A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010–2018
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paper_title: World Atlas of Mangroves

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Foreword Preface Introduction 1. Mangrove Ecosystems 2. Mangroves and Man 3. Mapping Mangroves Regional Chapters Western Indian Ocean 4. Eastern and Southern Africa 5. The Middle East 6. South Asia Eastern Asia and Australia/New Zealand 7. Southeast Asia 8. East Asia 9. Australia and New Zealand Pacific Ocean 10. Pacific Islands Americas 11. North and Central America and the Caribbean 12. South America West and Central Africa 13. West and Central Africa Annex 1: Species Ranges Annex 2: References for National Statistics Index
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paper_title: Seagrass Ecosystem Services and Their Variability across Genera and Geographical Regions

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Threats to and loss of seagrass ecosystems globally, impact not only natural resources but also the lives of people who directly or indirectly depend on these systems. Seagrass ecosystems play a multi-functional role in human well-being, e.g. food through fisheries, control of erosion and protection against floods. Quantifying these services reveals their contributions to human well-being and helps justify seagrass conservation. There has been no comprehensive assessment as to whether seagrass ecosystem services are perceived to vary over the globe or amongst genera. Our study compiles the most complete list of ecosystem services provided by seagrasses so far, including bioregional- and genus-specific information from expert opinion and published studies. Several seagrass ecosystem services vary considerably in their (known) provision across genera and over the globe. Seagrasses genera are clearly not all equal with regard to the ecosystem services they provide. As seagrass genera are not evenly distributed over all bioregions, the presence of an ecosystem service sometimes depends on the genera present. Larger sized seagrass genera (e.g. Posidonia, Enhalus) are perceived to provide more substantial and a wider variety of ecosystem services than smaller species (e.g. Halophila, Lepilaena). Nevertheless, smaller species provide important services. Our findings point out data gaps, provide new insight for more efficient management and recommend caution in economic valuation of seagrass services worldwide.
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paper_title: Accelerating loss of seagrasses across the globe threatens coastal ecosystems

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Coastal ecosystems and the services they provide are adversely affected by a wide variety of human activities. In particular, seagrass meadows are negatively affected by impacts accruing from the billion or more people who live within 50 km of them. Seagrass meadows provide important ecosystem services, including an estimated $1.9 trillion per year in the form of nutrient cycling; an order of magnitude enhancement of coral reef fish productivity; a habitat for thousands of fish, bird, and invertebrate species; and a major food source for endangered dugong, manatee, and green turtle. Although individual impacts from coastal development, degraded water quality, and climate change have been documented, there has been no quantitative global assessment of seagrass loss until now. Our comprehensive global assessment of 215 studies found that seagrasses have been disappearing at a rate of 110 km2 yr−1 since 1980 and that 29% of the known areal extent has disappeared since seagrass areas were initially recorded in 1879. Furthermore, rates of decline have accelerated from a median of 0.9% yr−1 before 1940 to 7% yr−1 since 1990. Seagrass loss rates are comparable to those reported for mangroves, coral reefs, and tropical rainforests and place seagrass meadows among the most threatened ecosystems on earth.
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paper_title: Seagrass ecosystems as a globally significant carbon stock

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Seagrass meadows are some of the most productive ecosystems on Earth. An analysis of organic carbon data from just under one thousand seagrass meadows indicates that, globally, these systems could store between 4.2 and 8.4 Pg carbon.
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paper_title: Impact of seagrass loss and subsequent revegetation on carbon sequestration and stocks

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Summary ::: Seagrass meadows are sites of high rates of carbon sequestration and they potentially support ‘blue carbon’ strategies to mitigate anthropogenic CO2 emissions. Current uncertainties on the fate of carbon stocks following the loss or revegetation of seagrass meadows prevent the deployment of ‘blue carbon’ strategies. ::: Here, we reconstruct the trajectories of carbon stocks associated with one of the longest monitored seagrass restoration projects globally. We demonstrate that sediment carbon stocks erode following seagrass loss and that revegetation projects effectively restore seagrass carbon sequestration capacity. We combine carbon chronosequences with 210Pb dating of seagrass sediments in a meadow that experienced losses until the end of 1980s and subsequent serial revegetation efforts. ::: Inventories of excess 210Pb in seagrass sediments revealed that its accumulation, and thus sediments, coincided with the presence of seagrass vegetation. They also showed that the upper sediments eroded in areas that remained devoid of vegetation after seagrass loss. Seagrass revegetation enhanced autochthonous and allochthonous carbon deposition and burial. Carbon burial rates increased with the age of the restored sites, and 18 years after planting, they were similar to that in continuously vegetated meadows (26.4 ± 0.8 gCorg m−2 year−1). ::: Synthesis. The results presented here demonstrate that loss of seagrass triggers the erosion of historic carbon deposits and that revegetation effectively restores seagrass carbon sequestration capacity. Thus, conservation and restoration of seagrass meadows are effective strategies for climate change mitigation.
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paper_title: RESPONSES OF COASTAL WETLANDS TO RISING SEA LEVEL

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Salt marsh ecosystems are maintained by the dominant macrophytes that regulate the elevation of their habitat within a narrow portion of the intertidal zone by accumulating organic matter and trapping inorganic sediment. The long-term stability of these ecosystems is explained by interactions among sea level, land elevation, primary production, and sediment accretion that regulate the elevation of the sediment surface toward an equilibrium with mean sea level. We show here in a salt marsh that this equilibrium is adjusted upward by increased production of the salt marsh macrophyte Spartina alterniflora and downward by an increasing rate of relative sea-level rise (RSLR). Adjustments in marsh surface elevation are slow in comparison to interannual anomalies and long-period cycles of sea level, and this lag in sediment elevation results in significant variation in annual primary productivity. We describe a theoretical model that predicts that the system will be stable against changes in relative mean sea level when surface elevation is greater than what is optimal for primary production. When surface elevation is less than optimal, the system will be unstable. The model predicts that there is an optimal rate of RSLR at which the equilibrium elevation and depth of tidal flooding will be optimal for plant growth. However, the optimal rate of RSLR also represents an upper limit because at higher rates of RSLR the plant community cannot sustain an elevation that is within its range of tol- erance. For estuaries with high sediment loading, such as those on the southeast coast of the United States, the limiting rate of RSLR was predicted to be at most 1.2 cm/yr, which is 3.5 times greater than the current, long-term rate of RSLR.
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paper_title: Centuries of human-driven change in salt marsh ecosystems

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Salt marshes are among the most abundant, fertile, and accessible coastal habitats on earth, and they provide more ecosystem services to coastal populations than any other environment. Since the Middle Ages, humans have manipulated salt marshes at a grand scale, altering species composition, distribution, and ecosystem function. Here, we review historic and contemporary human activities in marsh ecosystems—exploitation of plant products; conversion to farmland, salt works, and urban land; introduction of non-native species; alteration of coastal hydrology; and metal and nutrient pollution. Unexpectedly, diverse types of impacts can have a similar consequence, turning salt marsh food webs upside down, dramatically increasing top down control. Of the various impacts, invasive species, runaway consumer effects, and sea level rise represent the greatest threats to salt marsh ecosystems. We conclude that the best way to protect salt marshes and the services they provide is through the integrated approach of ecosystem-based management.
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paper_title: WETLAND RESOURCES: Status, Trends, Ecosystem Services, and Restorability

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▪ Abstract Estimates of global wetland area range from 5.3 to 12.8 million km2. About half the global wetland area has been lost, but an international treaty (the 1971 Ramsar Convention) has helped 144 nations protect the most significant remaining wetlands. Because most nations lack wetland inventories, changes in the quantity and quality of the world's wetlands cannot be tracked adequately. Despite the likelihood that remaining wetlands occupy less than 9% of the earth's land area, they contribute more to annually renewable ecosystem services than their small area implies. Biodiversity support, water quality improvement, flood abatement, and carbon sequestration are key functions that are impaired when wetlands are lost or degraded. Restoration techniques are improving, although the recovery of lost biodiversity is challenged by invasive species, which thrive under disturbance and displace natives. Not all damages to wetlands are reversible, but it is not always clear how much can be retained through re...
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paper_title: Multispectral Image Analysis Using the Object-Oriented Paradigm

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Introduction Background Objects and Human Interpretation Process Object-Oriented Paradigm Organization of the Book Multispectral Remote Sensing Spatial Resolution Spectral Resolution Radiometric Resolution Temporal Resolution Multispectral Image Analysis Why an Object-Oriented Approach? Object Properties Advantages of Object-Oriented Approach Creating Objects Image Segmentation Techniques Creating and Classifying Objects at Multiple Scales Object Classification Creating Multiple Levels Creating Class Hierarchy and Classifying Objects Final Classification Using Object Relationships between Levels Object-Based Image Analysis Image Analysis Techniques Supervised Classification Using Multispectral Information Exploring the Spatial Dimension Using Contextual Information Taking Advantage of Morphology Parameters Taking Advantage of Texture Adding Temporal Dimension Advanced Object Image Analysis Techniques to Control Image Segmentation within eCognition Techniques to Control Image Segmentation within eCognition Multi-Scale Approach for Image Analysis Objects vs. Spatial Resolution Exploring the Parent-Child Object Relationships Using Semantic Relationships Taking Advantage of Ancillary Data Accuracy Assessment Sample Selection Sampling Techniques Ground Truth Collection Accuracy Assessment Measures References Index
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paper_title: Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic

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An airborne infrared image was used to produce a map of land cover types in the Eastern shore of Lake Huron, Ontario province of Canada. Maximum likelihood pixel-based and nearest neighbor object-based methods were used in this approach. Land cover classes that obtained traditional pixel-based classification approaches showed a salt-and-pepper effect having the lowest producer accuracy (59.5%). Overall classification results increased up to 80% in object- based approach but still failed to distinguish buildings and creeks. Contours and DEM thematic layers enhanced classification results to a higher level (94%) and increased the producer accuracy for buildings and creek by creating reasonable objects in segmentation process in the object-based approach. ::: ::: ::: ::: Key words: Infrared image classification, pixel-based, object-based, DEM thematic layer, land cover mapping.
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paper_title: Dynamics of mangrove forests in the Mangoky River delta, Madagascar, under the influence of natural and human factors

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Mangroves in Madagascar cover 327 000 ha to 340 300 ha. Several authors have studied these mangroves, but we do not yet have a complete knowledge of these ecosystems. The present study is an attempt to respond to this concern by analyzing dynamics and changes in the extent of the mangrove forest in the Mangoky delta (SW Madagascar) between 1951 and 2000. The comparison between base maps and Landsat TM images shows that the mangrove area remained relatively stable, even increasing slightly (+189 ha) between 1951 and 1979. From 1979 to 1994, its area decreased from 21 426 ha to 11 706 ha (−9720 ha). It then increased again (+84 ha) between 1994 and 2000. The balance was a decrease of 41.74% between 1951 and 2000. These figures may be compared with national (−15% in 56 years) and world (−35% for the last 20 years) statistics. Three main reasons are given to explain these changes: (1) the hydrological and sedimentological behavior of the Mangoky River; (2) the mechanism of plant succession, which depends on tides and on the characteristics of the substratum and (3) human activities and logging, which have increased since the 1980s in the Mangoky delta.
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paper_title: Monitoring coastal zone land use and land cover changes of Abu Dhabi using remote sensing

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Coastal zones are most vulnerable for landuse changes in this rapid industrialization and urbanization epoch. It is necessary to evaluate land use — land cover (LULC) changes to develop efficient management strategies. The main objective of this paper is to evaluate and quantity Abu Dhabi coastal zone LULC changes from 1972 to 2000 using multi-temporal LANDSAT satellite data and digital change detection techniques. Supervised classification coupled with expert visual interpretation techniques were used to produce LULC classified images with an accuracy of 88%. Change detection process was achieved by applying post-classification comparison techniques in ENVI software. From this study it has been observed that the important coastal landuse types of Abu Dhabi coast .i.e. wetlands and woody Vegetation (Mangrove, represented by a single species,Avicennia marina) have been reduced drastically in their extent due to reclamation, dredging, tipping and other anthropogenic activities along the coastal zone. However, it has been observed that there is rapid increase in the man-made plantation and managed vegetation from 1990 to 2000 due to the Abu Dhabi government initiation. This study has given good insight into Abu Dhabi coastal zone changes during last 3 decades.
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paper_title: Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems

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Mangrove ecosystems help mitigate climate change, are highly biodiverse, and provide critical goods and services to coastal communities. Despite their importance, anthropogenic activities are rapidly degrading and deforesting mangroves world-wide. Madagascar contains 2% of the world’s mangroves, many of which have undergone or are starting to exhibit signs of widespread degradation and deforestation. Remotely sensed data can be used to quantify mangrove loss and characterize remaining distributions, providing detailed, accurate, timely and updateable information. We use USGS maps produced from Landsat data to calculate nation-wide dynamics for Madagascar’s mangroves from 1990 to 2010, and examine change more closely by partitioning the national distribution in to primary (i.e., >1000 ha) ecosystems; with focus on four Areas of Interest (AOIs): Ambaro-Ambanja Bays (AAB), Mahajamba Bay (MHJ), Tsiribihina Manombolo Delta (TMD) and Bay des Assassins (BdA). Results indicate a nation–wide net-loss of 21% (i.e., 57,359 ha) from 1990 to 2010, with dynamics varying considerably among primary mangrove ecosystems. Given the limitations of national-level maps for certain localized applications (e.g., carbon stock inventories), building on two previous studies for AAB and MHJ, we employ Landsat data to produce detailed, contemporary mangrove maps for TMD and BdA. These contemporary, AOI-specific maps provide improved detail and accuracy over the USGS national-level maps, and are being applied to conservation and restoration initiatives through the Blue Ventures’ Blue Forests programme and WWF Madagascar West Indian Ocean Programme Office’s work in the region.
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paper_title: A Hybrid Approach to Land Cover Classification from Multi Spectral Images

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This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The specific objective of this paper is to describe a new methodology for producing really exploitable results from automatic classification algorithms. Input data are basically constituted by multispectral images, integrated with textural and contextual measures. The output is constituted by an image with each pixel assigned to one out of 15 classes at the second level of the CLC legend or let unclassified (somehow a better solution than a classification error), plus a stability map that helps users to separate the regions classified with high accuracy from those whose classification result should be verified before being used.
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paper_title: Spatio-temporal changes of a mangrove–saltmarsh ecotone in the northeastern coast of Florida, USA

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Abstract General circulation models predict warming trends and changes in temperature and precipitation patterns that have the potential to alter the structure and function of coastal habitats. The purpose of this study was to quantify the expansion and contraction of mangroves and saltmarsh habitats and assess the impact of climate on these landscape changes. The study was conducted in a mangrove/saltmarsh ecotone in Flagler County, FL, near the northern range limit of mangroves along the Atlantic coast of North America. We used time series of historical aerial photography and high-resolution multispectral satellite imagery from 1942 to 2013 to quantify changes in the extent of mangrove and saltmarsh vegetation and compared these changes to climate variables of temperature and precipitation, temperature–seasonality, as well as historical sea-level data. Results showed increases in mangrove extent of 89% between 1942 and 1952, and a continuous increase from 1995 to 2013. Largest decrease in saltmarsh extent occurred between 1942 and 1952 (-136%) and between 2008 and 2013 (-81%). We found significant effects of precipitation, temperature, seasonality, and time on mangrove and saltmarsh areal extent. The statistical effect of sea-level was rather small, but we speculate that it might have ecological impacts on these two coastal ecosystems. Results also showed a cyclical dynamism as well as a reversal in habitat dominance, which may be the result of complex interactions between plant habitats and several environmental drivers of change such as species interactions, and hydrological changes induced by sea-level rise, in addition to temperature and precipitation effects. Our results on mangrove/saltmarsh expansion and contraction may contribute to the improvement of management and conservation strategies for coastal ecosystems being impacted by climate change.
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paper_title: Land cover changes on the coastal zone of Candarli Bay, Turkey using remotely sensed data.

