An overview of recent remote sensing and GIS based research in ecological informatics
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paper_title: Non-stationarity and local approaches to modelling the distributions of wildlife

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Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.
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paper_title: Environmental sensor networks in ecological research

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Environmental sensor networks offer a powerful combination of distributed sensing capacity, real-time data visualization and analysis, and integration with adjacent networks and remote sensing data streams. These advances have become a reality as a combined result of the continuing miniaturization of electronics, the availability of large data storage and computational capacity, and the pervasive connectivity of the Internet. Environmental sensor networks have been established and large new networks are planned for monitoring multiple habitats at many different scales. Projects range in spatial scale from continental systems designed to measure global change and environmental stability to those involved with the monitoring of only a few meters of forest edge in fragmented landscapes. Temporal measurements have ranged from the evaluation of sunfleck dynamics at scales of seconds, to daily CO2 fluxes, to decadal shifts in temperatures. Above-ground sensor systems are partnered with subsurface soil measurement networks for physical and biological activity, together with aquatic and riparian sensor networks to measure groundwater fluxes and nutrient dynamics. More recently, complex sensors, such as networked digital cameras and microphones, as well as newly emerging sensors, are being integrated into sensor networks for hierarchical methods of sensing that promise a further understanding of our ecological systems by revealing previously unobservable phenomena.
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paper_title: Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets

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This paper presents an application of fuzzy set theory for classification of individual tree crowns into species groups, in high spatial resolution colour infrared aerial photographs. In this type of digital image, the trees are visible as individual objects. The number of individuals to classify might be very large in the acquired set of photographs, but the applied grade of membership (GoM) model, which this paper focuses on, is suitable for dealing with large datasets.The extent of each tree crown in the image is defined using a previously published procedure. Based on colour information (hue), an optimal fuzzy thresholding technique divides the tree crown universal set into a dominant set and its minor complement. Nine different features of each image object are estimated, and transformed using principal component analysis (PCA). The first three or four PCs are subsequently used in the GoM model. Furthermore, the concept of fuzzy relation is applied to one of the descriptors: to predict a centroid of the star-shaped pattern of Norway spruce.The GoM model needs initial membership values, which are estimated using an unsupervised fuzzy clustering approach of small subareas (branches in the tree crowns) and their corresponding digital numbers in each colour band (RGB-images). The complete classification system comprises three independent components: decisions on coniferous/deciduous, Scots pine/Norway spruce, and Birch/Aspen. The accuracies (ground patches excluded), using the supervised GoM model with crossvalidation, are 87%, 76%, and 79%, respectively. The accuracy for the compounded system is 67%.
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paper_title: Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis

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Image fusion is one of the most commonly used image enhancement techniques for improving the spatial quality of the source image with minimal spectral distortion in remote sensing. Until now, data fusion algorithms were developed and applied to improve the spatial resolution of the multispectral images and also their performances were evaluated depending on the source images such as Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Multispectral (MS)/Panchromatic (PAN), Satellite pour l'Observation de la Terre (SPOT) XS/PAN and IKONOS MS/PAN datasets. This paper assesses whether hyperspectral images, having very narrow bands compared to multispectral images, can be fused with high spatial resolution panchromatic images using common and current new algorithms including Intensity-Hue Saturation (IHS), Principal Component Substitution (PCS), Gram Schmidt Transformation (GST), Smoothing Filter-based Intensity Modulation (SFIM), Discrete Wavelet Transform (DWT), wavelet-based IHS (DWT-IMS) and PCS (DWT-PCS) and Fast Fourier Transform (FFT)-enhanced IHS. We also examine the performance of the fused hyperspectral images with respect to the fused multispectral images. For this purpose, two different source datasets (EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN) were used. Some qualitative and quantitative analyses were implemented to assess the spatial and spectral quality of the fused images. The results show that it was possible to carry out the fusion of a narrow-band hyperspectral image and a high spatial resolution panchromatic image. The fusion of EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN datasets using the SFIM, DWT-PCS, DWT-IHS and FFT-IHS algorithms produces better results than other techniques. Also, the results show that the fusion methods behaved for both datasets in the same performances except the DWT algorithm. The DWT method has a lower performance for the hyperspectral image compared to the multispectral image. Therefore the DWT algorithm should be further studied to improve the spectral qualities of a fused hyperspectral image based on wavelet transformation.
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paper_title: Super‐resolution mapping of the waterline from remotely sensed data

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Methods for mapping the waterline at a subpixel scale from a soft image classification of remotely sensed data are evaluated. Unlike approaches based on hard classification, these methods allow the waterline to run through rather than between image pixels and so have the potential to derive accurate and realistic representations of the waterline from imagery with relatively large pixels. The most accurate predictions of waterline location were made from a geostatistical approach applied to the output of a soft classification (RMSE = 2.25 m) which satisfied the standards for mapping at 1 : 5000 scale from imagery with a 20 m spatial resolution.
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paper_title: Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America

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Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R 2 =68%
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paper_title: Multitemporal spectral analysis for cheatgrass (Bromus tectorum) classification

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Operational satellite remote sensing data can provide the temporal repeatability necessary to capture phenological differences among species. This study develops a multitemporal stacking method coupled with spectral analysis for extracting information from Landsat imagery to provide species-level information. Temporal stacking can, in an approximate mathematical sense, effectively increase the 'spectral' resolution of the system by adding spectral bands of several multitemporal images. As a demonstration, multitemporal linear spectral unmixing is used to successfully delineate cheatgrass (Bromus tectorum) from soil and surrounding vegetation (77% overall accuracy). This invasive plant is an ideal target for exploring multitemporal methods because of its phenological differences with other vegetation in early spring and, to a lesser degree, in late summer. The techniques developed in this work are directly applicable for other targets with temporally unique spectral differences.
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paper_title: The pixel: a snare and a delusion

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The pixel is an explicit feature of remotely-sensed imagery, and a primary concept of the raster GIS (Geographical Information System) which is the usual vehicle for integration. This Letter addresses the underlying spatial conceptualization of the pixel, which is the parallel of the grid cell in spatial analysis, and the regular grid in sampling. It is argued that integration of remote sensing and GIS can only possibly advance if we develop methods to address the conceptual short-comings of the pixel as a spatial entity, and stop pretending that it is a true geographical object. Three major strands of research which address this issue are highlighted, including mixture modelling, geostatistics and fuzzy classification.
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paper_title: Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

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Principal component analysis (PCA) is one of the most commonly adopted feature reduction techniques in remote sensing image analysis. However, it may overlook subtle but useful information if applied directly to the analysis of hyperspectral data, especially for discriminating between different vegetation types. In order to accurately map an invasive plant species (horse tamarind, Leucaena leucocephala) in southern Taiwan using Hyperion hyperspectral imagery, this study developed a spectrally segmented PCA based on the spectral characteristics of vegetation over different wavelength regions. The developed algorithm can not only reduce the dimensionality of hyperspectral imagery but also extracts helpful information for differentiating more effectively the target plant species from other vegetation types. Experiments conducted in this study demonstrated that the developed algorithm performs better than correlation-based segmented principal component transformation (SPCT) and conventional PCA (overall accuracy: 86%, 76%, 66%; kappa value: 0.81, 0.69, 0.57) in detecting the target plant species, as well as mapping other vegetation covers.
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paper_title: Mapping nonnative plants using hyperspectral imagery

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Nonnative plant species are causing enormous ecological and environmental impacts from local to global scale. Remote sensing images have had mixed success in providing spatial information on land cover characteristics to land managers that increase effective management of invasions into native habitats. However, there has been limited evaluation of the use of hyperspectral data and processing techniques for mapping specific invasive species based on their spectral characteristics. This research evaluated three different methods of processing hyperspectral imagery: minimum noise fraction (MNF), continuum removal, and band ratio indices for mapping iceplant (Carpobrotus edulis) and jubata grass (Cortaderia jubata) in California's coastal habitat. Validation with field sampling data showed high mapping accuracies for all methods for identifying presence or absence of iceplant (97%), with the MNF procedure producing the highest accuracy (55%) when the classes were divided into four different densities of iceplant.
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paper_title: Global mean values in linear spectral unmixing: double fallacy!

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Almost all conventional linear spectral unmixing techniques are based on the principle of least squares. The global mean digital number (DN) of an end-member is taken as the representative (i.e. contributory) DN for the end-member. This paper sets out to prove that the notion is a fallacy, and will always lead to negative percentages, super-positive percentages and non-100% sum of percentages if the unmixed pixel is not composed of, to within some tolerance, the global mean DNs only. Three sets of spectral end-members (two, three and four spectral end-members) are generated from Landsat ETM+ data. Practical percentages (between 0% and 100% and totalling 100%) of the end-members are returned by pixels in which the local mean DNs of the spectral end-members do not differ from the global mean DNs by, on average, 4.
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paper_title: Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

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Abstract Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, ho...
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paper_title: Mapping Chinese tallow with color-infrared photography

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Airborne color-infrared photography (CIR) (1:12,000 scale) was used to map localized occurrences of the widespread and aggressive Chinese tallow (Sapium sebiferum), an invasive species. Photography was collected during senescence when Chinese tallow's bright red leaves presented a high spectral contrast within the native bottomland hardwood and upland forests and marsh land-cover types. Mapped occurrences were conservative because not all senescing tallow leaves are bright red simultaneously. To simulate low spectral but high spatial resolution satellitelairborne image and digital video data, the CIR photography was transformed into raster images at spatial resolutions approximating 0.5 m and 1.0 m. The image data were then spectrally classified for the occurrence of bright red leaves associated with senescing Chinese tallow. Classification accuracies were greater than 95 percent at both spatial resolutions. There was no significant difference in either forest in the detection of tallow or inclusion of non-tallow trees associated with the two spatial resolutions. In marshes, slightly more tallow occurrences were mapped with the lower spatial resolution, but there were also more misclassifications of native land covers as tallow. Combining all land covers, there was no difference at detecting tallow occurrences (equal omission errors) between the two resolutions, but the higher spatial resolution was associated with less inclusion of nontallow land covers as tallow (lower commission error). Overall, these results confirm that high spatial (51 m) but low spectral resolution remote sensing data can be used for mapping Chinese tallow trees in dominant environments found in coastal and adjacent upland landscapes.
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paper_title: Multiframe demosaicing and super-resolution of color images

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In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteron estimation technique by minimizing a multiterm cost function. The L1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.
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paper_title: Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data

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The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multitemporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover.
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paper_title: Variability in Soft Classification Prediction and its implications for Sub-pixel Scale Change Detection and Super Resolution Mapping

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The impact of intra-class spectral variability on the estimation of sub-pixel land-cover class composition with a linear mixture model is explored. It is shown that the nature of intra-class variation present has a marked impact on the accuracy of sub-pixel class composition estimation, as it violates the assumption that a class can be represented by a single spectral endmember. It is suggested that a distribution of possible class compositions can be derived from pixels instead of a single class composition prediction. This distribution provides a richer indication of possible subpixel class compositions and highlights a limitation for super-resolution mapping. Moreover, the class composition distribution information may be used to derive different scenarios of changes when used in a post-classification comparison type approach to change detection. This latter issue is illustrated with an example of forest cover change in Brazil from Landsat TM data.
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paper_title: High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

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Abstract Characterization of ecosystem structure, diversity, and function is increasingly desired at finer spatial and temporal scales than have been derived in the past. Many ecological applications require detailed data representing large spatial extents, but these data are often unavailable or are impractical to gather using field-based techniques. Remote sensing offers an option for collecting data that can represent broad spatial extents with detailed attribute characterizations. Remotely sensed data are also appropriate for use in studies across spatial scales, in conjunction with field-collected data. This article presents the pertinent technical aspects of remote sensing for images at high spatial resolution (i.e., with a pixel size of 16 square meters or less), existing and future options for the processing and analysis of remotely sensed data, and attributes that can be estimated with these data for forest ecosystems.
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paper_title: Using remote sensing to assess biodiversity

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This review paper evaluates the potential of remote sensing for assessing species diversity, an increasingly urgent task. Existing studies of species distribution patterns using remote sensing can be essentially categorized into three types. The first involves direct mapping of individual plants or associations of single species in relatively large, spatially contiguous units. The second technique involves habitat mapping using remotely sensed data, and predictions of species distribution based on habitat requirements. Finally, establishment of direct relationships between spectral radiance values recorded from remote sensors and species distribution patterns recorded from field observations may assist in assessing species diversity. Direct mapping is applicable over smaller extents, for detailed information on the distribution of certain canopy tree species or associations. Estimations of relationships between spectral values and species distributions may be useful for the limited purpose of indicating areas with higher levels of species diversity, and can be applied over spatial extents of hundreds of square kilometres. Habitat maps appear most capable of providing information on the distributions of large numbers of species in a wider variety of habitat types. This is strongly limited by variation in species composition, and best applied over limited spatial extents of tens of square kilometres.
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paper_title: Classifying Eucalyptus forests with high spatial and spectral resolution imagery: an investigation of individual species and vegetation communities

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Mapping the spatial distribution of individual species is an important ecological and forestry issue that requires continued research to coincide with advances in remote-sensing technologies. In this study, we investigated the application of high spatial resolution (80 cm) Compact Airborne Spectrographic Imager 2 (CASI-2) data for mapping both spectrally complex species and species groups (subgenus grouping) in an Australian eucalypt forest. The relationships between spectral reflectance curves of individual tree species and identified statistical differences among species were analysed with ANOVA. Supervised maximum likelihood classifications were then performed to assess tree species separability in CASI-2 imagery. Results indicated that turpentine (Syncarpia glomulifera Smith), mesic vegetation (primarily rainforest species), and an amalgamated group of eucalypts could be readily distinguished. The discrimination of S. glomulifera was particularly robust, with consistently high classification accuracies. Eucalypt classification as a broader species group, rather than individual species, greatly improved classification performance. However, separating sunlit and shaded aspects of tree crowns did not increase classification accuracy.
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paper_title: Examining pine spectral separability using hyperspectral data from an airborne sensor : An extension of field-based results

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Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full-range spectroradiometer (400-2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near-infrared regions of the spectrum. Cross-validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air-or space-borne sensors.
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paper_title: Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study

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Super-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study.
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paper_title: Spatial patterns in species distributions reveal biodiversity change

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Interpretation of global biodiversity change is hampered by a lack of information on the historical status of most species in most parts of the world. Here we show that declines and increases can be deduced from current species distributions alone, using spatial patterns of occupancy combined with distribution size. Declining species show sparse, fragmented distributions for their distribution size, reflecting the extinction process; expanding species show denser, more aggregated distributions, reflecting colonization. Past distribution size changes for British butterflies were deduced successfully from current distributions, and former distributions had some power to predict future change. What is more, the relationship between distribution pattern and change in British butterflies independently predicted distribution change for butterfly species in Flanders, Belgium, and distribution change in British rare plant species is similarly related to spatial distribution pattern. This link between current distribution patterns and processes of distribution change could be used to assess relative levels of threat facing different species, even for regions and taxa lacking detailed historical and ecological information.
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paper_title: The factor of scale in remote sensing

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Abstract Thanks to such second- and third-generation sensor systems as Thematic Mapper, SPOT, and AVHRR, a user of digital satellite imagery for remote sensing of the earth's surface now has a choice of image scales ranging from 10 m to 1 km. The choice of an appropriate scale, or spatial resolution, for a particular application depends on several factors. These include the information desired about the ground scene, the analysis methods to be used to extract the information, and the spatial structure of the scene itself. A graph showing how the local variance of a digital image for a scene changes as the resolution-cell size changes can help in selecting an appropriate image scale. Such graphs are obtained by imaging the scene at fine resolution and then collapsing the image to successively coarser resolutions while calculating a measure of local variance. The local variance/resolution graphs for the forested, agricultural, and urban/suburban environments examined in this paper reveal the spatial structure of each type of scene, which is a function of the sizes and spatial relationships of the objects the scene contains. At the spatial resolutions of SPOT and Thematic Mapper imagery, local image variance is relatively high for forested and urban/suburban environments, suggesting that information-extracting techniques utilizing texture, context, and mixture modeling are appropriate for these sensor systems. In agricultural environments, local variance is low, and the more traditional classifiers are appropriate.
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paper_title: APPLICATION OF 1-M AND 4-M RESOLUTION SATELLITE DATA TO ECOLOGICAL STUDIES OF TROPICAL RAIN FORESTS

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Understanding the current status of the world's tropical rain forests (TRF) can be greatly advanced by global coverage of remotely sensed data at the scale of individual tree crowns. In 1999 the IKONOS satellite began offering worldwide 1-m panchromatic and 4-m multispectral data. Here we show that these data can be used to address diverse aspects of forest ecology and land-use classification in the tropics. Using crowns of emergent trees as control points, we georeferenced a 600-ha subset of IKONOS 1-m and 4-m data from an August 2000 image of the La Selva Biological Station, Costa Rica (root mean square error 5 4.3 m). Crown area measured on the image was highly correlated with crown area for the same tree measured from the ground. Using a 1988 aerial photograph as a baseline, all trees .1 m diameter in a long-term study that died over the ensuing 12-year period, and that could be located in the photograph, were detected as missing in the IKONOS image (N 5 7). Crown growth for large trees visible on both images averaged 12 m 2 /yr (N 5 16). We thus demonstrate that IKONOS imagery can provide data on four variables necessary for doing demographic research: tree size, location, mortality, and growth. Stand basal area, estimated aboveground biomass, and percentage of the canopy .15 m tall for 18 0.5-ha permanent forest inventory plots in old growth were all highly sig- nificantly correlated with different indices derived from the IKONOS data. We used summary statistics from the original IKONOS data as well as derived indices to characterize nine areas with well-documented land-use histories. Secondary forests were clearly separable from the other sites. One of the secondary forests was 40 years old, suggesting that IKONOS data can be used to detect significantly older secondary forest than is possible with coarser resolution satellite data. The selectively logged forest was distinguishable by measuring the size of the largest crowns on the 1-m image. This suggests a range of applications for detecting and quantifying biomass degradation due to selective logging and edge effects. Satellite data at 1-m and 4-m resolution make possible a truly global approach to fine spatial resolution remote-sensing studies of TRF ecology and land use.
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paper_title: Identifying species of individual trees using airborne laser scanner

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Abstract Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce ( Picea abies L. Karst.), Scots pine ( Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50×20 m 2 ) were measured in field and tree species was recorded. The dominating species (>80%) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95%. Crown base height estimations of individual trees were also evaluated ( r =0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.
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paper_title: Remote Sensing for Sustainable Forest Management

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INTRODUCTION Forest Management Questions Remote Sensing Data and Methods Categories of Applications of Remote Sensing Organization of the Book SUSTAINABLE FOREST MANAGEMENT Definition of Sustainable Forest Management Ecosystem Management Criteria and Indicators of Sustainable Forest Management Information Needs of Forest Managers Role of Remote Sensing ACQUISITION OF IMAGERY Field, Aerial, and Satellite Imagery Data Characteristics Resolution and Scale Aerial Platforms and Sensors Satellite Platforms and Sensors General Limits of Airborne and Satellite Remote Sensing Data IMAGE CALIBRATION AND PROCESSING Georadiometric Effects and Spectral Response Image Processing Systems and Functionality Image Analysis Support Functions Image Information Extraction Image Understanding FOREST MODELING AND GIS Geographical Information Science Ecosystem Process Models Spatial Pattern Modeling FOREST CLASSIFICATION Information on Forest Classes Classification Systems for Use with Remote Sensing Data Level I Classes Level II Classes Level III Classes FOREST STRUCTURE ESTIMATION Information on Forest Structure Forest Inventory Variables Biomass Volume and Growth Assessment FOREST CHANGE DETECTION Information on Forest Change Harvesting and Silviculture Activity Natural Disturbances Change in Spatial Structure CONCLUSION The Technological Approach - Revisited References
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paper_title: Tree Species Classification using Semi-automatic Delineation of Trees on Aerial Images

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The purpose of this study was to develop a method for classifying tree species from remote sensing images by combining a semi-automatic pattern recognition technique and spectral properties of trees. Five stands in southern Finland were studied. Individual trees in the digital colour infrared (CIR) aerial photographs were segmented by a method based on the recognition of tree crown patterns at subpixel accuracy. The images were filtered with the Gaussian N-by-N smoothing operator and local maxima above a threshold level were segmented. The segments were classified into three tree species classes. The kappa coefficients for stands varied from 0.43 to 0.86 when the training data and test data were from the same aerial photograph. When training data from other photographs were used as reference data, the kappa coefficients ranged from 0.40 to 0.75. The method described provides an interesting approach for detecting tree species semi-automatically in digital aerial data.
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paper_title: Landscape as a continuum: an examination of the urban landscape structures and dynamics of Indianapolis City, 1991-2000, by using satellite images

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The majority of the vast literature on remote sensing of urban landscapes has adopted a 'hard classification' approach, in which each image pixel is assigned a single land use and land cover category. Owing to the nature of urban landscapes, the confusion between land use and land cover definitions and the constraints of widely applied medium spatial resolution satellite images, high classification accuracy has been difficult to achieve with the conventional 'hard' classifiers. The prevalence of the mixed pixel problem in urban landscapes indicates a crucial need for an alternative approach to urban analyses. Identification, description and quantification, rather than classification, may provide a better understanding of the compositions and processes of heterogeneous landscapes such as urban areas. This study applied the Vegetation-Impervious Surface-Soil (V-I-S) model for characterizing urban landscapes and analysing their dynamics in Indianapolis, USA, between 1991 and 2000. To extract these landscape components from three dates of Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images in 1991 1995 and 2000, we used the technique of linear spectral mixture analysis (LSMA). These components were further classified into urban thematic classes, and used for analysis of the landscape patterns and dynamics. The results indicate that LSMA provides a suitable technique for detecting and mapping urban materials and V-I-S component surfaces in repetitive and consistent ways, and for solving the spectral mixing of medium spatial resolution satellite imagery. The reconciliation between the V-I-S model with LSMA for Landsat imagery allowed this continuum landscape model to be an alternative, effective approach to characterizing and quantifying the spatial and temporal changes of the urban landscape compositions in Indianapolis from 1991 to 2000. It is suggested that the model developed in this study offers a more realistic and robust representation of the true nature of urban landscapes, as compared with the conventional method based on 'hard classification' of satellite imagery. The general applicability of this continuum model, especially its spectral, spatial and temporal variability, is discussed.
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paper_title: Evaluation of global land cover data sets over the tundra-taiga transition zone in northernmost Finland

paper_content:

The remote sensing-based continental to global scale land cover data sets provide several land cover depictions over the circumpolar tundra-taiga transition zone. The aim of this study was to evaluate three data sets in northernmost Finland: the Global Land Cover 2000 Northern Eurasia map (GLC2000-NE), the MODIS global land cover map (MODIS-IGBP) and the tree cover layer of the MODIS vegetation continuous fields product (MODIS-VCF). The data sets were first compared both visually and statistically to biotope inventory data including tree cover, height, species composition and shrub cover information as continuous variables. The agreement with reference data was poor because the classifications do not correspond to the class descriptions. The MODIS-VCF tree cover overestimates the tree cover in the low values and underestimates it in the high values. The agreement was relatively good when the global data sets were aggregated to a forest-non-forest level and compared to the Finnish CORINE Land Cover 2000 map over a larger area. However, the inaccurate mapping of the deciduous broadleaf forests and mires reduced the agreement at the forest-non-forest level. The vegetation transitions are difficult to map using low-resolution satellite data and further improvements to the land cover characterization over the tundra-taiga transition zone are required.
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paper_title: Global composites of the MERIS Terrestrial Chlorophyll Index

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From the year 2006, the European Space Agency (ESA) supported the production of the global composite (Level 3) of a unique terrestrial chlorophyll product called the MERIS Terrestrial Chlorophyll Index (MTCI) (Dash and Curran 2004). The MTCI is calculated using three red/near infrared bands of Envisat MERIS data (Rast et al. 1999). This index estimates the relative location of the reflectance 'red edge' of vegetation and is more sensitive than red edge position to canopy chlorophyll content, notably at high chlorophyll contents. This product effectively combines information on leaf area index and the chlorophyll concentration of leaves to produce an image of chlorophyll content (i.e. the amount of chlorophyll per unit area of ground). Chlorophyll content plays an important role in determining the physiological status of a plant, is related to photosynthetic rate and varies temporarily and spatially. MTCI global composites can be used to estimate relative and land cover specific, chlorophyll content in space and time and this in turn can be a key input to models of terrestrial productivity, gas exchange and vegetation health. Two global monthly MTCI composites for March and August 2003 are presented on the cover. These images display the MTCI on a nominal scale of 0 to 6, with higher values indicating higher chlorophyll content. These images clearly capture the phenology of global vegetation. In March, a major part of the southern hemisphere (e.g. South Africa, South America) had high MTCI values during the peak of their growing season, whereas a major part of the northern hemisphere had low MTCI values in March. In August, the situation was reversed. A major part of the northern hemisphere (e.g. Europe, North America) had high MTCI values when the coverage of green leaves was at a maximum. In both images the tropical rain forests had relatively high MTCI values. It is interesting to note that even at the centre of these forests there is a change in MTCI values between March and August. This global MTCI product will be produced as weekly and monthly composites and is the only terrestrial chlorophyll product available from space. The MTCI along with oceanographic chlorophyll concentration estimates, also from MERIS, can be used to generate a 'global chlorophyll map' for the estimation of global productivity.
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paper_title: Remote sensing in physical geography: a twenty-first-century perspective

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Timely, high-quality data from remote sensing can benefi t the study of physical geography in many ways (Wolman, 2004). Rapid developments in remote sensing, particularly from satellite platforms, have generated much, often well-placed, enthusiasm about its potential as a powerful research tool (Skole, 2004). Indeed, remote sensing is viewed by many as having come of age (Tatem et al., 2008) and can be deemed a mature discipline with its physical principles well understood and its range of applications a showcase of its versatility (Warner et al., 2009). Historically, physical geography has benefited greatly from the emergence of new technologies, like remote sensing, and is likely to continue to reap benefi ts from further technological advances in the foreseeable future (Rhoads, 2004). The beginning of the twenty-fi rst century promised to be a period of technological innovation, with remote sensing identifi ed as one of 21 technologies well placed to meet contemporary issues and challenges facing society (AllBusiness, 2001). Given that many of these challenges involve the interaction between society and the physical environment (Demeritt, 2009), and that there is an increasing need for all areas of science, including physical geography, to iterate their value to society (Rediscovering Geography Committee, NRC, 1997), the close of the fi rst decade of the twenty-fi rst century is a suitable point at which to take stock of current trends and developments in remote sensing within physical geography and interrelated fi elds. Remote sensing is useful for the physical geographer not only as a tool for data collection, with superiority over other techniques by virtue of its spatial and temporal coverage, but also because of the logic implicit in the reasoning process employed to analyse the data (Estes et al., 1980). In addition to the experimental, hypothetical and case-study style of reasoning that tends to characterize the use of remote sensing within physical geography, there are increasingly operational modes of usage (eg, within meteorology and climatology, hazard management). Since the turn of the twenty-fi rst century more than 100 satellite sensors have been launched (www.itc.nl/research/products/sensordb/ searchsat.aspx) and numerous airborne and terrestrial sensors manufactured. Many of these sensors are the result of emerging technologies (eg, Krabach, 2000), with the processing and interpretation of the data acquired benefiting from advanced pro-
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paper_title: Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama

paper_content:

Mangrove stands of differing species composition are hard to distinguish in conventional, coarse resolution satellite images. The new generation of meter-level satellite imagery provides a unique opportunity to achieve this goal. In this study, an IKONOS Geo bundle image and a QuickBird Standard bundle image were acquired for a study area located at Punta Galeta on the Caribbean coast of Panama. The two images cover the same area and were acquired under equivalent conditions. Three comparison tests were designed and implemented, each with separate objectives. First, a comparison was conducted band by band by examining their spectral statistics and species by species by inspecting their textural roughness. The IKONOS image had a higher variance and entropy value in all the compared bands, whereas the QuickBird image displayed a finer textural roughness in the forest canopy. Second, maximum likelihood classification (MLC) was executed with two different band selections. When examining only multispectral bands, the IKONOS image had better spectral discrimination than QuickBird while the inclusion of panchromatic bands had no effect on the classification accuracy of either the IKONOS or QuickBird image. Third, first- and second-order texture features were extracted from the panchromatic images at different window sizes and with different grey level (GL) quantization levels and were compared through MLC classification. Results indicate that the consideration of image texture enhances classifications based on the IKONOS panchromatic band more than it does classifications based on comparable QuickBird imagery. An object-based classification was also utilized to compare underlying texture in both panchromatic and multispectral bands. On the whole, both IKONOS and QuickBird images produced promising results in classifying mangrove species.
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paper_title: Two improvement schemes of PAN modulation fusion methods for spectral distortion minimization

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Fusion of panchromatic (PAN) and multispectral (MS) images is one of the most promising issues in remote sensing. PAN modulation fusion methods are usually based on an assumption that a ratio of two different-resolution versions of an MS band is equal to a ratio of two different-resolution versions of a PAN image. In such fusion methods, image haze is rarely taken into account, and it may produce serious spectral distortion in synthetic images. In this paper, assuming that the previous ratio relationship only holds for haze-free images, two relevant improvement schemes are proposed to better express the ratio relationship of haze-included images. In a test on a spatially degraded IKONOS dataset, the first scheme synthesizes an image with minimum spectral distortion, and the second modifies several current PAN modulation fusion methods and generates high-quality synthetic products. The experiment results confirm that image haze can seriously impact the quality of fused images obtained by using PAN modulation fusion methods, and it should be taken into account in relevant image fusion.
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paper_title: Estimating area errors for fine‐scale feature‐based ecological mapping

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High spatial resolution feature‐based approaches are especially useful for ecological mapping in densely populated landscapes. This paper evaluates errors in estimating ecological map class areas from fine‐scale current (∼2002) and historical (∼1945) feature‐based ecological mapping by a set of trained interpreters across densely populated rural sites in China based on field‐validated interpretation of high spatial resolution (1 m) imagery. Median overall map accuracy, corrected for chance, was greater than 85% for mapping by trained interpreters, with greater accuracy for current versus historical mapping. An error model based on feature perimeter proved as reliable in predicting 90% confidence intervals for map class areas as did models derived from the conventional error matrix. A conservative error model combining these approaches was developed and tested for statistical reliability in predicting confidence intervals for ecological map class areas from fine‐scale feature‐based mapping by a set of tra...
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paper_title: Exploitation of Very High Resolution Satellite Data for Tree Species Identification

paper_content:

With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in “monoculture stands” identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).
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paper_title: One-Class Classification for Mapping a Specific Land-Cover Class: SVDD Classification of Fenland

paper_content:

Remote sensing is a major source of land-cover information. Commonly, interest focuses on a single land-cover class. Although a conventional multiclass classifier may be used to provide a map depicting the class of interest, the analysis is not focused on that class and may be suboptimal in terms of the accuracy of its classification. With a conventional classifier, considerable effort is directed on the classes that are not of interest. Here, it is suggested that a one-class-classification approach could be appropriate when interest focuses on a specific class. This is illustrated with the classification of fenland, a habitat of considerable conservation value, from Landsat Enhanced Thematic Mapper Plus imagery. A range of one-class classifiers is evaluated, but attention focuses on the support-vector data description (SVDD). The SVDD was used to classify fenland with an accuracy of 97.5% and 93.6% from the user's and producer's perspectives, respectively. This classification was trained upon only the fenland class and was substantially more accurate in fen classification than a conventional multiclass maximum-likelihood classification provided with the same amount of training data, which classified fen with an accuracy of 90.0% and 72.0% from the user's and producer's perspectives, respectively. The results highlight the ability to classify a single class using only training data for that class. With a one-class classification, the analysis focuses tightly on the class of interest, with resources and effort not directed on other classes, and there are opportunities to derive highly accurate classifications from small training sets
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paper_title: Status of land cover classification accuracy assessment

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The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of accuracy. This paper briefly reviews the background and methods of classification accuracy assessment that are commonly used and recommended in the research literature. It is, however, evident that the research community does not universally adopt the approaches that are often recommended to it, perhaps a reflection of the problems associated with accuracy assessment, and typically fails to achieve the accuracy targets commonly specified. The community often tends to use, unquestioningly, techniques based on the confusion matrix for which the correct application and interpretation requires the satisfaction of often untenable assumptions (e.g., perfect coregistration of data sets) and the provision of rarely conveyed information (e.g., sampling design for ground data acquisition). Eight broad problem areas that currently limit the ability to appropriately assess, document, and use the accuracy of thematic maps derived from remote sensing are explored. The implications of these problems are that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.
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paper_title: Optimizing Remote Sensing and GIS Tools for Mapping and Managing the Distribution of an Invasive Mangrove (Rhizophora mangle) on South Molokai, Hawaii

paper_content:

In 1902, the Florida red mangrove, Rhizophora mangle L., was introduced to the island of Molokai, Hawaii, and has since colonized nearly 25% of the south coast shoreline. By classifying three kinds of remote sensing imagery, we compared abilities to detect invasive mangrove distributions and to discriminate mangroves from surrounding terrestrial vegetation. Using three analytical techniques, we compared mangrove mapping accuracy for various sensor-technique combinations. ANOVA of accuracy assessments demonstrated significant differences among techniques, but no significant differences among the three sensors. We summarize advantages and disadvantages of each sensor and technique for mapping mangrove distributions in tropical coastal environments.
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paper_title: Tree species mapping with Airborne hyper-spectral MIVIS data: the Ticino Park study case

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The present work describes the procedure, which was studied for mapping the spatial distribution of tree forest communities in the Ticino Park located in Northern Italy. Ten overlapping airborne runs of the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were acquired to cover the entire park extent (920 km2). An integrated supervised classification procedure was developed using band ratios in the red edge portion (REP) of the spectrum and training collected by field survey and visual interpretation. Validation performed with a robust random stratified sampling scheme and taking into account the unequal distribution of the classes showed that, on large-scale application, high-resolution remotely sensed images can generate, in a cost-effective manner, accurate (overall accuracy 75%) local-scale thematic products.
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paper_title: Remote Sensing Research Priorities in Tropical Dry Forest Environments

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Abstract Satellite multi- and hyper-spectral sensors have evolved over the past three decades into powerful monitoring tools for ecosystem processes. Research in temperate environments, however, has tended to keep pace with new remote sensing technologies more so than in tropical environments. Here, we identify what we consider to be three priority areas for remote sensing research in Neotropical dry forests. The first priority is the use of improved sensor capabilities, which should allow for better characterization of tropical secondary forests than has been achieved. Secondary forests are of key interest due to their potential for sequestering carbon in relatively short periods of time. The second priority is the need to characterize leaf area index (LAI) and other biophysical variables by means of bidirectional reflectance function models. These biophysical parameters have importance linkages with net primary productivity and may be estimated through remote sensing. The third priority is to identify t...
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paper_title: Estimates of forest canopy height and aboveground biomass using ICESat

paper_content:

Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage. While the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and land Elevation Satellite (ICESat) has collected an unparalleled dataset of lidar waveforms over terrestrial targets, processing of ICESat data to estimate forest height is complicated by the pulse broadening associated with large-footprint, waveform-sampling lidar. We combined ICESat waveforms and ancillary topography from the Shuttle Radar Topography Mission to estimate maximum forest height in three ecosystems; tropical broadleaf forests in Brazil, temperate broadleaf forests in Tennessee, and temperate needleleaf forests in Oregon. Final models for each site explained between 59% and 68% of variance in field-measured forest canopy height (RMSE between 4.85 and 12.66 m). In addition, ICESat-derived heights for the Brazilian plots were correlated with field-estimates of aboveground biomass (r(2) = 73%, RMSE = 58.3 Mgha(-1)).
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paper_title: Modelling butterfly distribution based on remote sensing data

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Aim ::: ::: We tested the usefulness of satellite based remote sensing data and geographical information system (GIS) techniques (1) in explaining the observed distribution of the threatened clouded apollo butterfly (Parnassius mnemosyne) and (2) in predicting the occurrence of the butterfly in two independent test areas with different landscape structure. ::: ::: ::: ::: Location ::: ::: The three study areas are located along the rivers Rekijoki and Halikonjoki in a boreal agricultural landscape in south-western Finland (60°40′ N; 23°20′ E). ::: ::: ::: ::: Methods ::: ::: Landsat satellite images were used to generate habitat maps of the three study areas. Topographical variables were calculated from a digital elevation model. These data were used to construct a multiple logistic regression model fitted with the observed occurrence of clouded apollo in 126 grid squares of 0.25 km2 within an area of 31.5 km2 (model building area). The parameterized model was used to predict the occurrence of the butterfly in an adjacent test area with a similar landscape structure as in the model building area, as well as in a more distant test area with a more fragmented pattern of semi-natural grassland. ::: ::: ::: ::: Results ::: ::: In the model building area probability of clouded apollo occurrence increased with connectivity of semi-natural grassland, cover of deciduous forest, cover of semi-natural slope grassland and topographical heterogeneity. The model accuracy was high, the correct classification rate being 98.4% overall and 95.0% for the butterfly presence squares. In both test areas the overall correct classification of the model prediction was high (c. 92%). However, in predicting the actual butterfly presence squares the model succeeded substantially better in the adjacent than in the distant test area (correct classification rates 91.3% and 66.7%, respectively). ::: ::: ::: ::: Main conclusions ::: ::: The results showed that the distribution of a habitat specialized butterfly may be quite successfully explained and predicted based on pure satellite imagery and topographical data. Nevertheless, the decrease of accuracy of the model prediction when applied to a different landscape structure than the one in which the model was parameterized suggests that the useful application of such models is limited by the environmental variability of the original model building data.
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paper_title: Ground-based Laser Imaging for Assessing Three-dimensional Forest Canopy Structure

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Improved understanding of the role of forests in carbon, nutrient, and water cycling can be facilitated with improved assessments of canopy structure, better linking leaf-level processes to canopy structure and forest growth. We examined the use of high-resolution, ground-based laser imaging for the spatially explicit assessment of forest canopies. Multiple range images were obtained and aligned during both leaf-off and leaf-on conditions on a 20 m × 40 m plot. The plot location was within a mixed species broadleaved deciduous forest in western North Carolina. Digital terrain and canopy height models were created for a 0.25 m square grid. Horizontal, vertical, and three-dimensional distributions of plant area index, created using gap-fraction based estimation, had 0.5 m resolution for a cubic lattice. Individual tree measurements, including tree positions and diameter at breast height, were made from the scanner data with positions, on average, within 0.43 m and diameters within 5 cm of independent measurements, respectively. Our methods and results confirm that applications of ground-based laser scanning provide high-resolution, spatially-explicit measures of plot-level forest canopy structure.
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paper_title: Land use/cover changes using Landsat TM/ETM images in a tropical and biodiverse mountainous area of central-eastern Mexico

paper_content:

Land use/cover (LUC) and changes between 1990 and 2003 in a tropical mountainous watershed were analysed with Landsat TM images using a GIS-RS approach. The La Antigua River upper catchment is a 1325 km2, biodiverse hydrological region in central Veracruz, Mexico. A large set of training pixels was used to optimize the representation of environmental heterogeneity. Classification accuracy was assessed with spectral and field-checked error matrices. Overall classification accuracy for the 1990 (78.2%) and 2003 (79.7%) images was satisfactory. Ancillary data (DEM) was incorporated to improve discrimination between LUC categories. The Landsat TM sensor proved sensitive enough to separate the different spectral patterns related to the LUC classes in this complex landscape. The time interval and scale selected are suitable for strategic planning purposes. Depletion of tropical montane cloud forest, and its conversion to pasture and agriculture, was by far the most important LUC change over the period of study.
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paper_title: Can Error Explain Map Differences Over Time?

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This paper presents methods to test whether map error can explain the observed dif- ferences between two points in time among categories of land cover in maps. Such differences may be due to two reasons: error in the maps and change on the ground. Our methods use matrix algebra: (1) to determine whether error can explain specific types of observed categorical transitions between two maps, (2) to represent visually the differences between the maps that error cannot explain, and (3) to examine how the results are sensitive to possible variation in map error. The methods complement conventional accuracy assessment because they rely on standard confusion matrices that use either a random or a stratified sampling design. We illustrate the methods with maps from 1971 and 1999, which show seven land-cover categories for central Massachusetts. The methods detect four transitions from agriculture, range, forest, and barren in 1971 to built in 1999, which a 15 percent error cannot explain. Sensitivity analysis reveals that if the accuracy of the maps were less than 77 percent, then error could explain virtually all of the observed differences between the maps. The paper discusses the assumptions behind the methods and articulates priorities for future research.
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paper_title: Refining Biodiversity Conservation Priorities

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Although there is widespread agreement about conservation priorities at large scales (i.e., biodi- versity hotspots), their boundaries remain too coarse for setting practical conservation goals. Refining hotspot conservation means identifying specific locations (individual habitat patches) of realistic size and scale for managers to protect and politicians to support. Because hotspots have lost most of their original habitat, species endemic to them rely on what remains. The issue now becomes identifying where this habitat is and these species are. We accomplished this by using straightforward remote sensing and GIS techniques, identifying specific locations in Brazil's Atlantic Forest hotspot important for bird conservation. Our method requires a regional map of current forest cover, so we explored six popular products for mapping and quantifying forest: MODIS continuous fields and a MODIS land cover (preclassified products), AVHRR, SPOT VGT, MODIS (satellite images), and a GeoCover Landsat thematic mapper mosaic ( jpg). We compared subsets of these forest covers against a forest map based on a Landsat enhanced thematic mapper. The SPOT VGT forest cover predicted forest area and location well, so we combined it with elevation data to refine coarse distribution maps for forest endemic birds. Stacking these species distribution maps enabled identification of the subregion richest in threatened birds—the lowland forests of Rio de Janeiro State. We highlighted eight priority fragments, fo- cusing on one with finer resolved imagery for detailed study. This method allows prioritization of areas for conservation from a region >1 million km 2 to forest fragments of tens of square kilometers. To set priorities for biodiversity conservation, coarse biological information is sufficient. Hence, our method is attractive for tropical and biologically rich locations, where species location information is sparse.
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paper_title: The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest

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Abstract Vegetation structure is an important factor that influences wildlife-habitat selection, reproduction, and survival. However, field-based measurements of vegetation structure can be time consuming, costly, and difficult to undertake in areas that are remote and/or contain rough terrain. Light detection and ranging (lidar) is an active remote sensing technology that can quantify three-dimensional vegetation structure over large areas and thus holds promise for examining wildlife-habitat relationships. We used discrete-return airborne lidar data acquired over the Black Hills Experimental Forest in South Dakota, USA in combination with field-collected vegetation and bird data to assess the utility of lidar data in quantifying vegetation structural characteristics that relate to avian diversity, density, and occurrence. Indices of foliage height diversity calculated from lidar data were positively and significantly correlated with indices of bird species diversity, with the highest correlations observed when foliage height diversity categories contained proportionally more foliage layers near the forest floor (
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paper_title: An environmental domain classification of Canada using earth observation data for biodiversity assessment

paper_content:

Abstract Broad ecosystem based classifications are increasingly applied as a context to consider, understand, and manage biodiversity. The need for more spatially explicit, repeatable, transferable, transparent, and defensible environmental regionalization has become apparent. Increased computing power, sophisticated analysis software, and the availability of spatially explicit descriptions of the environment, principally derived from Earth observation data, have facilitated the development of statistical ecosystem regionalizations. These regionalizations are desired to produce environmentally unique ecoregions to provide the basis for stratification for ongoing biodiversity monitoring efforts. Using a suite of indicators of the physical environment, available energy such as vegetation production, and habitat suitability all derived from remote sensing technology at 1 km spatial resolution, we undertook an environmental regionalization using a two-stage multivariate classification of terrestrial Canada. A relatively large number of classes were initially derived (100) and a hierarchical clustering approach was then applied to derive a 40 level classification. These clusters where then used to assess which clusters were the most dissimilar to the majority thus providing indication of the most unique environmental domains across Canada. Secondly, a 14 class stratification was then produced to emulate the current ecozone stratification commonly used in Canada. Results indicated that a number of unique clusters exits across Canada, specifically the forest/urban-industrial/cropland mosaic in the southern portion of Ontario, the mixed wood forests in south–central Ontario and western Quebec, the foothills of south western Alberta, regions of the southern Arctic and the northern Boreal shield (particularly the areas south of Hudson Bay and Labrador). A resemblance between the 14 class stratification and the ecozone classification for Canada is evident; locations of within and between ecozone heterogeneity are also indicated. A critical key benefit of utilising ecoregions quantitatively using key indicators, such as those derived from remote sensing observations, is the capacity to establish, and quantify, how well particular networks of sites, or plot locations, represent the overall environment. As such, the incorporation of these types of methods, and remotely derived indicators, into biodiversity assessment is an important area of ongoing research.
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paper_title: Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data

