Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey
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paper_title: Urban structuring using multisensoral remote sensing data: By the example of the German cities Cologne and Dresden

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The urban landscape is a highly complex and small-structured, heterogeneous area as a result of permanent human settlement. Urban structure is scale-dependant and can be expressed on various levels of detail by satellite imagery. Very high resolution satellite (VHR) sensors are capable of mapping and monitoring cities - on house/block level - with their high degree of landcover diversity. However, detection of morphological features such as shape and elevation of single objects is performed much better when a digital surface model (DSM) - e.g. derived by airborne laserscanning - is incorporated. An object-oriented methodology for the joint analysis of optical satellite data and a digital surface model is presented for the classification of the urban morphology in terms of urban structural types. These are spatial units - mostly on block level - with aggregated information on the classified single features like landcover/landuse and urban fabric. Hence, a hierarchical, modular segmentation and classification workflow is implemented to extract the required information. The methodology is applied on two study areas in the cities of Cologne and Dresden, Germany, and a validation of the capability of the potential for transferability of the rulebase is shown.
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paper_title: Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

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Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within-class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21,000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.
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paper_title: Application of LandSat TM data for quality of life assessment in an urban environment

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Landsat TM data were used in conjunction with census data for urban quality of life assessment, using the Athens-Clarke County, Georgia in the United States as an example. The satellite image data were used to extract environmental variables of land cover, vegetation index, and surface temperature, while socioeconomic variables were obtained from the census data. Using the social space conceptualization put forth by Chombart de Lauwe, quality of life as a collective attribute that adheres to groups of people was assessed with reference to the morphological and sociocultural environment. A principal components analysis and a raster geographic information system overlay of variables were found to be useful approaches that effectively integrated biophysical and socioeconomic data together to give an accurate assessment of quality of life within the study area. It is concluded that high resolution satellite image data are a useful complement to census data by giving the environmental perspective in urban analysis.
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paper_title: Use of impervious surface in urban land-use classification

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Impervious surface has been recognized as a key indicator in assessing urban environments. However, accurate impervious surface extraction is still a challenge. Effectiveness of impervious surface in urban land-use classification has not been well addressed. This paper explored extraction of impervious surface information from Landsat Enhanced Thematic Mapper data based on the integration of fraction images from linear spectral mixture analysis and land surface temperature. A new approach for urban land-use classification, based on the combined use of impervious surface and population density, was developed. Five urban land-use classes (i.e., low-, medium-, high-, and very-high-intensity residential areas, and commercial/industrial/transportation uses) were developed in the city of Indianapolis, Indiana, USA. Results showed that the integration of fraction images and surface temperature provided substantially improved impervious surface image. Accuracy assessment indicated that the root-mean-square error and system error yielded 9.22% and 5.68%, respectively, for the impervious surface image. The overall classification accuracy of 83.78% for five urban land-use classes was obtained.
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paper_title: Spectral resolution requirements for mapping urban areas

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This study evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high within-class variability.
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paper_title: Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

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Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region.
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paper_title: Ecological evaluation of urban biotope types using airborne hyperspectral HyMap data

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Urban biotype mapping is the most important database for ecological urban planning. The mapping contains complex information about the outfit and structure of biotopes in the form of descriptive parameters. However, quantitative parameters are limited to a manual estimation of the five levels of surface sealing. Therefore, municipal authorities are interested in quantitative parameters for an advanced GIS-based evaluation of urban biotopes. In this study airborne hyperspectral HyMap data are analyzed to identify urban surface materials based on their material specific reflectance features. The small urban structures and the high spectral information content of the hyperspectral image data require advanced methods for surface identification. Thus, a combined classification and unmixing approach was applied [S. Roessner et al., 2001]. The resulting dataset includes surface abundances of spectrally different materials and forms the basis for the derivation of quantitative parameters for each biotope, such as the weighted surface sealing and the portion of overbuilt area. Spatial and statistical analyses show that the results represent a contribution towards an improved quantitative evaluation of biotopes. In this way valuable remote sensing based parameters can be integrated into ecological urban planning processes.
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paper_title: Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data

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We compared the capability of the Earth Observing-1 (EO-1) Hyperion hyperspectral (HS) data with that of the EO-1 Advanced Land Imager (ALI) multispectral (MS) data for discriminating different land-use and land-cover classes in Fremont, California. We designed a classification scheme of two levels with level I including general classes and level II including more specific classes. Classification shows that the HS data does not produce better results than the MS data when we directly applied a Mahalanobis distance (MD) classifier. We tested a number of feature reduction and extraction algorithms for the HS image. These algorithms include principal component analysis (PCA), segmented PCA (SEGPCA), linear discriminant analysis (LDA), segmented LDA (SEGLDA), penalized discriminant analysis (PDA) and segmented PDA (SEGPDA). Feature reductions were all followed by an MD classifier for image classification. With SEGPDA, SEGLDA, PDA, and LDA, similar accuracies were achieved while a segmentation-based approach we proposed (SEGPDA or SEGLDA) greatly improved computation efficiency. They all outperformed SEGPCA and PCA by 4 to 5 percent (level II) and 1 to 3 percent (level I) in classification accuracy. For level II classification, overall accuracies obtained by using the features extracted from the HS image were 2 to 3 percent greater than those obtained with the MS image. For various vegetation class and impervious land use categories, the HS data consistently produced better results than the MS data. For level I classification, the HS image generated a thematic map that is � 0.01 greater in kappa coefficient comparing to the MS image. When we collapsed the level II classification map to a level I map, 5 percent (HS) to 7 percent (MS) improvements were achieved.
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paper_title: Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm

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We investigate the spectral complexity and unique spectral characteristics of urban environments using a comprehensive regional field spectral library of more than 4500 individual spectra. Spectral properties of urban surface materials are presented and interpreted, and their separability is systematically analyzed using the Bhattacharyya distance (B-distance) as a quantitative measure of spectral discrimination. We find considerable spectral confusion between urban land cover types (i.e. specific roof and road types) but also show the potential of fine spectral-resolution remote sensing for detailed mapping of urban materials and their condition based on their spectral signal. An evaluation of the most suitable wavelengths for separation of urban land cover identified specific spectral features that provided the best separation. There is a strong indication that current multispectral systems, including IKONOS and LANDSAT ETM+, provide only marginal abilities to resolve these important features and are limited for urban land-cover mapping.
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paper_title: Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features

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Abstract With the advent of Earth Observation satellite sensors producing images in the visible wavelengths with resolutions better than 5 m, it is now possible to recognize man-made objects which were not visible at lower resolutions. Because of the size and the increasing quantity of remote sensing images, tools are needed for computer aided interpretation. In this work we present an image processing system for the detection and recognition of man-made objects in high resolution optical remote sensing images. Detection is understood here as finding a small rectangular area in the image containing an object. Recognition is the attribution of a class label. These algorithms are based on learning methods and on an example data base which contains eleven classes of objects. The examples (more that 150 for each class) have been manually extracted from SPOT 5 THR images (2.5 m resolution). In order to build a system which is independent of the type of object to be recognized, we have used a supervised learning approach based on support vector machines. The system learns a generic model for each class of objects by using a geometric characterization of the examples in the data base. The main novelty of this paper is the use of a high number of geometric image features which allows to characterise several classes of objects with different geometric properties using a supervised learning approach. The results show the possibility of discrimination of several classes of objects with classification rates higher than 80%.
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paper_title: How much is built? Quantifying and interpreting patterns of built space from different data sources

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Land-use/cover change (LUCC) has emerged as a crucial component of applied research in remote sensing. This work compares two methodologies, based on two data sources, for assessing amounts of land transformed from open to built space in three regions in Israel. We use a decision-tree methodology to define open and built space from remotely sensed (RS) Landsat data and a geographic information systems (GIS) platform for analysing 1:50 000 scale survey maps. The methodologies are developed independently, used to quantify and characterize the spatial pattern of built space, and then analysed for their strengths and weaknesses. We then develop a method for combining the built area maps derived from each methodology, capitalizing on the strengths of each. The RS methodology had higher omission errors for built space in areas with high vegetation levels and low-density exurban development, but high commission errors in the arid region. The GIS analysis generally had fewer errors, although systematically missed built surfaces that were not specifically buildings or roads, as well as structures intentionally omitted from the maps. We recommend using maps for baseline estimates whenever possible and then complementing the estimates with clusters of built areas identified with the RS methodology. The results of this comparative study are relevant to both researchers and practitioners who need to understand the strengths and weaknesses of mapping techniques they are using.
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paper_title: Urbanization in India - Spatiotemporal analysis using remote sensing data

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Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.
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paper_title: Use of airborne hyperspectral data and height information to support urban micro-climate characterisation

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The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. ::: ::: The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. ::: The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. ::: The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. ::: ::: The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. ::: ::: Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. ::: The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. ::: Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.
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paper_title: Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data

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In this article, Landsat TM images acquired during the same season from both 1984 and 1997 were analysed for urban built-up land change detection in Beijing, China, where great changes have taken place during the recent decades. To reduce the spectral confusion between urban 'built-up' and rural 'non built-up' land cover categories, we propose a new structural method based on road density combined with spectral bands for change detection. The road density represents one type of structural information while the multiple Landsat TM bands represent spectral information. Road density maps for both dates were produced using a gradient direction profile analysis (GDPA) algorithm and then integrated with spectral bands. Results from the spectral-structural postclassification comparison (SSPCC) and spectral-structural image differencing (SSID) methods were evaluated and compared with spectral-only change detection methods. The proposed SSPCC method greatly reduced spectral confusion and increased the accuracy of ...
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paper_title: Automated differentiation of urban surfaces based on airborne hyperspectral imagery

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The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.
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paper_title: Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

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This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.
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paper_title: NASA Mission to Measure Global Plant Physiology and Functional Types

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A NASA Earth mission concept has been developed that focuses on a set of science objectives related to the measurement of plant physiology and functional type for terrestrial and aquatic ecosystems. The NRC Decadal Survey specifically calls for the HyspIRI mission to measure terrestrial and aquatic ecosystems. A review of the literature in conjunction with analysis of ongoing ecosystem research established imaging spectroscopy in the solar reflected portion of the spectrum as the appropriate approach to address these objectives. For these topics a detailed requirement analysis was performed that specified the measurement objectives, measurement requirements, instrument requirement and other requirements. These were distilled into a single set of spectral, radiometric, spatial, uniformity and temporal requirement. Key among these are: spectral coverage from 380 to 2500 nm at 10 nm sampling, radiometric resolution and precision giving high signal-to-noise ratios for dark aquatic targets, spatial sampling of 60 m, spectral and spectral IFOV uniformity > 95%, and temporal coverage with a 19 day repeat at the equator.
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paper_title: Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

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Abstract The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level. In the present work, urban sprawl of the Ajmer city (situated in Rajasthan State of India) has been studied at a mid scale level, over a period of 25 years (1977–2002), to extract the information related to sprawl, area of impervious surfaces and their spatial and temporal variability. Statistical classification approaches have been used for the classification of the remotely sensed images obtained from various sensors viz. Landsat MSS, TM, ETM+ and IRS LISS-III. Urban sprawl and its spatial and temporal characteristics have been derived from the classified satellite images. The Shannon's entropy and landscape metrics (patchiness and map density) have been computed in terms of spatial phenomenon, in order to quantify the urban form (impervious area). Further, multivariate statistical techniques have been used to establish the relationship between the urban sprawl and its causative factors. Results reveal that land development (160.8%) in Ajmer is more than three times the population growth (50.1%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the urban sprawl over a period of last 25 years.
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paper_title: EnMAP A Hyperspectral Sensor for Environmental Mapping and Analysis

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The environmental mapping and analysis program (EnMAP) is a joint response of German Earth observation research institutions, value-adding (VA) resellers and space industry to the increasing demand on accurate, quantitative information about the evolution of terrestrial ecosystems. With its hyperspectral capabilities covering the visible, near- and short-wave infrared wavelengths, EnMAP will provide high quality, standardized, and consistent data on a timely and frequent basis. Its primary focus will be on the considerable improvement of already standardized products and the development of new quantitative and subsequently highly informative data and its derivatives. Only an imaging spectrometer, such as the EnMAP, can resolve and detect biophysical, biochemical, and geochemical variables in distinct detail. This will tremendously increase our understanding of coupled biospheric and geospheric processes and thus, enable the management and ensure the sustainability of our vital resources. After a competitive and successfully accomplished phase A, EnMAP has been approved by the German Aerospace Agency in the beginning of 2006. The instrument performance allows for a detailed monitoring, characterisation and parameter extraction of vegetation targets, rock/soils, and inland and coastal waters on a global scale. By the scientific lead of the GeoForschungsZentrum Potsdam (GFZ) and the industrial prime ship of Kayser-Threde, the ongoing planning aims towards an internationalisation of the mission approach. The EnMAP instrument provides information based on 218 contiguous spectral bands in the wavelength range from 420 nm to 2450 nm. It is characterized by a SNR of > 500:1 in the VNIR and >150:1 in the SWIR range at a ground resolution of 30 m times 30 m. A national and international broad science and application community can draw from an extensive and highly resolved pool of information, supporting the modeling and optimization process on their results. Operation of an airborne system (ARES - a joint venture of DLR and GFZ), starting in 2006, and the evolution of data handling and extraction procedures will further support this process.
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paper_title: The global trend of urbanization: spatiotemporal analysis of megacities using multi-temporal remote sensing, landscape metrics, and gradient analysis

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Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring ::: and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding ::: spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and ::: analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries ::: of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban ::: patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city ::: level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index, ::: edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into ::: spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of ::: spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial ::: characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.
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paper_title: Flight experience of the Compact High-Resolution Imaging Spectrometer (CHRIS)