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Land cover of the Earth is changing dramatically because of human activities. Information about changes is useful for management of natural resources. Rapid land cover changes have taken place in many coastal areas of Turkey over the last two decades due to urbanization and land degradation. In this paper, land cover change dynamics were investigated by the combined use of satellite remote sensing and geographical information systems. The main objective of the study was to determine land-cover transition rates among land cover types in coastal areas of Turkey. A time series of Landsat TM and ASTER images were used to gather land cover change data of the coastal line of Candarli Bay, Izmir, Turkey. The images were classified using supervised classification and a post-classification comparison approach was used in change detection. The results show significant increase in urban areas but decrease in semi natural and agricultural areas.
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paper_title: Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000☆

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Mangrove forests in many parts of the world are declining at an alarming ratedpossibly even more rapidly than inland tropical forests. The rate and causes of such changes are not known. The forests themselves are dynamic in nature and are undergoing constant changes due to both natural and anthropogenic forces. Our research objective was to monitor deforestation and degradation arising from both natural and anthropogenic forces. We analyzed multi-temporal satellite data from 1970s, 1990s, and 2000s using supervised classification approach. Our spatiotemporal analysis shows that despite having the highest population density in the world in its periphery, areal extent of the mangrove forest of the Sundarbans has not changed significantly (approximately 1.2%) in the last w25 years. The forest is however constantly changing due to erosion, aggradation, deforestation and mangrove rehabilitation programs. The net forest area increased by 1.4% from the 1970s to 1990 and decreased by 2.5% from 1990 to 2000. The change is insignificant in the context of classification errors and the dynamic nature of mangrove forests. This is an excellent example of the co-existence of humans with terrestrial and aquatic plant and animal life. The strong commitment of governments under various protection measures such as forest reserves, wildlife sanctuaries, national parks, and international designations, is believed to be responsible for keeping this forest relatively intact (at least in terms of area). While the measured net loss of mangrove forest is not that high, the change matrix shows that turnover due to erosion, aggradation, reforestation and deforestation was much greater than net change. The forest is under threat from natural and anthropogenic forces leading to forest degradation, primarily due to top-dying disease and over-exploitation of forest resources. 2007 Elsevier Ltd. All rights reserved.
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paper_title: A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform

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Abstract Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China.
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paper_title: An integrated comparative approach to mangrove vegetation mapping using advanced remote sensing and GIS technologies: preliminary results

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This study aims to develop an integrated methodology applying Remote Sensing and Geographic Information Systems (GIS) techniques for the assessment of the ecological status of mangrove forests. The study area is located at Phangnga Bay, Thailand. Various commonly available remote sensing data are evaluated for mangrove vegetation mapping. The satellite sensors used are covering the visible and infra-red (VIR) spectrum up to the microwave region of the electromagnetic spectrum. This study provides recommendations regarding the selection of a single sensor approach or sensor combination that fulfills a minimum requirement for practical mangrove mapping and inventory purposes (e.g. mangrove and non-mangrove areas, varying stocking density, dominant species composition and impact of human activities). Both their technical capabilities and their potentials are presented in correlation with the existing ground conditions.
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paper_title: Use of Archive Aerial Photography for Monitoring Black Mangrove Populations

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Abstract A study was conducted on the South Texas Gulf Coast to evaluate archive aerial color-infrared (CIR) photography combined with supervised image analysis techniques to quantify changes in black mangrove [Avicennia germinans (L.) L.] populations over a 26-year period. Archive CIR film from two study sites (sites 1 and 2) was studied. Photographs of site 1 from 1976, 1988, and 2002 showed that black mangrove populations made up 16.2%, 21.1%, and 29.4% of the study site, respectively. Photographs of site 2 from 1976 and 2002 showed that black mangrove populations made up 0.4% and 2.7% of the study site, respectively. Over the 26-year period, black mangrove had increases in cover of 77% and 467% on sites 1 and 2, respectively. These results indicate that aerial photographs coupled with image analysis techniques can be useful tools to monitor and quantify black mangrove populations over time.
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paper_title: Status and distribution of mangrove forests of the world using earth observation satellite data

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Aim Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved.Here,we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive. Methods We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth‐sun distance by converting the digital number values to the top-of-the-atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’. Results The total area of mangroves in the year 2000 was 137,760 km 2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world’s mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generallyconfinedtothetropicalandsubtropicalregionsandthelargestpercentage of mangroves is found between 5° N and 5° S latitude. Main conclusions We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations.We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created.We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.
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paper_title: An object-based method for mapping and change analysis in mangrove ecosystems

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Abstract Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to mangrove mapping. This research applied an object-based method to SPOT XS data to map the land cover in the mangrove ecosystem of Low Casamance, Senegal. In parallel, the object-based method was tested to analyse the changes in the mangrove area between 1986 and 2006. The object-based method for mangrove mapping applied a multi-resolution segmentation and implemented class-specific rules that incorporate spectral properties and relationships between image objects at different hierarchical levels. The object-based approach for change analysis conducted the segmentation on the multi-date composite of the 1986 and 2006 images and applied a nearest neighbour classifier. The object-based method clearly discriminated the different land cover classes within the mangrove ecosystem. The overall accuracy of the land cover classification was 86%, the overall kappa value was 0.83 and the user’s accuracy of the ‘mangroves’ class was higher than 97%. The estimated area of mangroves was 76,550 hectares in 2006. This result is an important update reference for mangrove studies in Senegal and the proposed method may represent a valid instrument for similar exercises in other regions. The image-to-image, object-based approach to change analysis clearly captured the fragmented and scattered pattern of change that prevails in the study area. The user’s accuracy of the increase and decrease classes of transition produced results better than 85%. The overall accuracy, however, is lower due to the method’s difficulties in detecting the small areas of change. To have conclusive evidence for the suitability of this method for change analysis of mangrove forest, this object-based approach should be tested in mangrove ecosystems where changes have different spatial patterns and modifications are more evident. Between 1986 and 2006, a small increase in the mangrove area was observed in Low Casamance. This was probably due to improved rainfall conditions after the droughts of the 1970s and 1980s. The pattern of change detected with the object-based approach corresponds to natural transitions and suggests that anthropogenic influence was limited.
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paper_title: Ecological Variability and Carbon Stock Estimates of Mangrove Ecosystems in Northwestern Madagascar

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Mangroves are found throughout the tropics, providing critical ecosystem goods and services to coastal communities and supporting rich biodiversity. Despite their value, world-wide, mangroves are being rapidly degraded and deforested. Madagascar contains approximately 2% of the world's mangroves, >20% of which has been deforested since 1990 from increased extraction for charcoal and timber and conversion to small to large-scale agriculture and aquaculture. Loss is particularly prominent in the northwestern Ambaro and Ambanja bays. Here, we focus on Ambaro and Ambanja bays, presenting dynamics calculated using United States Geological Survey (USGS) national-level mangrove maps and the first localized satellite imagery derived map of dominant land-cover types. The analysis of USGS data indicated a loss of 7659 ha (23.7%) and a gain of 995 ha (3.1%) from 1990-2010. Contemporary mapping results were 93.4% accurate overall (Kappa 0.9), with producer's and user's accuracies ≥85%. Classification results allowed partitioning mangroves in to ecologically meaningful, spectrally distinct strata, wherein field measurements facilitated estimating the first total carbon stocks for mangroves in Madagascar. Estimates suggest that higher stature closed-canopy mangroves have average total vegetation carbon values of 146.8 Mg/ha (±10.2) and soil organic
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paper_title: Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of Sunderban mangroves

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Mangrove conservation and management is a stupendous task chiefly due to the inaccessibility and the hostile substrate conditions. Remote sensing technology serves as an important tool in providing fast, accurate and up-to-date baseline information on the status of mangroves. It is almost impossible to carry out conventional field surveys in these swampy areas. The present study aims at the classification and mapping of the mangroves in Sunderban Biosphere Reserve (SBR) in the West Bengal province of India using IRS 1D LISS-III satellite data. Different classification approaches, viz., on-screen visual interpretation, supervised and unsupervised classifications were tried. The study showed that four mangroves classes, viz., Avicennia, Phoenix, mixed mangroves, and mangrove scrub and eight non-mangrove classes could be delineated using all the three approaches. All the mangrove and non-mangrove classes were field verified and the overall accuracy as well as user’s and producer’s accuracies for each category were determined. It was observed that among the three approaches, on-screen visual interpretation yielded higher classification accuracy (91.67%) compared to supervised (79.90%) and unsupervised classifications (71.08%). The results obtained through on-screen visual interpretation showed that all mangrove categories together cover 23.21% of the total geographical area of SBR, of which the mixed mangrove category covers maximum area (18.31%). Among the non-mangrove classes, the waterbody occupies largest area (35.36%) followed by agriculture (34.51%).
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paper_title: Identification of hot spots and well managed areas of Pichavaram mangrove using Landsat TM and Resourcesat—1 LISS IV: an example of coastal resource conservation along Tamil Nadu Coast, India

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The present work is a multi-temporal satellite based study on the spatial dynamic of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The Pichavaram mangrove forest near Chidambaram, South India is the second largest mangrove forest in the world. Unsupervised classification, the Iterative Self Organising Data Analysis Technique (ISODATA), has been used to classify the mangrove cover into the open and dense classes. The status of the classes has been monitored using Landsat TM of 1991, 2001, and Resourcesat–1 LISS IV of 2006 satellite data. The study demonstrated that by classifying mangrove ecosystem into just the 3 classes using remote sensing data and by studying their temporal variations, it is possible to get a reasonably accurate picture of the extent and condition of the mangrove ecosystem. The total area of the Pichavaram mangrove showed a net increase of 2.51 km2 within a span of 15 years (1991 to 2006). The hot spots that are at a risk of being degraded, and on the other hand, the mangrove areas that are well managed are identified using Geographical Information System (GIS) tools for the restoration and conservation measures.
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paper_title: Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery

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In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non‑mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.
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paper_title: Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia

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The lower reaches of River Kelantan form a vast delta (1200 ha) consisting of bay, mangrove and estuary on the northeast coast of Peninsular Malaysia. The present study was conducted to assess the mangrove vegetation at Tumpat based on ground-truth and remote sensing measurements. The mangroves are composed of several species including Nypa fruticans, Sonneratia caseolaris, Avicennia alba, Rhizophora apiculata, R. mucronata and Bruguiera gymnorrhiza, in order of dominance. The point-centred quarter method (PCQM) was used to estimate the stem density (number of stems/0.1 ha) and basal area (m2/0.1 ha) at selected sites on the ground. Recent high-resolution multispectral satellite data (QuickBird 2006, 2.4 m spatial resolution of the multispectral image) were used to produce land-use/cover classification and Normalized Differential Vegetation Index (NDVI) mapping for the delta. The area statistics reveal that mangroves occupy 339.6 ha, while coconut plantation dominates the vegetation (715.2 ha), followed by settlements (621.6 ha), sandbar (148.4 ha), agriculture (89 ha) and aquaculture (42.7 ha). Although the relationship between the spectral indices and dendrometric parameters was weak, we found a very high significance between the (mean) NDVI and stem density (p = 1.3 × 10-8). The sites with young/growing and also mature trees with lush green cover showed greater NDVI values (0.40-0.68) indicating healthy vegetation, while mature forests under environmental stress due to sand deposition and/or poor tidal inundation showed low NDVI values (0.38-0.47) and an unhealthy situation. Overall, a combination of ground survey and remote sensing provided valuable information for the assessment of mangrove vegetation types (i.e. young/growing or mature forest) and their health in Tumpat, Kelantan Delta.
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paper_title: Spatial coverage of mangrove communities in the Arabian Gulf

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Mangroves are the natural protectors of the coast, carbon sinks, and a nursery to many terrestrial as well as aquatic organisms. Different effects caused by natural forces together with anthropogenic factors have reduced mangrove cover on a global scale, yet little is known about the overall surface covered by mangroves in the Arabian Gulf. Therefore, the aim of this study was to examine their spatial coverage and distribution along the Gulf coastlines, using 25 satellite imagery recently acquired from Landsat 8 data for the year 2017. This study found about 165 km2 of fragmented scattered mangroves, mostly intense in the United Arab Emirates, where plantation projects have likely played a significant role in increasing their cover over the years. Whereas mangrove in Kuwait is rare, areas like Bahrain, Qatar and Saudi Arabia remained stable with a slight increase. However, mangroves in Iran appear to suffer a decline throughout the years.
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paper_title: REMOTE SENSING IN MANGROVE RESEARCH - RELATIONSHIP BETWEEN VEGETATION INDICES AND DENDROMETRIC PARAMETERS: A CASE FOR CORINGA, EAST COAST OF INDIA