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Abstract Structural characteristics of forest stands, e.g. in relation to carbon content and biodiversity, are of special interest. It has been stated in numerous publications that discrete return laser scanner data produce accurate tree canopy information since the quantiles of the height distribution of laser scanner data are related to the vertical structure of the tree canopy. Since some of the laser pulses will penetrate under the dominant tree layer, it is also possible to analyse multi-layered stands. In this study, the existence and number of understory trees were examined. This was carried out by analysing the height distributions of reflected laser pulses. In the laser data of this study, (Toposys Falcon, survey May 2003 in Kalkkinen, Finland, flight altitude 400 m AGL) the average number of laser pulse hits/1m 2 was 12. The reference data consisted of 28 accurately measured field sample plots. These plots include highly heterogeneous structures of boreal forests. The existence of lower canopy layers, i.e. understory trees, was analysed visually by viewing the plotwise 3D-images of laser scanner-based canopy height point data and examining distributions of canopy densities which were computed as proportions of laser hits above different height quantiles. Furthermore, a developed histogram thresholding method (HistMod) was applied to the height distribution of laser hits in order to separate different tree storeys. Finally, the number and Lorey's mean height of understory trees were predicted with estimated regression models. The results show that multi-layered stand structures can be recognised and quantified using quantiles of laser scanner height distribution data. However, the accuracy of the results is dependent on the density of the dominant tree layer.
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paper_title: Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees

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The paper describes a methodology for tree species classification using features that are derived from small-footprint full waveform Light Detection and Ranging (LIDAR) data. First, 3-dimensional coordinates of the laser beam reflections, the intensity, and the pulse width are extracted by a waveform decomposition, which fits a series of Gaussian pulses to the waveform. Since multiple reflections are detected, and even overlapping pulse reflections are distinguished, a much higher point density is achieved compared to the conventional first/last-pulse technique. Secondly, tree crowns are delineated from the canopy height model (CHM) using the watershed algorithm. The CHM posts are equally spaced and robustly interpolated from the highest reflections in the canopy. Thirdly, tree features computed from the 3-dimensional coordinates of the reflections, the intensity and the pulse width are used to detect coniferous and deciduous trees by an unsupervised classification. The methodology is applied to datasets that have been captured with the TopEye MK II scanner and the Riegl LMS-Q560 scanner in the Bavarian Forest National Park in leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification, which groups the data into two clusters (coniferous, deciduous), leads in the best case to an overall accuracy of 85% in a leaf-on situation and 96% in a leaf-off situation.
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paper_title: Change detection techniques for canopy height growth measurements using airborne laser scanner data

paper_content:

This paper analyzes the potential of airborne laser scanner data for measuring individual tree height growth in a boreal forest using 82 sample trees of Scots pine. Point clouds (10 points/m 2 , beam size 40 cm) illuminating 50 percent of the treetops were acquired in September 1998 and May 2003 with the Toposys 83 kHz lidar system. The reference height and height growth of pines were measured with a tacheometer in the field. Three different types of features were extracted from the point clouds representing each tree; they were the difference between the highest z values, the difference between the DSMs of the tree crown, and the differences between the 85 th , 90 th and 95 th percentiles of the canopy height histograms corresponding to the crown. The best correspondence with the field measurements was achieved with an R 2 value of 0.68 and a RMSE of 43 cm. The results indicate that it is possible to measure the growth of an individual tree with multi-temporal laser surveys. We also demonstrated a new algorithm for tree-to-tree matching. It is needed in operational growth estimation based on individual trees, especially in dense spruce forests. The method is based on minimizing the distances between treetops in the Ndimensional data space. The experiments showed that the use of the location (derived from laser data) and height of the trees were together adequate to provide reliable tree-totree matching. In the future, a fourth dimension (the crown area) should also be included in the matching.
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paper_title: Effect of coregistration error on patchy target detection using high-resolution imagery

paper_content:

Abstract Many factors influence classification accuracy and a typical error budget includes uncertainty arising from the 1) selection of processing algorithms, 2) selection of training sites, 3) quality of orthorectification, and 4) atmospheric effects. With the development of high spatial resolution imagery, the impact of errors in geographic coregistration between imagery and field sites has become apparent – and potentially limiting – for classification applications, especially those involving patchy target detection. The goal of this study was to document and quantify the effect of coregistration error between imagery and field sites on classification accuracy. Artificial patchy targets were randomly placed over a study area covered by a QuickBird image. Classification accuracy of these targets was assessed at two levels of coregistration. Results showed that producer's accuracy of target classification increased from 37.5% to 100% between low and high levels of coregistration respectively. In addition, “Error due to Location”, a measure of how well pixels were located within respective classes, decreased to zero at high coregistration levels. This study highlights the importance of considering coregistration between imagery and field sites in the error budget, especially with studies involving high spatial resolution imagery and patchy target detection.
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paper_title: Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI

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The ability to predict spatial patterns of species richness using a few easily measured environmental variables would facilitate timely evaluation of potential impacts of anthropogenic and natural disturbances on biodiversity and ecosystem functions. Two common hypotheses maintain that faunal species richness can be explained in part by either local vegetation heterogeneity or primary productivity. Although remote sensing has long been identified as a potentially powerful source of information on the latter, its principal application to biodiversity studies has been to develop classified vegetation maps at relatively coarse resolution, which then have been used to estimate animal diversity. Although classification schemes can be delineated on the basis of species composition of plants, these schemes generally do not provide information on primary productivity. Furthermore, the classification procedure is a time- and labour-intensive process, yielding results with limited accuracy. To meet decision-making ...
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paper_title: Laser scanning of forest resources: the nordic experience

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This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properti...
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paper_title: POVERTY AND CORRUPTION COMPROMISE TROPICAL FOREST RESERVES

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We used the global fire detection record provided by the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to determine the number of fires detected inside 823 tropical and subtropical moist forest reserves and for contiguous buffer areas 5, 10, and 15 km wide. The ratio of fire detection densities (detections per square kilometer) inside reserves to their contiguous buffer areas provided an index of reserve effectiveness. Fire detection density was significantly lower inside reserves than in paired, contiguous buffer areas but varied by five orders of magnitude among reserves. The buffer : reserve detection ratio varied by up to four orders of magnitude among reserves within a single country, and median values varied by three orders of magnitude among countries. Reserves tended to be least effective at reducing fire frequency in many poorer countries and in countries beset by corruption. Countries with the most successful reserves include Costa Rica, Jamaica, Malaysia, and Taiwan and the Indonesian island of Java. Countries with the most problematic reserves include Cambodia, Guatemala, Paraguay, and Sierra Leone and the Indonesian portion of Borneo. We provide fire detection density for 3964 tropical and subtropical reserves and their buffer areas in the hope that these data will expedite further analyses that might lead to improved management of tropical reserves.
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paper_title: A less‐is‐more approach to geovisualization – enhancing knowledge construction across multidisciplinary teams

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The 'less-is-more' concept in interface design for computer applications has recently gained ground. In this article, the concept is adopted for a user-centered design of geovisualization application. The premise is that using simple and clear design can lead to successful applications with improved ease of use. Over the last three decades, the development of GIS and geovisualization has seen a marked increase in the levels of interaction between the user, the system and the information. However, these enthusiastic advances in technology have not resulted in a significant increase in the number of users.This article suggests that types of user interaction should not simply emphasize traditional GIS functions such as zooming and panning but move towards interaction based on facilitating the knowledge construction process. Considerations are made for the complexity of the system, the task at hand and the skills and limitations of the users. These elements are particularly important when maps act as the mediators in collaboration with users across disciplinary backgrounds. In such cases, the emphasis on simplicity and usability becomes as important as functionality. In these situations a geovisualization application designed for specific uses can maximize effective development of geographic knowledge.In this article, a minimalistic design approach to geovisualization is adopted by creating a geographic profiling tool which shifts the emphasis from technological advances or interaction with the map to the interaction elements key to building the spatial knowledge of GIS experts and non-experts alike. To evaluate this notion of 'less-is-more geovisualization' the profiling tool is evaluated according to usability matrices: efficiency, effectiveness and learnability. How well the Suburban Profiler contributes to these elements is assessed by conducting a video analysis of the types and forms of user interaction available. The video analysis demonstrates the usefulness and usability of the Suburban Profiler, providing proof of concept for 'less-is-more geovisualization'.
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paper_title: A comparison of biophysical parameter retrieval for forestry using airborne and satellite LiDAR

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This paper compares vegetation height metrics and fractional cover derived from coincident small footprint, discrete return airborne Light Detection and Ranging (LiDAR) scanning data (Optech Airborne Laser Terrain Mapper (ALTM)) with those estimated from large footprint, full waveform LiDAR profiling using the Geoscience Laser Altimeter System (GLAS). Estimates of maximum canopy height showed correspondence between the two methods with R2 = 0.68 (rms. error (RMSE) = 4.4 m). The relationship between 99th percentiles (often associated with forestry top height) showed R2 = 0.75, RMSE = 3.5 m. Detection of surface elevation limits corresponded well, (R2 = 0.71, RMSE = 5.0 m). Correlations between satellite waveform and airborne LiDAR canopy cover estimates gave R2 = 0.41 and R2 = 0.63 for dominant cover of conifers or broadleaf species, respectively. The results suggest that the broad Ice, Cloud and land Elevation Satellite (ICESat)/GLAS footprints can provide estimates of mixed vegetation canopy height which...
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paper_title: Progress in the use of remote sensing for coral reef biodiversity studies

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Coral reefs are hotspots of marine biodiversity, and their global decline is a threat to our natural heritage. Conservation management of these precious ecosystems relies on accurate and up-to-date information about ecosystem health and the distribution of species and habitats, but such information can be costly to gather and interpret in the fi eld. Remote sensing has proven capable of collecting information on geomorphologic zones and substrate types for coral reef environments, and is cost-effective when information is needed for large areas. Remote sensing-based mapping of coral habitat variables known to infl uence biodiversity has only recently been undertaken and new sensors and improved data processing show great potential in this area. This paper reviews coral reef biodiversity, the infl uence of habitat variables on its local spatial distribution, and the potential for remote sensing to produce maps of these habitat variables, thus indirectly mapping coral reef biodiversity and fulfi lling information needs of coral reef managers.
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paper_title: Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

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Urban landscapes are expanding rapidly and are reshaping the distribution of many animal and plant species. With these changes, the need to understand and to include urban biodiversity patterns in research and management programmes is becoming vital. Recent studies have shown that remote sensing tools can be useful in studies examining biodiversity patterns in natural landscapes. The present study aimed to explore whether remote sensing tools can be applied in biodiversity research in an urban landscape. More specifically, the study examined whether the Landsat-derived Normalized Difference Vegetation Index (NDVI) and linear spectral unmixing of urban land cover can predict bird richness in the city of Jerusalem. Bird richness was sampled in 40 1-ha sites over a range of urban environments in 329 surveys. NDVI and the per cent cover of built-up area were strongly and negatively correlated with each other, and were both very successful in explaining the number of bird species in the study sites. Mean NDVI in each site was positively correlated with the site bird species richness. A hump-shaped relationship between NDVI and species richness was observed (when calculated over increasing spatial scales), with a maximum value (Pearson's R = 0.87, p<0.001, n = 40) at a scale of 15 ha. We suggest that remote sensing approaches may provide planners and conservation biologists with an efficient and cost-effective method to study and estimate biodiversity across urban environments that range between densely built-up areas, residential neighbourhoods, urban parks and the peri-urban environment.
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paper_title: Monitoring conservation effectiveness in a global biodiversity hotspot: the contribution of land cover change assessment

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Tropical forests, which play critical roles in global biogeochemical cycles, radiation budgets and biodiversity, have undergone rapid changes in land cover in the last few decades. This study examines the complex process of land cover change in the biodiversity hotspot of Western Ghats, India, specifically investigating the effects of conservation measures within the Indira Gandhi Wildlife Sanctuary. Current vegetation patterns were mapped using an IRS P6 LISS III image and this was used together with Landsat MSS data from 1973 to map land cover transitions. Two major and divergent trends were observed. A dominant degradational trend can be attributed to agricultural expansion and infrastructure development while a successional trend, resulting from protection of the area, showed the resilience of the system after prolonged disturbances. The sanctuary appears susceptible to continuing disturbances under the current management regime but at lower rates than in surrounding unprotected areas. The study demonstrates that remotely sensed land cover assessments can have important contributions to monitoring land management strategies, understanding processes underpinning land use changes and helping to inform future conservation strategies.
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paper_title: Land use and biodiversity relationships.

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Abstract The relationships between land use and biodiversity are fundamental to understanding the links between people and their environment. Biodiversity can be measured in many ways. The concept covers not only the overall richness of species present in a particular area but also the diversity of genotypes, functional groups, communities, habitats and ecosystems there. As a result, the relationships between biodiversity in its broadest sense and land use can be complex and highly context dependent. Moreover, the relationships between them are often two-way, so that simple relationships between cause and effect can be difficult to identify. In some places, specific land uses or land management practices may be important in sustaining particular patterns of biodiversity. Elsewhere, the uses to which land can be put are highly dependent on the biodiversity resources present. The review will consider how changes in the quantity, quality and spatial configuration of different aspects of land use can impact on different components of biodiversity, and what direct and indirect factors might drive these changes. The need to distinguish between land cover and land use will be discussed in relation to the economic and social drivers of land use change. The review will also consider whether framing biodiversity objectives involves society in placing constraints upon the types of land use and management practice that are possible, and will consider such arguments in relation to assessments of the costs of biodiversity loss. It would seem that while considerable progress has been made in mapping out plausible futures for land use and biodiversity at global and regional scales, closer integration of modelling, scenario and field-based monitoring is needed to strengthen the evidence base available to decision makers. Challenges that face us include how we take account of the qualitative changes in land cover, and the impacts of such modifications on biodiversity and ecosystem services. Broader perspectives on the value of biodiversity and ecosystem services are also needed as the basis for developing adaptive and flexible approaches to policy and management.
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paper_title: Integrating Habitat Status, Human Population Pressure, and Protection Status into Biodiversity Conservation Priority Setting

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Priority setting is an essential component of biodiversity conservation. Existing methods to identify priority areas for conservation have focused almost entirely on biological factors. We suggest a new relative ranking method for identifying priority conservation areas that integrates both biological and social aspects. It is based on the following criteria: the habitat's status, human population pressure, human efforts to protect habitat, and number of endemic plant and vertebrate species. We used this method to rank 25 hotspots, 17 megadiverse countries, and the hotspots within each megadiverse country. We used consistent, comprehensive, georeferenced,andmultibanddatasetsandanalyticalremotesensingandgeographicinformationsystemtools to quantify habitat status, human population pressure, and protection status. The ranking suggests that the Philippines,AtlanticForest,MediterraneanBasin,CaribbeanIslands,Caucasus,andIndo-Burmaarethehottest hotspots and that China, the Philippines, and India are the hottest megadiverse countries. The great variation in terms of habitat, protected areas, and population pressure among the hotspots, the megadiverse countries, and the hotspots within the same country suggests the need for hotspot- and country-specific conservation policies.
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paper_title: Landscape change and the dynamics of open formations in a natural reserve

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Remote sensing, when used in conjunction with landscape pattern metrics, is a powerful method for the study of ecological dynamics at the landscape scale by means of multi-temporal analysis. In this paper, we examine temporal change in open formations in the natural reserve of Poggio all’Olmo (central Italy). This area has undergone rural depopulation and the cessation of traditional methods of agriculture, resulting in the subsequent re-establishment and spread of other vegetation formations. Aerial photographs taken in 1954, 1977 and 1998 were orthorectified and classified based on the physiognomic characteristics of the vegetation. An objective definition of the minimum mapping unit (MMU) was guaranteed by using vector format grids for this classification. We applied landscape pattern metrics based on landscape composition, the shape and size of patches and patch isolation. Our results demonstrate the key roles of shrubland, woodland and coniferous plantations in the ongoing fragmentation of open formations in the landscape. Multi-temporal landscape analyses, and, in particular, a restricted suite of landscape metrics, proved useful for detecting and quantitatively characterizing dynamic ecological processes. We conclude with some recommendations on the management alternatives feasible for the protection of the remaining grassland formations in the natural reserve of Poggio all’Olmo. © 2005 Published by Elsevier B.V.
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paper_title: Amazonian biodiversity and protected areas: do they meet?

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Protected areas are crucial for Amazonian nature conservation. Many Amazonian reserves have been selected systematically to achieve biodiversity representativeness. We review the role natural-scientific understanding has played in reserve selection, and evaluate the theoretical potential of the existing reserves to cover a complete sample of the species diversity of the Amazonian rainforest biome. In total, 108 reserves (604,832 km2) are treated as strictly protected and Amazonian; 87 of these can be seen as systematically selected to sample species diversity (75.3% of total area). Because direct knowledge on all species distributions is unavailable, surrogates have been used to select reserves: direct information on some species distributions (15 reserves, 14.8% of total area); species distribution patterns predicted on the basis of conceptual models, mainly the Pleistocene refuge hypothesis, (5/10.3%); environmental units (46/27.3%); or a combination of distribution patterns and environmental units (21/22.9%). None of these surrogates are reliable: direct information on species distributions is inadequate; the Pleistocene refuge hypothesis is highly controversial; and environmental classifications do not capture all relevant ecological variation, and their relevance for species distribution patterns is undocumented. Hence, Amazonian reserves cannot be safely assumed to capture all Amazonian species. To improve the situation, transparency and an active dialogue with the scientific community should be integral to conservation planning. We suggest that the best currently available approach for sampling Amazonian species diversity in reserve selection is to simultaneously inventory indicator plant species and climatic and geological conditions, and to combine field studies with remote sensing.
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paper_title: GIS-Based Multicriteria Evaluation and Fuzzy Sets to Identify Priority Sites for Marine Protection

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There is an increasing momentum within the marine conservation community to develop representative networks of marine protected areas (MPAs) covering up to 30% of global marine habitats. However, marine conservation initiatives are perceived as uncoordinated at most levels of planning and decision-making. These initiatives also face the challenge of being in conflict with ongoing drives for sustained or increased resource extraction. Hence, there is an urgent need to develop large scale theoretical frameworks that explicitly address conflicting objectives that are embedded in the design and development of a global MPA network. Further, the frameworks must be able to guide the implementation of smaller scale initiatives within this global context. This research examines the applicability of an integrated spatial decision support framework based on geographic information systems (GIS), multicriteria evaluation (MCE) and fuzzy sets to objectively identify priority locations for future marine protection. MCE is a well-established optimisation method used extensively in land use resource allocation and decision support, and which has to date been underutilised in marine planning despite its potential to guide such efforts. The framework presented here was implemented in the Pacific Canadian Exclusive Economic Zone (EEZ) using two conflicting objectives - biodiversity conservation and fisheries profit-maximisation. The results indicate that the GIS-based MCE framework supports the objective identification of priority locations for future marine protection. This is achieved by integrating multi-source spatial data, facilitating the simultaneous combination of multiple objectives, explicitly including stakeholder preferences in the decisions, and providing visualisation capabilities to better understand how global MPA networks might be developed under conditions of uncertainty and complexity.
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paper_title: Land cover mapping of large areas from satellites: status and research priorities

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Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and 'fine' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies - including pixel or scene compositing, radiometric corrections, classification and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processing and classification in relation to forthcoming improvements in data sources as well as ne...
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paper_title: Development of a large area biodiversity monitoring system driven by remote sensing

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Biodiversity is a multifaceted concept that often eludes simple operational definitions. As a result, a variety of definitions have been proposed each with varying levels of complexity and scope. While different definitions of biodiversity exist, the basic unit of measurement for the vast majority of studies is conducted at the species level. Traditional approaches to measuring species richness provide useful, yet spatially constrained information. Remote sensing offers the opportunity for large area characterizations of biodiversity in a systematic, repeatable, and spatially exhaustive manner. Based on this review we examine the potential for a national biodiversity monitoring system for Canada driven by remote sensing, a country approaching 1 billion ha in area, with the aim of producing recommendations that are transferable for regional or continental applications. A combination of direct and indirect approaches is proposed, with four selected key indicators of diversity that can be derived from Earth ...
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paper_title: A stochastic approach to marine reserve design: Incorporating data uncertainty

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Abstract Marine reserves, or protected areas, are used to meet an array of biodiversity and conservation objectives. The design of regional networks of marine reserves is concerned with the problem of where to place the marine protected areas and how to spatially configure them. Quantitative methods for doing this provide important decision support tools for marine managers. The central problem is to balance the costs and benefits of the reserve network, whilst satisfying conservation objectives (hence solving a constrained optimization problem). Current optimization algorithms for reserve design are widely used, but none allow for the systematic incorporation of data uncertainty and its effect on the reserve design solutions. The central purpose of this study is to provide a framework for incorporating uncertain ecological input data into algorithms for designing networks of marine reserves. In order to do this, a simplified version of the marine reserve design optimization problem is considered. A Metropolis–Hastings random search procedure is introduced to systematically sample the model solution space and converge on an optimal reserve design. Incorporation of the uncertain input data builds on this process and relies on a parametric bootstrapping procedure. This allows for the solution (i.e. the marine reserve design) to be expressed as the probability of any planning unit being included in the marine reserve network. Spatial plots of this acceptance probability are easily interpretable for decision making under uncertainty. The bootstrapping methodology is also readily adapted to existing comprehensive reserve design algorithms. Here, a preliminary application of the algorithm is made to the Mesoamerican Barrier Reef System (in the Caribbean Sea) based on satellite-derived and mapped conservation features (from Landsat).
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paper_title: Hyperspectral Remote Sensing of Canopy Biodiversity in Hawaiian Lowland Rainforests

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Mapping biological diversity is a high priority for conservation research, management and policy development, but few studies have provided diversity data at high spatial resolution from remote sensing. We used airborne imaging spectroscopy to map woody vascular plant species richness in lowland tropical forest ecosystems in Hawai’i. Hyperspectral signatures spanning the 400–2,500 nm wavelength range acquired by the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) were analyzed at 17 forest sites with species richness values ranging from 1 to 17 species per 0.1–0.3 ha. Spatial variation (range) in the shape of the AVIRIS spectra (derivative reflectance) in wavelength regions associated with upper-canopy pigments, water, and nitrogen content were well correlated with species richness across field sites. An analysis of leaf chlorophyll, water, and nitrogen content within and across species suggested that increasing spectral diversity was linked to increasing species richness by way of increasing biochemical diversity. A linear regression analysis showed that species richness was predicted by a combination of four biochemically-distinct wavelength observations centered at 530, 720, 1,201, and 1,523 nm (r 2 = 0.85, p < 0.01). This relationship was used to map species richness at approximately 0.1 ha resolution in lowland forest reserves throughout the study region. Future remote sensing studies of biodiversity will benefit from explicitly connecting chemical and physical properties of the organisms to remotely sensed data.
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paper_title: Behavioral barriers to non-migratory movements of birds