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This paper describes the initial flight experience of the Compact High Resolution Imaging Spectrometer (CHRIS) developed at Sira Electro-Optics Ltd. The imaging spectrometer is flying on PROBA, a small agile satellite, which was launched in October 2001. The main purpose of the instrument is to provide images of land areas. The platform provides pointing in both across-track and along-track directions, for target acquisition and multi-angle observations, particularly for measurement of the Bi-directional Reflectance Distribution Function (BRDF) properties of selected targets. The platform also provides pitch motion compensation during imaging in order to increase the integration time of the instrument, increasing the number of spectral bands that can be read and enhancing radiometric resolution. The instrument covers a spectral range from 400nm to 1050nm, at ≤11nm resolution. The spatial sampling interval at perigee is approximately 17m. In this mode it is possible to read out 19 spectral bands. The locations and widths of the spectral bands are programmable. Selectable on-chip integration can increase the number of bands to 63 for a spatial sampling interval of 34m. The swath width imaged is 13km at perigee.
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paper_title: Spectral Dimensionality and Scale of Urban Radiance

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Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of AVIRIS radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the ~20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.
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paper_title: The EnMAP hyperspectral imager—An advanced optical payload for future applications in Earth observation programmes

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Abstract The Environmental Mapping and Analysis Program (EnMAP) is a joint response of German space industry, Earth observation research institutions and value-adding (VA) resellers. EnMAP fulfills the increasing demand on accurate, quantitative information about the evolution of terrestrial ecosystems. The payload consists of an imaging hyperspectral instrument covering the visible, near- and short-wave infrared wavelengths. EnMAP will provide high quality, standardized, and consistent data on a timely and frequent basis. The mission's primary focus will be on the considerable improvement of already standardized products and the development of new quantitative and subsequently highly informative data and its derivatives. The advanced hyperspectral instrument on EnMAP can resolve and detect biophysical, biochemical, and geochemical variables in distinct detail. These data can only be derived from an EnMAP type instrument and will tremendously increase our understanding of coupled biospheric and geospheric processes and thus, enable the management and ensure the sustainability of our vital resources. The instrument data set allows for a detailed monitoring, characterization and parameter extraction of vegetation targets, rock/soils, and inland and coastal waters on a global scale.
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paper_title: Monitoring Urban Structure Types as Spatial Indicators With CIR Aerial Photographs for a More Effective Urban Environmental Management

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This study focuses on the way urban dynamic processes challenge existing monitoring approaches and how urban structure types (UST) support an effective urban management. Due to their microstructure and the instability of shape, the differentiation of settlement structures is substantially difficult. Hence, highly sophisticated data such as color infrared (CIR) orthophotos as well as methods of image analysis, e.g., segmentation of objects will be employed. It will be shown how an object-oriented analysis strategy with CIR aerial photographs can be used to detect and discriminate different urban structure types by describing typical characteristics of color, texture, shape, and context. The urban structure classification is characterized by identifying different types of buildings (different types of housing, industrial buildings, infrastructure) and open spaces (woodland, community gardens, parks), their structural composition in terms of the amount, connectivity, and distribution of impervious surfaces, green spaces, and other open spaces on an aggregated neighborhood scale. This investigation contributes to a transferable methodology to monitor the urban dynamics and structure on a local level.
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paper_title: Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

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Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within-class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21,000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.
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paper_title: Monitoring urban to peri-urban development with integrated remote sensing and GIS information: a Leipzig, Germany case study

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Urban planning mainly concentrates on the management of cities as an administrative unit. Urban regions, their state and development, constitute the major interaction of urbanization processes. Such regions comprise the urban core and its associated peri-urban and rural areas. Urbanization processes along the rural-urban continuum need to be monitored by a complex set of indicators. In this paper the phenomena of growth and shrinkage patterns are investigated for an inner urban to peri-urban gradient. The area of investigation is a transect covering the eastern part of the urban region of the city of Leipzig, Germany, with its surrounding communes. Selected patterns of spatial processes are analysed for the gradient using Satellite pour l'Observation de la Terre (SPOT) XS data, and additionally, Landsat Thematic Mapper (TM) data where SPOT data were missing. Further statistical information on social, economic and environmental sustainability issues related to land-use changes is analysed. These spatial and statistical analyses are data-driven and reflect a wide range of spatial, socio-economic and ecological development being mutually intertwined. It will be shown how suburban communes and local districts within the city have developed disregarding sustainable planning. By the end of the 1990s peri-urban communes started to be organized in a better coordinated regional development plan, this case study being part of the Gruner Ring Leipzig (Green Belt of Leipzig).
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paper_title: Spectral resolution requirements for mapping urban areas

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This study evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high within-class variability.
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paper_title: A Housing-Unit-Level Approach to Characterizing Residential Sprawl

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This article reports on a study that used five spatial metrics at the housing-unit level for analyzing spatial patterns of urban growth in order to better identify the characteristics and qualities of urban sprawl. A multi-temporal land-use/land-cover dataset for Hunterdon County, New Jersey was utilized to measure new housing units developed between Time 1 (1986) and Time 2 (1995) for five traits defined as "sprawl" in the planning and policy literature: density, leapfrog, segregated land use, accessibility, and highway strip. The resulting housing-unit sprawl indicator measurements are summarized by municipality to provide a "sprawl report card." The authors conclude that sprawl indicator measures calculated at the housing-unit level provide an advantageous set of tools for evaluating and informing the development process. Measuring sprawl at the housing-unit level also facilitates investigation into other political and geographic factors that results in different grown at the municipal level or for any geographic unit of interest (such as neighborhood, census track, zoning region, congressional district, county, etc.).
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paper_title: Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

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Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region.
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paper_title: Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data

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We compared the capability of the Earth Observing-1 (EO-1) Hyperion hyperspectral (HS) data with that of the EO-1 Advanced Land Imager (ALI) multispectral (MS) data for discriminating different land-use and land-cover classes in Fremont, California. We designed a classification scheme of two levels with level I including general classes and level II including more specific classes. Classification shows that the HS data does not produce better results than the MS data when we directly applied a Mahalanobis distance (MD) classifier. We tested a number of feature reduction and extraction algorithms for the HS image. These algorithms include principal component analysis (PCA), segmented PCA (SEGPCA), linear discriminant analysis (LDA), segmented LDA (SEGLDA), penalized discriminant analysis (PDA) and segmented PDA (SEGPDA). Feature reductions were all followed by an MD classifier for image classification. With SEGPDA, SEGLDA, PDA, and LDA, similar accuracies were achieved while a segmentation-based approach we proposed (SEGPDA or SEGLDA) greatly improved computation efficiency. They all outperformed SEGPCA and PCA by 4 to 5 percent (level II) and 1 to 3 percent (level I) in classification accuracy. For level II classification, overall accuracies obtained by using the features extracted from the HS image were 2 to 3 percent greater than those obtained with the MS image. For various vegetation class and impervious land use categories, the HS data consistently produced better results than the MS data. For level I classification, the HS image generated a thematic map that is � 0.01 greater in kappa coefficient comparing to the MS image. When we collapsed the level II classification map to a level I map, 5 percent (HS) to 7 percent (MS) improvements were achieved.
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paper_title: Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

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The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid-scale level (5-25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977-2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.
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paper_title: Use of airborne hyperspectral data and height information to support urban micro-climate characterisation

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The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. ::: ::: The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. ::: The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. ::: The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. ::: ::: The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. ::: ::: Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. ::: The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. ::: Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.
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paper_title: Estimation of urban vegetation abundance by spectral mixture analysis

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The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembl...
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paper_title: A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data

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Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development.
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paper_title: Automated differentiation of urban surfaces based on airborne hyperspectral imagery

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The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.
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paper_title: A survey of image classification methods and techniques for improving classification performance

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Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.
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paper_title: A remote sensing approach to establish relationships among different land covers at the micro level

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Urbanization brings many changes in urban land‐cover composition. It generates the need to quantify the effect of increasing population on urban environmental quality that can draw together the relationship related to various land covers. The present scenario of urbanization in India poses challenges for sustainable development, which requires scientific vision about the existing scenario and relationship among various factors for careful planning. In this research, an attempt was made to understand the physical interrelationship of man‐made as well as natural environment at the micro level of Dehra Dun urban area using IKONOS satellite imagery. A detailed study was carried out on representative wards of different characteristics within Dehra Dun Municipal Corporation. For this purpose, six wards were selected considering various parameters including type of development (old/new), population density and land use. On the basis of surface functional characteristics, land‐cover composition was classified und...
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paper_title: A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing

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Abstract. This study aims at creating a holistic conceptual approach systematizing the interrelation of (natural) hazards, vulnerability and risk. A general hierarchical risk meta-framework presents potentially affected components of a given system, such as its physical, demographic, social, economic, political or ecological spheres, depending on the particular hazard. Based on this general meta-framework, measurable indicators are specified for the system "urban area" as an example. This framework is used as an outline to identify the capabilities of remote sensing to contribute to the assessment of risk. Various indicators contributing to the outline utilizing diverse remote sensing data and methods are presented. Examples such as built-up density, main infrastructure or population distribution identify the capabilities of remote sensing within the holistic perspective of the framework. It is shown how indexing enables a multilayer analysis of the complex and small-scale urban landscape to take different types of spatial indicators into account to simulate concurrence. The result is an assessment of the spatial distribution of risks within an urban area in the case of an earthquake and its secondary threats, using an inductive method. The results show the principal capabilities of remote sensing to contribute to the identification of physical and demographic aspects of vulnerability, as well as provide indicators for the spatial distribution of natural hazards. Aspects of social, economic or political indicators represent limitations of remote sensing for an assessment complying with the holistic risk framework.
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paper_title: Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

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Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux (QH). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m− 2, whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m− 2). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m− 2, suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m− 2 in mean QH averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study QH, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived QH values to 30 and 90 m spatial resolution are within ∼ 5 W m− 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.
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paper_title: Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data.

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Driven by a constantly accelerating increase of urban population in recent decades urban sprawl has become ::: one of the most dynamic processes in the context of global land use transformations. The expansion of urban ::: agglomerations is closely associated with a substantial increase of impervious surface. In Europe, methods for ::: an accurate, fast and cost-effective mapping and assessment of impervious surface on a state-wide or ::: national scale have not been established so far. This study presents an approach for estimating the ::: impervious surface based on a combined analysis of single-date Landsat images and road network and ::: railway vector data using Support Vector Machines and functionalities of geographic information processing. ::: The modeling aims at the provision of data on the impervious surface for the total of residential, industrial ::: and transportation-related areas. The derived information is provided for the administrative units of ::: communities. The output of the procedure is a vector data file providing the ‘percent impervious surface of ::: built-up areas’ (PISB) and the ‘percent impervious surface of the total of built-up and transportation-related ::: areas’ (PISBT) for the administrative units of communities. The developed method is tested for a study area ::: covering almost one third of the German territory. The results prove the suitability of the approach for a ::: widely automated and area-wide mapping of impervious surfaces. Using reference data sets of three cities ::: (Leipzig, Ludwigshafen, Passau) we realized a mean absolute error of 19.8% and an average error of 6.4% for ::: the percent impervious surface modeled on the basis of the Landsat images. The final product resulting from ::: a combination of the imperviousness raster derived from the satellite images with the transportation-related ::: vector information showed a mean difference of 1% to 4% compared to corresponding reference data and ::: results of previous studies. For the year 2000 our research shows that 45.3% of the area occupied by ::: settlements and transport infrastructure in the German federal state of Bavaria, 44.6% in the state of Baden- ::: Wurttemberg and 42.6% in the state of Saxony was covered by impervious surface.
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paper_title: Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

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Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.
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paper_title: Extraction of Buildings and Trees in Urban Environments

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In this article, two methods for data collection in urban environments are presented. The first method combines multispectral imagery and laser altimeter data in an integrated classification for the extraction of buildings, trees and grass-covered areas. The second approach uses laser data and 2D ground plan information to obtain 3D reconstructions of buildings.
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paper_title: Climate protection and compact urban structures in spatial planning and local construction plans in Germany

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Abstract The paper discusses opportunities for integration of the climate protection strategy declared by the German government into spatial and urban land-use plans in Germany. The paper gives a brief overview of German climate protection related legislation, gives some statistics on energy demand in the housing sector in Germany, and identifies opportunities for climate change mitigation in urban areas through enhanced spatial planning and energy-efficient homes. Drawing on statistical analysis the paper identifies discrepancies between trends in population densities and actual housing floor space. The paper further identifies opportunities for increasing heating efficiency of houses by managing the spatial arrangement of apartments and buildings. The overall conclusion of the paper is that adjustments in the spatial planning legal base in Germany are necessary to integrate specific climate protection tools that will allow an increase in energy efficiency in housing.
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paper_title: Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

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This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.
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paper_title: Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

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This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.
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paper_title: Automating the Build-Up Process of Feature-Based Fuzzy Logic Models for the Identification of Urban Biotopes from Hyperspectral Remote Sensing Data

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The automatic classification of urban biotopes from remote sensing (RS) data is not a task for a pixel-based classifier. All pixels of a biotope have to be taken into account during the classification of a biotope. Therefore, fuzzy logic models of biotope types are built with regard to the composition of the biotopes of different surface materials and their arrangement in the biotopes. The models consist of lists of numerical features and associated membership functions. The features have been calculated on RS data and are able to quantitatively assess characteristic differences between the biotopes of different types. There are not enough one-against-all features that separate one biotope type from all others. Therefore, the fully automated feature selection process aims at finding the set of features that separates two biotope types best with a pairwise maximum likelihood classification. The resulting list of features for the separation of two types is incorporated into the fuzzy logic models of these two types and serves there as one input variable. Thus, a model consists of n-1 input variables whereby n is the number of biotope types to distinguish. The application of the associated membership functions (Bayesian probability functions defined by means and covariances of training biotopes) on the input variables results in probability values between zero and one. The smallest of these probability values is taken as the crisp output value of a model and can be interpreted as a similarity value expressing the similarity of the classified biotope to the type of the model. The classification of the biotopes in the 14.5 km2 test area with the developed models yields an overall accuracy of 87%.
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paper_title: Application of satellite Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat wave in Paris

paper_content:

Satellite observations are used to monitor the August 2003 heat wave in Paris, and their applications to environmental risk assessment and to health alert systems are discussed. Fifty NOAA-AVHRR satellites images were processed to retrieve the diurnal variations of surface temperature. Land cover classification of the Paris basin was mapped from a multi-spectral SPOT-HRV image. Geographic Information Systems were used to merge SPOT-HRV and NOAA-AVHRR data. Results indicate large surface temperature gradients and contrasted warming patterns. Contrary to prevailing concepts, the magnitudes of temperature anomalies are larger in daytime than at night. Surface temperature gradients between urban parks and industrial areas are largest in daytime, the negative correlation between surface temperature and normalized vegetation index showing the cooling effect of vegetation. The temperature difference between downtown and suburban areas is largest at night, revealing the strong relationship of nighttime temperature anomaly with built density inferred from the classified SPOT image. Comparison between time-series for August 1998, a normal summer, and the anomalous August 2003, indicates a large difference in diurnal temperature amplitude, confirming the impact of high nighttime temperatures on the heat wave process. The areas of the Paris region most vulnerable to heat stress were identified. Thermal indices can be constructed for assimilation into logistic models, to assess the urban variability of risk factors and implement health alert systems. The method is general and applicable to other cities.
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paper_title: Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

paper_content:

In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and t...
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paper_title: Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing

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It is well known that vegetation has cooling effect on urban land surface temperature (ULST). However, the influence of vegetation's structure, component, and spatial distribution on ULST has rarely been quantitatively studied. This paper, taking Landsat ETM+ data to retrieve ULST, and IKONOS data to obtain vegetation information, studied the relationship between ULST and vegetation patches. Results showed that in the urban environment, (1) ULSTs over tree and shrub, lawn, and weed patches were not much different, because of the edge effects of the vegetation patches and their fragmentized distribution pattern; (2) the relationship between ULST and area, perimeter, and shape index of vegetation patches exhibited positive, while LST and the ratio of perimeter/area presented negative linear relationship; (3) using land use polygons as units, area density had quadratic curve relationship to ULST, edge density and mean area density, had strongly negative relationship to ULST. These results indicated that not only the characteristics of vegetation patches, but also their spatial distribution had a great effect on ULST, which should be considered in urban ecological environment management.
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paper_title: Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

paper_content:

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.
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paper_title: Quantifying high-resolution impervious surfaces using spectral mixture analysis

paper_content:

Impervious surface distribution and its temporal changes are considered key urbanization indicators and are utilized for analysing urban growth and influences of urbanization on natural environments. Recently, urban impervious surface information was extracted from medium/coarse resolution remote sensing imagery (e.g. Landsat ETM+ and AVHRR) through spectral analytical methods (e.g. spectral mixture analysis (SMA), regression tree, etc.). Few studies, however, have attempted to generate impervious surface information from high resolution remotely sensed imagery (e.g. IKONOS and Quickbird). High resolution images provide detailed information about urban features and are, therefore, more valuable for urban analysis. The improved spatial resolution, however, also brings new challenges when existing spectral analytical methods are applied. In particular, a higher spatial resolution leads to reduced boundary effects and increased within-class variability. Taking Grafton, Wisconsin, USA as a study site, this paper analyses the spectral characteristics of IKONOS imagery and explores the applicability of SMA for impervious surface estimation. Results suggest that with improved spatial resolution, IKONOS imagery contains 40-50% of mixed urban pixels for the study area, and the within-class variability is a severe problem for spectral analysis. To address this problem, this paper proposes two approaches, interior end-member set selection and spectral normalization, for SMA. Analysis of results indicates that these approaches can reasonably reduce the problems associated with boundary effects and within-class variability, therefore generating better impervious surface estimates.
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paper_title: Use of impervious surface in urban land-use classification

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Impervious surface has been recognized as a key indicator in assessing urban environments. However, accurate impervious surface extraction is still a challenge. Effectiveness of impervious surface in urban land-use classification has not been well addressed. This paper explored extraction of impervious surface information from Landsat Enhanced Thematic Mapper data based on the integration of fraction images from linear spectral mixture analysis and land surface temperature. A new approach for urban land-use classification, based on the combined use of impervious surface and population density, was developed. Five urban land-use classes (i.e., low-, medium-, high-, and very-high-intensity residential areas, and commercial/industrial/transportation uses) were developed in the city of Indianapolis, Indiana, USA. Results showed that the integration of fraction images and surface temperature provided substantially improved impervious surface image. Accuracy assessment indicated that the root-mean-square error and system error yielded 9.22% and 5.68%, respectively, for the impervious surface image. The overall classification accuracy of 83.78% for five urban land-use classes was obtained.
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paper_title: Assessing the environmental performance of land cover types for urban planning

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Urban planning and decision-making for sustainable development urgently need data of high spatial resolution to establish the relationship between the socio-economic performance of the urban system and its different sub-units (i.e. housing schemes, commercial and industrial developments, services) on the one hand and their environmental impacts of these sub-units on the other. ::: ::: To achieve this task, a system was developed to delineate urban land cover units. The approach was tested for the city of Munich. The units delineate the fine-grained pattern of urban built-up and open spaces. The typology was based on established categories of zoning and development plans. In addition, the units were delineated on an aggregated scale to generate a synoptic understanding of the urban “metabolism” for different “strategic” levels of urban planning. ::: ::: A case study on urban hydrology is presented to characterize aspects of the metabolism of the urban system. Additional case studies covered urban climates, energy demand for space heating and carbon dioxide emissions. Results showed that the cover units and types had distinct environmental features. Furthermore the causal links with urban physical and land use features were investigated. Based on the hydrological case study, environmental targets and standards to promote sustainable development are proposed.
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paper_title: Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center

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Abstract Study of the detailed structure and ecological functioning of urban and peri-urban systems is intensifying due to increasing concentration of the human population into urban centers. Much of this increase is expected to occur in semiarid to arid cites. Data from new high spatial, temporal, and spectral resolution satellite-based sensors promise to increase our understanding of global urban ecological and climatic processes and improve city and land planning capabilities. Two of these sensors, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectroradiometer (MODIS), provide an opportunity to compare urban spatial structure (distribution and configuration of discrete land cover/land use classes on the landscape) with contemporaneous measurements of surface biophysical composition at a variety of spatial and temporal scales. Such combined measurements are useful for modeling changes to urban climate, hydrology, and biogeochemical cycles caused by modification of the landscape. We compare gridded landscape metrics derived from expert system land cover classification of ASTER to corresponding MODIS NDVI data at scales of 250 m/pixel, 500 m/pixel, and 1 km/pixel in order to determine which of these scales is optimal for monitoring of urban biophysical processes and landscape structure change. Weak positive and negative correlations between NDVI and landscape structure were observed at all three spatial scales for the metrics Class Area, Mean Patch Size, Edge Density, and Interspersion/Juxtaposition Index.
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paper_title: Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data.

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Driven by a constantly accelerating increase of urban population in recent decades urban sprawl has become ::: one of the most dynamic processes in the context of global land use transformations. The expansion of urban ::: agglomerations is closely associated with a substantial increase of impervious surface. In Europe, methods for ::: an accurate, fast and cost-effective mapping and assessment of impervious surface on a state-wide or ::: national scale have not been established so far. This study presents an approach for estimating the ::: impervious surface based on a combined analysis of single-date Landsat images and road network and ::: railway vector data using Support Vector Machines and functionalities of geographic information processing. ::: The modeling aims at the provision of data on the impervious surface for the total of residential, industrial ::: and transportation-related areas. The derived information is provided for the administrative units of ::: communities. The output of the procedure is a vector data file providing the ‘percent impervious surface of ::: built-up areas’ (PISB) and the ‘percent impervious surface of the total of built-up and transportation-related ::: areas’ (PISBT) for the administrative units of communities. The developed method is tested for a study area ::: covering almost one third of the German territory. The results prove the suitability of the approach for a ::: widely automated and area-wide mapping of impervious surfaces. Using reference data sets of three cities ::: (Leipzig, Ludwigshafen, Passau) we realized a mean absolute error of 19.8% and an average error of 6.4% for ::: the percent impervious surface modeled on the basis of the Landsat images. The final product resulting from ::: a combination of the imperviousness raster derived from the satellite images with the transportation-related ::: vector information showed a mean difference of 1% to 4% compared to corresponding reference data and ::: results of previous studies. For the year 2000 our research shows that 45.3% of the area occupied by ::: settlements and transport infrastructure in the German federal state of Bavaria, 44.6% in the state of Baden- ::: Wurttemberg and 42.6% in the state of Saxony was covered by impervious surface.
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paper_title: A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification

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Abstract The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-2 will make a major contribution towards the advancement of the commercial remote sensing industry by providing improved capabilities, more frequent revisits and greater imaging flexibility with respect to the precursor QuickBird satellite. Remote sensing data from panchromatic systems have a potential for more detailed and accurate mapping of the urban environment with details of sub-meter ground resolution, but at the same time, they present additional complexities for information mining. In this study, very high-resolution panchromatic images from QuickBird and WorldView-1 have been used to accurately classify the land-use of four different urban environments: Las Vegas (U.S.A.), Rome (Italy), Washington D.C. (U.S.A.) and San Francisco (U.S.A.). The proposed method is based on the analysis of first- and second-order multi-scale textural features extracted from panchromatic data. For this purpose, textural parameters have been systematically investigated by computing the features over five different window sizes, three different directions and two different cell shifts for a total of 191 input features. Neural Network Pruning and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes. The results show that with a multi-scale approach it is possible to discriminate different asphalt surfaces, such as roads, highways and parking lots due to the different textural information content. This approach also makes it possible to differentiate building architectures, sizes and heights, such as residential houses, apartment blocks and towers with classification accuracies above 0.90 in terms of Kappa coefficient computed over more than a million independent validation pixels.
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paper_title: Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines

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We investigate the relevance of morphological operators for the classification of land use in urban scenes using sub-metric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.
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paper_title: Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

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In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and t...
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paper_title: Spectral Dimensionality and Scale of Urban Radiance

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Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of AVIRIS radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the ~20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.
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paper_title: Multi-resolution region-based clustering for urban analysis

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In the domain of urban planning and management, it may be necessary to map the territory at different scales, each corresponding to a semantic level. Three semantic levels are identified: (1) the object level, for mapping urban elements (buildings, etc.), (2) the block level, for mapping homogeneous patterns of urban elements, and (3) the area level, for mapping urban fabrics defined as sets of homogeneous patterns. Some of these levels are directly linked to specific satellite images presenting ad hoc resolutions (namely, medium spatial resolution (MSR) images for the area level and high spatial resolution (HSR) images for the object level); in such cases, a straightforward mapping can be performed by clustering the data. Conversely, classical clustering techniques do not enable the intermediate semantic level to be extracted directly. The purpose of this article is to propose a methodology enabling a clustering at this level to be generated. The proposed approach is, in particular, based on the segmentation and unsupervised, region-based and joined clustering of two images representing a same scene at MSR and HSR. The method has been applied to different and heterogeneous datasets composed of HSR images at 2.5 m and MSR images at 10 m and 20 m. Qualitative validations by an expert, and quantitative ones by comparison to other existing methods, tend to emphasize the soundness and efficiency of this methodology, thus justifying further developments.
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paper_title: Use of normalized difference built-up index in automatically mapping urban areas from TM imagery

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Remotely sensed imagery is ideally used to monitor and detect land cover changes that occur frequently in urban and peri-urban areas as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into land cover map using the existing methods of manual interpretation and parametric image classification digitally. In this paper we propose a new method based on Normalized Difference Built-up Index (NDBI) to automate the process of mapping built-up areas. It takes advantage of the unique spectral response of built-up areas and other land covers. Built-up areas are effectively mapped through arithmetic manipulation of re-coded Normalized Difference Vegetation Index (NDVI) and NDBI images derived from TM imagery. The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China. The mapped results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably. Compared with the maximum likelihood classification method, t...
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paper_title: Synergistic use of Landsat Multispectral Scanner with GIRAS land-cover data to retrieve impervious surface area for the Potomac River Basin in 1975

paper_content:

Abstract For many studies of urbanization, particularly those covering broad spatial extents and thus requiring the perspective afforded by satellite remote sensing, the longest time series of impervious surface area (ISA) initiate with the availability of Landsat Thematic Mapper (TM) data in the mid 1980s. Earlier generation remote sensing data, such as Landsat Multispectral Scanner (MSS) do not generally lead to robust representations of impervious surface area due to poor spectral differentiation between impervious and non-impervious surfaces lacking vegetation. In the 1970s, however, the USGS completed a comprehensive mapping of land cover based on aerial photography called the Geographic Information Retrieval and Analysis System (GIRAS). In this paper we describe a methodology for retrieving ISA estimates through the fusion of GIRAS land cover with historic and contemporary remote sensing data, resulting in a data set that is temporally comparable to more modern data sets (e.g. the National Land Cover Database (NLCD) ISA product). We calculate correlation coefficients between our data set and digitized aerial photography at 4 spatial resolutions, exhibiting a maximum correlation of 0.65 at 120-m and 240-m pixel sizes. Our highest resolution product (30-m) exhibited 94% accuracy and a kappa coefficient of 36% in rural areas, and 90.7% accuracy and a kappa coefficient of 59.6% in suburban areas. Further, we show that trends in urban area through time derived from these data exhibit accelerating rates of development throughout the Potomac River basin 1975–2001, including increased development in riparian buffer zones and on steeper topography. These results are applicable wherever low-resolution vector-based data sets of urban area are available or could be generated for the pre-Landsat TM era.
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paper_title: Monitoring urban to peri-urban development with integrated remote sensing and GIS information: a Leipzig, Germany case study

paper_content:

Urban planning mainly concentrates on the management of cities as an administrative unit. Urban regions, their state and development, constitute the major interaction of urbanization processes. Such regions comprise the urban core and its associated peri-urban and rural areas. Urbanization processes along the rural-urban continuum need to be monitored by a complex set of indicators. In this paper the phenomena of growth and shrinkage patterns are investigated for an inner urban to peri-urban gradient. The area of investigation is a transect covering the eastern part of the urban region of the city of Leipzig, Germany, with its surrounding communes. Selected patterns of spatial processes are analysed for the gradient using Satellite pour l'Observation de la Terre (SPOT) XS data, and additionally, Landsat Thematic Mapper (TM) data where SPOT data were missing. Further statistical information on social, economic and environmental sustainability issues related to land-use changes is analysed. These spatial and statistical analyses are data-driven and reflect a wide range of spatial, socio-economic and ecological development being mutually intertwined. It will be shown how suburban communes and local districts within the city have developed disregarding sustainable planning. By the end of the 1990s peri-urban communes started to be organized in a better coordinated regional development plan, this case study being part of the Gruner Ring Leipzig (Green Belt of Leipzig).
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paper_title: Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data