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The mangrove forest of the Godavari estuary, Andhra Pradesh, represents the second largest area of such vegetation formations along the East Coast of India, next to the Sunderbans (West-Bengal). Although declared as Wildlife Sanctuary since 1972, this rich but fragile ecosystem has undergone serious alterations largely induced by human activities. Continuous efficient retrieval of reliable information from the mangroves is therefore necessary for conservation purposes. Satellite remote sensing is a useful source of information as it provides timely and complete coverage of the study area, complementing field surveys of higher information content but which are more difficult to carry out, especially in mangroves. The purpose of the present study is 1) to map the mangrove formations and its surroundings based on a supervised classification of remote-sensing data and 2) to analyse the potential relationships between mangrove dendrometric parameters and spectral indices extracted from satellite data. The supervised classification was carried out with an IRS-1C LISS3 image of March 1999 and was trained from ground truth data and field knowledge. Among the resulting 14 classes, 3 correspond to different mangrove signatures. The ground truth includes 128 sampling locations for which mangrove vegetation parameters like basal area and tree density have been estimated using the Point Centred Quarter Method (PCQM) on transect lines of at least 100 m. In a second stage, Vegetation Indices (VI) have been calculated at locations for which mangrove parameters were obtained from field surveys. Various statistical tools, among which scatter-plots and analyses of variance (ANOVA), have been used in order to explore the relationships that may exist between VI and mangrove parameters. The first results show that a relationship exists between VI and basal area whereas this is not the case with density. Furthermore, when spectral indices and mangrove parameters are considered altogether, it appears that only two classes of mangrove can be discriminated.
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paper_title: Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan

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Abstract The purpose of this study is to apply different remote sensing techniques to monitor shifting mangrove vegetation in the Danshui River estuary in Taipei, Taiwan, in order to evaluate a long-term wetland conservation strategy compromising between comprehensive wetland ecosystem management and urban development. In the Danshui estuary, mangrove dominated by Kandelia candel is the major vegetation, and a large area of marsh with freshwater grasses has been protected in three reserves along the river shore. This study applied satellite imagery from different remote sensors of various resolutions for spectral analysis in order to compare shifting wetland vegetation communities at different times. A two-stage analytical process was used for extracting vegetation area and types. In the first-stage, a normalized difference vegetation index (NDVI) was adopted to analyze SPOT, Landsat, and QuickBird imagery to obtain the spatial distribution of vegetation covers. In the second stage, a maximum likelihood classification (MLC) program was used to classify mangrove and non-mangrove areas. The results indicated that the spatial distribution of mangroves expanded 15.18 and 40 ha in two monitoring sites in 10 years, demonstrating the success of establishing reserves for protecting mangrove habitats. The analytical results also indicated that satellite imagery can easily discern the difference in characteristics between imagery of mangrove and other vegetation types, and that the logistical disadvantages of monitoring long-term vegetation community changes as well as evaluating an inaccessible area may be overcome by applying remote sensing techniques.
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paper_title: A preliminary assessment of NigeriaSat-1 for sustainable mangrove forest monitoring

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Mangroves constitute an area of great ecological importance and regular assessment and monitoring of this ecosystem is an integral part of environmental management plan. The difficulty of access for ground survey has often limited the frequency of assessment of mangroves and remote sensing methods therefore provide a veritable means of assessment. However, accessibility to remotely sensed data as well as the cost have been major constraints for mangrove assessment in the developing countries. The launching of small satellites by some developing countries may therefore provide a solution to this problem. This paper is an attempt to evaluate the capability of NigeriaSat-1 which is one of the Disaster Management Constellation (DMC) small satellites for generation of baseline information on cover types and areal extents within the mangrove zone in Nigeria. This is important since cover information is always the first step for conservation and management. The study shows that the results obtained from NigeriaSat-1 have comparable accuracy with ASTER and Landsat ETM+. The findings documented in this paper could serve as a springboard for organized wetland management in Nigeria in particular and West Africa sub-region in general.
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paper_title: MONITORING A RECENT DELTA FORMATION IN A TROPICAL COASTAL WETLAND USING REMOTE SENSING AND GIS. CASE STUDY: GUAPO RIVER DELTA,

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The integrated use of remotely sensed data and GIS to monitor a rapid recent delta formation was undertaken in the Tacarigua Lagoon, a mangrove coastal protected wetland in the north-central coast of Venezuela. Recently, the resource value of coastal wetlands such as coastal lagoons and deltas, has brought about a need to protect and conserve these ecosystems. To that end, valuable resources such as these should be continuously monitored so that temporal changes in their environment can be analysed. The importance of determining the cause, extent, and spatial distribution of these changes can then be used in different aspects of environmental studies, land suitability analyses and for wise resource management. Aerospace data interpretation and a field survey were utilised to study the formation of the Guapo River delta within the Tacarigua Lagoon and to map the expansion that this depositional environment has undergone. A historical set of aerial photographs and a radar image, together with a GIS, were used to assess the growth of the delta from the beginning of its formation up to the present time.
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paper_title: Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

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This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the ‘salt and pepper effect’ in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas; young mangrove forest (less than 13 years old); and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.
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paper_title: Change Detection of Land use and Riverbank in Mekong Delta, Vietnam using Time Series Remotely Sensed Data

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Abstract: By taking the advantages of remote sensing technology, changes of land cover in Ca Mau Peninsular (the Southern part of the Mekong delta) and erosion of riverbank of the Mekong River have been defined. Change detection using time—series optical and radar satellite images (Landsat, SPOT and Radarsat) is the main approach in this study. Typical land cover in Ca Mau Peninsular is mangrove; its changes are observable by analysing imageries from 1973 to 2008. The results showed that more than half of the mangrove area was converted into shrimp farms. This made forest cover severely declined. Riverbank of the Mekong River also was detected to be eroded and accreted in a complicated form. Locations along the left and right banks of Tien and Hau river are defined as hot spots that are seriously suffered erosions. Topographic maps in 1966–1968 were taken into account as base line data, which was analyzed along with satellite images from 1989 to 2009. In addition, the Digital Shoreline Analysis System (DS...
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paper_title: Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure

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Abstract Mangrove forests grow in intertidal zones in tropical and subtropical regions and have suffered a dramatic decline globally over the past few decades. Remote sensing data, collected at various spatial resolutions, provide an effective way to map the spatial distribution of mangrove forests over time. However, the spectral signatures of mangrove forests are significantly affected by tide levels. Therefore, mangrove forests may not be accurately mapped with remote sensing data collected during a single-tidal event, especially if not acquired at low tide. This research reports how a decision-tree −based procedure was developed to map mangrove forests using multi-tidal Landsat 5 Thematic Mapper (TM) data and a Digital Elevation Model (DEM). Three indices, including the Normalized Difference Moisture Index (NDMI), the Normalized Difference Vegetation Index (NDVI) and NDVI L ·NDMI H (the multiplication of NDVI L by NDMI H , L: low tide level, H: high tide level) were used in this algorithm to differentiate mangrove forests from other land-cover and land-use types in Fangchenggang City, China. Additionally, the recent Landsat 8 OLI (Operational Land Imager) data were selected to validate the results and compare if the methodology is reliable. The results demonstrate that short-term multi-tidal remotely-sensed data better represent the unique nearshore coastal wetland habitats of mangrove forests than single-tidal data. Furthermore, multi-tidal remotely-sensed data has led to improved accuracies using two classification approaches: i.e. decision trees and the maximum likelihood classification (MLC). Since mangrove forests are typically found at low elevations, the inclusion of elevation data in the two classification procedures was tested. Given the decision-tree method does not assume strict data distribution parameters, it was able to optimize the application of multi-tidal and elevation data, resulting in higher classification accuracies of mangrove forests. When using multi-source data of differing types and distributions to map mangrove forests, a decision-tree method appears to be superior to traditional statistical classifiers.
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paper_title: Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic

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An airborne infrared image was used to produce a map of land cover types in the Eastern shore of Lake Huron, Ontario province of Canada. Maximum likelihood pixel-based and nearest neighbor object-based methods were used in this approach. Land cover classes that obtained traditional pixel-based classification approaches showed a salt-and-pepper effect having the lowest producer accuracy (59.5%). Overall classification results increased up to 80% in object- based approach but still failed to distinguish buildings and creeks. Contours and DEM thematic layers enhanced classification results to a higher level (94%) and increased the producer accuracy for buildings and creek by creating reasonable objects in segmentation process in the object-based approach. ::: ::: ::: ::: Key words: Infrared image classification, pixel-based, object-based, DEM thematic layer, land cover mapping.
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paper_title: Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

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Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.
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paper_title: Mapping the Philippines’ Mangrove Forests Using Landsat Imagery

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Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines' mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines' degraded mangrove forests.
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paper_title: An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme

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Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.
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paper_title: The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: case study of Kien Giang coast, Vietnam

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Fringe mangroves are recognised as a highly valuable resource due to their provision of multiple ecosystem services. However, accelerating human-pressures on mangrove forests are resulting in the loss of key mangrove ecosystem services. In this paper, we assessed spatial-temporal changes in the extent and width of fringe mangroves, and changes in adjacent land use. The study focused on the Kien Giang Province, Vietnam, for the period 1989-2009. The total mangrove extent was estimated at seven points in time using four Landsat TM images (1989, 1992, 2003, and 2006) and three SPOT images (1995, 2003 and 2009). Supervised classification was conducted for image classification and mapping changes using a Maximum Livelihood Classifier approach. Changes in mangrove width were calculated for 2003 and 2009 using the Digital Shoreline Analysis Application.An assessment of the four Landsat TM images showed a significant decrease in mangrove extent for the periods 1989-1992 (-2.7% yr-1) and 2003-2006 (-2.1% yr-1), while a significant increase in mangrove extent was observed during 1992-2003 (0.7% yr-1). SPOT images were used to classify shoreline land cover into four types, namely mangroves; shrimp farms; built-up areas; and bare and wet ground areas. The total loss of fringe mangrove extent was significantly correlated with the increase in extent of other land covers, especially shrimp farms and bare and wet ground, during the period of 2003-2009.The overall accuracy assessment of the classification using 2003 and 2009 SPOT 5 images was 90.1% and 90.5% respectively. The overall accuracy assessment of the 2003 Landsat image was 92.1%. This accuracy in classification of mangroves and non-mangroves showed that use of Landsat images was adequate for assessing spatial-temporal mangrove dynamics when higher spatial resolution satellite images were limited. The results highlight the dynamic nature of fringe mangrove communities in the region, and provide data useful for informing shoreline mangrove rehabilitation, conservation efforts, and national monitoring programs for estimating mangrove values of carbon sequestration and shoreline protection functions in Vietnam.
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paper_title: Eelgrass and Macroalgal Mapping to Develop Nutrient Criteria in New Hampshire's Estuaries using Hyperspectral Imagery

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ABSTRACT Pe'eri, S.; Morrison, J.R.; Short, F.; Mathieson, A., and Lippmann, T., 2016. Eelgrass and macroalgal mapping to develop nutrient criteria in New Hampshire's estuaries using hyperspectral imagery. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 209–218. Coconut Creek (Florida), ISSN 0749-0208. In recent years, mapping of seagrass beds for assessment of water quality has become more common in the United States and around the world. The static location of seagrass on marine sediments and its sensitivity to light make it a good environmental indicator and an alternative to water sampling of suspended particulates and dissolved matter. The New Hampshire (NH) Department of Environmental Services (DES) adopted the assumption that eelgrass survival could be used as the water quality target for nutrient criteria in NH's estuaries. One of the hypotheses...
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paper_title: Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows

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Abstract Estuaries and coasts are among the most productive ecosystems and constitute valuable habitats for biodiversity and ecosystem services. Amongst nearshore ecosystems, seagrass beds play a major role enhancing biodiversity and water quality. Consequently, the development of new approaches to create extensive and high-resolution habitat maps is required not only to implement conservation, restoration and management plans, but also to establish adaptation plans to face climate change impacts. This study particularly assesses the capability of hyperspectral airborne imagery acquired with Compact Airborne Spectrographic Imager (CASI) to discriminate and map estuarine habitats, with special focus on Zostera noltii seagrass meadows. To this end, 13 habitats were defined along the supralittoral, intertidal and subtidal zones of an estuary, including Z. noltii seagrass meadows. The CASI sensor was configured to acquire 25 bands in the visible and near infrared wavelengths with a ground sampling distance of 2 m. Spectral bands were selected for species discrimination based on the spectral signature of the different habitat classes. Six different band combinations were tested applying maximum likelihood classification algorithm. The most accurate classification was obtained with 10 band combination (a mean producer accuracy 92% and a mean user accuracy 94%). The classification of Z. noltii beds has been found to be restricted to moderate and high dense meadows, however a vegetation index has been defined which could be applied for mapping Z. noltii meadow cover. These results highlight the value of CASI data to discriminate and map estuarine habitats, providing key information to be used in supporting the implementation of environmental legislation, protection and conservation of coastal habitats.
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paper_title: Temporal patterns in species zonation in a mangrove forest in the Mekong Delta, Vietnam, using a time series of Landsat imagery

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Abstract Time-series analysis of Landsat imagery was used to evaluate trends in species zonation in a restored mangrove forest in the Mekong Delta, Vietnam. Dating back throughout the primary expansion of the forest, the Landsat archive provides a unique opportunity to examine the evolution of a restored forest in all stages of maturity. Through temporal trend analysis, areas of the forest were divided into four development stages: Pre-mangrove water, initial mangrove colonization, a period dominated by Sonneratia spp., and the arrival and zonation of secondary species. Field inventory data was used in conjunction with satellite data to investigate the geomorphic and hydrologic influences behind the species zonation. We hypothesize that the development of Sonneratia spp. facilitates initial sedimentation. The trees mature at higher soil elevations and a region develops with low forest density, high light availability, and reduced tidal inundation. Multi-species zonation then develops through the timely exploitation of the geomorphic conditions suitable for the establishment of secondary species.
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paper_title: Application of ALOS AVNIR-2 for the detection of seaweed and seagrass beds on the northeast of Brazil