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Although effects of physical barriers to animal movement are well established, the behavioral inhibition of individuals moving across habitat gaps, ecotones, and interpatch (matrix) habitat has received little attention. Birds are often cited as a taxon in which movements should not be disrupted by gaps in landscape connectivity. Here we synthesize evidence from the literature for behavioral inhibition of movements by birds, and fi nd that a wide variety of behavioral inhibitions to movements have been observed. We also present a model for describing edge or gap permeability that incorporates the propensity of an individual to cross an ecotone or enter a gap, and the effect of gap width. From published observations, we propose fi ve ecologically based patterns of behavioral inhibition of bird movements as hypotheses: that habitat specialists, understory-dwellers, tropical species, solitary species, and non-migratory species are more inhibited than are species that are their ecological counterparts. Understanding what animals perceive as impediments to movement will contribute to efforts to maintain populations through landscape design, and will allow us to predict the types and degrees of habitat fragmentation that will cause persistence problems for various species.
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paper_title: Crop classification by support vector machine with intelligently selected training data for an operational application

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The accuracy of a supervised classification is dependent to a large extent on the training data used. The aim in training is often to capture a large training set to fully describe the classes spectrally, commonly with the requirements of a conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes, especially if using a support vector machine (SVM) as the classifier. An SVM seeks to fit an optimal hyperplane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. An approach to identify informative training samples was demonstrated for the classification of agricultural classes in south-western part of Punjab state, India. A small, intelligently selected, training dataset was acquired in the field with the aid of ancillary information. This dataset contained the data from training sites that were predicted before the classification to be amongst the most informative for an SVM classification. The intelligent training collection scheme yielded a classification of comparable accuracy, ∼91%, to one derived using a larger training set acquired by a conventional approach. Moreover, from inspection of the training sets it was apparent that the intelligently defined training set contained a greater proportion of support vectors (0.70), useful training sites, than that acquired by the conventional approach (0.41). By focusing on the most informative training samples, the intelligent scheme required less investment in training than the conventional approach and its adoption would have reduced the total financial outlay in classification production and evaluation by ∼26%. Additionally, the analysis highlighted the possibility to further reduce the training set size without any significant negative impact on classification accuracy.
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paper_title: Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

paper_content:

In this study we use a technique referred to as Gaussian decomposition for processing and calibrating data acquired with a novel small-footprint airborne laser scanner that digitises the complete waveform of the laser pulses scattered back from the Earth's surface. This paper presents the theoretical basis for modelling the waveform as a series of Gaussian pulses. In this way the range, amplitude, and width are provided for each pulse. Using external reference targets it is also possible to calibrate the data. The calibration equation takes into account the range, the amplitude, and pulse width and provides estimates of the backscatter cross-section of each target. The applicability of this technique is demonstrated based on RIEGL LMS-Q560 data acquired over the city of Vienna.
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paper_title: Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability

paper_content:

Abstract The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.
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paper_title: Assessing forest metrics with a ground-based scanning lidar

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A ground-based scanning lidar (light detection and ranging) system was evaluated to assess its potential utility for tree-level forest mensuration data extraction. Ground-based-lidar and field-mens...
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paper_title: COUPLING ECOLOGY AND GIS TO EVALUATE EFFICACY OF MARINE PROTECTED AREAS IN HAWAII

paper_content:

In order to properly determine the efficacy of marine protected areas (MPAs), a seascape perspective that integrates ecosystem elements at the appropriate ecological scale is necessary. Over the past four decades, Hawaii has developed a system of 11 Marine Life Conservation Districts (MLCDs) to conserve and replenish marine resources around the state. Initially established to provide opportunities for public interaction with the marine environment, these MLCDs vary in size, habitat quality, and management regimes, providing an excellent opportunity to test hypotheses concerning MPA design and function using multiple discrete sampling units. Digital benthic habitat maps for all MLCDs and adjacent habitats were used to evaluate the efficacy of existing MLCDs using a spatially explicit stratified random sampling design. Analysis of benthic cover validated the a priori classification of habitat types and provided justification for using these habitat strata to conduct stratified random sampling and analyses of fish habitat utilization patterns. Results showed that a number of fish assemblage characteristics (e.g., species richness, biomass, diversity) vary among habitat types, but were significantly higher in MLCDs compared with adjacent fished areas across all habitat types. Overall fish biomass was 2.6 times greater in the MLCDs compared to open areas. In addition, apex predators and other species were more abundant and larger in the MLCDs, illustrating the effectiveness of these closures in conserving fish populations within their boundaries. Habitat type, protected area size, and level of protection from fishing were all important determinates of MLCD effectiveness with respect to their associated fish assemblages. Although size of these protected areas was positively correlated with a number of fish assemblage characteristics, all appear too small to have any measurable influence on the adjacent fished areas. These protected areas were not designed for biodiversity conservation or fisheries enhancement yet still provide varying degrees of protection for fish populations within their boundaries. Implementing this type of biogeographic process, using remote sensing technology and sampling across the range of habitats present within the seascape, provides a robust evaluation of existing MPAs and can help to define ecologically relevant boundaries for future MPA design in a range of locations.
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paper_title: Remote sensing and land cover area estimation

paper_content:

This article gives an overview of different ways to use satellite images for land cover area estimation. Approaches are grouped into three categories. (1) Estimates coming essentially from remote sensing. Ground data, are used as an auxiliary tool, mainly as training data for image classification, or sub-pixel analysis. Area estimates from pixel counting are sometimes used without a solid statistical justification. (2) Methods, such as regression, calibration and small area estimators, combining exhaustive but inaccurate information (from satellite images) with accurate information on a sample (most often ground surveys). (3) Satellite images can support area frame surveys in several ways: to define sampling units, for stratification; as graphic documents for the ground survey, or for quality control. Cost-efficiency is discussed. Operational use of remote sensing is easier now with cheaper Landsat Thematic Mapper images and computing, but many administrations are reluctant to integrate remote sensing in ...
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paper_title: Mediterranean ecosystems: problems and tools for conservation

paper_content:

Mediterranean ecosystems rival tropical ecosystems in terms of plant biodiversity. The Mediterranean Basin (MB) itself hosts 25 000 plant species, half of which are endemic. This rich biodiversity and the complex biogeographical and political issues make conservation a difficult task in the region. Species, habitat, ecosystem and landscape approaches have been used to identify conservation targets at various scales: ie, European, national, regional and local. Conservation decisions require adequate information at the species, community and habitat level. Nevertheless and despite recent improvements/efforts, this information is still incomplete, fragmented and varies from one country to another. This paper reviews the biogeographic data, the problems arising from current conservation efforts and methods for the conservation assessment and prioritization using GIS. GIS has an important role to play for managing spatial and attribute information on the ecosystems of the MB and to facilitate interactions with existing databases. Where limited information is available it can be used for prediction when directly or indirectly linked to externally built models. As well as being a predictive tool today GIS incorporate spatial techniques which can improve the level of information such as fuzzy logic, geostatistics, or provide insight about landscape changes such as 3D visualization. Where there are limited resources it can assist with identifying sites of conservation priority or the resolution of environmental conflicts (scenario building). Although not a panacea, GIS is an invaluable tool for improving the understanding of Mediterranean ecosystems and their dynamics and for practical management in a region that is under increasing pressure from human impact.
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paper_title: Interannual variability of NDVI and species richness in Kenya

paper_content:

Ecologists have long recognized the spatial variability of species richness. In an attempt to identify the factors responsible for this variability, ecologists have traditionally used environmental data obtained from sparse point samples (such as meteorological stations). However, remotely sensed data also provide a means of estimating relevant environmental factors and thereby improving predictions of species richness. The Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index (AVHRR NDVI) has been shown to be related to net primary productivity (NPP) and actual evapotranspiration (AET) for many vegetation types. NPP and AET have frequently been used as surrogate measures for species richness. Local spatial variability of NPP and AET, indicating habitat heterogeneity, is hypothesized as another influence on species richness. We examined the relationship between interannual maximum NDVI parameters and species richness of vascular plants and mammals. The study was done at a landsca...
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paper_title: Climate: Counting carbon in the Amazon

paper_content:

If the next climate treaty tackles deforestation, tropical nations will need to monitor the biomass of their forests. One ecologist has worked out a way to do that from the sky, finds Jeff Tollefson.
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paper_title: The first detailed land‐cover map of Socotra Island by Landsat/ETM+ data

paper_content:

Present study has produced first detailed land‐cover map of Socotra Island. A Landsat 7 ETM+ dataset was used as a main source of remotely sensed data. From numerous reference points (more than 250) coming from the ground data verification the set of training fields and the set of evaluation fields were digitised. As a classification method the supervised maximum likelihood classification without prior probabilities was used in combination with rule‐based post‐classification sorting, providing results of sufficient accuracy and subject resolution. Estimates of the area and degree of coverage of particular land‐cover classes within Socotra Island have brought excellent overview on state of island biotopes. Overall accuracy of the map achieved is more than 80%, 19 terrestrial land‐cover classes (including three types of Shrublands, three types of Woodlands, two types of Forests and Mangroves) have been distinguished. It consequently allows estimates of the current and potential occurrence of endemic plant p...
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paper_title: Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

paper_content:

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 20 10 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 x 10 km sampling sites systematically-distributed every 0.5 degrees over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest. (C) 2008 Elsevier Inc. All rights reserved.
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paper_title: Bird distributions relative to remotely sensed habitats in Great Britain : Towards a framework for national modelling

paper_content:

Abstract This paper develops a comprehensive and objective picture of bird distributions relative to habitats across Britain. Bird species presence/absence data from an extensive field survey and habitat data from the remotely sensed UK Land Cover Map 2000 were analysed in 36,920 tetrads (2 km×2 km) across Britain (a 65% sample of Britain's c. 240 000 km 2 ). Cluster analysis linked birds to generalised landscapes based on distinctive habitat assemblages. Maps of the clusters showed strong regional patterns associated with the habitat assemblages. Cluster centroid coordinates for each bird species and each habitat were combined across clusters to derive individualised bird–habitat preference indices and examine the importance of individual habitats for each bird species. Even rare species and scarce habitats showed successful linkages. Results were assessed against published accounts of bird–habitat relations. Objective corroboration strongly supported the associations. Relatively scarce coastal and wetland habitats proved particularly important for many birds. However, extensive arable farmland and woodland habitats were also favoured by many species, despite reported declines in bird numbers in these habitats. The fact that habitat-specialists do not or cannot move habitat is perhaps a reason for declining numbers where habitats have become unsuitable. This study showed that there are unifying principles determining bird–habitat relations which apply and can be quantified at the national scale, and which corroborate and complement the cumulative knowledge of many and varied surveys and ecological studies. This ‘generality’ suggests that we may be able, reliably and objectively, to integrate and scale up such disparate studies to the national scale, using this generalised framework. It also suggests the potential for a landscape ecology approach to bird–habitat analyses. Such developments will be important steps in building models to develop and test the sustainable management of landscapes for birds.
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paper_title: Advanced full-waveform lidar data echo detection: Assessing quality of derived terrain and tree height models in an alpine coniferous forest

paper_content:

Small footprint full-waveform airborne lidar systems offer large opportunities for improved forest characterization. To take advantage of full-waveform information, this paper presents a new processing method based on the decomposition of waveforms into a sum of parametric functions. The method consists of an enhanced peak detection algorithm combined with advanced echo modelling including Gaussian and generalized Gaussian models. The study focuses on the qualification of the extracted geometric information. Resulting 3D point clouds were compared to the point cloud provided by the operator. 40 to 60% additional points were detected mainly in the lower part of the canopy and in the low vegetation. Their contribution to Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) was then analysed. The quality of DTMs and CHM-based heights was assessed using field measurements on black pine plots under various topographic and stand characteristics. Results showed only slight improvements, up to 5 cm bias ...
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paper_title: Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods

paper_content:

The paper presents a case study for the application of satellite remote sensing and GIS data and methods in the context of habitat monitoring and landscape assessment at different scales. The range of work covers the production of overview maps for land covers, techniques of classification for detailed habitat maps, change detection as a management support tool for the updating of existing habitat databases and an integrative GIS model to delineate habitat suitability for key species. Furthermore the role of comprehensive indicators and historical satellite data in investigating landscape change over two decades on a regional scale is discussed. Future activities for transferring the respective approaches onto a pan-European scale are presented in a concluding discussion.
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paper_title: A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

paper_content:

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.
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paper_title: Landscape pattern and species richness; regional scale analysis from remote sensing

paper_content:

Concern about the future of biodiversity in the wider countryside is stimulating the development of methods for species and ecosystem monitoring over large areas. The objective of this paper is to explore the potential of remotely sensed data for measuring landscape structure as an important determinant of species diversity. Data from the satellite Land Cover Map of Great Britain, a land cover classification of Landsat Thematic Mapper scenes, were used to derive a set of simple measures of landscape structure within 2km x 2km tetrads for three vascular plant families. Results from a model to predict plant diversity from landscape structure alone proved difficult to interpret ecologically and highlighted the need to obtain data on both landscape quality and landscape structure.
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paper_title: Landsat's Role in Ecological Applications of Remote Sensing

paper_content:

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat.
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paper_title: Associations between wasp communities and forest structure: Do strong local patterns hold across landscapes?

paper_content:

Forest structure and habitat complexity have been used extensively to predict the distribution and abundance of insect assemblages in forest ecosystems.We tested empirically derived predictions of strong, consis- tent relationships between wasp assemblages and habitat complexity, using both field assessments and vegetation indices from remote sensing as measures of habitat complexity. Wasp samples from 26 paired 'high and low' complexity sites in two forests approximately 70 km apart, were compared with normalized difference vegetation indices (NDVIs) derived from multispectral videography of the survey sites.We describe a strong unequivocal link between habitat complexity and wasp communities, the patterns holding over coarse and fine landscape scales. NDVIs were also excellent predictors of habitat complexity and hence wasp community patterns. Sites with greater NDVIs consistently supported a greater abundance and species richness, and a different composition of wasps to sites with low NDVIs. Using vegetation indices from remote sensing to gauge habitat complexity has significant potential for ecosystem modelling and rapid biodiversity assessment.
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paper_title: PREDICTING WOODY-PLANT SPECIES RICHNESS IN TROPICAL DRY FORESTS: A CASE STUDY FROM SOUTH FLORIDA, USA

paper_content:

Tropical dry forests are one of the world's most endangered forest types. Currently there are no comparative data on extent or levels of species richness for remaining forest fragments. This research identifies landscape metrics and spectral indices that can be applied at the stand and patch level to predict woody-plant species richness in tropical dry forests. This study was undertaken in 18 stands of tropical dry forest with nine sites in the Florida Keys and nine sites within an urban–agricultural matrix in mainland Florida, USA. Woody-plant species richness was quantified at the stand level (belt transects totaling 500 m2) and patch level (presence/absence data for 65 native tropical plants ≤2.5 cm dbh) for all study sites. Landsat Enhanced Thematic Mapper Plus (ETM+) satellite images (pixel resolution 30 × 30 m) were used to assess the utility of landscape metrics (forest patch area, nearest neighbor distance, shape index, boundary complexity) and spectral indices (normalized-difference vegetation index [NDVI] for nine pixels and 500 pixels directly over transects, and all pixels in the forest patch area) for predicting stand- and patch-level species richness. The 18 stands of tropical dry forest sampled in this study included 4248 woody plants, representing 71 species. ::: ::: Islands in the Florida Keys had higher levels of woody-plant species richness than mainland sites. There was a significant positive relationship between mean NDVI for the nine pixels over each stand and stand species richness and a significant negative relationship between species richness and standard deviation of NDVI for nine pixels over each stand. The density of evergreen plants explained 66% of the variability in mean NDVI. At the patch level, forest patch area and mean NDVI at the stand, 500-pixel, and patch level were all positively associated with patch species richness. However, combining forest patch area with NDVI significantly improved the prediction of patch species richness. Results from this study support the species–energy theory at the level of a forest stand and patch and suggest that a first-order approximation of woody-plant species richness in stands and patches of tropical dry forest is possible in biodiversity hot spots.
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paper_title: Bias in land cover change estimates due to misregistration

paper_content:

Land cover change may be overestimated due to positional error in multi-temporal images. To assess the potential magnitude of this bias, we introduced random positional error to identical classified images and then subtracted them. False land cover change ranged from less than 5% for a 5-class AVHRR classification, to more than 33% for a 20-class Landsat TM classification. The potential for false change was higher with more classes. However, false change could not be reliably estimated simply by number of classes, since false change varied significantly by simulation trial when class size remained constant. Registration model root mean squared (rms) error may underestimate the actual image co-registration asccuracy. In simulations with 5 to 50 ground control locations, the mean model rms error was always less than the actual population rms error. The model rms error was especially unreliable when small sample sizes were used to develop second order rectification models. We introduce a bootstrap resampling...
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paper_title: Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada.

paper_content:

Although there is no shortage of potential explanations for the large-scale patterns of biological diversity, the hypothesis that energy-related factors are the primary determinants is perhaps most extensively supported, especially in cold-temperate regions. By using unusually high-resolution biodiversity and environmental data that have not previously been available, we demonstrate that habitat heterogeneity, as measured by remotely sensed land cover variation, explains Canadian butterfly richness better than any energy-related variable we measured across spatial scales. Although species-richness predictability declines with progressively smaller quadrat sizes, as expected, we demonstrate that most variability (>90%) in butterfly richness may be explained by habitat heterogeneity with secondary contributions from climatic energy. We also find that patterns of community similarity across Canada are strongly related to patterns of habitat composition but not to differences in energy-related factors. Energy should still be considered significant but its main role may be through its effects on within-habitat diversity and perhaps, indirectly, on the sorts of habitats that may be found in a region. Effects of sampling intensity and spatial autocorrelation do not alter our findings.
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paper_title: EDGE RESPONSES OF TROPICAL AND TEMPERATE BIRDS

paper_content:

Abstract Tropical birds may differ from temperate birds in their sensitivity to forest edges. We provide predictions about the proportions of tropical and temperate species that should avoid or exploit edges, and relationships between natural-history characters and edge responses. We conducted exploratory meta-analyses from 11 studies using 287 records of 220 neotropical and temperate species' responses to edges to address our predictions. A higher proportion of neotropical species were edge-avoiders compared with temperate species and a higher proportion of temperate species were edge-exploiters compared with neotropical species. Edge-avoiding responses were positively associated with being an insectivore for neotropical birds, and with being of small body mass and a latitudinal migrant for temperate birds. Temperate edge-exploiters were less likely to be insectivores and migrants than temperate birds that were not edge-exploiters. A greater proportion of neotropical birds than temperate birds may be at ...
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paper_title: PREDICTING BIRD SPECIES RICHNESS USING REMOTE SENSING IN BOREAL AGRICULTURAL‐FOREST MOSAICS

paper_content:

One of the main goals in nature conservation and land use planning is to identify areas important for biodiversity. One possible cost-effective surrogate for deriving appropriate estimates of spatial patterns of species richness is provided by predictive modeling based on remote sensing and topographic data. Using bird species richness data from a spatial grid system (105 squares of 0.25 km2 within an area of 26.25 km2), we tested the usefulness of Landsat TM satellite-based remote sensing and topographic data in bird species richness modeling in a boreal agricultural-forest mosaic in southwestern Finland. We built generalized linear models for the bird species richness and validated the accuracy of the models with an independent test area of 50 grid squares (12.5 km2). We evaluated separately the modeling performance of habitat structure, habitat composition, topographical-moisture variables and all variables in the model-building and model-test areas. Areas of high observed and predicted bird species ri...
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paper_title: A method to compare and improve land cover datasets: application to the GLC-2000 and MODIS land cover products

paper_content:

This paper presents a methodology for the comparison of different land cover datasets and illustrates how this can be extended to create a hybrid land cover product. The datasets used in this paper are the GLC-2000 and MODIS land cover products. The methodology addresses: 1) the harmonization of legend classes from different global land cover datasets and 2) the uncertainty associated with the classification of the images. The first part of the methodology involves mapping the spatial disagreement between the two land cover products using a combination of fuzzy logic and expert knowledge. Hotspots of disagreement between the land cover datasets are then identified to determine areas where other sources of data such as TM/ETM images or detailed regional and national maps can be used in the creation of a hybrid land cover dataset
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paper_title: Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA

paper_content:

Habitat heterogeneity has long been recognized as a fundamental variable indicative of species diversity, in terms of both richness and abundance. Satellite remote sensing data sets can be useful for quantifying habitat heterogeneity across a range of spatial scales. Past remote sensing analyses of species diversity have largely been limited to correlative studies based on the use of vegetation indices or derived land cover maps. A relatively new form of laser remote sensing (lidar) provides another means to acquire information on habitat heterogeneity. Here we examine the efficacy of lidar metrics of canopy structural diversity as predictors of bird species richness in the temperate forests of Maryland, USA. Canopy height, topography and the vertical distribution of canopy elements were derived from lidar imagery of the Patuxent National Wildlife Refuge and compared to bird survey data collected at referenced grid locations. The canopy vertical distribution information was consistently found to be the strongest predictor of species richness, and this was predicted best when stratified into guilds dominated by forest, scrub, suburban and wetland species. Similar lidar variables were selected as primary predictors across guilds. Generalized linear and additive models, as well as binary hierarchical regression trees produced similar results. The lidar metrics were also consistently better predictors than traditional remotely sensed variables such as canopy cover, indicating that lidar provides a valuable resource for biodiversity research applications.
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paper_title: Global land surface phenology trends from GIMMS database

paper_content:

A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter. A principal component analysis on the resulting time series indicates that the first components explain 36, 53 and 37% of the variance for the start, end and length of growing season, respectively, and shows generally good spatial homogeneity. Mann-Kendall trend tests have been carried out, and trends were estimated by linear regression. Maps of these trends show a global advance in spring dates of 0.38 days per year, a global delay in autumn dates of 0.45 days per year and a global increase of 0.8 days per year in the growing seasons validated by comparison with previous works. Correlations between retrieved phenological parameters and climate indices generally showed a good spatial coherence.
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paper_title: Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements

paper_content:

[1] In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.
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paper_title: Monitoring vegetation phenology using MODIS

paper_content:

Abstract Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.
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paper_title: The MERIS terrestrial chlorophyll index

paper_content:

The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and indirect evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS terrestrial chlorophyll index (MTCI), was evaluated using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP but unlike REP was sensitive to high values of chlorophyll content. As a result this index became an official MERIS level-2 product of the European Space Agency in March 2004. Further direct evaluation of the MTCI is proposed, using both greenhouse and field data.
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paper_title: Characterising the spatial pattern of phenology for the tropical vegetation of India using multi-temporal MERIS chlorophyll data

paper_content:

The annual growth cycles of terrestrial ecosystems are related to long-term regional/global climatic patterns. Understanding vegetation phenology and its spatio-temporal variation is required to reveal and predict ongoing changes in Earth system dynamics. The study attempts to characterize the phenology of the major tropical vegetation types in India, since such information is not yet available for India. Multi-temporal Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data were utilized to derive onset of greenness (OG) and end of senescence (ES) for four major tropical vegetation types. The study found that Fourier-smoothed results using the first four components revealed adequately the annual phenological variation of the natural vegetation types in India. From these smoothed data, inflection points were located iteratively through a spatio-temporal search, spanning over 18 months of 8-day composite data, per pixel such as to derive the OG and ES. The median OG and ES was extracted from the available annual results for the years 2003–04, 2004–05, 2005–06 and 2006–07. The GLC2000 land cover map (1 km spatial resolution) was utilized to determine the locations of the major vegetation types. The percentage of each vegetation type falling beneath a MTCI composite pixel (4.6 km spatial resolution) was calculated. MTCI composite pixels with homogeneity of ≥80% vegetative cover were used for examining pattern of phenology in different regions, different years and at different latitudes. The most common dates for the occurrence of OG for the tropical evergreen, semi-evergreen, moist-deciduous, and dry-deciduous vegetation types were found to be during February–April, January–April, March–May, and February–May, respectively. Similarly, for ES the most common dates were in February–April, January–April, February–April, and December–April, respectively. The phenological pattern was uniquely different for each vegetation type, as expected, and also differed with regions and latitudes. A general trend of early occurrence of OG in the lower latitudes was observed.
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paper_title: Intercalibration of vegetation indices from different sensor systems

paper_content:

Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR-2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 1–2%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change.
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paper_title: Landsat continuity : Issues and opportunities for land cover monitoring

paper_content:

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.
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paper_title: Responses of spring phenology to climate change

paper_content:

Summary ::: Climate change effects on seasonal activity in terrestrial ecosystems are significant and well documented, especially in the middle and higher latitudes. Temperature is a main driver of many plant developmental processes, and in many cases higher temperatures have been shown to speed up plant development and lead to earlier switching to the next ontogenetic stage. Qualitatively consistent advancement of vegetation activity in spring has been documented using three independent methods, based on ground observations, remote sensing, and analysis of the atmospheric CO2 signal. However, estimates of the trends for advancement obtained using the same method differ substantially. We propose that a high fraction of this uncertainty is related to the time frame analysed and changes in trends at decadal time scales. Furthermore, the correlation between estimates of the initiation of spring activity derived from ground observations and remote sensing at interannual time scales is often weak. We propose that this is caused by qualitative differences in the traits observed using the two methods, as well as the mixture of different ecosystems and species within the satellite scenes.
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paper_title: Measuring phenological variability from satellite imagery

paper_content:

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiom- eter (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variabil- ity of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and pre- dicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demon- strated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particu- larly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.
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paper_title: MERIS: the re-branding of an ocean sensor

paper_content:

MERIS (Medium Resolution Imaging Spectrometer) is a fine spectral and medium spatial resolution satellite sensor and is part of the core instrument payload of Envisat, the European Space Agency's (ESA) environmental research satellite, launched in March 2002. Designed primarily for ocean (‘MER’) and coastal zone remote sensing, this imaging spectrometer (‘IS’) now has a much broader environmental remit covering also land and atmospheric applications. This paper reviews (i) MERIS's development history, focusing on its changing mission objectives; (ii) MERIS's technical specification, including its radiometric, spectral and geometric characteristics, programmability and onboard calibration; (iii) decisions that led to modifications of MERIS's spectral, geometric and radiometric performance for land applications; (iv) MERIS's data products; and (v) some of the ways in which MERIS data might be used to provide information on terrestrial vegetation.
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paper_title: Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data

paper_content:

Recent studies using both field measurements and satellite-derived-vegetation indices have demonstrated that global warming is influencing vegetation growth and phenology. To accurately predict the future response of vegetation to climate variation, a thorough understanding of vegetation phenological cycles and their relationship to temperature and precipitation is required. In this paper, vegetation phenological transition dates identified using data from the moderate-resolution imaging spectroradiometer (MODIS) in 2001 are linked with MODIS land surface temperature (LST) data from the northern hemisphere between 351N and 701N. The results show well-defined patterns dependent on latitude, in which vegetation greenup gradually migrates northward starting in March, and dormancy spreads southward from late September. Among natural vegetation land-cover types, the growing-season length for forests is strongly correlated with variation in mean annual LST. For urban areas, the onset of greenup is 4‐9 days earlier on average, and the onset of dormancy is about 2‐16 days later, relative to adjacent natural vegetation. This difference (especially for urban vs. forests) is apparently related to urban heat island effects that result in both the average spring temperature and the mean annual temperature in urban areas being about 1‐31C higher relative to rural areas. The results also indicate that urban heat island effects on vegetation phenology are stronger in North America than in Europe and Asia. Finally, the onset of forest greenup at continental scales can be effectively described using a thermal time-chilling model, which can be used to infer the delay or advance of greenup onset in relation to climatic warming at global scale.
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paper_title: The MERIS Global Vegetation Index (MGVI): Description and preliminary application

paper_content:

This paper describes the physical and mathematical approach followed to design a vegetation index optimized for the Medium Resolution Imaging Spectrometer (MERIS) sensor, i.e. the MERIS Global Vegetation Index (MGVI). It complements an earlier feasibility study presented elsewhere in this issue by Govaerts and collaborators. Specifically, the crucial issue of the dependency of the vegetation index on changes in illumination and observing geometries is addressed, together with the atmospheric contamination problem. The derivation of the optimal MGVI index formulae allows a comparison of its performance with that of the widely used Normalized Difference Vegetation Index (NDVI), both from a theoretical and an experimental point of view. Data collected by the MOS/IRS-P3 instrument since March 1996 in spectral bands analogous to those that will be available from MERIS can be used to evaluate the MVGI.
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paper_title: Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

paper_content:

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of ...
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paper_title: Variations in satellite-derived phenology in China's temperate vegetation

paper_content:

The relationship between vegetation phenology and climate is a crucial topic in global change research because it indicates dynamic responses of terrestrial ecosystems to climate changes. In this study, we investigate the possible impact of recent climate changes on growing season duration in the temperate vegetation of China, using the advanced very high resolution radiometer (AVHRR)/normalized difference vegetation index (NDVI) biweekly time-series data collected from January 1982 to December 1999 and concurrent mean temperature and precipitation data. The results show that over the study period, the growing season duration has lengthened by 1.16 days yr � 1 in temperate region of China. The green-up of vegetation has advanced in spring by 0.79 days yr � 1 and the dormancy delayed in autumn by 0.37 days yr � 1 . The dates of onset for phenological events are most significantly related with the mean temperature during the preceding 2–3 months. A warming in the early spring (March to early May) by 11C could cause an earlier onset of green-up of 7.5 days, whereas the same increase of mean temperature during autumn (mid-August through early October) could lead to a delay of 3.8 days in vegetation dormancy. Variations in precipitation also influenced the duration of growing season, but such influence differed among vegetation types and phenological phases.
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paper_title: A comparative study of satellite and ground-based phenology

paper_content:

Long time series of ground-based plant phenology, as well as more than two decades of satellite-derived phenological metrics, are currently available to assess the impacts of climate variability and trends on terrestrial vegetation. Traditional plant phenology provides very accurate information on individual plant species, but with limited spatial coverage. Satellite phenology allows monitoring of terrestrial vegetation on a global scale and provides an integrative view at the landscape level. Linking the strengths of both methodologies has high potential value for climate impact studies. We compared a multispecies index from ground-observed spring phases with two types (maximum slope and threshold approach) of satellite-derived start-of-season (SOS) metrics. We focus on Switzerland from 1982 to 2001 and show that temporal and spatial variability of the multispecies index correspond well with the satellite-derived metrics. All phenological metrics correlate with temperature anomalies as expected. The slope approach proved to deviate strongly from the temporal development of the ground observations as well as from the threshold-defined SOS satellite measure. The slope spring indicator is considered to indicate a different stage in vegetation development and is therefore less suited as a SOS parameter for comparative studies in relation to ground-observed phenology. Satellite-derived metrics are, however, very susceptible to snow cover, and it is suggested that this snow cover should be better accounted for by the use of newer satellite sensors.
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paper_title: A web-based GIS tool for exploring the world's biodiversity: The Global Biodiversity Information Facility Mapping and Analysis Portal Application (GBIF-MAPA)

paper_content:

Abstract Legacy biodiversity data from natural history and survey collections are rapidly becoming available in a common format over the Internet. Over 110 million records are already being served from the Global Biodiversity Information Facility (GBIF). However, our ability to use this information effectively for ecological research, management and conservation lags behind. A solution is a web-based Geographic Information System for enabling visualization and analysis of this rapidly expanding data resource. In this paper we detail a case study system, GBIF Mapping and Analysis Portal Application (MAPA), developed for deployment at distributed database portals. Building such a system requires overcoming a series of technical and research challenges. These challenges include: assuring fast speed of access to the vast amounts of data available through these distributed biodiversity databases; developing open standards based access to suitable environmental data layers for analyzing biodiversity distribution; building suitably flexible and intuitive map interfaces for refining the scope and criteria of an analysis; and building appropriate web-services based analysis tools that are of primary importance to the ecological community and make manifest the value of online biodiversity GBIF data. After discussing how we overcome these challenges, we provide case studies showing two examples of the use of GBIF-MAPA analysis tools.
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paper_title: Fieldservers and Sensor Service Grid as Real-time Monitoring Infrastructure for Ubiquitous Sensor Networks

paper_content:

The fieldserver is an Internet based observation robot that can provide an outdoor solution for monitoring environmental parameters in real-time. The data from its sensors can be collected to a central server infrastructure and published on the Internet. The information from the sensor network will contribute to monitoring and modeling on various environmental issues in Asia, including agriculture, food, pollution, disaster, climate change etc. An initiative called Sensor Asia is developing an infrastructure called Sensor Service Grid (SSG), which integrates fieldservers and Web GIS to realize easy and low cost installation and operation of ubiquitous field sensor networks.
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paper_title: Avian Information Systems: Developing Web-Based Bird Avoidance Models

paper_content:

Collisions between aircraft and birds, so-called "bird strikes," can result in serious damage to aircraft and even in the loss of lives. Information about the distribution of birds in the air and on the ground can be used to reduce the risk of bird strikes and their impact on operations en route and in and around air fields. Although a wealth of bird distribution and density data is collected by numerous organizations, these data are not readily available nor interpretable by aviation. This paper presents two national efforts, one in the Netherlands and one in the United States, to develop bird avoidance nodels for aviation. These models integrate data and expert knowledge on bird distributions and migratory behavior to provide hazard maps in the form of GIS-enabled Web services. Both models are in operational use for flight planning and flight alteration and for airfield and airfield vicinity management. These models and their presentation on the Internet are examples of the type of service that would be very useful in other fields interested in species distribution and movement information, such as conservation, disease transmission and prevention, or assessment and mitigation of anthropogenic risks to nature. We expect that developments in cyber-technology, a transition toward an open source philosophy, and higher demand for accessible biological data will result in an increase in the number of biological information systems available on the Internet.
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paper_title: Integrated research of parallel computing: Status and future

paper_content:

In the past twenty years, the research group in University of Science and Technology of China has developed an integrated research method for parallel computing, which is a combination of “Architecture-Algorithm-Programming-Application”. This method is also called the ecological environment of parallel computing research. In this paper, we survey the current status of integrated research method for parallel computing and by combining the impact of multi-core systems, cloud computing and personal high performance computer, we present our outlook on the future development of parallel computing.
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paper_title: Environmental sensor networks in ecological research

paper_content:

Environmental sensor networks offer a powerful combination of distributed sensing capacity, real-time data visualization and analysis, and integration with adjacent networks and remote sensing data streams. These advances have become a reality as a combined result of the continuing miniaturization of electronics, the availability of large data storage and computational capacity, and the pervasive connectivity of the Internet. Environmental sensor networks have been established and large new networks are planned for monitoring multiple habitats at many different scales. Projects range in spatial scale from continental systems designed to measure global change and environmental stability to those involved with the monitoring of only a few meters of forest edge in fragmented landscapes. Temporal measurements have ranged from the evaluation of sunfleck dynamics at scales of seconds, to daily CO2 fluxes, to decadal shifts in temperatures. Above-ground sensor systems are partnered with subsurface soil measurement networks for physical and biological activity, together with aquatic and riparian sensor networks to measure groundwater fluxes and nutrient dynamics. More recently, complex sensors, such as networked digital cameras and microphones, as well as newly emerging sensors, are being integrated into sensor networks for hierarchical methods of sensing that promise a further understanding of our ecological systems by revealing previously unobservable phenomena.
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paper_title: Developing a grid-enabled spatial Web portal for Internet GIServices and geospatial cyberinfrastructure

paper_content:

Geospatial cyberinfrastructure integrates distributed geographic information processing (DGIP) technology, high-performance computing resources, interoperable Web services, and sharable geographic knowledge to facilitate the advancement of geographic information science (GIScience) research, geospatial technology, and geographic education. This article addresses three major development issues of geospatial cyberinfrastructure: the performance of grid-enabled DGIP services, the integration of Internet GIService resources, and the technical challenges of spatial Web portal implementation. A four-tier grid-enabled Internet GIService framework was designed for geospatial cyberinfrastructure. The advantages of the grid-enabled framework were demonstrated by a spatial Web portal. The spatial Web portal was implemented based on current available Internet technologies and utilizes multiple computing resources and high-performance systems, including local PC clusters and the TeraGrid. By comparing their performance testing results, we found that grid computing (TeraGrid) is more powerful and flexible than local PC clusters. However, job queuing time and relatively poor performance of cross-site computation are the major obstacles of grid computing for geospatial cyberinfrastructure. Detailed analysis of different computational settings and performance testing contributes to a deeper understanding of the improvements of DGIP services and geospatial cyberinfrastructure. This research demonstrates that resource/service integration and performance improvement can be accomplished by deploying the new four-tier grid-enabled Internet GIService framework. This article also identifies four research priorities for developing geospatial cyberinfrastructure: the design of GIS middleware, high-performance geovisualization methods, semantic GIService, and the integration of multiple GIS grid applications.
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paper_title: XML Web Service‐based development model for Internet GIS applications

paper_content:

Most of the current Internet Geographic Information System (GIS) applications cannot be shared and are not interoperable because of their heterogeneous environments. With the growth of Internet GIS, many difficulties have occurred in integrating GIS components because of their diversity. The main objective of this study is to suggest a new development model for dynamic and interoperable Internet GIS applications. The model is based mainly on the dynamic integration of Internet GIS components by applying Extensible Markup Language (XML), XML Web Service, Geography Markup Language (GML), and Scalable Vector Graphics (SVG), etc. The relevant technologies of Internet GIS were reviewed thoroughly, and then a new model was designed. During the design of the new model, typical examples of GIS Web Service components were suggested, together with practical structures for applications using these components. Six examples of components and four types of applications were suggested, and they were experimentally implemented for model validation and improvement. The suggested model and components will enable easier and more rapid development of Internet GIS applications through the dynamic integration of distributed GIS components. Users will be able to avoid redundancy and consequently reduce both cost and time during each GIS project.
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paper_title: A global organism detection and monitoring system for non-native species

paper_content:

Abstract Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: “Where, Who & When, and What.” Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from “map servers” to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species.
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paper_title: GOBLET: An open-source geographic overlaying database and query module for spatial targeting in agricultural systems

paper_content:

We describe the development of an open-source mapping and query utility. The impetus for its development came originally from the need to be able to locate, spatially, target populations of resource-poor livestock keepers in the developing world, and to identify livestock management interventions that may be appropriate for these households. The tool was developed using open-source software, to facilitate its transfer and use. It brings together a considerable amount of spatial data from many sources, and allows users to overlay these geographic areas to identify a ''domain'' where characteristics like human population, area, and the number of livestock may vary. The tool serves as the core of several applications, including an ex ante impact assessment framework to help answer questions relating to improved targeting of fodder interventions in Kenya, and a communication tool for poverty related information in Africa.
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paper_title: Free and Open Source Geographic Information Tools for Landscape Ecology

paper_content:

Abstract Geographic Information tools (GI tools) have become an essential component of research in landscape ecology. In this article we review the use of GIS (Geographic Information Systems) and GI tools in landscape ecology, with an emphasis on free and open source software (FOSS) projects. Specifically, we introduce the background and terms related to the free and open source software movement, then compare eight FOSS desktop GIS with proprietary GIS to analyse their utility for landscape ecology research. We also provide a summary of related landscape analysis FOSS applications, and extensions. Our results indicate that (i) all eight GIS provide the basic GIS functionality needed in landscape ecology, (ii) they all facilitate customisation, and (iii) they all provide good support via forums and email lists. Drawbacks that have been identified are related to the fact that most projects are relatively young. This currently affects the size of their user and developer communities, and their ability to include advanced spatial analysis functions and up-to-date documentation. However, we expect these drawbacks to be addressed over time, as systems mature. In general, we see great potential for the use of free and open source desktop GIS in landscape ecology research and advocate concentrated efforts by the landscape ecology community towards a common, customisable and free research platform.
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paper_title: An overview on current free and open source desktop GIS developments

paper_content:

Over the past few years the world of free and open source geospatial software has experienced some major changes. For instance, the website FreeGIS.org currently lists 330 GIS‐related projects. Besides the advent of new software projects and the growth of established projects, a new organisation known as the OSGeo Foundation has been established to offer a point of contact. This paper will give an overview on existing free and open source desktop GIS projects. To further the understanding of the open source software development, we give a brief explanation of associated terms and introduce the two most established software license types: the General Public License (GPL) and the Lesser General Public License (LGPL). After laying out the organisational structures, we describe the different desktop GIS software projects in terms of their main characteristics. Two main tables summarise information on the projects and functionality of the currently available software versions. Finally, the advantages and disad...
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paper_title: The GIS Weasel: An interface for the development of geographic information used in environmental simulation modeling

paper_content:

The GIS Weasel is a freely available, open-source software package built on top of ArcInfo Workstation^(R) [ESRI, Inc., 2001, ArcInfo Workstation (8.1 ed.), Redlands, CA] for creating maps and parameters of geographic features used in environmental simulation models. The software has been designed to minimize the need for GIS expertise and automate the preparation of the geographic information as much as possible. Although many kinds of data can be exploited with the GIS Weasel, the only information required is a raster dataset of elevation for the user's area of interest (AOI). The user-defined AOI serves as a starting point from which to create maps of many different types of geographic features, including sub-watersheds, streams, elevation bands, land cover patches, land parcels, or anything else that can be discerned from the available data. The GIS Weasel has a library of over 200 routines that can be applied to any raster map of geographic features to generate information about shape, area, or topological association with other features of the same or different maps. In addition, a wide variety of parameters can be derived using ancillary data layers such as soil and vegetation maps.
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paper_title: Web service based spatial forest information system using an open source software approach

paper_content:

For technical and other reasons there is a dilemma that data providers cannot find an appropriate way to redistribute spatial forest data and data users who need spatial data cannot access and integrate available forest resources information. To overcome this dilemma, this paper proposed a spatial forest information system based art Web service using an open source software approach. With Web service based architecture, the system can enable interoperability, integrate Web services from other application servers, reuse codes, and shorten the development time and cost, At the same time, it is possible to extend the local system to a regional or national spatial forest information system. The growth of Open Source Software (OSS) provides an alternative choice to proprietary software for operating systems, web servers, Web-based GIS applications and database management systems. Using open source software to develop spatial forest information systems can greatly reduce the cost while providing high performance and sharing spatial forest information. We chose open source software to build a prototype system for Xixia County, Henan Province, China. By integrating OSS packages Deegree and IJMN MapServer which are compliant to the OGC open specifications, the prototype system enables users to access spatial forest information and travelling information of Xixia County which come from two different data servers via a standard Web browser and promotes spatial forest information sharing.
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paper_title: A global organism detection and monitoring system for non-native species

paper_content:

Abstract Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: “Where, Who & When, and What.” Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from “map servers” to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species.
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paper_title: A less‐is‐more approach to geovisualization – enhancing knowledge construction across multidisciplinary teams

paper_content:

The 'less-is-more' concept in interface design for computer applications has recently gained ground. In this article, the concept is adopted for a user-centered design of geovisualization application. The premise is that using simple and clear design can lead to successful applications with improved ease of use. Over the last three decades, the development of GIS and geovisualization has seen a marked increase in the levels of interaction between the user, the system and the information. However, these enthusiastic advances in technology have not resulted in a significant increase in the number of users.This article suggests that types of user interaction should not simply emphasize traditional GIS functions such as zooming and panning but move towards interaction based on facilitating the knowledge construction process. Considerations are made for the complexity of the system, the task at hand and the skills and limitations of the users. These elements are particularly important when maps act as the mediators in collaboration with users across disciplinary backgrounds. In such cases, the emphasis on simplicity and usability becomes as important as functionality. In these situations a geovisualization application designed for specific uses can maximize effective development of geographic knowledge.In this article, a minimalistic design approach to geovisualization is adopted by creating a geographic profiling tool which shifts the emphasis from technological advances or interaction with the map to the interaction elements key to building the spatial knowledge of GIS experts and non-experts alike. To evaluate this notion of 'less-is-more geovisualization' the profiling tool is evaluated according to usability matrices: efficiency, effectiveness and learnability. How well the Suburban Profiler contributes to these elements is assessed by conducting a video analysis of the types and forms of user interaction available. The video analysis demonstrates the usefulness and usability of the Suburban Profiler, providing proof of concept for 'less-is-more geovisualization'.
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paper_title: Mobile environmental visualization

paper_content:

AbstractEnvironmental processes are a major point of concern for researchers, environmental engineers and the general public. This paper presents a multi-user mobile system to visualize environmental processes. This system enables multiple users to visualize and simulate environmental processes, and also retrieve additional information in real time, while moving through the environmental area under analysis. Each user is able to contribute to the simulation and assess the impact of adding or removing agents to or from the model, respectively.This system uses a modular approach, allowing its deployment through different platforms. Two main modules compose the system under development: a geo-referenced model and an Augmented Reality (AR) composition module. The geo-referenced model describes the environmental processes being modelled and tracks all users. The AR composition module allows users to visualize the geo-referenced model evolution and to interact with the model through two different views, namely ...
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paper_title: GIS Visualization and Analysis of River Operations Impacts on Endangered Species Habitat

paper_content:

Many river systems include natural and manmade backwater areas providing habitat for a diverse community of aquatic and aviary species, including several listed as endangered by the U.S. Fish and Wildlife Service. Concern has been raised over ecological impacts of river operations on these species and their habitat in these backwater areas. A methodology is presented that utilizes a geographic information system (GIS) in association with a numerical hydraulic model to assess these impacts. The GIS provides geostatistical estimates of water surface elevations within the backwaters during passage of a hydrograph created by reservoir releases, and then quantifies and provides animated visualization of the changes in habitat for many species dwelling in these areas. This generalized tool is applied to a portion of the Lower Colorado River in Arizona/California, which includes several dams and diversion structures controlling flow for a variety of important purposes. It is demonstrated that this GIS-based tool provides effective support for river system operators in assessing the impacts of operations on endangered species habitat and evaluating remedial measures.
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paper_title: SFMN GeoSearch: An interactive approach to the visualization and exchange of point-based ecological data

paper_content:

Abstract Recent advances in computer networks and information technologies have created exciting new possibilities for sharing and analyzing scientific research data. Although individual datasets can be studied efficiently, many scientists are still largely limited to considering data collected by themselves, their students, or closely affiliated research groups. Increasingly widespread high-speed network connections and the existence of large, coordinated research programs suggest the potential for scientists to access and learn from data from outside their immediate research circle. We are developing a web-based application that facilitates the sharing of scientific data within a research network using the now-common “virtual globe” in combination with advanced visualization methods designed for geographically distributed scientific data. Two major components of the system enable the rapid assessment of geographically distributed scientific data: a database built from information submitted by network members, and a module featuring novel and sophisticated geographic data visualization techniques. By enabling scientists to share results with each other and view their shared data through a common virtual-globe interface, the system provides a new platform for important meta-analyses and the analysis of broad-scale patterns. Here we present the design and capabilities of the SFMN GeoSearch platform for the Sustainable Forest Management Network, a pan-Canadian network of forest researchers who have accumulated data for more than a decade. Through the development and dissemination of this new tool, we hope to help scientists, students, and the general public to understand the depth and breadth of scientific data across potentially large areas.
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paper_title: Integration of augmented reality and GIS : A new approach to realistic landscape visualisation

paper_content:

This paper describes the development of a photo-realistic visualisation method which uses the combination of a geographic information system (GIS) with off-line augmented reality (AR) techniques. The technique makes a linkage between GIS-based modelling and realistic panoramic video frames to dynamically augment a landscape view with modelled temporal changes. By using real object textures, computer-generated objects can be well matched to background frames and become hardly detectable as computer objects. The technique has been tested with data from an area in northeast Victoria, Australia and has successfully represented the dynamic spread of weeds (blackberries) and their effects on landscape over a period of 14 years. The proposed approach has the potential to improve the communication between policy makers and non-experts and to improve decision-making process. Extensions to other model types and to real-time usage are envisaged.
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paper_title: Exploratory spatio-temporal visualization: an analytical review

paper_content:

Abstract Current software tools for visualization of spatio-temporal data, on the one hand, utilize the opportunities provided by modern computer technologies, on the other hand, incorporate the legacy from the conventional cartography. We have considered existing visualization-based techniques for exploratory analysis of spatio-temporal data from two perspectives: (1) what types of spatio-temporal data they are applicable to; (2) what exploratory tasks they can potentially support. The technique investigation has been based on an operational typology of spatio-temporal data and analytical tasks we specially devised for this purpose. The result of the study is a structured inventory of existing exploratory techniques related to the types of data and tasks they are appropriate for. This result is potentially helpful for data analysts—users of geovisualization tools: it provides guidelines for selection of proper exploratory techniques depending on the characteristics of data to analyze and the goals of analysis. At the same time the inventory as well as the suggested typology of tasks could be useful for tool designers and developers of various domain-specific geovisualization applications. The designers can, on the one hand, see what task types are insufficiently supported by the existing tools and direct their creative activities towards filling the gaps, on the other hand, use the techniques described as basic elements for building new, more sophisticated ones. The application developers can, on the one hand, use the task and data typology in the analysis of potential user needs, on the other hand, appropriately select and combine existing tools in order to satisfy these needs.
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paper_title: Refining predictions of climate change impacts on plant species distribution through the use of local statistics

paper_content:

Abstract Bioclimate envelope models are often used to predict changes in species distribution arising from changes in climate. These models are typically based on observed correlations between current species distribution and climate data. One limitation of this basic approach is that the relationship modelled is assumed to be constant in space; the analysis is global with the relationship assumed to be spatially stationary. Here, it is shown that by using a local regression analysis, which allows the relationship under study to vary in space, rather than conventional global regression analysis it is possible to increase the accuracy of bioclimate envelope modelling. This is demonstrated for the distribution of Spotted Meddick in Great Britain using data relating to three time periods, including predictions for the 2080s based on two climate change scenarios. Species distribution and climate data were available for two of the time periods studied and this allowed comparison of bioclimate envelope model outputs derived using the local and global regression analyses. For both time periods, the area under the receiver operating characteristics curve derived from the analysis based on local statistics was significantly higher than that from the conventional global analysis; the curve comparisons were also undertaken with an approach that recognised the dependent nature of the data sets compared. Marked differences in the future distribution of the species predicted from the local and global based analyses were evident and highlight a need for further consideration of local issues in modelling ecological variables.
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paper_title: Measurement and meaningfulness in conservation science.

paper_content:

Incomplete databases often require conservation scientists to estimate data either through expert judgment or other scoring, rating, and ranking procedures. At the same time, ecosystem complexity has led to the use of increasingly sophisticated algorithms and mathematical models to aid in conservation theorizing, planning, and decision making. Understanding the limitations imposed by the scales of measurement of conservation data is important for the development of sound conservation theory and policy. In particular, biodiversity valuation methods, systematic conservation planning algorithms, geographic information systems (GIS), and other conservation metrics and decision-support tools, when improperly applied to estimated data, may lead to conclusions based on numerical artifact rather than empirical evidence. The representational theory of measurement is described here, and the description includes definitions of the key concepts of scale, scale type, and meaningfulness. Representational measurement is the view that measurement entails the faithful assignment of numbers to empirical entities. These assignments form scales that are organized into a hierarchy of scale types. A statement involving scales is meaningful if its truth value is invariant under changes of scale within scale type. I apply these concepts to three examples of measurement practice in the conservation literature. The results of my analysis suggest that conservation scientists do not always investigate the scale type of estimated data and hence may derive results that are not meaningful. Recognizing the complexity of observation and measurement in conservation biology, and the constraints that measurement theory imposes, the examples are accompanied by suggestions for informal estimation of the scale type of conservation data and for conducting meaningful analysis and synthesis of this information.
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paper_title: Non-stationarity and local approaches to modelling the distributions of wildlife

paper_content:

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.
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paper_title: Modelling species’ range shifts in a changing climate: The impacts of biotic interactions, dispersal distance and the rate of climate change

paper_content:

Abstract There is an urgent need for accurate prediction of climate change impacts on species ranges. Current reliance on bioclimatic envelope approaches ignores important biological processes such as interactions and dispersal. Although much debated, it is unclear how such processes might influence range shifting. Using individual-based modelling we show that interspecific interactions and dispersal ability interact with the rate of climate change to determine range-shifting dynamics in a simulated community with two growth forms—mutualists and competitors. Interactions determine spatial arrangements of species prior to the onset of rapid climate change. These lead to space-occupancy effects that limit the rate of expansion of the fast-growing competitors but which can be overcome by increased long-distance dispersal. As the rate of climate change increases, lower levels of long-distance dispersal can drive the mutualists to extinction, demonstrating the potential for subtle process balances, non-linear dynamics and abrupt changes from species coexistence to species loss during climate change.
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paper_title: Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity

paper_content:

Recent attempts at projecting climate change impacts on biodiversity have used the IUCN Red List Criteria to obtain estimates of extinction rates based on projected range shifts. In these studies, the Criteria are often misapplied, potentially introducing substantial bias and uncertainty. These misapplications include arbitrary changes to temporal and spatial scales; confusion of the spatial variables; and assume a linear relationship between abundance and range area. Using the IUCN Red List Criteria to identify which species are threatened by climate change presents special problems and uncertainties, especially for shorter-lived species. Responses of most species to future climate change are not understood well enough to estimate extinction risks based solely on climate change scenarios and projections of shifts and/or reductions in range areas. One way to further such understanding would be to analyze the interactions among habitat shifts, landscape structure and demography for a number of species, using a combination of models. Evaluating the patterns in the results might allow the development of guidelines for assigning species to threat categories, based on a combination of life history parameters, characteristics of the landscapes in which they live, and projected range changes.
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paper_title: Forecasting the Effects of Global Warming on Biodiversity

paper_content:

AbstractThe demand for accurate forecasting of the effects of global warming on biodiversity is growing, but current methods for forecasting have limitations. In this article, we compare and discuss the different uses of four forecasting methods: (1) models that consider species individually, (2) niche-theory models that group species by habitat (more specifically, by environmental conditions under which a species can persist or does persist), (3) general circulation models and coupled ocean–atmosphere–biosphere models, and (4) species–area curve models that consider all species or large aggregates of species. After outlining the different uses and limitations of these methods, we make eight primary suggestions for improving forecasts. We find that greater use of the fossil record and of modern genetic studies would improve forecasting methods. We note a Quaternary conundrum: While current empirical and theoretical ecological results suggest that many species could be at risk from global warming, during t...
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paper_title: Predicting the impacts of climate change on the distribution of species : are bioclimate envelope models useful ?

paper_content:

Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent
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paper_title: Inferring habitat-suitability areas with ecological modelling techniques and GIS: A contribution to assess the conservation status of Vipera latastei

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Some snakes are highly vulnerable to extinction due to several life history traits. However, the elusive behavior and secretive habits of some widespread species constrain the collection of demographic and ecological data necessary for the identification of extinction-prone species. In this scenario, the enhancement of ecological modelling techniques in Geographical Information Systems (GIS) is providing researchers with robust tools to apply to such species. This study has identified the environmental factors that limit the current distribution of Vipera latastei, a species with secretive behavior, and has evaluated how human activities affect its current conservation status, identifying areas of best habitat suitability in the Iberian Peninsula. Ecological-niche factor analysis (ENFA) indicated low marginality (0.299) and high tolerance (0.887) scores, suggesting strong tendency for the species to live in average conditions throughout the study area and to inhabit any of the environmental conditions. The analysis also revealed that this viper tends to select particular Mediterranean habitats, although topographic factors (altitude and slope) were the major environmental constraints for the Iberian distribution pattern of the species. The presence of other parapatric viper species in the north of the Iberian Peninsula (V. aspis and V. seoanei) and two human-related variables (landscape transformation and human density) also had a negative relation with the occurrence of V. latastei. All factors can explain its absence in northern Iberia and its fragmented distribution as currently is found mostly in mountains and relatively undisturbed low-altitude areas. The historical destruction and alteration of natural Mediterranean habitats and several life-history traits of the species contribute to its vulnerability to extinction. The ENFA analysis proved to be an outstanding method to evaluate the factors that limit the distribution range of secretive and widespread species such as V. latastei, updating evaluation of their conservation status.
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paper_title: The role of land cover in bioclimatic models depends on spatial resolution

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Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. ::: ::: ::: ::: Location Finland, northern Europe. ::: ::: ::: ::: Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20-km, 40-km and 80-km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross-validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. ::: ::: ::: ::: Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10-km and 20-km resolutions, respectively. There was no significant improvement at the 40-km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. ::: ::: ::: ::: Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large-scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20-km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.
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paper_title: Maximising the natural capital benefits of habitat creation : Spatially targeting native woodland using GIS

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Abstract The establishment of the Common Agricultural Policy has dramatically transformed the relationship between the natural environment and agriculture in the UK. Accordingly, the Government now acknowledges that our stock of ‘natural capital’ is being managed unsustainably and is undertaking Common Agricultural Policy reforms to provide a more sustainable form of agriculture. Such reforms will be based on the economic rationale of payment in return for the provision of natural capital benefits such as biodiversity, carbon sequestration, landscape and recreation. A basic lower tier payment is proposed for general environmental practices, with higher tiers of payments being available for ‘benefit generating’ habitat maintenance and creation. In order to maximise the benefits of such habitat creation, some form of spatial targeting is required. Using geographical information systems (GIS), a suite of spatially explicit criteria are adopted to measure how the potential benefits of native woodland creation vary across the agricultural landscape of the Chilterns natural area. Rather than regarding habitat conservation solely in terms of biodiversity benefits, a more holistic natural capital benefit approach is thus adopted. Public preference on the provision of each benefit is integrated into the GIS-based suitability analysis through multicriteria evaluation. We demonstrate how such a targeted approach leads to large improvements in the delivery of natural capital benefits, with the attainment of biodiversity, landscape and recreation benefits being particularly complementary. As such, the targeted pursuit of natural capital benefits does not compromise the attainment of biodiversity goals, but actually aids in their achievement. However, due to limitations in data availability and accuracy, GIS should be regarded as a decision support tool, with validation of targeted sites being undertaken through a farm audit system.
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paper_title: GBD-Explorer: Extending open source java GIS for exploring ecoregion-based biodiversity data

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Abstract Biodiversity and ecosystem data are both geo-referenced and “species-referenced”. Ecoregion classification systems are relevant to basic ecological research and have been increasingly used for making policy and management decisions. There are practical needs to integrate taxonomic data with ecoregion data in a GIS to visualize and explore species distribution conveniently. In this study, we represent the species distributed in an ecoregion as a taxonomic tree and extend the classic GIS data model to incorporate operations on taxonomic trees. A prototype called GBD-Explorer was developed on top of the open source JUMP GIS. We use the World Wildlife Fund (WWF) terrestrial ecoregion and WildFinder species databases as an example to demonstrate the rich capabilities implemented in the prototype.
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paper_title: Modelling breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus) in relation to topography, disturbance, climate and land use at different spatial scales

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Predictive models on breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus; Aves: Accipitridae) have been performed at four different spatial scales in Castellon province, East of Iberian Peninsula. The scales considered were: (1) nest site scale (1×1 km2 Universal Transverse Mercator (UTM) square containing the nest); (2) near nest environment (3×3 km2 UTM square); (3) home range scale (5×5 km2 UTM square); and (4) landscape level scale (9×9 km2 UTM square containing the above mentioned ones). Topographic, disturbance, climatic and land use factors were measured on a geographic information system (GIS) at occupied and unoccupied UTM squares. Logistic regression was performed by means of a stepwise addition procedure. We tested whether inclusion of new subset of variables improved the models by increasing the area under the receiver operator characteristic plot. At nest site scale, only topographic factors were considered as the most parsimonious predictors. Probability of species occurrence increases with slope in craggy areas at lower altitudes. At the 3×3 km2 scale, climate and disturbance variables were included. At home range and landscape level scales, models included climate, disturbance, topographic and land use factors. Higher temperatures in January, template ones in July, higher rainfall in June, lower altitudes and higher slope in the sample unit increase probability of occurrence of Bonelli’s eagle at broadest scales. The species seems to prefer disperse forests, scrubland and agricultural areas. From our results, we consider that there is a hierarchical framework on habitat selection procedure. We suggest that it is necessary to analyse what key factors are affecting Bonelli’s eagle nest-site selection at every study area to take steps to ensure appropriate conservation measures. The combination of regression modelling and GIS will become a powerful tool for biodiversity and conservation studies, taking into account that application depends on sampling design and the model assumptions of the statistical methods employed. Finally, predictive models obtained could be used for the efficient monitoring of this scarce species, to predict range expansions or identify suitable locations for reintroductions, and also to design protected areas and to help on wildlife management.
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paper_title: SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species

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A model, A Spatial Evaluation of Climate Impact on the Envelope of Species (SPECIES), is presented which has been developed to evaluate the impacts of climate change on the bioclimatic envelope of plant species in Great Britain. SPECIES couples an artificial neural network with a climate–hydrological process model. The hybrid model has been successfully trained to estimate current species distributions using climate and soils data at the European scale before application at a finer resolution national scale. Using this multi-scale approach ensures encapsulation of the full extent of future climate scenarios within Great Britain without extrapolating outside of the model's training dataset. Application of the model to 32 plant species produced a mean Pearson correlation coefficient of 0.841 and a mean Kappa statistic of 0.772 between observed and simulated distributions. Simulations of four climate change scenarios revealed that changes to suitable climate space in Great Britain is highly species dependent and that distribution changes may be multidirectional and temporally non-linear. Analysis of the SPECIES results suggests that the neural network methodology can provide a feasible alternative to more classical spatial statistical techniques.
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paper_title: Mediterranean ecosystems: problems and tools for conservation

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Mediterranean ecosystems rival tropical ecosystems in terms of plant biodiversity. The Mediterranean Basin (MB) itself hosts 25 000 plant species, half of which are endemic. This rich biodiversity and the complex biogeographical and political issues make conservation a difficult task in the region. Species, habitat, ecosystem and landscape approaches have been used to identify conservation targets at various scales: ie, European, national, regional and local. Conservation decisions require adequate information at the species, community and habitat level. Nevertheless and despite recent improvements/efforts, this information is still incomplete, fragmented and varies from one country to another. This paper reviews the biogeographic data, the problems arising from current conservation efforts and methods for the conservation assessment and prioritization using GIS. GIS has an important role to play for managing spatial and attribute information on the ecosystems of the MB and to facilitate interactions with existing databases. Where limited information is available it can be used for prediction when directly or indirectly linked to externally built models. As well as being a predictive tool today GIS incorporate spatial techniques which can improve the level of information such as fuzzy logic, geostatistics, or provide insight about landscape changes such as 3D visualization. Where there are limited resources it can assist with identifying sites of conservation priority or the resolution of environmental conflicts (scenario building). Although not a panacea, GIS is an invaluable tool for improving the understanding of Mediterranean ecosystems and their dynamics and for practical management in a region that is under increasing pressure from human impact.
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paper_title: Surveyor consistency in presence/absence sampling for monitoring vegetation in a boreal forest

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Vegetation assessments are a central part of many large-scale monitoring programmes. To accurately estimate change over time, consistent field methods are important. Presence/absence (P/A) sampling is considered to be less susceptible to judgement and measurement errors in comparison with visual cover assessment, although errors also occur with this method in complete species inventories. Few studies have evaluated surveyor consistency in P/A sampling with a limited list of species. In this study, the consistency of results in P/A sampling was evaluated in a field test, with different surveyors assessing the same sample plots. The results indicated a good consistency between surveyors and high accuracy according to a reference survey for many of the tested species, although some differences both between surveyors and in comparison with the reference survey were found. Comparing a group of surveyors with a larger experience of vegetation assessments with a group of surveyors with less experience indicated that the former were more accurate and consistent. No clear differences were found between different plot sizes tested. The main conclusion from the study is that P/A sampling is slightly affected by observer judgement bias, but that in comparison with the consistency of visual cover assessment observed in other studies, the difference between surveyors for many species is reasonably low.
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paper_title: Species distribution models and ecological theory: A critical assessment and some possible new approaches

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Given the importance of knowledge of species distribution for conservation and climate change management, continuous and progressive evaluation of the statistical models predicting species distributions is necessary. Current models are evaluated in terms of ecological theory used, the data model accepted and the statistical methods applied. Focus is restricted to Generalised Linear Models (GLM) and Generalised Additive Models (GAM). Certain currently unused regression methods are reviewed for their possible application to species modelling. A review of recent papers suggests that ecological theory is rarely explicitly considered. Current theory and results support species responses to environmental variables to be unimodal and often skewed though process-based theory is often lacking. Many studies fail to test for unimodal or skewed responses and straight-line relationships are often fitted without justification. Data resolution (size of sampling unit) determines the nature of the environmental niche models that can be fitted. A synthesis of differing ecophysiological ideas and the use of biophysical processes models could improve the selection of predictor variables. A better conceptual framework is needed for selecting variables. Comparison of statistical methods is difficult. Predictive success is insufficient and a test of ecological realism is also needed. Evaluation of methods needs artificial data, as there is no knowledge about the true relationships between variables for field data. However, use of artificial data is limited by lack of comprehensive theory. Three potentially new methods are reviewed. Quantile regression (QR) has potential and a strong theoretical justification in Liebig’s law of the minimum. Structural equation modelling (SEM) has an appealing conceptual framework for testing causality but has problems with curvilinear relationships. Geographically weighted regression (GWR) intended to examine spatial non-stationarity of ecological processes requires further evaluation before being used.
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paper_title: Where will species go? Incorporating new advances in climate modelling into projections of species distributions

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Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985‐1995) and future (2025‐2035). Initially, we compared the simulated values for each climate variable (P, Tmax, Tmin ,a ndTmean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future.
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paper_title: An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data

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Summary 1. Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo-absences. 2. In this study we tested a new approach to generating pseudo-absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum . This method of generating pseudo-absences was compared with two others: (i) use of a GLM with pseudo-absences generated totally at random, and (ii) use of an ENFA only. 3. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above-mentioned methods with the two grains generated a distinct HS map. 4. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. 5. Results showed that (i) GLM models using ENFA-weighted pseudo-absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. 6. Synthesis and applications. We suggest that the use of ENFA-weighted pseudoabsence is a possible way to enhance the quality of GLM-based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.
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paper_title: Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile

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Forest fragmentation threatens biodiversity in one of the last remaining temperate rainforests that occur in South America. We study the current and future impacts of fragmentation on spatial configuration of forest habitats at the landscape level time in southern Chile. For this purpose, we identify the geophysical variables (“pattern drivers”) that explain the spatial patterns of forest loss and fragmentation between 1976 and 1999 using both a GIS-based land-use change model (GEOMOD) and spatially explicit logistic regression. Then, we project where and how much forest fragmentation will occur in the future by extrapolation of the current rate of deforestation to 2010 and 2020. Both modeling approaches showed consistent and complementary results in terms of the pattern drivers that were most related to deforestation. Between 1976 and 1999, forest fragmentation has occurred mainly from the edges of small fragments situated on gentle slopes (less than 10°) and far away from rivers. We predict that patch density will decline from 2010 to 2020, and that total forest interior area and patch proximity will further decline as a result of forest fragmentation. Drivers identified by these approaches suggest that deforestation is associated with observed local socio-economic activities such as clearance of forest for pasture and crops and forest logging for fuelwood.
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paper_title: Uncertainty of bioclimate envelope models based on the geographical distribution of species

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Aim We explored the effects of prevalence, latitudinal range and spatial autocorrelation of species distribution patterns on the accuracy of bioclimate envelope models of butterflies. ::: ::: ::: ::: Location Finland, northern Europe. ::: ::: ::: ::: Methods The data of a national butterfly atlas survey (NAFI) carried out in 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAM) were constructed, for each of 98 species, to estimate the probability of occurrence as a function of climate variables. Model performance was measured using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were related to the species’ geographical attributes using multivariate GAM. ::: ::: ::: ::: Results Accuracies of the climate–butterfly models varied from low to very high (AUC values 0.59–0.99), with a mean of 0.79. The modelling performance was related negatively to the latitudinal range and prevalence, and positively to the spatial autocorrelation of the species distribution. These three factors accounted for 75.2% of the variation in the modelling accuracy. Species at the margin of their range or with low prevalence were better predicted than widespread species, and species with clumped distributions better than scattered dispersed species. ::: ::: ::: ::: Main conclusions The results from this study indicate that species’ geographical attributes highly influence the behaviour and uncertainty of species–climate models, which should be taken into account in biogeographical modelling studies and assessments of climate change impacts.
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paper_title: Designing the core zone in a biosphere reserve based on suitable habitats: Yancheng Biosphere Reserve and the red crowned crane (Grus japonensis)