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The island of Puerto Rico has both a high population density and a long history of ineffective land use planning. This study integrates geospatial technology and population census data to understand how people use and develop the lands. We define three new regions for Puerto Rico: Urban (16%), Densely Populated Rural (36%), and Sparsely Populated Rural (48%). Eleven percent of the island is composed of urban/built-up surfaces. A large part of these developments occur in both low-density patterns of construction and sparsely populated neighborhoods. Half of the urban development occurs outside of urban centers. This analysis helps differentiate zones in the landscape with different uses and conditions, identifying not only urban and rural settings, but also the interface where development occurs in a territory dominated by forests and pastures, analogous to a wildland urban interface. The ineffective plan of land development has left a high degree of urban sprawl in 40% of island, where cities and towns appear typically surrounded by sprawl. The San Juan Metropolitan Area is one of the most expanded urbanized areas with a population of 2–2.5 million, comparable with the most sprawled cities of the U.S. mainland. This study reinforces the need for an efficient land use planning, and provides information to support research and planning efforts related to land development and conservation. It represents the first approach integrating satellite imagery with population census data for studying the human environment in the Caribbean.
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paper_title: A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure

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A procedure for the calculation of a texture-derived built-up presence index (PanTex) from textural characteristics of panchromatic satellite data is presented. The index is based on fuzzy rule-based composition of anisotropic textural co-occurrence measures derived from the satellite data by the gray-level co-occurrence matrix (GLCM). Examples are produced how the PanTex index reduces the edge effects of the nonbuilt-up linear features and improves capacity to discriminate between built-up and nonbuilt-up areas. The accuracy and robustness of the PanTex measure against seasonal changes, multisensor, multiscene, and data degradation by wavelet-based compression and histogram stretching is discussed with some examples.
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paper_title: URBAN GROWTH DETECTION USING TEXTURE ANALYSIS ON MERGED LANDSAT TM AND SPOT-P DATA

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This paper develops a texture analysis procedure to distinguish "built" from "non-built" pixels at a meaningful scale of resolution to serve planning needs, and applies the technique to imagery from two dates in order to monitor and measure urban growth patterns accurately. The methodology used digitally merged Landsat thematic mapping and SPOT-P data, resampled to 10 meters, for 1990 and 1995. For each date, a two-step texturing analysis resulted in a binary "built/non-built" map defining urban versus non-urban super pixels. Results from the study clearly defined "growth" pixels for the five-year time interval, with an accuracy of 92 percent. These growth pixels were then compared to a growth potential map produced by a geographic information systems analysis based on environmental inducements and constraints to growth. Results showed that the portions of the study area that were rated highest in growth potential did in fact experience the largest amount of urban expansion. Application of this technique over time is shown to provide an effective tool to identify, map, monitor and quantify patterns of urban growth and change.
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paper_title: Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier.

paper_content:

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.
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paper_title: A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data

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Urban sprawl has been identified as one of the most negative effects of global population growth on the environment and biodiversity. Frequent monitoring of urban sprawl is needed to limit the impact of this ongoing phenomenon. This paper proposes precise monitoring of building construction using an object-based classification methodology applied to Spot 5 images with a 2.5 m resolution. An application at a regional scale on Reunion Island in the Indian Ocean shows that this building extraction methodology has limitations in the production of reference urban maps because of difficulties in defining the shape and the number of buildings compared to classical photo-interpretation of aerial photography. However, these results are of great value for planning in urban sprawl areas where up-to-date information is lacking because of the rapid pace of house construction and residential development.
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paper_title: The global trend of urbanization: spatiotemporal analysis of megacities using multi-temporal remote sensing, landscape metrics, and gradient analysis

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Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring ::: and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding ::: spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and ::: analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries ::: of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban ::: patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city ::: level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index, ::: edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into ::: spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of ::: spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial ::: characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.
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paper_title: An object-oriented approach for analysing and characterizing urban landscape at the parcel level

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This paper presents an object-oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high-resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three-level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object-oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.
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paper_title: Satellite Image Analysis for Disaster and Crisis-Management Support

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This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines
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paper_title: Hyperspectral remote sensing and GIS techniques application for the evaluation and monitoring of interactions between natural risks and industrial hazards

paper_content:

The aim of this paper is to outline the potential of imaging spectroscopy and GIS techniques as tool for the management of data rich environments, as complex fluvial areas, exposed to geological, geomorphological, and hydrogeological risks. The area of study, the Pescara River Basin is characterized by the presence of important industrial sites and by the occurrence of floods, landslides and seismic events. Data were collected, during a specific flight, using an hyperspectral MIVIS sensor. Images have been processed in order to obtain updated and accurate land-cover and land-use maps that have been inserted in a specific GIS database and integrated with further information like lithology, geological structure, geomorphology, hydrogeological features, productive plants location and characters. The processing of data layers was performed, using a dedicated software, through typical GIS operators like indexing, recording, matrix analysis, proximity analysis. The interactions between natural risks, industrial installations, agricultural areas, water resources and urban settlements have been analyzed. This allowed the creation and processing of thematic layers like vulnerability, risk and impact maps.
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paper_title: Integrating imaging spectroscopy (445–2543 nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney

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This paper demonstrates a methodology for the analysis and integration of airborne hyperspectral sensor data (445–2543 nm) with GIS data in order to develop a vulnerability map which has the potential to assist in decision making during post-disaster emergency operations. Hailstorms pose a threat to people as well as property in Sydney, Australia. Emergency planning demands current, large-scale spatio-temporal information on urban areas that may be susceptible to hailstones. Several regions, dominated by less resistant roofing materials, have a higher vulnerability to hailstorm damage than others. Post-disaster operations must focus on allocating dynamic resources to these areas. Remote sensing data, particularly airborne hyperspectral sensor data, consist of spectral bands with narrow bandwidths, and have the potential to quantify and distinguish between urban features such as roofing materials and other man-made features. A spectral library of surface materials from urban areas was created by using a fu...
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paper_title: A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing

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Abstract. This study aims at creating a holistic conceptual approach systematizing the interrelation of (natural) hazards, vulnerability and risk. A general hierarchical risk meta-framework presents potentially affected components of a given system, such as its physical, demographic, social, economic, political or ecological spheres, depending on the particular hazard. Based on this general meta-framework, measurable indicators are specified for the system "urban area" as an example. This framework is used as an outline to identify the capabilities of remote sensing to contribute to the assessment of risk. Various indicators contributing to the outline utilizing diverse remote sensing data and methods are presented. Examples such as built-up density, main infrastructure or population distribution identify the capabilities of remote sensing within the holistic perspective of the framework. It is shown how indexing enables a multilayer analysis of the complex and small-scale urban landscape to take different types of spatial indicators into account to simulate concurrence. The result is an assessment of the spatial distribution of risks within an urban area in the case of an earthquake and its secondary threats, using an inductive method. The results show the principal capabilities of remote sensing to contribute to the identification of physical and demographic aspects of vulnerability, as well as provide indicators for the spatial distribution of natural hazards. Aspects of social, economic or political indicators represent limitations of remote sensing for an assessment complying with the holistic risk framework.
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paper_title: Application of satellite Remote Sensing for Urban Risk Analysis: a case study of the 2003 extreme heat wave in Paris

paper_content:

Satellite observations are used to monitor the August 2003 heat wave in Paris, and their applications to environmental risk assessment and to health alert systems are discussed. Fifty NOAA-AVHRR satellites images were processed to retrieve the diurnal variations of surface temperature. Land cover classification of the Paris basin was mapped from a multi-spectral SPOT-HRV image. Geographic Information Systems were used to merge SPOT-HRV and NOAA-AVHRR data. Results indicate large surface temperature gradients and contrasted warming patterns. Contrary to prevailing concepts, the magnitudes of temperature anomalies are larger in daytime than at night. Surface temperature gradients between urban parks and industrial areas are largest in daytime, the negative correlation between surface temperature and normalized vegetation index showing the cooling effect of vegetation. The temperature difference between downtown and suburban areas is largest at night, revealing the strong relationship of nighttime temperature anomaly with built density inferred from the classified SPOT image. Comparison between time-series for August 1998, a normal summer, and the anomalous August 2003, indicates a large difference in diurnal temperature amplitude, confirming the impact of high nighttime temperatures on the heat wave process. The areas of the Paris region most vulnerable to heat stress were identified. Thermal indices can be constructed for assimilation into logistic models, to assess the urban variability of risk factors and implement health alert systems. The method is general and applicable to other cities.
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paper_title: Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

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Abstract By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.
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paper_title: Potential of hyperspectral data for urban micro climate analysis

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The urban micro climate is important for the well-being of citizens. With simulation models the urban micro climate can be studied and recommendations can be made to improve the micro climatic situation. However, mainly synthetic scenarios are simulated due to a lack of information. The objective of this paper is to explore the potential of airborne hyperspectral and height data to provide area wide information for micro climate simulations, by the example of the model ENVI-met. First the requirements for such a model are presented. Then an approach is presented in which a surface material map, surface albedo and leaf area index are derived from hyperspectral HyMap data and the object height from HRSC height data. These products are implemented as input parameters for the urban micro climate model. The paper shows that based on hyperspectral and height data realistic urban micro climate simulations can be carried out.
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paper_title: Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

paper_content:

Global warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.
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paper_title: Analysis of the influence of urban form and materials on sensible heat flux — a case study of Japan's largest housing development “Tama New Town”

paper_content:

Abstract In this study, the relationship between the form and materials of urban blocks and sensible heat flux from total surfaces was analyzed in the case of `Tama New Town', which is one of the largest housing developments in Japan and is under continuing development. First, urban blocks were divided into five categories depending on the building plot types. The characteristics of the form and thermal properties of each building, as well as the land cover condition (area of vegetation, bare soil, asphalt pavement, and built area) of each category was considered. Furthermore, 6 urban blocks were selected for numerical simulation of heat balance of the total surfaces, and sensible heat flux from the total surfaces of each urban block on clear sky summer day was calculated. It was confirmed that the influence of the direction that buildings faced and floor area ratio was as great as that of building materials upon the amount of sensible heat flux in each urban block.
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paper_title: Urban radiation balance of two coastal cities in a hot and dry environment

paper_content:

Four Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) satellite scenes of Dubai and Abu Dhabi with channels from the visible-near infrared (VNIR) to the thermal infrared (TIR) were analysed to show variations in surface temperature, albedo, emissivity and net radiation in different urban and rural classes. For a better understanding of the spatial coherences of surface properties a land use classification was derived. The different classes were then spatially and temporally compared. The investigations show in the daytime a distinct surface cool island for Dubai and surface cool areas at Abu Dhabi city and its surrounding mangrove areas. Net radiation is mainly controlled by the albedo. The albedo in urban areas is lower than in their surrounding desert environments, therefore the net radiation is higher in the urban areas. The surface temperatures behave contrary to the net radiation and are higher in land use classes, where water is available.
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paper_title: Generating global surface albedo products from multiple geostationary satellites

paper_content:

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.
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paper_title: Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

paper_content:

Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux (QH). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m− 2, whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m− 2). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m− 2, suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m− 2 in mean QH averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study QH, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived QH values to 30 and 90 m spatial resolution are within ∼ 5 W m− 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.
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paper_title: Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

paper_content:

This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.
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paper_title: Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem

paper_content:

Regional climate change induced by rapid urbanization is responsible for and may result from changes in coupled human-ecological systems. Specifically, the distribution of urban vegetation may be an important intermediary between patterns of human settlement and regional climate spatial variability. To test this hypothesis we identified the relationships between surface temperature, one component of regional climate, vegetation, and human settlement patterns in the Phoenix, AZ, USA region. Combining satellite-derived surface temperature and vegetation data from an early summer day with US Census and topographic data, we found substantial surface temperature differences within the city that correlate primarily with an index of vegetation cover. Furthermore, both of these patterns vary systematically with the social characteristics of neighborhoods through the region. Overall, every $10,000 increase in neighborhood annual median household income was associated with a 0.28°C decrease in surface temperature on an early summer day in Phoenix. Temperature variation within a neighborhood was negatively related to population density. A multivariate model generated using path analysis supports our hypothesis that social impacts on surface temperature occur primarily through modifications of vegetation cover. Higher income neighborhoods were associated with increased vegetation cover and higher density neighborhoods were associated with decreased vegetation variability. These results suggest that settlement patterns in the central Arizona region influence regional climate through multiple pathways that are heterogeneously distributed throughout the city.
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paper_title: Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

paper_content:

As more than 50% of the human population are situated in cities of the world, urbanization has become an important contributor to global warming due to remarkable urban heat island (UHI) effect. UHI effect has been linked to the regional climate, environment, and socio-economic development. In this study, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery, respectively acquired in 1989 and 2001, were utilized to assess urban area thermal characteristics in Fuzhou, the capital city of Fujian province in south-eastern China. As a key indicator for the assessment of urban environments, sub-pixel impervious surface area (ISA) was mapped to quantitatively determine urban land-use extents and urban surface thermal patterns. In order to accurately estimate urban surface types, high-resolution imagery was utilized to generate the proportion of impervious surface areas. Urban thermal characteristics was further analysed by investigating the relationships between the land surface temperature (LST), percent impervious surface area, and two indices, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The results show that correlations between NDVI and LST are rather weak, but there is a strong positive correlation between percent ISA, NDBI and LST. This suggests that percent ISA, combined with LST, and NDBI, can quantitatively describe the spatial distribution and temporal variation of urban thermal patterns and associated land-use/land-cover (LULC) conditions.
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paper_title: Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

paper_content:

Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within-class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21,000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.
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paper_title: Use of impervious surface in urban land-use classification

paper_content:

Impervious surface has been recognized as a key indicator in assessing urban environments. However, accurate impervious surface extraction is still a challenge. Effectiveness of impervious surface in urban land-use classification has not been well addressed. This paper explored extraction of impervious surface information from Landsat Enhanced Thematic Mapper data based on the integration of fraction images from linear spectral mixture analysis and land surface temperature. A new approach for urban land-use classification, based on the combined use of impervious surface and population density, was developed. Five urban land-use classes (i.e., low-, medium-, high-, and very-high-intensity residential areas, and commercial/industrial/transportation uses) were developed in the city of Indianapolis, Indiana, USA. Results showed that the integration of fraction images and surface temperature provided substantially improved impervious surface image. Accuracy assessment indicated that the root-mean-square error and system error yielded 9.22% and 5.68%, respectively, for the impervious surface image. The overall classification accuracy of 83.78% for five urban land-use classes was obtained.
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paper_title: Potential of hyperspectral remote sensing for analyzing the urban environment

paper_content:

In contrast to widely used multispectral data, hyperspectral imagery resolves material-specific spectral reflection and absorption features making them especially suitable for detailed and comprehensive mapping of urban surface materials. However, this requires development of automated methods for efficient information extraction that take into account the special conditions in urban areas characterized by a big variety of materials and a large heterogeneity of small-sized urban structures. This chapter deals with the current state of methodological developments in urban hyperspectral remote sensing emphasizing the research of the authors towards the development of an automated system for comprehensive mapping of urban surface materials. This includes field- and image-based spectral investigations aiming at the automated derivation of robust quantitative spectral features. They serve as input information for the developed multi-step processing system allowing detailed mapping of urban surface materials. In this context, the iterative procedure is capable of analyzing urban structures at a sub-pixel level and provides area-wide information about the fractional coverage of surface materials for each pixel. Thus, the obtained results are characterized by a new level of thematic and spatial detail which significantly increases their suitability for subsequent modeling, evaluation and monitoring of the urban environment.
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paper_title: Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities†

paper_content:

Abstract Growing interest in urban systems as ecological entities calls for some standards in parameterizing biophysical composition of urban environments. A vegetation-impervious surface-soil ( V-I-S) model is presented as a possible basis for standardization. The V-I-S model may serve as a foundation for characterizing urban/near-urban environments universally, and for comparison of urban morphology within and between cities. Inasmuch as the model may be driven by satellite digital data, it may serve as a global model of urban ecosystem analysis and comparison world-wide. The V-I-S model may prove useful for urban change detection and growth modelling, for environmental impact analysis from urbanization, for energy- and water-related investigations, and for certain dimensions of human ecosystem analysis of the city as well.
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paper_title: Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm

paper_content:

We investigate the spectral complexity and unique spectral characteristics of urban environments using a comprehensive regional field spectral library of more than 4500 individual spectra. Spectral properties of urban surface materials are presented and interpreted, and their separability is systematically analyzed using the Bhattacharyya distance (B-distance) as a quantitative measure of spectral discrimination. We find considerable spectral confusion between urban land cover types (i.e. specific roof and road types) but also show the potential of fine spectral-resolution remote sensing for detailed mapping of urban materials and their condition based on their spectral signal. An evaluation of the most suitable wavelengths for separation of urban land cover identified specific spectral features that provided the best separation. There is a strong indication that current multispectral systems, including IKONOS and LANDSAT ETM+, provide only marginal abilities to resolve these important features and are limited for urban land-cover mapping.
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paper_title: Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier.

paper_content:

Mapping and monitoring impervious surface dynamic change in a complex urban-rural frontier with medium or coarse spatial resolution images is a challenge due to the mixed pixel problem and the spectral confusion between impervious surfaces and other non-vegetation land covers. This research selected Lucas do Rio Verde County in Mato Grosso State, Brazil as a case study to improve impervious surface estimation performance by the integrated use of Landsat and QuickBird images and to monitor impervious surface change by analyzing the normalized multitemporal Landsat-derived fractional impervious surfaces. This research demonstrates the importance of two step calibrations. The first step is to calibrate the Landsat-derived fraction impervious surface values through the established regression model based on the QuickBird-derived impervious surface image in 2008. The second step is to conduct the normalization between the calibrated 2008 impervious surface image with other dates of impervious surface images. This research indicates that the per-pixel based method overestimates the impervious surface area in the urban-rural frontier by 50-60%. In order to accurately estimate impervious surface area, it is necessary to map the fractional impervious surface image and further calibrate the estimates with high spatial resolution images. Also normalization of the multitemporal fractional impervious surface images is needed to reduce the impacts from different environmental conditions, in order to effectively detect the impervious surface dynamic change in a complex urban-rural frontier. The procedure developed in this paper for mapping and monitoring impervious surface area is especially valuable in urban-rural frontiers where multitemporal Landsat images are difficult to be used for accurately extracting impervious surface features based on traditional per-pixel based classification methods as they cannot effectively handle the mixed pixel problem.
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paper_title: Automated differentiation of urban surfaces based on airborne hyperspectral imagery

paper_content:

The urban environment is characterized by an intense use of the available space, where the preservation of open green spaces is of special ecological importance. Because of dynamic urban development and high mapping costs, municipal authorities are interested in effective methods for mapping urban surface cover types that can be used for evaluating ecological conditions in urban structures and supporting updates of biotope mapping. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for their potential in automated area-wide differentiation of ecologically meaningful urban surface cover types for a study area in the city of Dresden, Germany. The small urban structures and the high spectral information content of the hyperspectral image data require the development of special methods capable of dealing with the resulting large number of mixed pixels. In this paper, a new approach is presented that combines advantages of classification with linear spectral unmixing. Since standard unmixing techniques are not suitable for an area-wide analysis of urban surfaces representing a large number of spectrally similar endmembers (EMs), the mathematical model, were extended and a new method for pixel-oriented EM selection was developed. This method reduces the number of possible EM combination for each pixel by introducing spectrally pure seedlings and a list of possible EM combinations into a neighborhood-oriented iterative unmixing procedure. The results and their comparison with standard spectral classification methods show that the new pixel- and contest-based approach enables reasonable material-oriented differentiation of urban surfaces.
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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

paper_content:

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: A Multi-scale Approach to Urban Thermal Analysis

paper_content:

Abstract An environmental consequence of urbanization is the urban heat island effect, a situation where urban areas are warmer than surrounding rural areas. The urban heat island phenomenon results from the replacement of natural landscapes with impervious surfaces such as concrete and asphalt and is linked to adverse economic and environmental impacts. In order to better understand the urban microclimate, a greater assessment of the overall urban thermal pattern, including an analysis of the thermal properties of individual land covers, is needed. This study examines the surface thermal pattern by means of thermal land cover response for the Salt Lake City, Utah, study area at two scales: 1) the community level, and 2) the regional or valleywide level. Airborne ATLAS (Advanced Thermal and Land Applications Sensor) data, a high spatial resolution (10-meter) dataset appropriate for an environment containing a concentration of diverse land covers, are used for both land cover and thermal analysis at the community level. The ATLAS data consist of 15 channels covering the visible, near-IR, mid-IR and thermal-IR wavelengths. At the regional level Landsat TM data are used for land cover analysis while the ATLAS channel 13 data are used for the thermal analysis. Results show that a ground-level heat island is evident at both the community and the valleywide level where there is an abundance of impervious surfaces. ATLAS data perform well in community level studies in terms of land cover and thermal exchanges, but other, more coarse-resolution data sets are more appropriate for large-area thermal studies. Thermal response per land cover is consistent at both levels, which suggests potential for comparison with more coarse spatial resolution studies.
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paper_title: Expert system classification of urban land use/cover for Delhi, India

paper_content:

This study presents the results of classifying land use/land cover for Delhi, India using an expert system approach. For this study Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of 22 September 2003 were used. The research goals of this project are two-fold. In one respect, the research goal is to report on the extent covered by urbanization using the classified image. Thirteen different land-cover categories were identified with an 85.55% overall classification accuracy based on 256 random points for validation and 50 on the ground observations. Secondly, we report on our efforts to duplicate an expert system model previously developed for Phoenix Arizona as a generalized approach for urban land use classification. Results suggest that while some of the methodology could be duplicated, there are local factors (e.g. data availability and specific land features) that required the approach to be modified.
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paper_title: Use of impervious surface in urban land-use classification

paper_content:

Impervious surface has been recognized as a key indicator in assessing urban environments. However, accurate impervious surface extraction is still a challenge. Effectiveness of impervious surface in urban land-use classification has not been well addressed. This paper explored extraction of impervious surface information from Landsat Enhanced Thematic Mapper data based on the integration of fraction images from linear spectral mixture analysis and land surface temperature. A new approach for urban land-use classification, based on the combined use of impervious surface and population density, was developed. Five urban land-use classes (i.e., low-, medium-, high-, and very-high-intensity residential areas, and commercial/industrial/transportation uses) were developed in the city of Indianapolis, Indiana, USA. Results showed that the integration of fraction images and surface temperature provided substantially improved impervious surface image. Accuracy assessment indicated that the root-mean-square error and system error yielded 9.22% and 5.68%, respectively, for the impervious surface image. The overall classification accuracy of 83.78% for five urban land-use classes was obtained.
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paper_title: Spectral resolution requirements for mapping urban areas

paper_content:

This study evaluated how spectral resolution of high-spatial resolution optical remote sensing data influences detailed mapping of urban land cover. A comprehensive regional spectral library and low altitude data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were used to characterize the spectral properties of urban land cover. The Bhattacharyya distance was applied as a measure of spectral separability to determine a most suitable subset of 14 AVIRIS bands for urban mapping. We evaluated the performance of this spectral setting versus common multispectral sensors such as Ikonos by assessing classification accuracy for 26 urban land cover classes. Significant limitations for current multispectral sensors were identified, where the location and broadband character of the spectral bands only marginally resolved the complex spectral characteristics of the urban environment, especially for built surface types. However, the AVIRIS classification accuracy did not exceed 66.6% for 22 urban cover types, primarily due to spectral similarities of specific urban materials and high within-class variability.
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paper_title: Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

paper_content:

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region.
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paper_title: A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure

paper_content:

A procedure for the calculation of a texture-derived built-up presence index (PanTex) from textural characteristics of panchromatic satellite data is presented. The index is based on fuzzy rule-based composition of anisotropic textural co-occurrence measures derived from the satellite data by the gray-level co-occurrence matrix (GLCM). Examples are produced how the PanTex index reduces the edge effects of the nonbuilt-up linear features and improves capacity to discriminate between built-up and nonbuilt-up areas. The accuracy and robustness of the PanTex measure against seasonal changes, multisensor, multiscene, and data degradation by wavelet-based compression and histogram stretching is discussed with some examples.
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paper_title: Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis

paper_content:

Abstract Both moderate and high spatial resolution imagery can be used to quantify abundance and distribution of urban vegetation for urban landscape management and to provide inputs to physical process models. Estimation of vegetation fraction from Landsat ETM+ and Quickbird allows for operational monitoring and reconnaissance at moderate resolution with calibration and vicarious validation at higher resolution. Establishing a linear correspondence between ETM-derived vegetation fraction and Quickbird-derived vegetation fraction facilitates the validation task by extending the spatial scale from 30 × 30 m to a more manageable 2.8 × 2.8 m. A comparative analysis indicates that urban reflectance can be accurately represented with a three component linear mixture model for both Landsat ETM+ and Quickbird imagery in the New York metro area. The strong linearity of the Substrate Vegetation Dark surface (SVD) mixture model provides consistent estimates of illuminated vegetation fraction that can be used to constrain physical process models that require biophysical inputs related to vegetation abundance. When Quickbird-derived 2.8 m estimates of vegetation fraction are integrated to 30 m scales and coregistered to Landsat-derived 30 m estimates, median estimates agree with the integrated fractions to within 5% for fractions > 0.2. The resulting Quickbird-ETM+ scatter distribution cannot be explained with estimate error alone but is consistent with a 3% to 6% estimation error combined with a 17 m subpixel registration ambiguity. The 3D endmember fraction space obtained from ETM+ imagery forms a ternary distribution of reflectance properties corresponding to distinct biophysical surface types. The SVD model is a reflectance analog to Ridd's V–I–S land cover model but acknowledges the fact that permeable and impermeable surfaces cannot generally be distinguished on the basis of broadband reflectance alone. We therefore propose that vegetation fraction be used as a proxy for permeable surface distribution to avoid the common erroneous assumption that all nonvegetated surfaces along the gray axis are completely impermeable. Comparison of mean vegetation fractions to street tree counts in New York City shows a consistent relationship between minimum fraction and tree count. However, moderate and high resolution areal estimates of vegetation fraction provide complementary information because they image all illuminated vegetation, including that not counted by the in situ street tree inventory.
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paper_title: The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean

paper_content:

The aim of this study was to develop an efficient and accurate procedure for classifying Mediterranean land cover with remotely sensed data. Combinations of artificial neural networks (ANN) and texture analysis on a per-field basis were used to classify a Landsat Thematic Mapper image of the Cukurova Deltas, Turkey, into eight land cover classes. This study integrated spectral information with measures of texture, in the form of the variance and the variogram. The accuracy of the ANN was greater than that of maximum likelihood (ML) when using spectral data alone and when using spectral and textural data. The use of texture measures through the per-pixel and per-field majority rule approaches were found to reduce classification accuracy because the field boundaries were enlarged and so overwhelmed the measures of texture. In contrast, the per-field approach (where the field was specified prior to analysis) combined with texture information increased significantly classification accuracy. However, the accuracy decreased as the variogram lag increased. The accuracy with which land cover could be classified in this region was maximised at 89% by using a per-field, ANN approach in which semivariance at a lag of 1 pixel was incorporated as textural information. This is 15% greater than the accuracy achieved using a standard per-pixel ML classification. The primary limitation of the use of the per-field approach was noted to be the need for prior knowledge of field boundaries which may be resolved using existing data or through some form of edge-detection routine.
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paper_title: A survey of image classification methods and techniques for improving classification performance

paper_content:

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.
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paper_title: Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center

paper_content:

Abstract Study of the detailed structure and ecological functioning of urban and peri-urban systems is intensifying due to increasing concentration of the human population into urban centers. Much of this increase is expected to occur in semiarid to arid cites. Data from new high spatial, temporal, and spectral resolution satellite-based sensors promise to increase our understanding of global urban ecological and climatic processes and improve city and land planning capabilities. Two of these sensors, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Moderate Resolution Imaging Spectroradiometer (MODIS), provide an opportunity to compare urban spatial structure (distribution and configuration of discrete land cover/land use classes on the landscape) with contemporaneous measurements of surface biophysical composition at a variety of spatial and temporal scales. Such combined measurements are useful for modeling changes to urban climate, hydrology, and biogeochemical cycles caused by modification of the landscape. We compare gridded landscape metrics derived from expert system land cover classification of ASTER to corresponding MODIS NDVI data at scales of 250 m/pixel, 500 m/pixel, and 1 km/pixel in order to determine which of these scales is optimal for monitoring of urban biophysical processes and landscape structure change. Weak positive and negative correlations between NDVI and landscape structure were observed at all three spatial scales for the metrics Class Area, Mean Patch Size, Edge Density, and Interspersion/Juxtaposition Index.
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paper_title: Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques

paper_content:

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour l'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Seve...
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paper_title: Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data

paper_content:

Using a linear unconstrained least squares (LSS) method and a non-linear artificial neural network (ANN) algorithm, we conducted a spectral mixture analysis to the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image data in Yokohama city, Japan, for mapping the abundance of the urban surface components. ASTER is a newly developed research facility instrument. The regions of interest of four endmembers (Vegetation, Soil, High/Low albedo impervious surfaces) were determined in Maximum Noise Fraction (MNF) feature spaces. The spectral signatures of the four endmembers were then extracted from the ASTER VNIR (15-m resolution) and SWIR (30-m resolution) imagery by referring to high spatial resolution airborne imagery (The Airborne Imaging Spectrometer, AISA, with 2-m resolution) and land use/land cover map for training and testing the LSS and ANN algorithms. Experimental results indicate that ASTER VNIR and SWIR image data are capable of mapping the abundances of urban surface components with a reasonable accuracy and that the ANN outperforms the unconstrained LSS in this spectral mixture analysis.
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paper_title: High spatial resolution spectral mixture analysis of urban reflectance

paper_content:

This study uses IKONOS imagery to quantify the combined spatial and spectral characteristics of urban reflectance in 14 urban areas worldwide. IKONOS 1-m panchromatic imagery provides a detailed measure of spatial variations in albedo while IKONOS 4-m multispectral imagery allows the relative contributions of different materials to the spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Spatial autocorrelation analyses indicate that the characteristic scale of urban reflectance is consistently between 10 and 20 m for the cities in this study. Spectral mixture analysis quantifies the relative contributions of the dominant spectral endmembers to the overall reflectance of the urban mosaic. Spectral mixing spaces defined by the two low-order principal components account for 96% to 99% of image variance and have a consistent triangular structure spanned by high albedo, low albedo and vegetation endmembers. Spectral mixing among these endmembers is predominantly linear although some nonlinear mixing is observed along the gray axis spanning the high and low albedo endmembers. Inversion of a constrained three-component linear mixing model produces stable, consistent estimates of endmember abundance. RMS errors based on the misfit between observed radiance vectors and modeled radiance vectors (derived from fraction estimates and image endmembers) are generally less than 3% of the mean of the observed radiance. Agreement between observed radiance and fraction estimates does not guarantee the accuracy of the areal fraction estimates, but it does indicate that the three-component linear model provides a consistent and widely applicable physical characterization of urban reflectance. Field validated fraction estimates have applications in urban vegetation monitoring and pervious surface mapping.
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paper_title: Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data.

paper_content:

Driven by a constantly accelerating increase of urban population in recent decades urban sprawl has become ::: one of the most dynamic processes in the context of global land use transformations. The expansion of urban ::: agglomerations is closely associated with a substantial increase of impervious surface. In Europe, methods for ::: an accurate, fast and cost-effective mapping and assessment of impervious surface on a state-wide or ::: national scale have not been established so far. This study presents an approach for estimating the ::: impervious surface based on a combined analysis of single-date Landsat images and road network and ::: railway vector data using Support Vector Machines and functionalities of geographic information processing. ::: The modeling aims at the provision of data on the impervious surface for the total of residential, industrial ::: and transportation-related areas. The derived information is provided for the administrative units of ::: communities. The output of the procedure is a vector data file providing the ‘percent impervious surface of ::: built-up areas’ (PISB) and the ‘percent impervious surface of the total of built-up and transportation-related ::: areas’ (PISBT) for the administrative units of communities. The developed method is tested for a study area ::: covering almost one third of the German territory. The results prove the suitability of the approach for a ::: widely automated and area-wide mapping of impervious surfaces. Using reference data sets of three cities ::: (Leipzig, Ludwigshafen, Passau) we realized a mean absolute error of 19.8% and an average error of 6.4% for ::: the percent impervious surface modeled on the basis of the Landsat images. The final product resulting from ::: a combination of the imperviousness raster derived from the satellite images with the transportation-related ::: vector information showed a mean difference of 1% to 4% compared to corresponding reference data and ::: results of previous studies. For the year 2000 our research shows that 45.3% of the area occupied by ::: settlements and transport infrastructure in the German federal state of Bavaria, 44.6% in the state of Baden- ::: Wurttemberg and 42.6% in the state of Saxony was covered by impervious surface.
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paper_title: A competitive pixel-object approach for land cover classification

paper_content:

This paper describes a novel remote sensing land cover classification approach named competitive pixel-object classification, based on Bayesian neural networks and image segmentation. This approach makes use of both pixel spectral features and object features resulting from image segmentation through a competitive mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve. The competitive pixel-object method reduces the unreliability of object feature information produced by over- or under-segmentation of the image through a competitive mechanism. The experiment shows that the competitive pixel-object approach produces higher classification accuracy than either pixel-based classification or object-oriented classification.
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paper_title: An object-oriented approach for analysing and characterizing urban landscape at the parcel level

paper_content:

This paper presents an object-oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high-resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three-level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object-oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.
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paper_title: Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery

paper_content:

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object-oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object-oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey-level co-occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object-oriented classification of urban areas using very high resolution imagery.
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paper_title: Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

paper_content:

In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and t...
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paper_title: Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems: IMAGING SPECTROSCOPY REMOTE SENSING

paper_content:

[1] Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision-making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.
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paper_title: Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

paper_content:

Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within-class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21,000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.
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paper_title: Endmember variability in Spectral Mixture Analysis: A review

paper_content:

The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.
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paper_title: Earth system science related imaging spectroscopy - An assessment

paper_content:

The science of spectroscopy has existed for more than three centuries, and imaging spectroscopy for the Earth system for three decades. We first discuss the historical background of spectroscopy, followed by imaging spectroscopy, introducing a common definition for the latter. The relevance of imaging spectroscopy is then assessed using a comprehensive review of the cited literature. Instruments, technological advancements and (pre-)processing approaches are discussed to set the scene for application related advancements. We demonstrate these efforts using four examples that represent progress due to imaging spectroscopy, namely (i) bridging scaling gaps from molecules to ecosystems using coupled radiative transfer models (ii) assessing surface heterogeneity including clumping, (iii) physical based (inversion) modeling, and iv) assessing interaction of light with the Earth surface. Recent advances of imaging spectroscopy contributions to the Earth system sciences are discussed. We conclude by summarizing the achievements of thirty years of imaging spectroscopy and strongly recommend this community to increase its efforts to convince relevant stakeholders of the urgency to acquire the highest quality imaging spectrometer data for Earth observation from operational satellites capable of collecting consistent data for climatically-relevant periods of time.
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paper_title: Automated Labeling of Materials in Hyperspectral Imagery

paper_content:

We present a technique for automatically labeling segmented hyperspectral imagery with semantically meaningful material labels. The technique compares the mean signatures of each image segment to a spectral library of known materials, and material labels are assigned to image segments according to the most similar library entry. The similarity between spectral signatures is evaluated using our recently proposed CICRd similarity measure designed specifically for hyperspectral imagery. This measure considers both the continuum-intact reflectance spectrum and its continuum-removed representation. We provide a thorough assessment of this measure by comparison to several commonly used similarity measures on a well-studied low-altitude Airborne Visible/Infrared Imaging Spectrometer image of an urban area. We evaluate our results using both information-theoretic techniques and visual validation of the resulting spectral matches.
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paper_title: Automatic shadow detection in hyperspectral VIS-NIR images

paper_content:

Depending on the landscape type high amounts of shadow can be present in remote sensing images. These areas are usually masked using shadow detection techniques and excluded from further analysis. Although significant research has been conducted on the detection of shadows there is still room for improvements. In this investigation we focus on the development of a new shadow detection algorithm capable to be automatically applied without user knowledge on any hyperspectral VIS-NIR image and thus can be implemented in automated pre-processing chains. The analysis is strictly focussed on the VIS-NIR part of the electromagnetic spectrum due to the growing number of VIS-NIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential shadow pixels and the removal of no-shadow pixels from this mask. In this context the separation between shadow and water is the most challenging task. By analysing different images containing inland and ocean water types we found the slope of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR range to be a diagnostic feature for water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. These aspects have been considered in the development of a knowledge-based classifier. First results indicate the great potential of the developed algorithm for urban, rural and coastal scenes of different sensor data (AISA, HyMap). * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.
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paper_title: Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm

paper_content:

We investigate the spectral complexity and unique spectral characteristics of urban environments using a comprehensive regional field spectral library of more than 4500 individual spectra. Spectral properties of urban surface materials are presented and interpreted, and their separability is systematically analyzed using the Bhattacharyya distance (B-distance) as a quantitative measure of spectral discrimination. We find considerable spectral confusion between urban land cover types (i.e. specific roof and road types) but also show the potential of fine spectral-resolution remote sensing for detailed mapping of urban materials and their condition based on their spectral signal. An evaluation of the most suitable wavelengths for separation of urban land cover identified specific spectral features that provided the best separation. There is a strong indication that current multispectral systems, including IKONOS and LANDSAT ETM+, provide only marginal abilities to resolve these important features and are limited for urban land-cover mapping.
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paper_title: Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments

paper_content:

Abstract Remote sensing has considerable potential for providing accurate, up-to-date information in urban areas. Urban remote sensing is complicated, however, by very high spectral and spatial complexity. In this paper, Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to map urban land cover using HyMap data acquired over the city of Bonn, Germany. MESMA is well suited for urban environments because it allows the number and types of endmembers to vary on a per-pixel basis, which allows controlling the large spectral variability in these environments. We employed a hierarchical approach, in which MESMA was applied to map four levels of complexity ranging from the simplest level consisting of only two classes, impervious and pervious, to 20 classes that differentiated material composition and plant species. Lower levels of complexity, mapped at the highest accuracies, were used to constrain spatially models at higher levels of complexity, reducing spectral confusion between materials. A spectral library containing 1521 endmembers was created from the HyMap data. Three endmember selection procedures, Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA) and Count Based Endmember Selection (COB), were used to identify the most representative endmembers for each level of complexity. Combined two-, three- or four-endmember models – depending on the hierarchical level – were applied, and the highest endmember fractions were used to assign a land cover class. Classification accuracies of 97.2% were achieved for the two lowest complexity levels, consisting of impervious and pervious classes, and a four class map consisting of vegetation, bare soil, water and built-up. At the next level of complexity, consisting of seven classes including trees, grass, bare soil, river, lakes/basins, road and roof/building, classification accuracies remained high at 81.7% with most classes mapped above 85% accuracy. At the highest level, consisting of 20 land cover classes, a 75.9% classification accuracy was achieved. The ability of MESMA to incorporate within-class spectral variability, combined with a hierarchical approach that uses spatial information from one level to constrain model selection at a higher level of complexity was shown to be particularly well suited for urban environments.
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paper_title: Large-scale bidirectional reflectance model for urban areas

paper_content:

A bidirectional reflectance factor (BRF) model for urban areas for pixel sizes of more than 500 m/spl times/500 m is developed. Possible applications include albedo calculation, improvement of classification, and change detection algorithms, simulated global BRF maps, and refinement of atmospheric correction algorithms. The model combines the BRF effects at several scales (street grid, intermediate-sized objects, and microscale). The authors present modeling results as well as a comparison with measured data. The basic features of the urban BRF are the hotspot and the independence of its shape from wavelength in the range 450 nm/spl les//spl lambda//spl les/2300 nm. An analytical function that approximates the model is proposed for easy implementation and fast computation.
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paper_title: Spectral variability and bidirectional reflectance behaviour of urban materials at a 20 cm spatial resolution in the visible and near¿infrared wavelengths. A case study over Toulouse (France)

paper_content:

This letter presents an experiment carried out in Toulouse in May 2004 to study the spectral variability and bidirectional reflectance behaviour of urban materials. The measurements were carried out at a 20 cm spatial resolution in the visible and near‐infrared (350–2500 nm). These measurements allow quantification of three main types of reflectance spatial variability. In addition to these in situ experiments, the bidirectional properties of urban material samples were studied in the laboratory with a goniometer.
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paper_title: Integrating imaging spectroscopy (445–2543 nm) and geographic information systems for post-disaster management: a case of hailstorm damage in Sydney

paper_content:

This paper demonstrates a methodology for the analysis and integration of airborne hyperspectral sensor data (445–2543 nm) with GIS data in order to develop a vulnerability map which has the potential to assist in decision making during post-disaster emergency operations. Hailstorms pose a threat to people as well as property in Sydney, Australia. Emergency planning demands current, large-scale spatio-temporal information on urban areas that may be susceptible to hailstones. Several regions, dominated by less resistant roofing materials, have a higher vulnerability to hailstorm damage than others. Post-disaster operations must focus on allocating dynamic resources to these areas. Remote sensing data, particularly airborne hyperspectral sensor data, consist of spectral bands with narrow bandwidths, and have the potential to quantify and distinguish between urban features such as roofing materials and other man-made features. A spectral library of surface materials from urban areas was created by using a fu...
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paper_title: Decision level fusion in classification of hyperspectral data from urban areas

paper_content:

Classification of hyperspectral data with high spatial resolution using both spatial and spectral approaches is discussed. The spatial approach is based on mathematical morphology. In the method, several principal components (PCs) from the hyperspectral data are used. From each of the PCs, a morphological profile is built. These profiles are used together in one extended morphological profile, which is then classified with a neural network. The spectral classification approach is based on maximum likelihood classification and nonparametric weighted feature extraction (NWFE). The results from the spectral and spatial modeling are finally fused together using several different fusion rules. Experimental results are given on a hyperspectral data from an urban area.
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paper_title: A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images

paper_content:

This paper proposes a novel pixel-based system for the supervised classification of very high geometrical (spatial) resolution images. This system is aimed at obtaining accurate and reliable maps both by preserving the geometrical details in the images and by properly considering the spatial-context information. It is made up of two main blocks: 1) a novel feature-extraction block that, extending and developing some concepts previously presented in the literature, adaptively models the spatial context of each pixel according to a complete hierarchical multilevel representation of the scene and 2) a classifier, based on support vector machines (SVMs), capable of analyzing hyperdimensional feature spaces. The choice of adopting an SVM-based classification architecture is motivated by the potentially large number of parameters derived from the contextual feature-extraction stage. Experimental results and comparisons with a standard technique developed for the analysis of very high spatial resolution images confirm the effectiveness of the proposed system
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paper_title: Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data

paper_content:

Abstract The urban environment is characterized by an intense multifunctional use of available spaces, where the preservation of open green spaces is of special importance. For this purpose, area-wide urban biotope mapping based on CIR aerial photographs has been carried out for the large cities in Germany during the last 10 years. Because of dynamic urban development and high mapping costs, the municipal authorities are interested in effective methods for mapping urban surface cover types, which can be used for evaluation of ecological conditions in urban structures and supporting updates of biotope maps. Against this background, airborne hyperspectral remote sensing data of the DAIS 7915 instrument have been analyzed for a test site in the city of Dresden (Germany) with regard to their potential for automated material-oriented identification of urban surface cover types. Previous investigations have shown that the high spectral and spatial variabilities of these data require the development of special methods, which are capable of dealing with the resulting mixed-pixel problem in its specific characteristics in urban areas. Earlier, methodological developments led to an approach based on a combination of spectral classification and pixel-oriented unmixing techniques to facilitate sensible endmember selection based on the reflective bands of the DAIS instrument. This approach is now extended by a shape-based classification technique including the thermal bands of the DAIS instrument to improve the detection of buildings during the process of identifying seedling pixels, which represent the starting points for linear spectral unmixing. This new approach increases the reliability of differentiation between buildings and open spaces, leading to more accurate results for the spatial distribution of surface cover types. Thus, the new approach significantly enhances the exploitation of the information potential of the hyperspectral DAIS 7915 data for an area-wide identification of urban surface cover types.
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paper_title: Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

paper_content:

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.
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paper_title: The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data

paper_content:

Abstract The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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paper_title: Recent Advances in Techniques for Hyperspectral Image Processing

paper_content:

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific appli- cations, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of- the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.
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paper_title: De-shadowing of satellite/airborne imagery

paper_content:

A de‐shadowing technique is presented for multispectral and hyperspectral imagery over land acquired by satellite/airborne sensors. The method requires a channel in the visible and at least one spectral band in the near‐infrared (0.8–1 µm) region, but performs much better if bands in the short‐wave infrared region (around 1.6 and 2.2 µm) are available as well. The algorithm consists of these major components: (i) calculation of the covariance matrix and zero‐reflectance matched filter vector, (ii) derivation of the unscaled and scaled shadow function, (iii) histogram thresholding of the unscaled shadow function to define the core shadow areas, (iv) region growing to include the surroundings of the core shadow areas for a smooth shadow/clear transition, and (v) de‐shadowing of the pixels in the final shadow mask. The critical parameters of the method are discussed. Example images from different climates and landscapes are presented to demonstrate the successful performance of the shadow removal process ove...
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paper_title: Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing

paper_content:

In this study, Landsat 5-TM data were used to map urban land classes and the changes that occurred within them over a period of six years. The land classes were identified by Landsat 5-TM scenes taken in the same season in 1988 and 1994. The phenomena of land class changes were evaluated by adopting two remote sensing approaches, namely mapping and modelling, in a case study of the Bangkok Metropolitan area of Thailand. The quantitative results of changes, which were computed from a post-classification method, were used to analyse the pattern of changes in the urban land classes. The change-detection analysis indicated that 2% of agricultural land was lost, and there was a 14% increase in the commercial areas. The results demonstrated that the pattern of change in the urban land classes in Bangkok was that of agriculture lands to open lands; open lands to residential, and residential to commercial. The highest commercial land growth was observed in the high-density residential areas along main roads and t...
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paper_title: Construction of synthetic spectral reflectance of remotely sensed imagery for planning purposes

paper_content:

Abstract Urban and environment development plans commonly lack spectrally based value-added information layers such as expected albedo, emissivity and temperature of the planned landscapes. These can be integrated into plans in order to assist in using specific materials or in the way new landscapes and urban spaces are designed. In contrast to existing space-borne remotely sensed imagery from which information layers as such can be extracted using atmospheric correction tools, development plans are set on paper, in a geographic information system (GIS) or as perspective “artistic images” at best. This paper describes a new software tool within the environment for visualization of images (ENVI 4.1) software, for automatic simulation of such multispectral reflectance images, given thematic maps of planned landscapes and associated spectral signatures. We discuss issues related to the image generation process, the method of spectral signature integration, and to quality assessment measures. An example is provided. We assess the simulated output quantitatively using a pixel-based “goodness-of-fit” measure and by calculating Pearson's correlation coefficients. Results show that simulation of images based on local neighborhood spectral mixtures, have all, mean total-goodness-of-fit measures amounting 99%, and have a general positive linear correlation of around 0.86 with real data. A class-wise correlation of a simulated image with a real reference image shows that large image segments of homogenous land-cover classes, such as vegetation classes, inland waters and some soils, match about 80–90% of corresponding real data. On the other hand, simulated data will match only 20–40% of real values for highly textured land-cover classes with relatively small spatial extent over the image, such as for built-up areas. We conclude with two prospective applications related to the validation of classification algorithms, and to planning exercises.
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paper_title: SVM-based segmentation and classification of remotely sensed data

paper_content:

Support Vector Machines (SVM) is becoming a popular alternative to traditional image classification methods because it makes possible accurate classification from small training samples. Nevertheless, concerns regarding SVM parameterization and computational effort have arisen. This Letter is an evaluation of an automated SVM-based method for image classification. The method is applied to a land-cover classification experiment using a hyperspectral dataset. The results suggest that SVM can be parameterized to obtain accurate results while being computationally efficient. However, automation of parameter tuning does not solve all SVM problems. Interestingly, the method produces fuzzy image-regions whose contextual properties may be potentially useful for improving the image classification process.
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paper_title: Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions

paper_content:

A maximum a posteriori (MAP) estimation method for improving the spatial resolution of a hyperspectral image using a higher resolution auxiliary image is extended to address several practical remote sensing situations. These include cases where: 1) the spectral response of the auxiliary image is unknown and does not match that of the hyperspectral image; 2) the auxiliary image is multispectral; and 3) the spatial point spread function for the hyperspectral sensor is arbitrary and extends beyond the span of the detector elements. The research presented follows a previously reported MAP approach that makes use of a stochastic mixing model (SMM) of the underlying spectral scene content to achieve resolution enhancement beyond the intensity component of the hyperspectral image. The mathematical formulation of a generalized form of the MAP/SMM estimate is described, and the enhancement algorithm is demonstrated using various image datasets.
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paper_title: Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems: IMAGING SPECTROSCOPY REMOTE SENSING

paper_content:

[1] Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision-making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.
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paper_title: Investigating the angular and spectral properties of natural targets using CHRIS-PROBA images of San Rossore test site

paper_content:

This paper addresses the use of the bidirectional reflectance distribution function (BRDF) of natural surfaces for remote sensing applications and environmental monitoring. Multiangle images, acquired by the compact high resolution imaging spectrometer (CHRIS) over the San Rossore test site, have been analysed in order to exploit the potential of using the BRDF for data mining and precise retrieval of biophysical and geophysical parameters. The apparent reflectance and the anisotropy factor (ANIF) of selected soil cover types have been computed, after correcting the at-sensor radiance for atmospheric effects. The departure of soil reflectance from the standard Lambertian behaviour has been investigated, together with the hypothesis of BRDF spectral-angular separability. Then, the ANIF dependence on surface optical properties, illumination and viewing geometry has been assessed and demonstrated using an ANIF image. The stability of the atmospheric correction procedure was analysed, studying the retrieved reflectance versus the solar zenith angle.
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paper_title: SEGMENT BASED IMAGE ANALYSIS AND IMAGE FUSION

paper_content:

Over the last years, image fusion techniques have gained a renewed interest within the remote sensing community. The reason for this is that in most cases the new generation of remote sensors with very high spatial resolution records image datasets in two separate modes: the highest spatial resolution is obtained for panchromatic images whereas multispectral information is associated with lower spatial resolution. The ratios between panchromatic and multispectral imaging modes of one sensor vary between 1:2 and 1:5. For multisensor fusion, ratios can exceed 1:20 (e.g. Ikonos and SPOT merge). An emerging technique for the analysis of high-resolution images, on the other hand, is the use of segmentation algorithms as a pre-processing step. In this paper, we investigate techniques for the combination of image fusion and segment based image analysis. We will present a color preserving iconic fusion with subsequent segmentation and classification, a ‘cookie cutter’ approach for the integration of high resolution RGB and low resolution hyperspectral image data and a GIS based fusion technique for rapid image enhancement.
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paper_title: An automated and adaptable approach for characterizing and partitioning cities into urban structure types

paper_content:

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability.
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paper_title: Multi-resolution, multi-sensor image fusion: general fusion framework

paper_content:

Multi-resolution image fusion also known as pansharpening aims to include spatial information from a high resolution image, e.g. panchromatic or Synthetic Aperture Radar (SAR) image, into a low resolution image, e.g. multi-spectral or hyper-spectral image, while preserving spectral properties of a low resolution image. A signal processing view at this problem allowed us to perform a systematic classification of most known multi-resolution image fusion approaches and resulted in a General Framework for image Fusion (GFF) which is very well suitable for a fusion of multi-sensor data such as optical-optical and optical-radar imagery. Examples are presented for WorldView-1/2 and TerraSAR-X data.
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paper_title: Unmixing-based multisensor multiresolution image fusion

paper_content:

Constrained and unconstrained algorithms of the multisensor multiresolution technique (MMT) are discussed. They can be applied to unmix low-resolution images using the information about their pixel composition from co-registered high-resolution images. This makes it possible to fuse the low- and high-resolution images for a synergetic interpretation. The constrained unmixing preserves all the available radiometric information of the low-resolution image. On the other hand, the unconstrained unmixing may be preferable in case of noisy data. An analysis of the MMT sensitivity to sensor errors showed that the strongest requirement is the accuracy of geometric co-registration of the data; the co-registration errors should not exceed 0.1-0.2 of the low-resolution pixel size. Applications of the constrained and unconstrained algorithms are illustrated on examples of unmixing and fusion of the multiresolution reflective and thermal bands of a real TM/LANDSAT image as well as of a simulated image of the future ASTER/EOS-AMI sensor.
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paper_title: Multi-angular reflectance properties of a hemiboreal forest : An analysis using CHRIS PROBA data

paper_content:

Abstract Forest types differ in their hyperspectral anisotropy patterns mainly due to species-specific geometrical structure, spatial arrangement of canopies and subsequent shadow patterns. This paper examines the multi-angular, hyperspectral reflectance properties of typical hemiboreal forests during summer time using three simultaneous CHRIS PROBA (mode 3) scenes and stand inventory data from the Jarvselja Training and Experimental Forestry District in southeastern Estonia. We investigated the magnitude and reasons for the differences in the anisotropy patterns of deciduous and coniferous stands at three backward viewing angles. A forest reflectance model (FRT) was used as a tool to provide a theoretical basis to the discussion, and to estimate the directional contribution of scattering from crowns and ground to total stand reflectance for the two forest types. The FRT model simulated successfully the HDRF (hemispherical–directional reflectance factor) curves of the study stands to match those obtained from the CHRIS image, yet it produced a smaller and less wavelength-dependent angular reflectance effect than was observed in the satellite image. The main results of this study provide new information for separating the spectral contribution of the forest floor (or understory layer) from the tree canopy layer: (1) the red edge domain was identified to have the largest contribution from forest understory, and (2) the more oblique the viewing angle, the smaller the contribution from the understory. In addition, coniferous stands were observed to have a specific angular effect at the red and red edge domain, possibly as a result of the hierarchical structure and arrangement of coniferous canopies.
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paper_title: Multi-sensor image fusion for pansharpening in remote sensing

paper_content:

The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a panchromatic image of high spatial resolution with multispectral image data of lower spatial resolution to obtain a high-resolution multispectral image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for image fusion to show their ability to fuse multitemporal and multi-sensor image data. A series of eight multitemp...
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paper_title: Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

paper_content:

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO-1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high-quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI-derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion-derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low-albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low-albedo surfaces came mainly from additional bands in the mid-infrared region.
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paper_title: Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data

paper_content:

We compared the capability of the Earth Observing-1 (EO-1) Hyperion hyperspectral (HS) data with that of the EO-1 Advanced Land Imager (ALI) multispectral (MS) data for discriminating different land-use and land-cover classes in Fremont, California. We designed a classification scheme of two levels with level I including general classes and level II including more specific classes. Classification shows that the HS data does not produce better results than the MS data when we directly applied a Mahalanobis distance (MD) classifier. We tested a number of feature reduction and extraction algorithms for the HS image. These algorithms include principal component analysis (PCA), segmented PCA (SEGPCA), linear discriminant analysis (LDA), segmented LDA (SEGLDA), penalized discriminant analysis (PDA) and segmented PDA (SEGPDA). Feature reductions were all followed by an MD classifier for image classification. With SEGPDA, SEGLDA, PDA, and LDA, similar accuracies were achieved while a segmentation-based approach we proposed (SEGPDA or SEGLDA) greatly improved computation efficiency. They all outperformed SEGPCA and PCA by 4 to 5 percent (level II) and 1 to 3 percent (level I) in classification accuracy. For level II classification, overall accuracies obtained by using the features extracted from the HS image were 2 to 3 percent greater than those obtained with the MS image. For various vegetation class and impervious land use categories, the HS data consistently produced better results than the MS data. For level I classification, the HS image generated a thematic map that is � 0.01 greater in kappa coefficient comparing to the MS image. When we collapsed the level II classification map to a level I map, 5 percent (HS) to 7 percent (MS) improvements were achieved.
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paper_title: Spectroradiometric requirements for the reflective module of the airborne spectrometer ARES

paper_content:

The Airborne Reflective/Emissive Spectrometer is specified as a whisk-broom imaging spectrometer for remote sensing of land surfaces covering the wavelength regions 0.47-2.45 /spl mu/m and 8-12 /spl mu/m with 160 spectral bands. The instrument is being built by Integrated Spectronics, financed by the German Aerospace Agency (DLR) and the GeoResearch Centre Potsdam (GFZ) and will be available to the scientific community from end 2005 on. The spectroradiometric design is based on scientific requirements derived from three main application scenarios comprising vegetation, soil, and mineral sciences. Two of these are described in this letter. Measured or modeled reflectance spectra are input to a simulation model that calculates at-sensor radiance spectra, resamples them with the channel-specific response functions, adds different amounts of noise in the radiance domain, and performs a retrieval to get the corresponding noisy surface reflectance spectra. The retrieval results as a function of the sensor noise level are compared with the accuracy requirements imposed by the different application fields taking into account the technical boundary conditions. The final specifications account for the most demanding requirements of the three application fields: a spectral sampling distance of 13-14 nm in the 470-1800 nm region, and 12 nm in the 2000-2450-nm region. The required noise-equivalent radiances are 5, 3, and 2 nW/spl middot/cm/sup -2//spl middot/sr/sup -1//spl middot/nm/sup -1/ for the spectral regions 470-1000, 1000-1800, and 2000-2450 nm, respectively.
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paper_title: Large-scale bidirectional reflectance model for urban areas

paper_content:

A bidirectional reflectance factor (BRF) model for urban areas for pixel sizes of more than 500 m/spl times/500 m is developed. Possible applications include albedo calculation, improvement of classification, and change detection algorithms, simulated global BRF maps, and refinement of atmospheric correction algorithms. The model combines the BRF effects at several scales (street grid, intermediate-sized objects, and microscale). The authors present modeling results as well as a comparison with measured data. The basic features of the urban BRF are the hotspot and the independence of its shape from wavelength in the range 450 nm/spl les//spl lambda//spl les/2300 nm. An analytical function that approximates the model is proposed for easy implementation and fast computation.
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paper_title: Urbanization in India - Spatiotemporal analysis using remote sensing data

paper_content:

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.
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paper_title: Modeling the directional reflectance (BRDF) of a corrugated roof and experimental verification

paper_content:

Remotely sensed images with a pixel size of about 1 m can nowadays be acquired by airborne scanners and in the near future also by high resolution satellites. With such a high spatial resolution, remotely sensed data of urban areas can resolve structures like a roof into the different surface segments with different inclinations, e.g. in the case of a gabled roof. The authors have measured the BRDF (bidirectional reflectance distribution function) effects thoroughly on a roof covered with corrugated (sinusoidally shaped) roof tiles and on a sample of flat roof tiles. They modeled the shape of the corrugated tiles by a cosine function and assumed that every infinitesimal surface patch of the roof tile has a BRDF proportional to the BRDF of the flat roof tile. Model results and measurements agree well. The most critical parameters are the ratio height over wavelength of the sinusoidal roof tiles and the intensity of the specular peak of the surface patch. It is possible to retrieve these parameters from the measurements.
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paper_title: Estimation of urban vegetation abundance by spectral mixture analysis

paper_content:

The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembl...
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paper_title: Infrared stealthy surfaces : Why TES and THEMIS may miss some substantial mineral deposits on Mars and implications for remote sensing of planetary surfaces

paper_content:

[1] Infrared spectral observations have yielded many important insights into the nature of Mars. However, spectral effects tied to surface roughness and particle size can make mineral deposits substantially more difficult to detect and identify than is generally known, including for current investigations. The effects hold for Mars and other planets, but here we focus on the impact on the exploration of Mars. Laboratory spectra of pure, smooth-surfaced minerals have previously been used to conclude that the 1996 Global Surveyor Thermal Emission Spectrometer (TES) and the 2001 Mars Odyssey Thermal Emission Imaging System (THEMIS) will detect a mineral deposit that covers ∼10% of a pixel. Those laboratory spectra have formed the foundation for modeling and interpretations of TES/THEMIS data and for development of the 2003 Mars rover MiniTES. Here, for the first time, we compare TES/THEMIS foundation data to laboratory, field, and unique airborne spectra of rough surfaces. We demonstrate that substantial, well-crystalline, regional mineral deposits, including rock outcrops, can be 100% exposed and remain undetected at the sensitivity of TES/THEMIS. We then detail the physics, which apply to all geologic materials. Previous studies have demonstrated the complicating physical effects for solids versus particulates for a range of minerals, including oxides, sulfates, carbonates, and each of the seven silicate classes. We identify three cases of spectral behavior that can impact interpretations: In Case 1, physical effects cause a target to be undetectable by a given infrared instrument (“infrared stealthy”). In Case 2, variations in spectral contrast mimic abundance variations, which confounds abundance mapping. In Case 3, alteration of the spectral band shape impacts compositional interpretations. Optically smooth materials will be well represented in TES/THEMIS/MiniTES identifications. Conversely, if a material forms an optically rough surface or sufficiently small particles on Mars, then it will be comparatively underrepresented or missed. Surface roughness occurs on three broad scales, and roughness at the grain scale can be a primary variable in whether a mineral is detectable. Nondetection of certain key minerals (e.g., carbonates) can significantly impact geologic and climatic interpretations and landing site selection. The TES/THEMIS detection threshold may be 10% coverage on the basis of specific laboratory samples, but for field measurements, that prediction represents an idealized lower bound. Interpretations built on nondetection should be revisited after defining the uncertainties in detection capabilities for specific instruments as applied to expected mineral surface textures on Mars, including at the grain scale. The impact on future measurement strategies (including MiniTES) should also be assessed.
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paper_title: Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach

paper_content:

Urbanization related alterations to the surface energy balance impact urban warming (‘heat islands’), the growth of the boundary layer, and many other biophysical processes. Traditionally, in situ heat flux measures have been used to quantify such processes, but these typically represent only a small local-scale area within the heterogeneous urban environment. For this reason, remote sensing approaches are very attractive for elucidating more spatially representative information. Here we use hyperspectral imagery from a new airborne sensor, the Operative Modular Imaging Spectrometer (OMIS), along with a survey map and meteorological data, to derive the land cover information and surface parameters required to map spatial variations in turbulent sensible heat flux (QH). The results from two spatially-explicit flux retrieval methods which use contrasting approaches and, to a large degree, different input data are compared for a central urban area of Shanghai, China: (1) the Local-scale Urban Meteorological Parameterization Scheme (LUMPS) and (2) an Aerodynamic Resistance Method (ARM). Sensible heat fluxes are determined at the full 6 m spatial resolution of the OMIS sensor, and at lower resolutions via pixel aggregation and spatial averaging. At the 6 m spatial resolution, the sensible heat flux of rooftop dominated pixels exceeds that of roads, water and vegetated areas, with values peaking at ∼ 350 W m− 2, whilst the storage heat flux is greatest for road dominated pixels (peaking at around 420 W m− 2). We investigate the use of both OMIS-derived land surface temperatures made using a Temperature–Emissivity Separation (TES) approach, and land surface temperatures estimated from air temperature measures. Sensible heat flux differences from the two approaches over the entire 2 × 2 km study area are less than 30 W m− 2, suggesting that methods employing either strategy maybe practica1 when operated using low spatial resolution (e.g. 1 km) data. Due to the differing methodologies, direct comparisons between results obtained with the LUMPS and ARM methods are most sensibly made at reduced spatial scales. At 30 m spatial resolution, both approaches produce similar results, with the smallest difference being less than 15 W m− 2 in mean QH averaged over the entire study area. This is encouraging given the differing architecture and data requirements of the LUMPS and ARM methods. Furthermore, in terms of mean study QH, the results obtained by averaging the original 6 m spatial resolution LUMPS-derived QH values to 30 and 90 m spatial resolution are within ∼ 5 W m− 2 of those derived from averaging the original surface parameter maps prior to input into LUMPS, suggesting that that use of much lower spatial resolution spaceborne imagery data, for example from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is likely to be a practical solution for heat flux determination in urban areas.
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paper_title: Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

paper_content:

This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.
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paper_title: Development of a new spectral library classifier for airborne hyperspectral images on heterogeneous environments

paper_content:

The classification of hyperspectral images on heterogeneous environments without prior knowledge about the study area is a challenging task. Finding potential pure spectral signatures or endmembers (EM) of the various surface materials within an image is essential for obtaining accurate classification results. Automated endmember selection techniques, in many cases, return an unlabelled result without a relationship to a known material. This study demonstrates the potential of an automated spectral classification approach for hyperspectral imagery by using a comprehensive spectral library including a generalized class structure without the use of prior knowledge of the given scene. The classifier works by comparing every unknown image pixel to all labelled known spectra in the spectral library using a mixed measure similarity analysis of the spectral information divergence SID (Chang, 2000), the spectral angle mapper SAM (Kruse et. al., 1993) and the tangent trigonometric function (Du et. al., 2004). These similarity measures are the main criteria used to assign the class membership to a given pixel. In addition, a statistical analysis of the best ten scores identifies the statistical dominant material class from the similarity analysis. This statistical approach allows a pixel-related estimation of the classification reliability. The spectral library comparison classifier (SLC-Classifier) enables the classification of hyperspectral images on heterogeneous environments to be as complete as possible (depends on the input spectral library) with results containing both labelled potential pure ::: spectra and spectra with low similarity agreement. Pixels with low similarity agreement are mixed pixels and pixels related to materials without good representative spectra in the comprehensive spectral library. We demonstrate that this classifier is suitable for the identification of surface materials using hyperspectral images were detailed knowledge about the environments does not exist.
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paper_title: Multi-source remote sensing data fusion: status and trends

paper_content:

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fus...
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paper_title: The global trend of urbanization: spatiotemporal analysis of megacities using multi-temporal remote sensing, landscape metrics, and gradient analysis

paper_content:

Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring ::: and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding ::: spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and ::: analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries ::: of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban ::: patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city ::: level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index, ::: edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into ::: spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of ::: spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial ::: characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.
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paper_title: Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission

paper_content:

The simulation of remote sensing images is a valuable tool for defining future Earth observation systems, optimizing instrument parameters, and developing and validating data-processing algorithms. A scene simulator for optical Earth observation data has been developed within the Environmental Mapping and Analysis Program (EnMAP) hyperspectral mission. It produces EnMAP-like data following a sequential processing approach consisting of five independent modules referred to as reflectance, atmospheric, spatial, spectral, and radiometric modules. From a modeling viewpoint, the spatial module is the most complex. The spatial simulation process considers the satellite-target geometry, which is adapted to the EnMAP orbit and operating characteristics, the instrument spatial response, and the sources of spatial nonuniformity (keystone, telescope distortion and smile, and detector coregistration). The spatial module of the EnMAP scene simulator is presented in this paper. The EnMAP spatial and geometric characteristics will be described, the simulation methodology will be presented in detail, and the capability of the EnMAP simulator will be shown by illustrative examples.
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paper_title: ICARE: A physically-based model to correct atmospheric and geometric effects from high spatial and spectral remote sensing images over 3D urban areas

paper_content:

Automatic mapping of urban materials from remotely sensed radiance images remains difficult because of the complex phenomena induced by relief. Indeed, shadows and environment effects disrupt the radiance reaching the sensor. The measured radiance also depends on the illumination conditions of the observed area.
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paper_title: De-shadowing of satellite/airborne imagery

paper_content:

A de‐shadowing technique is presented for multispectral and hyperspectral imagery over land acquired by satellite/airborne sensors. The method requires a channel in the visible and at least one spectral band in the near‐infrared (0.8–1 µm) region, but performs much better if bands in the short‐wave infrared region (around 1.6 and 2.2 µm) are available as well. The algorithm consists of these major components: (i) calculation of the covariance matrix and zero‐reflectance matched filter vector, (ii) derivation of the unscaled and scaled shadow function, (iii) histogram thresholding of the unscaled shadow function to define the core shadow areas, (iv) region growing to include the surroundings of the core shadow areas for a smooth shadow/clear transition, and (v) de‐shadowing of the pixels in the final shadow mask. The critical parameters of the method are discussed. Example images from different climates and landscapes are presented to demonstrate the successful performance of the shadow removal process ove...
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Title: Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

Section 1: Introduction
Description 1: This section introduces the paper, highlighting the role of remote sensing in urban analysis, the limitations of current imaging spectrometers, and the objectives of the study.

Section 2: The Hyperspectral EnMAP Mission
Description 2: This section outlines the EnMAP mission, including its technical parameters, objectives, and the toolbox developed for hyperspectral remote sensing data analysis.

Section 3: Application Fields of Urban Remote Sensing
Description 3: Discusses the various application fields where urban remote sensing is utilized, such as urban development and planning, urban growth assessment, risk and vulnerability assessment, and urban climate analysis.

Section 4: Urban Development and Planning
Description 4: Describes how remote sensing aids in urban development and planning, focusing on impervious surface mapping, urban structure analysis, and urban ecology.

Section 5: Urban Growth Assessment
Description 5: Examines the use of remote sensing for monitoring urban growth, built-up areas, land cover changes, and dynamics of urban developments.

Section 6: Risk and Vulnerability Assessment
Description 6: Explores how remote sensing provides data for assessing risks and vulnerabilities in urban areas, useful for disaster management and risk mitigation.

Section 7: Urban Climate
Description 7: Focuses on the use of remote sensing in urban climate studies, including the urban heat island effect and microclimate analysis.

Section 8: Image Analysis Approaches
Description 8: Evaluates methodologies for extracting information from earth observation data, focusing on classification schemes, multispectral, and hyperspectral approaches.

Section 9: Discussion of the Potential of EnMAP for Urban Studies
Description 9: Discusses future research directions for optimizing EnMAP HSI data for urban analysis, including methodological advancements and application-oriented uses.

Section 10: Conclusions and Outlook
Description 10: Summarizes the potential contributions of the EnMAP mission to urban applications, emphasizing the need for new methods and strategies to exploit its full spectral information content.