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ABSTRACTSeaweed and seagrass beds play a multiplicity of functions within ecosystems, and have both ecological and economical value. However, anthropogenic activities, as well as climate change have contributed on the degradation. This study used data from Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer Type 2 (AVNIR-2) orbital images to detect seaweeds and seagrasses. Qualitative and quantitative samples were used to validate the images, classified using the kappa coefficient (κ). The supervised classifications performed by the algorithms fuzzy logic – Fuzclass, maximum likelihood – Maxlike, minimum distance to means – Mindist, parallelepiped – Piped, showed an accuracy level of 0.93, 0.84, 0.83 (both excellent), and 0.62 (substantial), respectively. The results of the hard classifiers (Maxlike, Mindist, and Piped) submitted a new classification based on fuzzy logic (Fuzclass) demonstrated accuracy level of 0.74, 0.61 (both substantial), and 0.50 (moderate), respect...
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paper_title: An approach to monitoring mangrove extents through time-series comparison of JERS-1 SAR and ALOS PALSAR data

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Between 2007 and 2010, Japan’s Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) captured dual polarization HH and HV data across the tropics and sub-tropics. A pan tropical dataset of Japanese Earth Resources Satellite (JERS-1) SAR (HH) data was also acquired between 1995 and 1998. The provision of these comparable cloud-free datasets provided an opportunity for observing changes in the extent of coastal mangroves over more than a decade. Focusing on nine sites distributed through the tropics, this paper demonstrates how these data can be used to backdate and update existing baseline maps of mangrove extent. The benefits of integrating dense time-series of Landsat sensor data for both validating assessments of change and determining the causes of change are outlined. The approach is evaluated for wider application across the geographical range of mangroves in order to advance the development of JAXA’s Global Mangrove Watch (GMW) program.
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paper_title: Integrating Indian remote sensing multi-spectral satellite and field data to estimate seagrass cover change in the Andaman and Nicobar Islands, India

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Environmental resource managers and policy makers require a reliable tool to quickly assess the spatial extent of any natural resources, including seagrasses, in order to develop management plans. Even small natural or anthropogenic disturbances can cause severe changes in the distributional pattern of seagrass meadows. Satellite imageries provide a suitable means to detect and assess such changes in space and time in remote and inaccessible areas. Present study aims to understand the distribution pattern of seagrasses after the Indian Ocean Tsunami in 2004 with the help of Indian Remote Sensing satellite data and in situ ground surveys with hand held GPS. As no geospatial data bases were available for the pre-tsunami period, the changes in seagrass cover were compared with the ground estimates available in the literature and also using pre-tsunami satellite data sets. The study found severe loss of seagrasses in the northern Andaman particularly in the Interview and North reef islands and in the Nicobar group of islands including Great Nicobar and Trinket islands. The investigation revealed the presence of 2,943.38 ha of seagrass covering the entire Andaman and Nicobar islands, and that 1,619.41 ha of seagrasses had been denuded during this period. The earthquake and subsequent tsunami in 2004 was the major reason for the loss of seagrasses in these islands. The seagrass spatial map generated in the present study can be used for the development of conservation and management plans and also to restore the denuded seagrasses of this region.
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paper_title: Madagascar’s Mangroves: Quantifying Nation-Wide and Ecosystem Specific Dynamics, and Detailed Contemporary Mapping of Distinct Ecosystems

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Mangrove ecosystems help mitigate climate change, are highly biodiverse, and provide critical goods and services to coastal communities. Despite their importance, anthropogenic activities are rapidly degrading and deforesting mangroves world-wide. Madagascar contains 2% of the world’s mangroves, many of which have undergone or are starting to exhibit signs of widespread degradation and deforestation. Remotely sensed data can be used to quantify mangrove loss and characterize remaining distributions, providing detailed, accurate, timely and updateable information. We use USGS maps produced from Landsat data to calculate nation-wide dynamics for Madagascar’s mangroves from 1990 to 2010, and examine change more closely by partitioning the national distribution in to primary (i.e., >1000 ha) ecosystems; with focus on four Areas of Interest (AOIs): Ambaro-Ambanja Bays (AAB), Mahajamba Bay (MHJ), Tsiribihina Manombolo Delta (TMD) and Bay des Assassins (BdA). Results indicate a nation–wide net-loss of 21% (i.e., 57,359 ha) from 1990 to 2010, with dynamics varying considerably among primary mangrove ecosystems. Given the limitations of national-level maps for certain localized applications (e.g., carbon stock inventories), building on two previous studies for AAB and MHJ, we employ Landsat data to produce detailed, contemporary mangrove maps for TMD and BdA. These contemporary, AOI-specific maps provide improved detail and accuracy over the USGS national-level maps, and are being applied to conservation and restoration initiatives through the Blue Ventures’ Blue Forests programme and WWF Madagascar West Indian Ocean Programme Office’s work in the region.
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paper_title: Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands, India, using IRS LISS III satellite images

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A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.
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paper_title: Comparison of bathymetry and seagrass mapping with hyperspectral imagery and airborne bathymetric lidar in a shallow estuarine environment

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We compared hyperspectral imagery and single-wavelength airborne bathymetric light detection and ranging lidar for shallow water <2 m bathymetry and seagrass mapping. Both the bathymetric results from hyperspectral imagery and airborne bathymetric lidar reveal that the presence of a strongly reflecting benthic layer under seagrass affects the elevation estimates towards the bottom depth instead of the top of seagrass canopy. Full waveform lidar was also investigated for bathymetry and similar performance to discrete lidar was observed. A provisional classification was performed with limited ground reference samples and four supervised classifiers were applied in the study to investigate the capability of airborne bathymetric lidar and hyperspectral imagery to identify seagrass genera. The overall classification accuracy is highly variable and strongly dependent on the classification strategy used. Features from bathymetric lidar alone are not sufficient for substrate classification, while hyperspectral imagery alone showed significant capability for substrate classification with over 95% overall accuracy. The fusion of hyperspectral imagery and bathymetric lidar only marginally improved the overall accuracy of seagrass classification.
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paper_title: Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

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Abstract Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of
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paper_title: Coral classification with hybrid feature representations

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Coral reefs exhibit significant within-class variations, complex between-class boundaries and inconsistent image clarity. This makes coral classification a challenging task. In this paper, we report the application of generic CNN representations combined with hand-crafted features for coral reef classification to take advantage of the complementary strengths of these representation types. We extract CNN based features from patches centred at labelled pixels at multiple scales. We use texture and color based hand-crafted features extracted from the same patches to complement the CNN features. Our proposed method achieves a classification accuracy that is higher than the state-of-art methods on the MLC benchmark dataset for corals.
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paper_title: Ecoregional scale seagrass mapping: A tool to support resilient MPA network design in the Coral Triangle

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Seagrass beds are of exceptional economic, ecological and social value in the Coral Triangle. The large number of people who live close to the coast and rely directly on marine resources for food and income paradoxically increases the value of, but also the threats to, these ecosystems. A key strategy of the Coral Triangle Initiative is to protect shallow coastal ecosystems through the design and implementation of resilient networks of marine protected areas (MPAs). This strategy requires accurate spatial data on the distribution and extent of coastal habitats (coral reef, seagrass and mangrove) at scales which match conservation planning decisions. In the Coral Triangle, seagrass distribution maps are not readily available at ecoregional scales. The Lesser Sunda ecoregion, extending from Bali, Indonesia to Timor-Leste, is one of 11 ecoregions of the Coral Triangle and a high priority for conservation and sustainable management of marine resources. To support the design of a resilient MPA network for the Lesser Sunda ecoregion, a seagrass distribution map was generated based on Landsat imagery, literature review and groundtruth data. Seagrass beds were estimated to cover an area of 273 km 2 at an overall accuracy of 78%. Use of the seagrass distribution map in the MPA design improved the habitat representation and connectivity e key criteria for resilient MPA design. The final MPA design included 80 km 2 of seagrass beds, with more than half the beds adjacent to coral reefs and mangroves. This study demonstrates the effective use of Landsat imagery and remote sensing techniques to derive ecoregional scale seagrass maps supporting MPA network design.
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paper_title: Use of Archive Aerial Photography for Monitoring Black Mangrove Populations

paper_content:

Abstract A study was conducted on the South Texas Gulf Coast to evaluate archive aerial color-infrared (CIR) photography combined with supervised image analysis techniques to quantify changes in black mangrove [Avicennia germinans (L.) L.] populations over a 26-year period. Archive CIR film from two study sites (sites 1 and 2) was studied. Photographs of site 1 from 1976, 1988, and 2002 showed that black mangrove populations made up 16.2%, 21.1%, and 29.4% of the study site, respectively. Photographs of site 2 from 1976 and 2002 showed that black mangrove populations made up 0.4% and 2.7% of the study site, respectively. Over the 26-year period, black mangrove had increases in cover of 77% and 467% on sites 1 and 2, respectively. These results indicate that aerial photographs coupled with image analysis techniques can be useful tools to monitor and quantify black mangrove populations over time.
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paper_title: Evaluating the complementarity of acoustic and satellite remote sensing for seagrass landscape mapping

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Accurate maps representing seagrass spatial distribution are essential components for effective monitoring and management of coastal vegetated habitats. Satellite and acoustic remote sensing provide valuable spatial data for seagrass mapping, though few studies have evaluated the complementarity of these methods. In this study, the complementarity of seagrass mapping was assessed through comparison of acoustic and satellite remote-sensing data sets. QuickBird® satellite imagery representing the seagrass landscape of the Richibucto estuary, New Brunswick, Canada, was classified through an object-based procedure and evaluated against a single-beam sonar data set. Acoustic percentage cover values were classified into binary presence/absence format through the application of a decision threshold, allowing comparison with satellite data using the error matrix and derived metrics. Though the binary satellite classification resulted in relatively high accuracy compared with independent ground reference data, agreement between satellite and acoustic data sets was limited. Local differences in seagrass prevalence and patchiness affected classification accuracy, highlighting the potential for under-or overestimating seagrass cover when applying bay-scale classification to areas with different landscape structure. These results emphasize the importance of landscape context in seagrass mapping. Satellite and acoustic remote sensing were seen to fundamentally differ in their depiction of the landscape. Comparison of multiple remote-sensing methods allowed for assessment of complementarity as well as ecologically relevant insight to seagrass spatial dynamics, with implications for mapping and monitoring of seagrass habitats.
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paper_title: An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand

paper_content:

Accurate and up-to-date maps of seagrass biodiversity are important for marine resource management but it is very challenging to test the accuracy of remote sensing techniques for mapping seagrass in coastal waters with variable water turbidity. In this study, Worldview-2 (WV-2) imagery was combined with field sampling to demonstrate the capability of mapping species type, percentage cover, and above-ground biomass of seagrasses in monsoonal southern Thailand. A high accuracy positioning technique, involving the Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS), was used to record field sample data positions and reduce uncertainties in matching locations between satellite and field data sets. Our results showed high accuracy (90.67%) in mapping seagrass distribution and moderate accuracies for mapping percentage cover and species type (73.74% and 75.00%, respectively). Seagrass species type mapping was successfully achieved despite discrimination confusion among Halophila ovalis, Thalassia hemprichii, and Enhalus acoroides species with greater than 50% cover. The green, yellow, and near infrared spectral channels of WV-2 were used to estimate the above-ground biomass using a multiple linear regression model (RMSE of ±10.38 g·DW/m2, R = 0.68). The average total above-ground biomass was 23.95 ± 10.38 g·DW/m2. The seagrass maps produced in this study are an important step towards measuring the attributes of seagrass biodiversity and can be used as inputs to seagrass dynamic models and conservation efforts.
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paper_title: Automatic Seagrass Disturbance Pattern Identification on Sonar Images

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Natural and anthropogenic disturbances are causing degradation and loss of seagrass cover, often in the form of bare patches (potholes) and propeller-scaring from vessels. Degradation of seagrass habitat has increased significantly in recent years with losses totaling some 110 km2 per year. With seagrass habitat disappearing at historically unprecedented rates, development of new tools for mapping these disturbances is critical to understanding habitat distribution and seagrass abundance. Current methods for mapping seagrass coverage rely on appropriate meteorological conditions (satellite imagery), are high in cost (aerial photography), or lack resolution ( in situ point surveys). All of these methods require low turbidity, and none is capable of automatically detecting bare patches (potholes) in seagrass habitat. Sonar-based methods for mapping seagrass can function in high turbidity, and are not affected by meteorological conditions. Here, we present an automatic method for detecting and quantifying potholes in sidescan sonar images collected in a very shallow, highly disturbed seagrass bed. Acoustic studies of shallow seagrass beds (<2 m) are scarce due to traditional approaches being limited by reduced horizontal swath in these depth ranges. The main challenges associated with these sidescan sonar images are random ambient noise and uneven backscatter intensity across the image. Our method combines adaptive histogram equalization and top-hat mathematical morphology transformation to remove image noises and irregularities. Then, boundaries of potholes are detected using optimum binarization as well as closing and erosion mathematical morphology filters. This method was applied to several sonar images taken from the Lower Laguna Madre in Texas at less than 2-m depth. Experimental results in comparison with ground-truthing demonstrated the effectiveness method by identifying potholes with 97% accuracy.
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paper_title: Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

paper_content:

The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remote sensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery – even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical capability, incorporating a term for potential systematic errors in radiometric alignment may be advisable. While Thalassia testudinium and Syringodium filiforme were too spectrally similar to be discriminated purely on spectral grounds, mapping of these and other species may be achievable by exploiting co-incident factors based on ecological zonation.
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paper_title: Deep Learning on Underwater Marine Object Detection: A Survey

paper_content:

Deep learning, also known as deep machine learning or deep structured learning based techniques, have recently achieved tremendous success in digital image processing for object detection and classification. As a result, they are rapidly gaining popularity and attention from the computer vision research community. There has been a massive increase in the collection of digital imagery for the monitoring of underwater ecosystems, including seagrass meadows. This growth in image data has driven the need for automatic detection and classification using deep neural network based classifiers. This paper systematically describes the use of deep learning for underwater imagery analysis within the recent past. The analysis approaches are categorized according to the object of detection, and the features and deep learning architectures used are highlighted. It is concluded that there is a great scope for automation in the analysis of digital seabed imagery using deep neural networks, especially for the detection and monitoring of seagrass.
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paper_title: Mapping Mediterranean seagrasses with Sentinel-2 imagery

paper_content:

Abstract Mediterranean seagrasses have been hailed for their numerous ecosystem services, yet they are undergoing a decline in their coverage. The major complication with resolving this tendency is the sparsity of data on their overall distribution. This study addresses the suitability of the recently launched Sentinel-2 satellite for mapping the distribution of Mediterranean seagrass meadows. A comprehensive methodology is presented which applies atmospheric and analytical water column corrections and compares the performance of three different supervised classifiers. Remote sensing of the Thermaikos Gulf, northwestern Aegean Sea (Greece, eastern Mediterranean Sea) reveals that the utilization of Support Vector Machines on water column corrected reflectances yields best accuracies. Two Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa, cover a total submerged area of 1.48 km2 between depths of 1.4–16.5 m. With its 10-m spatial resolution and 5-day revisit frequency, Sentinel-2 imagery can mitigate the Mediterranean seagrass distribution data gap and allow better management and conservation in the future in a retrospective, time- and cost-effective fashion.
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paper_title: Texture analysis of seabed images: Quantifying the presence of Posidonia oceanica at Palma Bay

paper_content:

An automatic classifier algorithm has been designed to assess the population of Posidonia oceanica over a set of underwater images at Palma Bay. Law's energy filters and statistical descriptors of the Gray Level Co-occurrence Matrix have been use to correctly classify the input image patches in two classes: Posidonia oceanica or not Posidonia oceanica. The input images have been first preprocessed and splitted in three different patch sizes in order to find the best patch size to better classify this seagrass. From all the attributes obtained in these patches, a best subset algorithm has been run to choose the best ones and a decision tree classifier has been trained. The classifier was made by training a Logistic Model Tree from 125 pre-classified images. This classifier was finally tested on 100 new images. The classifier outputs gray level images where black color indicates Posidonia oceanica presence and white no presence. Intermediate values are obtained by overlapping the processed patches, resulting in a smoother final result. This images can be merged in an offline process to obtain density maps of this algae in the sea.
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paper_title: Integrating field survey data with satellite image data to improve shallow water seagrass maps : the role of AUV and snorkeller surveys

paper_content:

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow ( 20 m). Here we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (< 5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photos of the seagrass were collected along transects via snorkelling or an AUV. Photos from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller collected field data sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison to snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5 % of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programs.
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paper_title: Establishing a Baseline for Regional Scale Monitoring of Eelgrass (Zostera marina) Habitat on the Lower Alaska Peninsula

paper_content:

Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.
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paper_title: Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

paper_content:

Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS) to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF) algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV) ensembles for Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), and Bagging (BAG) classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.
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paper_title: Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam

paper_content:

Mui Ca Mau at the southern tip of Vietnam sup- ports a large area of mangroves and has a high value for biodiversity and scenic beauty. This area is affected by ero- sion along the East Sea and accretion along the Gulf of Thai- land, leading to the loss of huge stretches of mangroves along the East Sea and, in some cases, loss of environmental and ecosystem services provided by mangroves. In this study, we used remotely sensed aerial (1953), Landsat (1979, 1988 and 2000) and SPOT (1992, 1995, 2004, 2008, 2009 and 2011) images and the Digital Shoreline Analysis System (DSAS) to quantify the rate of mangrove shoreline change for a 58 yr period. There were 1129 transects sampled at 100 m inter- vals along the mangrove shoreline and two statistical meth- ods, namely end point rate (EPR) and linear regression rate (LRR), were used to calculate the rate of change of mangrove shorelines and distance from 1953 to 2011. The study con- firms that erosion and accretion, respectively, are significant at the East Sea and Gulf of Thailand sides of Mui Ca Mau. The East Sea side had a mean erosion LRR of 33.24 m yr 1 . The accretion trend at the Gulf of Thailand side had an av- erage rate of 40.65 m yr 1 . The results are important in pre- dicting changes of coastal ecosystem boundaries and enable advanced planning for specific sections of coastline, to min- imize or neutralize losses, to inform provincial rehabilitation efforts and reduce threats to coastal development and human safety.
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paper_title: ZooplanktoNet: Deep convolutional network for zooplankton classification

paper_content:

Zooplankton are quite significant to the ocean ecosystem for stabilizing balance of the ecosystem and keeping the earth running normally. Considering the significance of zooplantkon, research about zooplankton has caught more and more attentions. And zooplankton recognition has shown great potential for science studies and mearsuring applications. However, manual recognition on zooplankton is labour-intensive and time-consuming, and requires professional knowledge and experiences, which can not scale to large-scale studies. Deep learning approach has achieved remarkable performance in a number of object recognition benchmarks, often achieveing the current best performance on detection or classification tasks and the method demonstrates very promising and plausible results in many applications. In this paper, we explore a deep learning architecture: ZooplanktoNet to classify zoolankton automatically and effectively. The deep network is characterized by capturing more general and representative features than previous predefined feature extraction algorithms in challenging classification. Also, we incorporate some data augmentation to aim at reducing the overfitting for lacking of zooplankton images. And we decide the zooplankton class according to the highest score in the final predictions of ZooplanktoNet. Experimental results demonstrate that ZooplanktoNet can solve the problem effectively with accuracy of 93.7% in zooplankton classification.
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paper_title: Alternative spatially enhanced integrative techniques for mapping seagrass in Florida's marine ecosystem

paper_content:

Seagrass is an important component of coastal marine ecosystems. Seagrass mapping provides a means for assessing seagrass health by monitoring the spatial distribution and density of seagrass habitat in coastal waters. Recent image processing and satellite technologies present the opportunity to leverage quantitative techniques that have the potential to improve upon traditional photo-interpretation techniques in terms of cost, mapping fidelity, and objectivity. Integrated spatial and spectral processing techniques were identified as an alternative method for mapping seagrass extent and density from an IKONOS satellite image of Springs Coast, Florida. These spatially enhanced integrative mapping techniques objectively standardize seagrass-monitoring efforts and enhance mapping capabilities by characterizing spatial seagrass density gradients. A combination of water column correction, pixel classification, and image segmentation techniques provided a seagrass density index map that represented seagrass density and distribution with high spatial detail and overall accuracy 77% comparable to photo-interpretation techniques. Satellite imagery-based spatially enhanced image processing techniques were found to provide a consistent, quantitative, and cost-effective alternative for seagrass mapping in Springs Coast with the potential to be transferred to other parts of the world. A cost savings analysis concluded that there was a 13% cost saving using satellite photo-interpretation and a 47% cost saving using enhanced satellite classification when compared to aerial photo-interpretation.
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paper_title: Mapping of mangrove extent and zonation using high and low tide composites of Landsat data

paper_content:

Monitoring mangrove health and distribution requires reliable methods that can be undertaken rapidly and at a resolution that optimises costs and accuracy. The Landsat record has been used for this purpose, but its application has been limited by the capacity to provide accurate results that distinguish mangrove from adjoining communities. The Australian Geoscience Data Cube provides a framework for exploring the Landsat record from 1987 onwards, and as pre-processing has already been undertaken there are efficiencies gained using this resource. Using the Data Cube, we exploited the differential spectral signature of mangrove under high tide and low tide conditions at Darwin Harbour, Australia, a relatively stable mangrove ecosystem, using image composites that combined imagery corresponding to the highest 10% and lowest 10% of tides. By applying the automated RandomForest classification technique, we demonstrate the capacity to accurately determine the extent of mangrove zones. Classification identified five mangrove zones: (1) seaward margin dominated by Sonneratia alba, (2) Rhizophora zone dominated by Rhizophora stylosa, (3) tidal flat dominated by Ceriops tagal, (4) landward salt flat and (5) marginal hinterland. Image composites that included high and low tide images achieved the best outcomes with kappa co-efficient of 0.81 and overall accuracy of 82%.
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paper_title: A multi-method approach for benthic habitat mapping of shallow coastal areas with high-resolution multibeam data

paper_content:

The coastal waters of the Maltese Islands, central Mediterranean Sea, sustain a diversity of marine habitats and support a wide range of human activities. The islands’ shallow waters are characterised by a paucity of hydrographic and marine geo-environmental data, which is problematic in view of the requirements of the Maltese Islands to assess the state of their coastal waters by 2012 as part of the EU Marine Strategy Directive. Multibeam echosounder (MBES) systems are today recognised as one of the most effective tools to map the seafloor, although the quantitative characterisation of MBES data for seafloor and habitat mapping is still an underdeveloped field. The purpose of this study is to outline a semi-automated, Geographic Information System-based methodology to map the distribution of habitats in shallow coastal waters using high-resolution MBES data. What distinguishes our methodology from those proposed in previous studies is the combination of a suite of geomorphometric and textural analytical techniques to map specific types of seafloor morphologies and compositions; the selection of the techniques is based on identifying which geophysical parameter would be influenced by the seabed type under consideration. ::: ::: We tested our approach in a 28 km2 area of Maltese coastal waters. Three data sets were collected from this study area: (i) MBES bathymetry and backscatter data; (ii) Remotely Operated Vehicle imagery and (iii) photographs and sediment samples from dive surveys. The seabed was classified into five elementary morphological zones and features – flat and sloping zones, crests, depressions and breaks of slope – using morphometric derivatives, the Bathymetric Position Index and geomorphometric mapping. Segmentation of the study area into seagrass-covered and unvegetated seafloor was based on roughness estimation. Further subdivision of these classes into the four predominant types of composition – medium sand, maerl associated with sand and gravel, seagrass settled on sand and gravel, and seagrass settled on bedrock – was carried out through supervised classifications of morphometric derivatives of the bathymetry and textural indices of backscatter, based on information from training stations. The resulting morphologic and seabed composition maps were combined to plot the distribution of the predominant habitats in the coastal waters offshore NE Malta, some of which are of high conservation value. Ground-truthing of the habitat map using ROV imagery and dive observations confirms that our approach produces a simplified and accurate representation of seafloor habitats while using all the information available within the MBES data sets.
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paper_title: Mapping and Classification of Ecologically Sensitive Marine Habitats Using Unmanned Aerial Vehicle (UAV) Imagery and Object-Based Image Analysis (OBIA)

paper_content:

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resources.
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paper_title: Shallow water substrate mapping using hyperspectral remote sensing

paper_content:

Abstract During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastal region of Western Australia. These data were processed by inversion of a semi-analytical shallow water optical model to classify the substrate. Inputs to the optical model include water column constituent specific inherent optical properties (SIOPs), view and illumination geometry, surface condition (based on wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap data covering approximately 4 km 2 was processed such that each 3×3 m 2 pixel was classed as sand, seagrass, brown algae or various mixtures of these three components. Coincident video data were collected and used to estimate substrate types. We present comparisons of the habitat classifications determined by these two methods and show that the percentage validation of the remotely sensed habitat map may be optimised by selection of appropriate optical model parameters. The optical model was able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentages of approximately 50% for sand and seagrass classification, and 90% for brown algae classification. The semi-analytical model inversion approach to classification can be expected to be applied to any shallow water region where substrate reflectance spectra and SIOPs are known or can be inferred.
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paper_title: Seagrass habitat mapping: how do Landsat 8 OLI, Sentinel-2, ZY-3A, and Worldview-3 perform?

paper_content:

ABSTRACTThis study assessed recently launched multispectral sensors to map seagrass properties for a ~ 150 km2 shallow bank in Moreton Bay, Australia. We utilised a previously developed semi-automated, object-based image analysis classification routine for our comparison, utilising field and image data as input. Field data were collected through georeferenced photograph transects which were analysed for species composition and percentage cover. Field data collection occurred close to image capture from Landsat 8 Operational Land Imager (30 m pixel), Sentinel-2 (10 m pixel), Ziyuan-3A (5 m pixel), and WorldView-3 (2 m pixel) sensors. The output maps had average overall accuracies of 66% for species and 57% for percentage cover maps. The study concluded that all sensors tested were suitable for mapping seagrass meadows in clear shallow waters, but the higher resolution sensors provided more detail and were considered more representative. The choice of sensor would depend upon the extent of the seagrass mead...
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paper_title: Looking for Seagrass: Deep Learning for Visual Coverage Estimation

paper_content:

Underwater videography enables marine researchers to collect enormous amounts of seagrass image data. This collection is fast and cheap but the manual analysis of such data is slow and expensive. Therefore, we propose a machine-learning approach for the automatic seagrass coverage estimation of the sea bottom. Our contribution is the investigation of CNN features to describe patches and superpixels of seagrass. CNN features are the activations of a specific layer in a deep convolutional neural network. We also provide the first public available dataset of seagrass images that can be used as a benchmark for automatic seagrass segmentation. Our best method achieves an accuracy of 94.5% for seagrass segmentation on the provided dataset. Our code and dataset is available on GitHub: https://enviewfulda.github.io/LookingForSeagrass/
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paper_title: Seagrass detection in the mediterranean: A supervised learning approach

paper_content:

Abstract We deal with the problem of detecting seagrass presence/absence and distinguishing seagrass families in the Mediterranean via supervised learning methods. By merging datasets about seagrass presence and other external environmental variables, we develop suitable training data, enhanced by seagrass absence data algorithmically produced based on certain hypotheses. Experiments comparing several popular classification algorithms yield up to 93.4% accuracy in detecting seagrass presence. In a feature strength analysis, the most important variables determining presence–absence are found to be Chlorophyll- α levels and Distance-to-Coast. For determining family, variables cannot be easily singled out; several different variables seem to be of importance, with Chlorophyll- α surpassing all others. In both problems, tree-based classification algorithms perform better than others, with Random Forest being the most effective. Hidden preferences reveal that Cymodocea and Posidonia favor the low, limited-range chlorophyll- α levels ( 3 ), Halophila tolerates higher salinities (>39), while Ruppia prefers euryhaline conditions (37.5–39).
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paper_title: Mapping of seagrass and other benthic habitats in Bolinao, Pangasinan using Worldview-2 satellite image

paper_content:

The objective of this research is to determine the suitability of Worldview-2 high resolution multispectral data in classifying and mapping benthic habitats, specifically seagrass. Worldview-2 offers an increased number of spectral bands for high-resolution image, from the traditional 4bands to 8 bands. It boasts of the ability to enhance mapping and monitoring of benthic habitats with the addition of the Coastal Band. This was investigated in this research using a Worldview-2 image of Bolinao, Pangasinan acquired on March 2010. The study site, Bolinao, has the highest single concentration of seagrass in the northern part of the Philippines. To achieve more accurate results, geometric, atmospheric and water column corrections were applied to the images. For geometric correction, a Differential Global Positioning System Topcon Hiper Ga model receiver was used to obtain highly accurate ground control points. Atmospheric correction was performed in ENVI using the Fast Line-of-Sight Atmospheric Analysis (FLAASH) model. Three water column correction models were applied and compared in this research, Lyzenga's Optical Model, Stumpf's Ratio Model and Simple Radiative Transfer Model. A spectral library was created using in situ reflected spectral radiances on both submerged and emerged vegetation to aid in image classification. Different benthic covers, seagrass, sand, corals and rocks are significantly separable spectrally based on spectral signatures obtained on field using a USB 4000 Fiber Optic Spectrometer. Maximum likelihood supervised classification in ENVI 4.8 is utilized for mapping. Using Worldview 2's coastal, green, yellow and red bands and applying the Simple Radiative Transfer Model produced the highest overall accuracy (88.3%) among the classification results. Using the same bands, Stumpf's Ratio Model produced 87.84% overall accuracy while Lyzenga's optical model achieved 75.54%. Moran's I spatial autocorrelation was implemented to increase the classification accuracy. Using lag 1 slightly increased Stumpf's Model's overall accuracy, from 87.84% to 88.08% while using lags 5 and 10 decreased the overall accuracy with 83.91% and 84.25% respectively.
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paper_title: Observation of typhoon-induced seagrass die-off using remote sensing

paper_content:

Abstract Satellite remote sensing has been used as an effective tool to monitor and manage seagrass beds, but this tool is not appropriate to use in highly turbid waters along the coast of Korea. In this study, satellite data were used to identify the method for detecting seagrass beds in turbid bay waters and describing temporal changes over the past 24 years. Moreover, we found the cause of seagrass die-off phenomenon by observing the long-term satellite images and evaluating the impact of the typhoon on seagrass beds. All seagrass spectra from different sensors (Landsat TM/ETM+, Aster, Spot-4, and Kompsat-2) have low reflectance in green and high in NIR regions, whereas unvegetated seawater has the opposite spectrum features. A training area in the green band and Mahalanobis Distance Classification algorithm was adopted for classification and mapping of seagrass beds. The error matrix using the in situ reference data for Kompsat-2 image classification was 72.9% accurate. The average area of seagrass beds was 4.6 km 2 from May 1990 to January 2012, and it was consistently >4.0 km 2 . A die-off of seagrass beds was observed in September 2012 after Typhoon Bolaven, Tembin, and Sanba consecutively passed the study site. When Typhoon Sanba passed the study site, it had sustained maximum wind speed (147.6 km h −1 ) during low tide, unlike the other typhoons. We found that the water column buffer did not have any effect on resistance of seagrass bed to disturbance, which led to greater damage. Additionally, the three sequential typhoons that passed through during the short time probably produced a cumulative effect. These results allow an understanding of past changes, and reveal the sudden and influential changes in seagrass distribution. As seagrass removal might have negative effect on other associated communities, comprehensive monitoring is required to ensure that disturbed seagrass habitats is recovered naturally.
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paper_title: Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia

paper_content:

The lower reaches of River Kelantan form a vast delta (1200 ha) consisting of bay, mangrove and estuary on the northeast coast of Peninsular Malaysia. The present study was conducted to assess the mangrove vegetation at Tumpat based on ground-truth and remote sensing measurements. The mangroves are composed of several species including Nypa fruticans, Sonneratia caseolaris, Avicennia alba, Rhizophora apiculata, R. mucronata and Bruguiera gymnorrhiza, in order of dominance. The point-centred quarter method (PCQM) was used to estimate the stem density (number of stems/0.1 ha) and basal area (m2/0.1 ha) at selected sites on the ground. Recent high-resolution multispectral satellite data (QuickBird 2006, 2.4 m spatial resolution of the multispectral image) were used to produce land-use/cover classification and Normalized Differential Vegetation Index (NDVI) mapping for the delta. The area statistics reveal that mangroves occupy 339.6 ha, while coconut plantation dominates the vegetation (715.2 ha), followed by settlements (621.6 ha), sandbar (148.4 ha), agriculture (89 ha) and aquaculture (42.7 ha). Although the relationship between the spectral indices and dendrometric parameters was weak, we found a very high significance between the (mean) NDVI and stem density (p = 1.3 × 10-8). The sites with young/growing and also mature trees with lush green cover showed greater NDVI values (0.40-0.68) indicating healthy vegetation, while mature forests under environmental stress due to sand deposition and/or poor tidal inundation showed low NDVI values (0.38-0.47) and an unhealthy situation. Overall, a combination of ground survey and remote sensing provided valuable information for the assessment of mangrove vegetation types (i.e. young/growing or mature forest) and their health in Tumpat, Kelantan Delta.
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paper_title: A bayesian spatial approach for predicting seagrass occurrence

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Abstract We implement a Bayesian spatial approach to predict and map the probability of occurrence of seagrass Posidonia oceanica at high spatial resolution based environmental variables. We found that depth, near-bottom orbital velocities and a spectral pattern of Landsat imagery were relevant environmental variables, although there was no effect of slope or water residence time. We generated a data inventory of P. oceanica samples at Palma Bay, NW Mediterranean, from three main sources: side scan sonar, aerial imagery and a customized drop-camera system. A hierarchical Bayesian spatial model for non-Gaussian data was used to relate presence-absence data of P. oceanica with environmental variables in the presence of spatial autocorrelation (SA). A spatial dimension reduction method, the predictive process approach, was implemented to overcome computational constraints for moderately large datasets. Our results suggest that incorporating spatial random effects removes SA from the residuals and improves model fit compared to non-spatial regression models. The main products of this work were probability and uncertainty model maps, which could benefit seagrass management and the assessment of the ecological status of seagrass meadows.
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paper_title: Simulation of seagrass bed mapping by satellite images based on the radiative transfer model

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Seagrass and seaweed beds play important roles in coastal marine ecosystems. They are food sources and habitats for many marine organisms, and influence the physical, chemical, and biological environment. They are sensitive to human impacts such as reclamation and pollution. Therefore, their management and preservation are necessary for a healthy coastal environment. Satellite remote sensing is a useful tool for mapping and monitoring seagrass beds. The efficiency of seagrass mapping, seagrass bed classification in particular, has been evaluated by mapping accuracy using an error matrix. However, mapping accuracies are influenced by coastal environments such as seawater transparency, bathymetry, and substrate type. Coastal management requires sufficient accuracy and an understanding of mapping limitations for monitoring coastal habitats including seagrass beds. Previous studies are mainly based on case studies in specific regions and seasons. Extensive data are required to generalise assessments of classification accuracy from case studies, which has proven difficult. This study aims to build a simulator based on a radiative transfer model to produce modelled satellite images and assess the visual detectability of seagrass beds under different transparencies and seagrass coverages, as well as to examine mapping limitations and classification accuracy. Our simulations led to the development of a model of water transparency and the mapping of depth limits and indicated the possibility for seagrass density mapping under certain ideal conditions. The results show that modelling satellite images is useful in evaluating the accuracy of classification and that establishing seagrass bed monitoring by remote sensing is a reliable tool.
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paper_title: Consensus forecasting of intertidal seagrass habitat in the Wadden Sea

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1. After the dramatic eutrophication-induced decline of intertidal seagrasses in the 1970s, theWadden Sea has shown diverging developments. In the northern Wadden Sea, seagrass bedshave expanded and become denser, while in the southern Wadden Sea, only small beds withlow shoot densities are found. A lack of documentation of historical distributions hampersconservation management. Yet, the recovery in the northern Wadden Sea provides opportunityto construct robust habitat suitability models to support management.2. We tuned habitat distribution models based on 17 years of seagrass surveys in the northernWadden Sea and high-resolution hydrodynamics and geomorphology for the entire WaddenSea using five machine learning approaches. To obtain geographically transferablemodels, hyperparameters were tuned on the basis of prediction accuracy assessed by non-random,spatial cross-validation. The spatial cross-validation methodology was combined with aconsensus modelling approach.3. The predicted suitability scores correlated amongst each other and with the hold-out observationsin the training area indicating that the models converged and were transferable acrossspace. Prediction accuracy was improved by averaging the predictions of the best models.4. We graphically examined the relationship between the consensus suitability score andindependent presence-only data from outside the training area using the area-adjusted seagrassfrequency per suitability class (continuous Boyce index). The Boyce index was positivelycorrelated with the suitability score indicating the adequacy of the prediction methodology.5. We used the plot of the continuous Boyce index against habitat suitability score to demarcatethree habitat classes – unsuitable, marginal and suitable – for the entire international WaddenSea. This information is valuable for habitat conservation and restoration management.6. Divergence between predicted suitability and actual distributions from the recent past indicatesthat unaccounted factors limit seagrass development in the southern Wadden Sea.7. Synthesis and applications. Our methodology and data enabled us to produce a robust andvalidated consensus habitat suitability model. We identified highly suitable areas where intertidalseagrass meadows may establish and persist. Our work provides scientific underpinningfor effective conservation planning in a dynamic landscape and sets monitoring priorities.
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paper_title: A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

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Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae.
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paper_title: Mapping and modeling eelgrass Zostera marina distribution in the western Baltic Sea

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In the northern hemisphere, eelgrass Zostera marina L. is the most important and widespread seagrass species. Despite its ecological importance, baseline data on eelgrass distribution and abundance are mostly absent, particularly in subtidal areas with relatively turbid waters. Here, we report a combined approach of vegetation mapping in the Baltic Sea coupled to a species distribution model (SDM). Eelgrass cover was mapped continuously in the summers of 2010 and 2011 with an underwater towed camera along ~400 km of seafloor. Eelgrass populated 80% of the study region and occurred at water depths between 0.6 and 7.6 m at sheltered to moderately exposed coasts. Mean patch length was 128.6 m but was higher at sheltered locations, with a maximum of >2000 m. The video observations (n = 7824) were used as empiric input to the SDMs. Using generalized additive models, 3 predictor variables (depth, wave exposure, and slope), which were selected based on Akaike’s information criterion, were sufficient to predict eelgrass presence/absence. Along with a very good overall discriminative ability (area under the receiver-operating characteristic curve ROC/AUC = 0.82), depth (as a proxy for light), wave exposure, and slope contributed 66, 29, and 5%, respectively, to the final model. The estimated total areal extent of eelgrass in the study region amounts to 140.5 km2 and comprises about 11.5% of all known Baltic seagrass beds. The present work is, to the best of our knowledge, the largest study undertaken to date on vegetation mapping and the first to assess distribution of eelgrass quantitatively in the western Baltic Sea.
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paper_title: An improved water-depth correction algorithm for seagrass mapping using hyperspectral data

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Remote detection of seagrass has been limited because of numerous factors including the influence of the water column, which interferes with reflected signals from the seafloor. In a previously published study, a water-depth correction algorithm was developed to improve the detection of underwater vegetation spectral signals. The algorithm successfully corrected laboratory-measured submerged vegetation spectra for water effects, but the water absorption coefficients, derived from the data collected over a white surface, tended to underestimate the actual water absorption when applied to hyperspectral image data. The experimental conditions were modified to reduce the errors associated with the effects of enhanced multi-path scattering, to improve the algorithm using the new empirical data and to apply the algorithm to an airborne hyperspectral image data obtained over Halodule wrightii seagrass beds at Grand Bay National Estuarine Research Reserve, Mississippi, USA. The water absorption and scattering fac...
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paper_title: Mapping seagrass and colonized hard bottom in Springs Coast, Florida using WorldView-2 satellite imagery

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Abstract The offshore extent of seagrass habitat along the West Florida (USA) coast represents an important corridor for inshore-offshore migration of economically important fish and shellfish. Surviving at the fringe of light requirements, offshore seagrass beds are sensitive to changes in water clarity. Beyond and intermingled with the offshore seagrass areas are large swaths of colonized hard bottom. These offshore habitats of the West Florida coast have lacked mapping efforts needed for status and trends monitoring. The objective of this study was to propose an object-based classification method for mapping offshore habitats and to compare results to traditional photo-interpreted maps. Benthic maps were created from WorldView-2 satellite imagery using an Object Based Image Analysis (OBIA) method and a visual photo-interpretation method. A logistic regression analysis identified depth and distance from shore as significant parameters for discriminating spectrally similar seagrass and colonized hard bottom features. Seagrass, colonized hard bottom and unconsolidated sediment (sand) were mapped with 78% overall accuracy using the OBIA method compared to 71% overall accuracy using the photo-interpretation method. This study suggests an alternative for mapping deeper, offshore habitats capable of producing higher thematic and spatial resolution maps compared to those created with the traditional photo-interpretation method.
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paper_title: Damage to seagrass and seaweed beds in Matsushima Bay, Japan, caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011

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Damage to seagrass and seaweed beds caused by the huge tsunami of the Great East Japan Earthquake on 11 March 2011 was investigated in Matsushima Bay, Miyagi Prefecture, Japan, by comparing satellite images taken before the tsunami in November 2009 and after the tsunami in May 2011. The results showed that the tsunami destroyed 220 ha of seagrass and seaweed beds, from 320 ha in 2009 to 100 ha just after the tsunami. Zostera marina beds on the sandy and muddy bottom were rather severely damaged compared with brown seaweed beds of Sargassum horneri on the rocky substrate. Substrates of the beds and directions of the tsunami seem to be responsible for the magnitude of the damage to the beds. Maps of the spatial distributions of the beds before and after the tsunami can provide information for the restoration of seagrass and seaweed beds as natural infrastructures of coastal fisheries resources.
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paper_title: Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