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Although much research has been undertaken to design nature reserves, there are few practical methods to determine the interior structure of a reserve. A procedure for design of the core zone in reserves is proposed. As a case study, the core zone in Yancheng Biosphere Reserve, People's Republic of China, which was established to preserve the endangered red crowned crane (Grus japonensis) is designed. A statistical habitat model using geographic information system (GIS) is developed to predict crane presence or absence. Based on predicted suitable habitats, the minimum core zone is defined. More suitable habitats can be contained in the designated core zone, and this will be beneficial to the conservation and restoration for crane habitats. (C) 1999 Elsevier Science Ltd. All rights reserved.
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paper_title: Methods and uncertainties in bioclimatic envelope modelling under climate change

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Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic 'envelope' models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non- climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.
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paper_title: Network connectivity and dispersal barriers: using geographical information system (GIS) tools to predict landscape scale distribution of a key predator (Esox lucius) among lakes

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The keystone piscivore northern pike Esox lucius can structure fish communities, and models predicting pike-focused connectivity will be important for management of many waters. We explored the ability of pike to colonize upstream locations and modelled presence-absence in lakes based on landscape features derived from maps. An upstream connectivity model (UC model) was generated using data from 87 lakes. We validated the UC model with retrospective whole-lake experiments involving introductions (n = 49) and extirpations (by rotenone) of pike (n = 96), as well as with the natural distribution of pike in lakes (n = 1365) within 26 drainage basin networks in northern Sweden. The UC model predicted the incidence of pike in lakes with stream-connections with 95.4% accuracy, based mainly on a single variable, S-V5max, that measures the minimum distance found between 5 m elevation intervals (= maximum stream slope) along watercourses from nearest downstream source of potential immigrants. Recolonizations of pike in rotenone lakes generated a near-identical classification tree, as in the UC model. The classification accuracy of pike presence in the external validation procedure ranged from 88.7 to 98.7% between different drainage basins. Predictions of pike absence were not as accurate, due possibly to undetected introductions, but still lead to 86.6% overall accuracy of the external validation. Most lakes lacking pike, but misclassified as having pike based on low S-V5max, were isolated from downstream sources of pike by subsurface stream flow through bouldery areas (SSB). Synthesis and applications. The variable S-V5max provides managers with a tool for revealing the location and severity of natural dispersal barriers to pike (and logically also barriers to other species with equivalent or less dispersal capacity). Because presented models only require map-based information, and have high predictive power, they may have the potential to be of fundamental use in predicting distribution of freshwater fish. These predictions may provide the means for prioritizing in risk assessment and control programmes to combat pike invasions, as well as contribute to determining a reference state of species incidence in specific lakes. Our results also point towards a possibility that, even where stream slope is low, long-term effective barriers may be designed that mimic natural SSB.
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paper_title: POTENTIAL EFFECTS OF CLIMATE CHANGE ON ECOSYSTEM AND TREE SPECIES DISTRIBUTION IN BRITISH COLUMBIA

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A new ecosystem-based climate envelope modeling approach was applied to assess potential climate change impacts on forest communities and tree species. Four orthogonal canonical discriminant functions were used to describe the realized climate space for British Columbia's ecosystems and to model portions of the realized niche space for tree species under current and predicted future climates. This conceptually simple model is capable of predicting species ranges at high spatial resolutions far beyond the study area, including outlying populations and southern range limits for many species. We analyzed how the realized climate space of current ecosystems changes in extent, elevation, and spatial distribution under climate change scenarios and evaluated the implications for potential tree species habitat. Tree species with their northern range limit in British Columbia gain potential habitat at a pace of at least 100 km per decade, common hardwoods appear to be generally unaffected by climate change, and some of the most important conifer species in British Columbia are expected to lose a large portion of their suitable habitat. The extent of spatial redistribution of realized climate space for ecosystems is considerable, with currently important sub-boreal and montane climate regions rapidly disappearing. Local predictions of changes to tree species frequencies were generated as a basis for systematic surveys of biological response to climate change.
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paper_title: Accessible habitat: an improved measure of the effects of habitat loss and roads on wildlife populations

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Habitat loss is known to be the main cause of the current global decline in biodiversity, and roads are thought to affect the persistence of many species by restricting movement between habitat patches. However, measuring the effects of roads and habitat loss separately means that the configuration of habitat relative to roads is not considered. We present a new measure of the combined effects of roads and habitat amount: accessible habitat. We define accessible habitat as the amount of habitat that can be reached from a focal habitat patch without crossing a road, and make available a GIS tool to calculate accessible habitat. We hypothesize that accessible habitat will be the best predictor of the effects of habitat loss and roads for any species for which roads are a major barrier to movement. We conducted a case study of the utility of the accessible habitat concept using a data set of anuran species richness from 27 ponds near a motorway. We defined habitat as forest in this example. We found that accessible habitat was not only a better predictor of species richness than total habitat in the landscape or distance to the motorway, but also that by failing to consider accessible habitat we would have incorrectly concluded that there was no effect of habitat amount on species richness.
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paper_title: Bioclimatic Analysis to Enhance Reintroduction Biology of the Endangered Helmeted Honeyeater (Lichenostomus melanops cassidix) in Southeastern Australia

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Reintroduction programs are a high-risk conservation strategy for restoring populations of endangered species. The success of these programs often depends on the ability to identify suitable habitat within the species' former range. Bioclimatic analysis offers an empirical, explicit, robust, and repeatable method to analyze large areas rapidly using a small number of locality records, and in turn predicting (and/or reconstructing) its potential distribution limits. This approach therefore can estimate the broad limits of the distribution of a taxon, using data that may be inadequate for standard forms of statistical analysis. We illustrate the potential value of bioclimatic modeling for reintroduction biology using a case study of the highly endangered Helmeted Honeyeater (Lichenostomus melanops cassidix) from Victoria, southeastern Australia. The results of our analyses assisted us to both predict the former range limits of the Helmeted Honeyeater and determine the broad limits of those areas that may contain potentially suitable sites for future reintroduction programs for the subspecies. The analysis predicted that the range of the Helmeted Honeyeater extends from the Yarra River district east of Melbourne, south to the Western Port Bay and east as far as the Morwell area of Victoria. The climatic characteristics of habitat occupied by the extant population of the Helmeted Honeyeater were found to be unique within its predicted range. We recommend that reintroduction efforts therefore be concentrated within this small area, as has occurred to date.
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paper_title: Making better biogeographical predictions of species' distributions

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Summary 1. Biogeographical models of species’ distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species’ distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species’ predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species’ distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species’ distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.
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paper_title: A global organism detection and monitoring system for non-native species

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Abstract Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: “Where, Who & When, and What.” Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from “map servers” to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species.
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paper_title: Interactions Among Spatial Scales Constrain Species Distributions in Fragmented Urban Landscapes

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Understanding species' responses to habitat loss is a major challenge for ecologists and conservation biologists, who need quantitative, yet practical, frameworks to design landscapes better able to sustain native species. I here develop one such framework by synthesizing two ecological paradigms: scale-dependence and constraint-like interactions in biological phenomena. I develop a model and apply it to birds around Tucson, USA, investigating the manner in which spatial scales interact to constrain species distributions in fragmented urban landscapes. Species' responses vary in interesting ways. Surprisingly, most show situations in which habitat at one spatial scale constrains the influence of habitat at another scale. I discuss the implications of this work for conservation in human-dominated landscapes, and the need to recognize constraint-like interactions among processes and spatial scales in ecology.
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paper_title: Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

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Aim Several studies have found that more accurate predictive models of species’ occurrences can be developed for rarer species; however, one recent study found the relationship between range size and model performance to be an artefact of sample prevalence, that is, the proportion of presence versus absence observations in the data used to train the model. We examined the effect of model type, species rarity class, species’ survey frequency, detectability and manipulated sample prevalence on the accuracy of distribution models developed for 30 reptile and amphibian species. Location Coastal southern California, USA. Methods Classification trees, generalized additive models and generalized linear models were developed using species presence and absence data from 420 locations. Model performance was measured using sensitivity, specificity and the area under the curve (AUC) of the receiver-operating characteristic (ROC) plot based on twofold cross-validation, or on bootstrapping. Predictors included climate, terrain, soil and vegetation variables. Species were assigned to rarity classes by experts. The data were sampled to generate subsets with varying ratios of presences and absences to test for the effect of sample prevalence. Join count statistics were used to characterize spatial dependence in the prediction errors. Results Species in classes with higher rarity were more accurately predicted than common species, and this effect was independent of sample prevalence. Although positive spatial autocorrelation remained in the prediction errors, it was weaker than was observed in the species occurrence data. The differences in accuracy among model types were slight. Main conclusions Using a variety of modelling methods, more accurate species distribution models were developed for rarer than for more common species. This was presumably because it is difficult to discriminate suitable from unsuitable habitat for habitat generalists, and not as an artefact of the effect of sample prevalence on model estimation.
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paper_title: A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa

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Modelling techniques used in binary classification problems often result in a predicted probability surface, which is then translated into a presence–absence classification map. However, this translation requires a (possibly subjective) choice of threshold above which the variable of interest is predicted to be present. The selection of this threshold value can have dramatic effects on model accuracy as well as the predicted prevalence for the variable (the overall proportion of locations where the variable is predicted to be present). The traditional default is to simply use a threshold of 0.5 as the cut-off, but this does not necessarily preserve the observed prevalence or result in the highest prediction accuracy, especially for data sets with very high or very low observed prevalence. Alternatively, the thresholds can be chosen to optimize map accuracy, as judged by various criteria. Here we examine the effect of 11 of these potential criteria on predicted prevalence, prediction accuracy, and the resulting map output. Comparisons are made using output from presence–absence models developed for 13 tree species in the northern mountains of Utah. We found that species with poor model quality or low prevalence were most sensitive to the choice of threshold. For these species, a 0.5 cut-off was unreliable, sometimes resulting in substantially lower kappa and underestimated prevalence, with possible detrimental effects on a management decision. If a management objective requires a map to portray unbiased estimates of species prevalence, then the best results were obtained from thresholds deliberately chosen so that the predicted prevalence equaled the observed prevalence, followed closely by thresholds chosen to maximize kappa. These were also the two criteria with the highest mean kappa from our independent test data. For particular management applications the special cases of user specified required accuracy may be most appropriate. Ultimately, maps will typically have multiple and somewhat conflicting management applications. Therefore, providing users with a continuous probability surface may be the most versatile and powerful method, allowing threshold choice to be matched with each maps intended use.
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paper_title: Mapping urban areas on a global scale: which of the eight maps now available is more accurate?

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Eight groups from government and academia have created 10 global maps that offer a ca 2000 portrait of land in urban use. Our initial investigation found that their estimates of the total amount of urban land differ by as much as an order of magnitude (0.27–3.52 ×106 km2). Since it is not possible for these heterogeneous maps to all represent urban areas accurately, we undertake the first global accuracy assessment of these maps using a two-tiered approach that draws on a stratified random sample of 10 000 high-resolution Google Earth validation sites and 140 medium-resolution Landsat-based city maps. Employing a wide range of accuracy measures at different spatial scales, we conclude that the new MODIS 500 m resolution global urban map has the highest accuracy, followed by a thresholded version of the Global Impervious Surface Area map based on the Night-time Lights and LandScan datasets.
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paper_title: Harshness in image classification accuracy assessment

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Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.
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paper_title: Sample size determination for image classification accuracy assessment and comparison

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Many factors influence the quality and value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for accuracy assessment and includes factors linked to accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appro...
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paper_title: The abuse of power: The pervasive fallacy of power calculations for data analysis

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It is well known that statistical power calculations can be valuable in planning an experiment. There is also a large literature advocating that power calculations be made whenever one performs a statistical test of a hypothesis and one obtains a statistically nonsignificant result. Advocates of such post-experiment power calculations claim the calculations should be used to aid in the interpretation of the experimental results. This approach, which appears in various forms, is fundamentally flawed. We document that the problem is extensive and present arguments to demonstrate the flaw in the logic.
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paper_title: More complex distribution models or more representative data

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Distribution models for species are increasingly used to summarize species’ geography in conservation analyses. These models use increasingly sophisticated modeling techniques, but often lack detailed examination of the quality of the biological occurrence data on which they are based. I analyze the results of the best comparative study of the performance of different modeling techniques, which used pseudo-absence data selected at random. I provide an example of variation in model accuracy depending on the type of absence information used, showing that good model predictions depend most critically on better biological data.
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paper_title: Statistical Power of Presence-Absence Data to Detect Population Declines

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Abstract: Population declines may be inferred from a decrease in the number of sites at which a species is detected. Although such presence-absence data often are interpreted informally, it is simple to test the statistical significance of changes in the number of sites occupied by a species. I used simulations to examine the statistical power (i.e., the probability of making the Type II error that no population decline has occurred when the population actually has declined) of presence-absence designs. Most presence-absence designs have low power to detect declines of <20–50% in populations but have adequate power to detect steeper declines. Power was greater if the population disappeared entirely from a subset of formerly occupied sites than if it declined evenly over its entire range. Power also rose with (1) increases in the number of sites surveyed; (2) increases in population density or sampling effort at a site; and (3) decreases in spatial variance in population density. Because of potential problems with bias and inadequate power, presence-absence designs should be used and interpreted cautiously.
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paper_title: A review of methods for the assessment of prediction errors in conservation presence/absence models

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Summary Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models.
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paper_title: Threshold criteria for conversion of probability of species presence to either–or presence–absence

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ABSTRACT For many applications the continuous prediction afforded by species distribution modeling must be converted to a map of presence or absence, so a threshold probability indicative of species presence must be fixed. Because of the bias in probability outputs due to frequency of presences (prevalence), a fixed threshold value, such as 0.5, does not usually correspond to the threshold above which the species is more likely to be present. In this paper four threshold criteria are compared for a wide range of sample sizes and prevalences, modeling a virtual species in order to avoid the omnipresent error sources that the use of real species data implies. In general, sensitivity–specificity difference minimizer and sensitivity–specificity sum maximizer criteria produced the most accurate predictions. The widely-used 0.5 fixed threshold and Kappa-maximizer criteria are the worst ones in almost all situations. Nevertheless, whatever the criteria used, the threshold value chosen and the research goals that determined its choice must be stated.
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paper_title: Effect of errors in ground truth on classification accuracy

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The effect of errors in ground truth on the estimated thematic accuracy of a classifier is considered. A relationship is derived between the true accuracy of a classifier relative to ground truth without errors, the actual accuracy of the ground truth used, and the measured accuracy of the classifier as a function of the number of classes. We show that if the accuracy of the ground truth is known or can be estimated, the true accuracy of a classifier can be estimated from the measured accuracy. In a series of simulations our method is shown to produce unbiased estimates of the true accuracy of the classifier with an uncertainty that depends on the number of samples and the accuracy of the ground truth. A method for determining the relative performance of two or more classifiers over the same area is then discussed. The results indicate that, as the number of samples increases, the performance of the classifiers can be effectively differentiated using inaccurate ground truth. It is argued that relative acc...
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paper_title: Effects of species and habitat positional errors on the performance and interpretation of species distribution models

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Aim A key assumption in species distribution modelling is that both species and environmental data layers contain no positional errors, yet this will rarely be true. This study assesses the effect of introduced positional errors on the performance and interpretation of species distribution models. Location Baixo Alentejo region of Portugal. Methods Data on steppe bird occurrence were collected using a random ::: stratified sampling design on a 1-km2 pixel grid. Environmental data were sourced from satellite imagery and digital maps. Error was deliberately introduced into the species data as shifts in a random direction of 0–1, 2–3, 4–5 and 0–5 pixels. Whole habitat layers were shifted by 1 pixel to cause mis-registration, and the cumulative effect of one to three shifted layers investigated. Distribution models were built for ::: three species using three algorithms with three replicates. Test models were compared with controls without errors. ::: Results Positional errors in the species data led to a drop in model performance (larger errors having larger effects – typically up to 10% drop in area under the curve on average), although not enough for models to be rejected. Model interpretation was more severely affected with inconsistencies in the contributing variables. Errors in the habitat layers had similar although lesser effects. Main conclusions Models with species positional errors are hard to detect, often ::: statistically good, ecologically plausible and useful for prediction, but interpreting them is dangerous. Mis-registered habitat layers produce smaller effects probably ::: because shifting entire layers does not break down the correlation structure to the same extent as random shifts in individual species observations. Spatial autocorrelation in the habitat layers may protect against species positional errors to some extent but the relationship is complex and requires further work. The key recommendation must be that positional errors should be minimised through careful field design and data processing.
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paper_title: Measuring the accuracy of species distribution models: a review

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Species distribution models (SDMs) are empirical models relating species occurrence to environmental variables based on statistical or other response surfaces. Species distribution modeling can be used as a tool to solve many theoretical and applied ecological and environmental problems, which include testing biogeographical, ecological and evolutionary hypotheses, assessing species invasion and climate change impact, and supporting conservation planning and reserve selection. The utility of SDM in real world applications requires the knowledge of the model’s accuracy. The accuracy of a model includes two aspects: discrimination capacity and reliability. The former is the power of the model to differentiate presences from absences; and the latter refers to the capability of the predicted probabilities to reflect the observed proportion of sites occupied by the subject species. Similar methodology has been used for model accuracy assessment in different fields, including medical diagnostic test, weather forecasting and machine learning, etc. Some accuracy measures are used in all fields, e.g. the overall accuracy and the area under the receiver operating characteristic curve; while the use of other measures is largely restricted to specific fields, e.g. F-measure is mainly used in machine learning field, or is referred to by different names in different fields, e.g. “true skill statistic” is used in atmospheric science and it is called “Youden’s J” in medical diagnostic field. In this paper we review those accuracy measures typically used in ecology. Generally, the measures can be divided into two groups: threshold-dependent and thresholdindependent. Measures in the first group are used for binary predictions, and those in the second group are used for continuous predictions. Continuous predictions may be transformed to binary ones if a specific threshold is employed. In such cases, the threshold-dependent accuracy measures can also be used. The threshold-dependent indices used in or introduced to SDM field include overall accuracy, sensitivity, specificity, positive predictive value, negative predictive value, odds ratio, true skill statistic, F-measure, Cohen’s kappa, and normalized mutual information (NMI). However, since NMI only measures the agreement between two patterns, it cannot differentiate the worse-than-random models from the better-thanrandom models, which reduces its utility as an accuracy measure. The threshold-independent indices used in or introduced to the SDM field include the area under the receiver operating characteristic curve (AUC), Gini index, and point biserial correlation coefficient. The proportion of explained deviance 2 D and its adjusted form have been also introduced into SDM field. But this adjusted metric has no theoretical foundation in the context of generalized linear modeling. Therefore, we provide another adjusted form, which was proposed by H. V. Houwelingen based on the asymptotic 2 χ distribution of the log-likelihood statistics. Its superiority over other related measures has been found through previous simulation studies. We also provide another analogous measure, the coefficient of determination 2 R , which has had a long history in weather forecast verification and was also recommended for use in medical diagnosis. Though these measures 2 D and 2 R are routinely used to evaluate generalized linear models (GLMs), we argue that nothing prevents them from being applied to other GLM-like models. In SDM accuracy assessment, discrimination capacity is often considered, but model reliability is frequently ignored. The primary reason for this is that no reliability measure has been introduced into the ecological literature. To meet this need we also suggest that root mean square error be used as a reliability measure. Its squared form, mean square error, has been used in meteorology for a long time, and is called Brier’s score. We also discuss the effect of prevalence dependence of accuracy measures and the precision of accuracy estimates.
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paper_title: Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

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Summary ::: 1In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion. ::: 2In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel. ::: 3The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence. ::: 4When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence. ::: 5Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.
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paper_title: WAS IT THERE? DEALING WITH IMPERFECT DETECTION FOR SPECIES PRESENCE/ABSENCE DATA†

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Summary ::: ::: Species presence/absence surveys are commonly used in monitoring programs, metapopulation studies and habitat modelling, yet they can never be used to confirm that a species is absent from a location. Was the species there but not detected, or was the species genuinely absent? Not accounting for imperfect detection of the species leads to misleading conclusions about the status of the population under study. Here some recent modelling developments are reviewed that explicitly allow for the detection process, enabling unbiased estimation of occupancy, colonization and local extinction probabilities. The methods are illustrated with a simple analysis of presence/absence data collected on larvae and metamorphs of tiger salamander (Ambystoma tigrinum) in 2000 and 2001 from Minnesota farm ponds, which highlights that misleading conclusions can result from naive analyses that do not explicitly account for imperfect detection.
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paper_title: Statistical Techniques for Evaluating the Diagnostic Utility of Laboratory Tests

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Clinical laboratory data is used to help classify patients into diagnostic disease categories so that appropriate therapy may be implemented and prognosis estimated. Unfortunately, the process of correctly classifying patients with respect to disease status is often difficult. Patients may have several concurrent disease processes and the clinical signs and symptoms of many diseases lack specificity. In addition, results of laboratory tests and other diagnostic procedures from healthy and diseased individuals often overlap. Finally, advances in computer technology and laboratory automation have resulted in an extraordinary increase in the amount of information produced by the clinical laboratory; information which must be correctly evaluated and acted upon so that appropriate treatment and additional testing, if necessary, can be implemented. Clinical informatics refers to a broad array of statistical methods used for the evaluation and management of diagnostic information necessary for appropriate patient care. Within the realm of clinical chemistry, clinical informatics may be used to indicate the acquisition, evaluation, representation and interpretation of clinical chemistry data. This review discusses some of the techniques that should be used for the evaluation of the diagnostic utility of clinical laboratory data. The major topics to be covered include probabilistic approaches to data evaluation, and information theory. The latter topic will be discussed in some detail because it introduces important concepts useful in providing for cost-effective, quality patient care. In addition, an example illustrating how the informational value of diagnostic tests can be determined is shown.
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paper_title: On the accuracy of landscape pattern analysis using remote sensing data

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Advances in remote sensing technologies have provided practical means for land use and land cover mapping which is critically important for landscape ecological studies. However, all classifications of remote sensing data are subject to different kinds of errors, and these errors can be carried over or propagated in subsequent landscape pattern analysis. When these uncertainties go unreported, as they do commonly in the literature, they become hidden errors. While this is apparently an important issue in the study of landscapes from either a biophysical or socioeconomic perspective, limited progress has been made in resolving this problem. Here we discuss how errors of mapped data can affect landscape metrics and possible strategies which can help improve the reliability of landscape pattern analysis.
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paper_title: The influence of spatial errors in species occurrence data used in distribution models

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Summary 1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species’ environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species’ occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications . To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error. Journal of Applied Ecology (2007)
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paper_title: Obtaining Environmental Favourability Functions from Logistic Regression

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Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes them more useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.
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paper_title: Evaluating Diagnostic Tests with Imperfect Standards

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New diagnostic tests frequently are evaluated against gold standards that are assumed to classify patients with unerring accuracy according to the presence or absence of disease. In practice, gold standards rarely are perfect predictors of disease and tend to misclassify a small number of patients. When an imperfect standard is used to evaluate a diagnostic test, many commonly used measures of test performance are distorted. It is not widely appreciated that these distortions occur in predictable directions and that they may be of considerable magnitude, even when the gold standard has a high degree of accuracy. The diagnostic powers of clinical tests will be more accurately reported if consideration is given to the types of biases that result from the use of imperfect standards. Several different approaches may be used to minimize these distortions when evaluating new tests.
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paper_title: Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority

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The comparison of classification accuracy statements has generally been based upon tests of difference or inequality when other scenarios and approaches may be more appropriate. Procedures for evaluating two scenarios with interest focused on the similarity in accuracy values, non-inferiority and equivalence, are outlined following a discussion of tests of difference (inequality). It is also suggested that the confidence interval of the difference in classification accuracy may be used as well as or instead of conventional hypothesis testing to reveal more information about the disparity in the classification accuracy values compared.
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paper_title: Surveyor consistency in presence/absence sampling for monitoring vegetation in a boreal forest

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Vegetation assessments are a central part of many large-scale monitoring programmes. To accurately estimate change over time, consistent field methods are important. Presence/absence (P/A) sampling is considered to be less susceptible to judgement and measurement errors in comparison with visual cover assessment, although errors also occur with this method in complete species inventories. Few studies have evaluated surveyor consistency in P/A sampling with a limited list of species. In this study, the consistency of results in P/A sampling was evaluated in a field test, with different surveyors assessing the same sample plots. The results indicated a good consistency between surveyors and high accuracy according to a reference survey for many of the tested species, although some differences both between surveyors and in comparison with the reference survey were found. Comparing a group of surveyors with a larger experience of vegetation assessments with a group of surveyors with less experience indicated that the former were more accurate and consistent. No clear differences were found between different plot sizes tested. The main conclusion from the study is that P/A sampling is slightly affected by observer judgement bias, but that in comparison with the consistency of visual cover assessment observed in other studies, the difference between surveyors for many species is reasonably low.
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paper_title: Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study

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BackgroundA common feature of diagnostic research is that results for a diagnostic gold standard are available primarily for patients who are positive for the test under investigation. Data from such studies are subject to what has been termed "verification bias". We evaluated statistical methods for verification bias correction when there are few false negatives.MethodsA simulation study was conducted of a screening study subject to verification bias. We compared estimates of the area-under-the-curve (AUC) corrected for verification bias varying both the rate and mechanism of verification.ResultsIn a single simulated data set, varying false negatives from 0 to 4 led to verification bias corrected AUCs ranging from 0.550 to 0.852. Excess variation associated with low numbers of false negatives was confirmed in simulation studies and by analyses of published studies that incorporated verification bias correction. The 2.5th – 97.5th centile range constituted as much as 60% of the possible range of AUCs for some simulations.ConclusionScreening programs are designed such that there are few false negatives. Standard statistical methods for verification bias correction are inadequate in this circumstance.
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paper_title: The impact of imperfect ground reference data on the accuracy of land cover change estimation

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Error in the ground reference data set used in studies of land cover change can be a source of bias in the estimation of land cover change and of change detection accuracy. The magnitude of the bias introduced may be very large even if the ground reference data set is of a high accuracy. Sometimes the bias is of a predictable systematic nature and so may be reduced or even removed. The impacts of ground reference data error on the accuracy of estimates of the extent of change and on change detection accuracy were explored with simulated data. In one scenario illustrated, the producer's accuracy of change detection was estimated to be ∼61% when in reality it was 80%, the substantial underestimation of accuracy arising through the use of a ground reference data set with an accuracy of 90%. In the same scenario, the extent of change was also substantially overestimated at 26%, when in reality a change of only 20% had occurred. Reducing the effect of error in ground reference data will enable more accurate estimation of land cover change and a more realistic appraisal of the quality of remote sensing as a source of data on land cover change.
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paper_title: Ecology and geography of avian influenza (HPAI H5N1) transmission in the Middle East and northeastern Africa

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BackgroundThe emerging highly pathogenic avian influenza strain H5N1 ("HPAI-H5N1") has spread broadly in the past decade, and is now the focus of considerable concern. We tested the hypothesis that spatial distributions of HPAI-H5N1 cases are related consistently and predictably to coarse-scale environmental features in the Middle East and northeastern Africa.We used ecological niche models to relate virus occurrences to 8 km resolution digital data layers summarizing parameters of monthly surface reflectance and landform. Predictive challenges included a variety of spatial stratification schemes in which models were challenged to predict case distributions in broadly unsampled areas.ResultsIn almost all tests, HPAI-H5N1 cases were indeed occurring under predictable sets of environmental conditions, generally predicted absent from areas with low NDVI values and minimal seasonal variation, and present in areas with a broad range of and appreciable seasonal variation in NDVI values. Although we documented significant predictive ability of our models, even between our study region and West Africa, case occurrences in the Arabian Peninsula appear to follow a distinct environmental regime.ConclusionOverall, we documented a variable environmental "fingerprint" for areas suitable for HPAI-H5N1 transmission.
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paper_title: Improved abundance prediction from presence-absence data

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Aim Many ecological surveys record only the presence or absence of species in the cells of a rectangular grid. Ecologists have investigated methods for using these data to predict the total abundance of a species from the number of grid cells in which the species is present. Our aim is to improve such predictions by taking account of the spatial pattern of occupied cells, in addition to the number of occupied cells. Innovation We extend existing prediction models to include a spatial clustering variable. The extended models can be viewed as combining two macroecological regularities, the abundance‐occupancy regularity and a spatial clustering regularity. The models are estimated using data from five tropical forest censuses, including three Panamanian censuses (4, 6 and 50 ha), one Costa Rican census (16 ha) and one Puerto Rican census (16 ha). A serpentine grassland census (8 × 8 m) from northern California is also studied. Main conclusions Taking account of the spatial clustering of occupied cells improves abundance prediction from presence‐absence data, reducing the mean square error of log-predictions by roughly 54% relative to a benchmark Poisson predictor and by roughly 34% relative to current prediction methods. The results have high statistical significance.
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paper_title: Evaluating presence-absence models in ecology: the need to account for prevalence

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1. Models for predicting the distribution of organisms from environmental data are widespread in ecology and conservation biology. Their performance is invariably evaluated from the percentage success at predicting occurrence at test locations. 2. Using logistic regression with real data from 34 families of aquatic invertebrates in 180 Himalayan streams, we illustrate how this widespread measure of predictive accuracy is affected systematically by the prevalence (i.e. the frequency of occurrence) of the target organism. Many evaluations of presence-absence models by ecologists are inherently misleading. 3. With the same invertebrate models, we examined alternative performance measures used in remote sensing and medical diagnostics. We particularly explored receiver-operating characteristic (ROC) plots, from which were derived (i) the area under each curve (AUC), considered an effective indicator of model performance independent of the threshold probability at which the presence of the target organism is accepted, and (ii) optimized probability thresholds that maximize the percentage of true absences and presences that are correctly identified. We also evaluated Cohen's kappa, a measure of the proportion of all possible cases of presence or absence that are predicted correctly after accounting for chance effects. 4. AUC measures from ROC plots were independent of prevalence, but highly significantly correlated with the much more easily computed kappa. Moreover, when applied in predictive mode to test data, models with thresholds optimized by ROC erroneously overestimated true occurrence among scarcer organisms, often those of greatest conservation interest. We advocate caution in using ROC methods to optimize thresholds required for real prediction. 5. Our strongest recommendation is that ecologists reduce their reliance on prediction success as a performance measure in presence-absence modelling. Cohen's kappa provides a simple, effective, standardized and appropriate statistic for evaluating or comparing presence-absence models, even those based on different statistical algorithms. None of the performance measures we examined tests the statistical significance of predictive accuracy, and we identify this as a priority area for research and development.
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paper_title: Sensitivity of species-distribution models to error, bias, and model design : An application to resource selection functions for woodland caribou

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Models that predict distribution are now widely used to understand the patterns and processes of plant and animal occurrence as well as to guide conservation and management of rare or threatened species. Application of these methods has led to corresponding studies evaluating the sensitivity of model performance to requisite data and other factors that may lead to imprecise or false inferences. We expand upon these works by providing a relative measure of the sensitivity of model parameters and prediction to common sources of error, bias, and variability. We used a one-at-a-time sample design and GPS location data for woodland caribou (Rangifer tarandus caribou) to assess one common species-distribution model: a resource selection function. Our measures of sensitivity included change in coefficient values, prediction success, and the area of mapped habitats following the systematic introduction of geographic error and bias in occurrence data, thematic misclassification of resource maps, and variation in model design. Results suggested that error, bias and model variation have a large impact on the direct interpretation of coefficients. Prediction success and definition of important habitats were less responsive to the perturbations we introduced to the baseline model. Model coefficients, prediction success, and area of ranked habitats were most sensitive to positional error in species locations followed by sampling bias, misclassification of resources, and variation in model design. We recommend that researchers report, and practitioners consider, levels of error and bias introduced to predictive species-distribution models. Formal sensitivity and uncertainty analyses are the most effective means for evaluating and focusing improvements on input data and considering the range of values possible from imperfect models.
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paper_title: Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease.

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Although frequentist approaches to prevalence estimation are simple to apply, there are circumstances where it is difficult to satisfy assumptions of asymptotic normality and nonsensical point estimates (greater than 1 or less than 0) may result. This is particularly true when sample sizes are small, test prevalences are low and imperfect sensitivity and specificity of diagnostic tests need to be incorporated into calculations of true prevalence. Bayesian approaches offer several advantages including direct computation of range-respecting interval estimates (e.g. intervals between 0 and 1 for prevalence) without the requirement of transformations or large-sample approximations, direct probabilistic interpretation, and the flexibility to model in a straightforward manner the probability of zero prevalence. In this review, we present frequentist and Bayesian methods for animal- and herd-level true prevalence estimation based on individual and pooled samples. We provide statistical methods for detecting differences between population prevalence and frequentist methods for sample size and power calculations. All examples are motivated using Mycobacterium avium subspecies paratuberculosis infection and we provide WinBUGS code for all examples of Bayesian estimation.
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paper_title: The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?

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Summary ::: ::: ::: 1 ::: Conservation scientists and resource managers increasingly employ empirical distribution models to aid decision-making. However, such models are not equally reliable for all species, and range size can affect their performance. We examined to what extent this effect reflects statistical artefacts arising from the influence of range size on the sample size and sampling prevalence (proportion of samples representing species presence) of data used to train and test models. ::: ::: 2 ::: Our analyses used both simulated data and empirical distribution models for 32 bird species endemic to South Africa, Lesotho and Swaziland. Models were built with either logistic regression or non-linear discriminant analysis, and assessed with four measures of model accuracy: sensitivity, specificity, Cohen's kappa and the area under the curve (AUC) of receiver-operating characteristic (ROC) plots. Environmental indices derived from Fourier-processed satellite imagery served as predictors. ::: ::: 3 ::: We first followed conventional modelling practice to illustrate how range size might influence model performance, when sampling prevalence reflects species’ natural prevalences. We then demonstrated that this influence is primarily artefactual. Statistical artefacts can arise during model assessment, because Cohen's kappa responds systematically to changes in prevalence. AUC, in contrast, is largely unaffected, and thus a more reliable measure of model performance. Statistical artefacts also arise during model fitting. Both logistic regression and discriminant analysis are sensitive to the sample size and sampling prevalence of training data. Both perform best when sample size is large and prevalence intermediate. ::: ::: 4 ::: Synthesis and applications. Species’ ecological characteristics may influence the performance of distribution models. Statistical artefacts, however, can confound results in comparative studies seeking to identify these characteristics. To mitigate artefactual effects, we recommend careful reporting of sampling prevalence, AUC as the measure of accuracy, and fixed, intermediate levels of sampling prevalence in comparative studies.
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paper_title: Land change in the Brazilian Savanna (Cerrado), 1986–2002: Comparative analysis and implications for land-use policy

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The Brazilian Cerrado, a biodiverse savanna ecoregion covering ∼1.8 million km2 south and east of the Amazon rainforest, is in rapid decline because of the expansion of modern agriculture. Previous studies of Cerrado land-use and land-cover (LULC) change imply spatial homogeneity, report widely varying rates of land conversion, use ambiguous LULC categories, and generally do not attempt to validate results. This study addresses this gap in the literature by analyzing moderate-resolution, multi-spectral satellite remote sensing data from 1986 to 2002 in two regions with identical underlying drivers. Unsupervised classification by the ISODATA algorithm indicates that Cerrado was converted to agro-pastoral land covers in 31% (3646 km2) of the study region in western Bahia and 24% (3011 km2) of the eastern Mato Grosso study region, while nearly 40% (4688 km2 and 5217 km2, respectively) of each study region remained unchanged. Although aggregate land change is similar, large and contiguous fragments persist in western Bahia, while smaller fragments remain in eastern Mato Grosso. These findings are considered in the current context of Cerrado land-use policy, which is dominated by the conservation set-aside and command-control policy models. The spatial characteristics of Cerrado remnants create considerable obstacles to implement the models; an alternative approach, informed by countryside biogeography, may encourage collaboration between state officials and farmer-landowners toward conservation land-use policies.
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paper_title: Bias and prevalence effects on kappa viewed in terms of sensitivity and specificity

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Abstract Paradoxical effects of bias and prevalence on the kappa coefficient are examined using the concepts of sensitivity and specificity. Results that appear paradoxical when viewed as a 2 × 2 table of frequencies do not appear paradoxical when viewed as a pair of sensitivity and specificity measures where each observer is treated as a predictor of the other observer. An adjusted kappa value can be obtained from these sensitivity/specificity measures but simulation studies indicate that it would result in substantial overestimation of reliability when bias or prevalence effects are observed. It is suggested that investigators concentrate on obtaining populations with trait prevalence near 50% rather than searching for statistical indices to rescue or excuse inefficient experiments.
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paper_title: Effects of sample size on accuracy of species distribution models

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Abstract Given increasing access to large amounts of biodiversity information, a powerful capability is that of modeling ecological niches and predicting geographic distributions. Because, sampling species’ distributions is costly, we explored sample size needs for accurate modeling for three predictive modeling methods via re-sampling of data for well-sampled species, and developed curves of model improvement with increasing sample size. In general, under a coarse surrogate model, and machine-learning methods, average success rate at predicting occurrence of a species at a location, or accuracy, was 90% of maximum within ten sample points, and was near maximal at 50 data points. However, a fine surrogate model and logistic regression model had significantly lower rates of increase in accuracy with increasing sample size, reaching similar maximum accuracy at 100 data points. The choice of environmental variables also produced unpredictable effects on accuracy over the range of sample sizes on the logistic regression method, while the machine-learning method had robust performance throughout. Examining correlates of model performance across species, extent of geographic distribution was the only significant ecological factor.
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paper_title: A method to compare and improve land cover datasets: application to the GLC-2000 and MODIS land cover products

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This paper presents a methodology for the comparison of different land cover datasets and illustrates how this can be extended to create a hybrid land cover product. The datasets used in this paper are the GLC-2000 and MODIS land cover products. The methodology addresses: 1) the harmonization of legend classes from different global land cover datasets and 2) the uncertainty associated with the classification of the images. The first part of the methodology involves mapping the spatial disagreement between the two land cover products using a combination of fuzzy logic and expert knowledge. Hotspots of disagreement between the land cover datasets are then identified to determine areas where other sources of data such as TM/ETM images or detailed regional and national maps can be used in the creation of a hybrid land cover dataset
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paper_title: Land cover and global productivity: A measurement strategy for the NASA programme

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NASA's Earth science programme is developing an improved understanding of terrestrial productivity and its relationship to global environmental change. Environmental change includes changes that are anthropogenic, caused for example by increasing population and resource use, as well as those that are natural, caused by interannual or decadal variability in climate and intrinsic vegetation dynamics. In response to current science and policy concerns, the Earth science programme has carbon and the major biogeochemical cycles as a primary focus but is broad enough to include related topics such as land-atmosphere interactions associated with the hydrological cycle and the chemical composition of the atmosphere. The research programme includes the study of ecosystems both as respondents to change and as mediators of feedback to the atmosphere. Underlying all the research elements are important questions of natural resources and sustainable land management. The land cover and land use change element of the pro...
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paper_title: Intercalibration of vegetation indices from different sensor systems

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Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR-2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 1–2%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change.
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paper_title: Satellite remote sensing of forest resources: three decades of research development

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Three decades have passed since the launch of the first international satellite sensor programme designed for monitoring Earth’s resources. Over this period, forest resources have come under increasing pressure, thus their management and use should be underpinned by information on their properties at a number of levels. This paper provides a comprehensive review of how satellite remote sensing has been used in forest resource assessment since the launch of the first Earth resources satellite sensor (ERTS) in 1972. The use of remote sensing in forest resource assessment provides three levels of information; namely (1) the spatial extent of forest cover, which can be used to assess the spatial dynamics of forest cover; (2) forest type and (3) biophysical and biochemical properties of forests. The assessment of forest information over time enables the comprehensive monitoring of forest resources. This paper provides a comprehensive review of how satellite remote sensing has been used to date and, building on...
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paper_title: Landsat continuity : Issues and opportunities for land cover monitoring

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Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.
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paper_title: Modelling coral reef habitat trajectories: Evaluation of an integrated timed automata and remote sensing approach

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The rapid degradation of many reefs worldwide calls for more effective monitoring and predictions of the trajectories of coral reef habitats as they cross cycles of disturbance and recovery. Current approaches include in situ monitoring, computer modelling, and remote sensing observations. We aimed to combine these three sources of information for Abor ´ e
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paper_title: Analysis of remotely sensed data: the formative decades and the future

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Developments in the field of image understanding in remote sensing over the past four decades are reviewed, with an emphasis, initially, on the contributions of David Landgrebe and his colleagues at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, culminating with a commentary on methods commonly adopted for multisource image analysis. The treatment concludes by examining the requirements of an operational multisource thematic mapping process, in which it is suggested that the most practical approach is to analyze each data type separately, by techniques optimized to that data's characteristics, and then to fuse at the label level.
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paper_title: Remote sensing in the coming decade: the vision and the reality

paper_content:

Investment in understanding the Earth pays off twice. It enables pursuit of scientific questions that rank among the most interesting and profound of our time. It also serves society's practical need for increased prosperity and security. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide the raw information from which we derive Earth knowledge. This network has served us well in the development of science and the provision of operational services. In the next decade, the demand for such information will grow dramatically. New remote sensing capabilities will emerge. Rapid evolution of Internet geospatial and location-based services will make communication and sharing of Earth knowledge much easier. Governments, businesses, and consumers will all benefit. But this exciting future is threatened from many directions. Risks range from technology and market uncertainties in the private sector to budget cuts and project setbacks in the public sector. The coming decade will see a dramatic confrontation between the vision of what needs to be accomplished in Earth remote sensing and the reality of our resources and commitment. The outcome will have long-term implications for both the remote sensing community and society as a whole.
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paper_title: Multiple Methods in the Study of Driving Forces of Land Use and Land Cover Change: A Case Study of SE Kajiado District, Kenya

paper_content:

This landscape-scale study combines analysis of multitemporal satellite imagery spanning 30 years and information from field studies extending over 25 years to assess the extent and causes of land use and land cover change in the Loitokitok area, southeast Kajiado District, Kenya. Rain fed and irrigated agriculture, livestock herding, and wildlife and tourism have all experienced rapid change in their structure, extent, and interactions over the past 30 years in response to a variety of economic, cultural, political, institutional, and demographic processes. Land use patterns and processes are explored through a complementary application of interpretation of satellite imagery and case study analysis that explicitly addresses the local–national spatial scale over a time frame appropriate to the identification of fundamental causal processes. The results illustrate that this combination provides an effective basis for describing and explaining patterns of land use and land cover change and their root causes.
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paper_title: Landsat's Role in Ecological Applications of Remote Sensing

paper_content:

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat.
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Title: An overview of recent remote sensing and GIS based research in ecological informatics

Section 1: Background
Description 1: Provide an introduction to geotechnology and its significance in the 21st century, emphasizing recent developments in GIS and remote sensing and their impact on ecological research.

Section 2: Remote sensing: overcoming limitations in spatial resolution
Description 2: Discuss the advancements in remote sensing technology, particularly in relation to overcoming spatial resolution limitations, and the challenges and solutions associated with mixed pixels and image classification.

Section 3: Remote sensing: targeted mapping
Description 3: Explain the use of remote sensing for tailored mapping of specific environmental features or species, and discuss the benefits and challenges of using airborne and spaceborne lidar systems.

Section 4: Remote sensing: capturing the temporal dimension
Description 4: Describe the importance of temporal data in remote sensing for ecological studies, particularly for monitoring vegetative phenological events and understanding climate change impacts.

Section 5: GIS: the internet revolution
Description 5: Explore the recent developments in WebGIS, mobile GIS, and sensor webs/networks, highlighting the benefits of internet technology for geographic data processing and sharing.

Section 6: Free and Open Source GIS Software and data
Description 6: Discuss the rise of Free and Open Source GIS software, including examples of projects, their benefits for ecological informatics, and the growing adoption within the GIS community.

Section 7: Geovisualization
Description 7: Outline the advancements in geovisualization technologies, their applications in ecological research, and the integration of approaches from various domains to enhance spatial data analysis.

Section 8: Species distribution modelling
Description 8: Provide an overview of the use of GIS in species distribution modelling, highlighting recent research trends, tools, and the impact of environmental changes on species distributions.

Section 9: Accuracy and comparison
Description 9: Discuss the importance of accuracy in ecological data and modelling, the challenges of data quality and quantity, and the methods used for accuracy assessment and validation.

Section 10: Prospects
Description 10: Summarize the future prospects of remote sensing and GIS in ecological informatics, considering potential advancements in sensor technology, data continuity, and the integration of multidisciplinary data.