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Remote sensing is widely used in coastal management. Lyzenga's model has been traditionally used to explain the relationship between bottom surface reflectance and the radiance level measured by satellite. Due to its central assumption, this model lacks accuracy compared with the other radiative transfer models. Nonetheless, it enables, with a single and simple equation, representation of the multiple optical processes taking place in coastal areas. Mapping processes associated with this model may include radiometric correction, a technique previously pointed out as a major driver of mapping accuracy. Radiometric correction is generally based on a depth-invariant index, efficient for clear waters (Jerlov water type I to II) but largely unsuitable when transparency decreases (Jerlov water type II to III). In order to overcome this problem, we developed a new index for radiometric correction, which combines bathymetry data with attenuation coefficients. The improved efficiency of our model with regard to the traditional depth invariant index was demonstrated through two case studies: Funakoshi Bay (Japan; Jerlov water type II) and the Gabes Gulf part located off Mahares (Tunisia; Jerlov water type II to III).
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paper_title: Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

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The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100m2) scales. However, landscape scale (>100km2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142km2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.
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paper_title: Improving the accuracy of Multispectral-based benthic habitats mapping using image rotations: the application of Principle Component Analysis and Independent Component Analysis

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AbstractThe low number of water penetration bands in multispectral images limits the maximum descriptive resolution and the accuracy of the resulting benthic habitats maps, especially at higher levels of benthic habitats scheme complexities. This research aimed at improving the accuracy of benthic habitats mapping by exploiting the spectral performance of multispectral images using image rotation techniques, which is very beneficial for fast, accurate, and repeatable mapping. Kemujan Island as part of the Karimunjawa archipelago in Indonesia is selected as the study area. Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were applied on Worldview-2 prior to image classification. The inputs for PCA and ICA are deglint bands and water column-corrected bands. Field benthic data collected from photo-transect technique were used to train the rotated datasets in the classification process and to assess the accuracy of the resulting benthic habitat maps. Three levels of benthic habitats...
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paper_title: Spatial and temporal variability of seagrass at Lizard Island, Great Barrier Reef

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Increasing threats to natural ecosystems from local and global stressors are reinforcing the need for baseline data on the distribution and abundance of organisms. We quantified spatial and/or temporal patterns of seagrass distribution, shoot density, leaf area index, biomass, productivity, and sediment carbon content in shallow water (0-5 m) at Lizard Island, Great Barrier Reef, Australia, in field surveys conducted in December 2011 and October 2012. Seagrass meadows were mapped using satellite imagery and field validation. A total of 18.3 ha of seagrass, composed primarily of Thalassia hemprichii and Halodule uninervis, was mapped in shallow water. This was 46% less than the area of seagrass in the same region reported in 1995, although variations in mapping methods may have influenced the magnitude of change detected. There was inter-annual variability in shoot density and length, with values for both higher in 2011 than in 2012. Seagrass properties and sediment carbon content were representative of shallow-water seagrass meadows on a mid-shelf Great Barrier Reef island. The data can be used to evaluate change, to parameterize models of the impact of anthropogenic or environmental variability on seagrass distribution and abundance, and to assess the success of management actions.
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paper_title: Towards Posidonia Meadows Detection, Mapping and Automatic recognition using Unmanned Marine Vehicles

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Abstract This paper reports the development of a new methodology for automatic detection and mapping of underwater vegetation by means of highly autonomous marine robotic platforms. In particular, the work describes the exploitation of a Remotely Operated Vehicle (ROV), equipped with a multi-parametric sensors package, for the exploration and characterization of sea-bottoms interested by the presence of the Posidonia oceanica seagrass, which represents a valuable indicator of the environmental health. The proposed methodology relies on the systematic exploration of the sea-bottom by means of the ROV acquiring acoustic data and video imagery of the seabed, in order to reconstruct a 2.5D model of the environment (i.e. an elevation map of the sea-bottom). The data collection is achieved by the employment of a single beam echosounder for seabed range measurements and a down-looking underwater camera. Furthermore, an acoustic data procedural analysis is developed to automatically detect the Posidonia presence, so that in future works it will be possible to operate also in low-visibility conditions. Data acquisition was carried out over different seafloor types in coastal area near Biograd Na Moru (Croatia) and the results are reported in the paper.
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paper_title: Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon, FL

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Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, SlopeRED and SlopeNIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.
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paper_title: A Systematic Approach toward Detection of Seagrass Patches from Hyperspectral Imagery

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Changes in the coverage of seagrass populations are considered to be a key indicator of the health and biodiversity of coastal ecosystems. The overall extent of seagrass meadows is declining worldwide, primarily due to human-induced disturbances. In Tampa Bay, Florida, a nearly 35% loss of seagrass coverage occurred from the 1950s to the 2000s. This decline was primarily due to the effects of human population growth. To examine closely the continuing declining trend of this major indicator of the health of coastal ecosystems, a systematic approach for extracting seagrass patches using EO-1 Hyperion hyperspectral imagery has been developed. In our previous work, a method based on Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) was developed and successfully applied to military object recognition using hyperspectral and multispectral imagery. It showed great potential in target detection of hyperspectral imagery. In this work, it is extend and applied in seagrass extraction. This study i...
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paper_title: Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977–2010)

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Abstract Mangrove forests, which are found in saline coastal environments around the tropical and subtropical latitudes, are among the most productive terrestrial ecosystems in the world and provide valuable ecological and societal goods and services. The objective of this work was to characterize the spatio-temporal changes in mangrove distribution and fragmentation patterns in the Zhanjiang National Mangrove Forest Nature Reserve, Guangdong province of Southern China, from 1977 through 2010. In addition, a major goal was to assess the socio-economic drivers contributing to the chronic changes taking place within and around the mangrove reserve. Land use and land cover data sets were generated for the reserve for multiple years via unsupervised classification using Landsat time series images. Mangrove fragmentation patterns were then assessed with a fragmentation model. Results revealed that the mangrove spatial extent decreased sharply during the period from 1977 to 1991 due to deforestation caused by diverse development programs, particularly shrimp farming. Afterwards, there was a continuous increase in mangrove extent from 1991 to 2010 due to afforestation and conservation efforts. The mangrove fragmentation trends depicted by the fragmentation model had a high degree of correlation with the observed areal changes. Additionally, the recorded dynamics of the local biodiversity (mainly birds) were consistent with the mangrove ecosystem fragmentation trends over time, and different fragmentation components, including interior, perforated and edge, had distinct impacts on the local mangrove-dependent biodiversity. The most effective way to protect and expand the current mangroves include the following: (1) establishment of mangrove natural reserves, (2) forceful implementation of regulations, (3) establishment of educational programs related to mangrove management, (4) deepening international exchanges and cooperation and (5) increasing the transparency of the project implementation process. Together such management measures will lead towards responsible and sustainable utilization of the mangrove ecosystems.
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paper_title: Towards Visual Detection, Mapping and Quantification of Posidonia Oceanica using a Lightweight AUV*

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Abstract: Posidonia Oceanica (P.O.) is a Mediterranean endemic seagrass strongly related to the health of the coastal ecosystems. Monitoring the presence and state of P.O. is essential not only for safeguarding the shallow-water life diversity, but also as an indicator of the water quality. Nowadays, the control of P.O. is done by divers in successive missions of a duration limited by the capacity of the scuba tanks. This paper proposes the application of robotic and computer vision technologies to upgrade these current methods, namely: 1) employing a lightweight Autonomous Underwater Vehicle (AUV) equipped with cameras to survey and image marine areas, 2) the automatic discrimination of P.O. from the rest of the seafloor, using several techniques based on image texture analysis and machine learning , and, 3) the fast computation of 2D maps (photo-mosaics) of the surveyed areas from all the images included in the grabbed video sequences; these mosaics are extremely useful to measure the real extension of the meadows and some of the descriptors needed for a biological analysis. Experiments conducted with an AUV in several marine areas of Mallorca reveal promising results in the discrimination of different patterns of P.O. and in the construction of highly realistic photo-mosaics of the surveyed areas.
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paper_title: Change Detection of Land use and Riverbank in Mekong Delta, Vietnam using Time Series Remotely Sensed Data

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Abstract: By taking the advantages of remote sensing technology, changes of land cover in Ca Mau Peninsular (the Southern part of the Mekong delta) and erosion of riverbank of the Mekong River have been defined. Change detection using time—series optical and radar satellite images (Landsat, SPOT and Radarsat) is the main approach in this study. Typical land cover in Ca Mau Peninsular is mangrove; its changes are observable by analysing imageries from 1973 to 2008. The results showed that more than half of the mangrove area was converted into shrimp farms. This made forest cover severely declined. Riverbank of the Mekong River also was detected to be eroded and accreted in a complicated form. Locations along the left and right banks of Tien and Hau river are defined as hot spots that are seriously suffered erosions. Topographic maps in 1966–1968 were taken into account as base line data, which was analyzed along with satellite images from 1989 to 2009. In addition, the Digital Shoreline Analysis System (DS...
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paper_title: Remote sensing of seagrasses in a patchy multi-species environment

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We tested the utility of IKONOS satellite imagery to map seagrass distribution and biomass in a 4.1 km2 area around Chumbe Island, Zanzibar, Tanzania. Considered to be a challenging environment to map, this area is characterized by a diverse mix of inter-and subtidal habitat types. Our mapped distribution of seagrasses corresponded well to field data, although the total seagrass area was underestimated due to spectral confusion and misclassification of areas with sparse seagrass patches as sparse coral and algae-covered limestone rock. Seagrass biomass was also accurately estimated (r2 = 0.83), except in areas with Thalassodendron ciliatum (r2 = 0.57), as the stems of T. ciliatum change the relationship between light interception and biomass from that of other species in the area. We recommend the use of remote sensing over field-based methods for seagrass mapping because of the comprehensive coverage, high accuracy and ability to estimate biomass. The results obtained with IKONOS imagery in our complex study area are encouraging, and support the use of this data source for seagrass mapping in similar areas.
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paper_title: Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

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AbstractInformation of seagrass LAI is still lacking in most parts of the world due to the high cost of comprehensive mapping. In this paper, we described the use of remote sensing as the cost and time effective solution to perform continuous seagrass LAI mapping, and discussed the issues and difficulties encountered during the mapping. ASTER VNIR and ALOS AVNIR-2 were used to perform the mapping. We proposed at life-form seagrass classification scheme to accommodate the low accuracy of at species level mapping. We also developed sampling mapping unit consist of several factors affecting the distribution of seagrass LAI. The results showed that sensor, method, and environmental limitation contribute to the low accuracy of seagrass LAI mapping.
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paper_title: Improved seagrass mapping using linear spectral unmixing of aerial photographs

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Abstract Mapping of seagrass is challenging, particularly in areas where seagrass cover ranges from extensive, continuous meadows to aggregations of patchy mounds often no more than a meter across. Manual delineation of seagrass habitat polygons through visual photointerpretation of high resolution aerial imagery remains the most widely adopted approach for mapping seagrass extent but polygons often include unvegetated gaps. Although mapped polygon data exist for many estuaries, these are likely insufficient to accurately characterize spatial pattern or estimate area actually occupied by seagrass. We evaluated whether a linear spectral unmixing (LSU) classifier applied to manually-delineated seagrass polygons clipped from digital aerial images could improve mapping of seagrass in North Carolina. Representative seagrass endmembers were chosen directly from images and used to unmix image-clipped polygons, resulting in fraction planes (maps) of the proportion of seagrass present in each image pixel. Thresholding was used to generate seagrass maps for each pixel proportion from 0 (no thresholding, all pixel proportions included) to 1 (only pixels having 100% seagrass) in 0.1 increments. The optimal pixel proportion for identifying seagrass was assessed using Euclidean distance calculated from Receiver Operating Characteristic (ROC) curves and overall thematic accuracy calculated from confusion matrices. We assessed overall classifier performance using Kappa statistics and Area Under the (ROC) Curve (AUC). We compared seagrass area calculated from each threshold map to the total area of the corresponding manually-delineated polygon. LSU effectively classified seagrass and performed better than a random classification as indicated by high values for both Kappa statistics (0.72–98) and AUC (0.80–0.99). The LSU classifier effectively distinguished between seagrass and bare substrate resulting in fine-scale seagrass maps with overall thematic accuracies that exceeded our expected accuracy target of 85% (range: 86.3–99.0%) and were comparable to those reported from previous seagrass mapping studies utilizing aerial image spectral data. The pixel proportion producing seagrass maps with highest accuracy varied among sites (range: 0 to 0.5). The optimal pixel proportion determined from Euclidean distance varied among sites (range: 0.2 to 0.6) and differed from those having highest accuracy. Importantly, the classifier identified small patches of seagrass, resulting in seagrass area estimates that were 2–94% lower than the area of the corresponding habitat polygon. We conclude that where seagrass polygon data exist, LSU can be used together with photointerpretation to generate spatially resolved maps suitable for analysis of seagrass spatial configuration and provide improved estimates of actual seagrass acreage.
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paper_title: Satellite Images for Monitoring Mangrove Cover Changes in a Fast Growing Economic Region in Southern Peninsular Malaysia

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Effective monitoring is necessary to conserve mangroves from further loss in Malaysia. In this context, remote sensing is capable of providing information on mangrove status and changes over a large spatial extent and in a continuous manner. In this study we used Landsat satellite images to analyze the changes over a period of 25 years of mangrove areas in Iskandar Malaysia (IM), the fastest growing national special economic region located in southern Johor, Malaysia. We tested the use of two widely used digital classification techniques to classify mangrove areas. The Maximum Likelihood Classification (MLC) technique provided significantly higher user, producer and overall accuracies and less “salt and pepper effects” compared to the Support Vector Machine (SVM) technique. The classified satellite images using the MLC technique showed that IM lost 6740 ha of mangrove areas from 1989 to 2014. Nevertheless, a gain of 710 ha of mangroves was observed in this region, resulting in a net loss of 6030 ha or 33%. The loss of about 241 ha per year of mangroves was associated with a steady increase in urban land use (1225 ha per year) from 1989 until 2014. Action is necessary to protect the existing mangrove cover from further loss. Gazetting of the remaining mangrove sites as protected areas or forest reserves and introducing tourism activities in mangrove areas can ensure the continued survival of mangroves in IM.
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paper_title: Mapping detailed seagrass habitats using satellite imagery

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Essentially, maintenance of adequate seagrass cover is intimately related to coastal ecosystem health and thus monitoring of seagrass habitats is a priority of coastal managers. Remote sensing techniques, especially satellite remote sensing, can provide seagrass habitat information spatially and temporally. In this study, we propose to evaluate and compare the capability of four satellite sensors' (Landsat TM, EO-1 ALI and Hyperion and IKONOS) data for mapping detailed seagrass habitats. After depth-invariant bands were created from the four sensors' data, a maximum likelihood classifier was used to classify the submerged aquatic vegetation (SAV) cover percentage into 3 classes and 5 classes in the study area. The SAV mapping results indicate that Hyperion sensor has produced the best mapping results of seagrass habitats in the two classification schemes: 3-class (Overall accuracy (OAA)=96%, Kappa=0.936) and 5-class (OAA=79%, Kappa=0.730). ALI outperformed TM for mapping SAV due to its additional blue band.
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paper_title: A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI/Hyperion images

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Abstract Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. Remote sensing techniques can help collect spatial and temporal information about seagrass resources. In this study, we evaluate a protocol that utilizes image optimization algorithms followed by atmospheric and sunglint corrections to the three satellite sensors [Landsat 5 Thematic Mapper (TM), Earth Observing-1 (EO-1) Advanced Land Imager (ALI) and Hyperion (HYP)] and a fuzzy synthetic evaluation technique to map and assess seagrass abundance in Pinellas County, FL, USA. After image preprocessed with image optimization algorithms and atmospheric and sunglint correction approaches, the three sensors’ data were used to classify the submerged aquatic vegetation cover (%SAV cover) into 5 classes with a maximum likelihood classifier. Based on three biological metrics [%SAV, leaf area index (LAI), and Biomass] measured from the field, nine multiple regression models were developed for estimating the three biometrics with spectral variables derived from the three sensors’ data. Then, five membership maps were created with the three biometrics along with two environmental factors (water depth and distance-to-shoreline). Finally, seagrass abundance maps were produced by using a fuzzy synthetic evaluation technique and five membership maps. The experimental results indicate that the HYP sensor produced the best results of the 5-class classification of %SAV cover (overall accuracy = 87% and Kappa = 0.83 vs. 82% and 0.77 by ALI and 79% and 0.73 by TM) and better multiple regression models for estimating the three biometrics ( R 2 = 0.66, 0.62 and 0.61 for %SAV, LAI and Biomass vs. 0.62, 0.61 and 0.55 by ALI and 0.58, 0.56 and 0.52 by TM) for creating seagrass abundance maps along with two environmental factors. Combined our results demonstrate that the image optimization algorithms and the fuzzy synthetic evaluation technique were effective in mapping detailed seagrass habitats and assessing seagrass abundance with the 30-m resolution data collected by the three sensors.
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paper_title: Monitoring Mangrove Forest Changes in Cat Ba Biosphere Reserve Using ALOS PALSAR Imagery and a GIS-Based Support Vector Machine Algorithm

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Cat Ba is one of the most well-known islands located in North Vietnam, which has been recognized as a biosphere reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) since 2004. Despite the large potential carbon stocks in mangrove forests of Cat Ba, the mangrove ecosystem of this island has suffered severe deforestation and forest degradation due to the conversion to shrimp aquaculture. Monitoring mangrove forest changes plays an important role for effective mangrove conservation and management. The objectives of this study were to map the spatial distribution of mangrove forest and to assess their changes between 2010 and 2015 in Cat Ba Biosphere Reserve, Hai Phong city of Vietnam using ALOS PALSAR data and a GIS-based support vector machine algorithm. For this purpose, ALOS PALSAR imagery for the above period and GIS data were collected. Then, spatial distributions of mangroves were derived using the support vector machine classifier. The results showed that the ALOS-2 PALSAR for 2015 achieves the overall accuracy of 85% and the kappa coefficient of 0.81, compared with those of 81% and 0.77, respectively from the ALOS PALSAR for 2010. The mangrove forest areas in the Cat Ba Biosphere Reserve, Vietnam decreased by 15% from 2010 to 2015. This research shows the potential use of ALOS PALSAR data combined with machine learning techniques in monitoring mangrove forest changes in tropical and semi-tropical climates.
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paper_title: The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: case study of Kien Giang coast, Vietnam

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Fringe mangroves are recognised as a highly valuable resource due to their provision of multiple ecosystem services. However, accelerating human-pressures on mangrove forests are resulting in the loss of key mangrove ecosystem services. In this paper, we assessed spatial-temporal changes in the extent and width of fringe mangroves, and changes in adjacent land use. The study focused on the Kien Giang Province, Vietnam, for the period 1989-2009. The total mangrove extent was estimated at seven points in time using four Landsat TM images (1989, 1992, 2003, and 2006) and three SPOT images (1995, 2003 and 2009). Supervised classification was conducted for image classification and mapping changes using a Maximum Livelihood Classifier approach. Changes in mangrove width were calculated for 2003 and 2009 using the Digital Shoreline Analysis Application.An assessment of the four Landsat TM images showed a significant decrease in mangrove extent for the periods 1989-1992 (-2.7% yr-1) and 2003-2006 (-2.1% yr-1), while a significant increase in mangrove extent was observed during 1992-2003 (0.7% yr-1). SPOT images were used to classify shoreline land cover into four types, namely mangroves; shrimp farms; built-up areas; and bare and wet ground areas. The total loss of fringe mangrove extent was significantly correlated with the increase in extent of other land covers, especially shrimp farms and bare and wet ground, during the period of 2003-2009.The overall accuracy assessment of the classification using 2003 and 2009 SPOT 5 images was 90.1% and 90.5% respectively. The overall accuracy assessment of the 2003 Landsat image was 92.1%. This accuracy in classification of mangroves and non-mangroves showed that use of Landsat images was adequate for assessing spatial-temporal mangrove dynamics when higher spatial resolution satellite images were limited. The results highlight the dynamic nature of fringe mangrove communities in the region, and provide data useful for informing shoreline mangrove rehabilitation, conservation efforts, and national monitoring programs for estimating mangrove values of carbon sequestration and shoreline protection functions in Vietnam.
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paper_title: Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques

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Accurate and informative vegetation maps are in urgent demand to support the Kissimmee-Okeechobee-Everglades ecosystem restoration project in South Florida. In this study, we evaluated the applicability of fine spatial resolution hyperspectral data collected from the HyMap sensor for both community- and species-level vegetation mapping. Informative and accurate vegetation maps were produced by combining machine learning methods (Support Vector Machines (SVM) and Random Forest (RF)), object-based image analysis techniques, and Minimum Noise Fraction (MNF) data transformation. An overall accuracy of 90% was obtained in discriminating 14 vegetation communities. Classification of a large number of species is also promising. An overall accuracy of 85% was achieved in identifying 55 species using a SVM model. The results indicate that fine spatial resolution hyperspectral data classification using such automated procedure has great potential to replace the manual interpretation of aerial photos for vegetation mapping in heterogeneous wetland ecosystems.
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paper_title: Mapping specific habitats from remotely sensed imagery: Support vector machine and support vector data description based classification of coastal saltmarsh habitats

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Abstract Remote sensing has considerable potential for the provision of information on the distribution of habitats that may be used to inform a variety of activities such as those required through the European Union's Habitats Directive. Such programmes are often resource-limited with a need for innovative methods that optimise resource use. This paper explores two approaches to resource savings when mapping habitats from remotely sensed imagery. The first approach realises that in an area of study often interest is focused on a specific habitat with the remaining land cover classes in the region of no importance. In such circumstances conventional statistical supervised classification analyses may be inefficient and yield a map of sub-optimal accuracy. The second approach seeks to further reduce the training requirements of a supervised classification. For this support vector machine (SVM) based approaches to classification are explored to map coastal saltmarsh habitats in North Norfolk, UK from a Landsat Enhanced Thematic Mapper (ETM+) image. A series of classifications using SVM based approaches and the Maximum Likelihood classifier (MLC) were undertaken. Classification accuracies were significantly higher using the SVM based approaches (e.g., 92.0% overall accuracy) than the MLC (64.8% overall accuracy). The SVM based classifications were demonstrated to be attractive for mapping a priority habitat in that the focus is on the habitat of interest to be mapped throughout the classification process resulting in a reduced need for training data. Moreover, it was shown that this can be further optimised through the use of intelligent training. This approach, based on the use of the support vector data description (SVDD), saved resource requirements even further in that training data were required only for the class of interest and yet still obtained high classification accuracies (95.2% overall accuracy) . The wider adoption of SVM based classification of remotely sensed imagery is advocated for use in conservation activities.
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paper_title: Remote sensing of salt marsh vegetation in the first four proposed Thematic Mapper bands

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Abstract The potential for using data collected in the first four bands of the proposed thematic mapper for monitoring intertidal salt marsh vegetation is assessed, using ground spectral data collected with a portable multiband radiometer. Results from a field study in Chichester Harbour, Southern England, show that certain species can be separated using transformations of these data. The estimation of above ground biomass from ground spectral data was not possible for Spartina × townsendii agg., due to the combined effects of mixtures of different substrate types and mixtures of live and dead vegetation within the field of view of the radiometer. However, biomass estimation was possible for both Zostera marina L. and Fucus vesiculosus L. The relationships between biomass and bidirectional reflectance for these species followed a similar pattern, despite Fucus vesiculosus having carotenoids rather than chlorophylls as the dominant leaf pigments.
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paper_title: Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery

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Freshwater wetlands are highly diverse, spatially heterogeneous, and seasonally dynamic systems that present unique challenges to remote sensing. Maximum likelihood and support vector machine-supervised classification were compared to map wetland plant species distributions in a deltaic environment using high-resolution WorldView-2 satellite imagery. The benefits of the sensor’s new coastal blue, yellow, and red-edge bands were tested for mapping coastal vegetation and the eight-band results were compared to classifications performed using band combinations and spatial resolutions characteristic of other available high-resolution satellite sensors. Unlike previous studies, this study found that support vector machine classification did not provide significantly different results from maximum likelihood classification. The maximum likelihood classifier provided the highest overall classification accuracy, at 75%, with user’s and producer’s accuracies for individual species ranging from 0% to 100%. Overall,...
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paper_title: Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands, India, using IRS LISS III satellite images

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A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.
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paper_title: Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

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The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (≈200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.
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paper_title: Separation of Ground and Low Vegetation Signatures in LiDAR Measurements of Salt-Marsh Environments

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Light detection and ranging (LiDAR) has been shown to have a great potential in the accurate characterization of forest systems; however, its application to salt-marsh environments is challenging because the characteristic short vegetation does not give rise to detectable differences between first and last LiDAR returns. Furthermore, the lack of precisely identifiable references (e.g., buildings, roads, etc.) in marsh areas makes the registration and bias correction of the LiDAR data much more difficult than in conventional urban- or forested-area applications. In this paper, we introduce reliable methods to remove random and systematic errors and to register raw data, as well as a new procedure, to determine the optimal filter window size to separate ground and canopy returns. A limited amount of field observations is used to determine the size of the filtering window which produces the minimally biased estimates of the digital terrain model (DTM). The digital surface model (DSM, representing the canopy top) is then obtained in a similar manner, and the digital vegetation model (DVM, representing the vegetation height) is computed as the difference between the DSM and the DTM. We apply this procedure to a study marsh within the Venice Lagoon, Italy, and obtain a high-accuracy DTM. The error (z_LiDAR-z_field) is 2.2 cm, with a standard deviation of 6.4 cm. The comparison of the estimated DVM with field observations shows an underestimation of the height of the canopy top (17.7 cm, on average). The height of the lowest canopy elements (e.g., basal leaves), however, is significantly correlated to the LiDAR-derived DVM, showing that this contains useful information on the canopy structure.
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paper_title: Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing

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Tidal marshes are characterized by complex patterns both in their geomorphic and ecological features. Such patterns arise through the elaboration of a network structure driven by the tidal forcing and through the interaction between hydrodynamical, geophysical and ecological components (chiefly vegetation). Intertidal morphological and ecological structures possess characteristic extent (order of kilometers) and small-scale features (down to tens of centimeters) which are not simultaneously accessible through field observations, thus making remote sensing a necessary observation tool. This paper describes a set of remote sensing observations from several satellite and airborne platforms, the collection of concurrent ground reference data and the vegetation distributions that may be inferred from them, with specific application to the Lagoon of Venice (Italy). The data set comprises ROSIS, CASI, MIVIS, IKONOS and QuickBird acquisitions, which cover a wide range of spatial and spectral resolutions. We show that spatially-detailed and quantitatively reliable vegetation maps may be derived from remote sensing in tidal environments through unsupervised (K-means) and supervised algorithms (Maximum Likelihood and Spectral Angle Mapper). We find that, for the objective of intertidal vegetation classification, hyperspectral data contain largely redundant information. This in particular implies that a reduction of the spectral features is required for the application of the Maximum Likelihood classifier. A large number of experiments with different feature extraction/selection algorithms show that the use of four bands derived from Maximum Noise Fraction transforms and four RGBI broad bands obtained by spectral averaging yield very similar classification performances. The classifications from hyperspectral data are somewhat superior to those from multispectral data, but the close performance and the results of the features reduction experiments show that spatial resolution affects classification accuracy much more importantly than spectral resolution. Monitoring schemes of tidal environment vegetation may thus be based on high-resolution satellite acquisitions accompanied by systematic ancillary field observations at a relatively limited number of reference sites, with practical consequences of some relevance.
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paper_title: Twelve-Year Mapping and Change Analysis of Eelgrass (Zostera marina) Areal Abundance in Massachusetts (USA) Identifies Statewide Declines

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In 1994, 1995, and 1996, seagrasses in 46 of the 89 coastal embayments and portions of seven open-water near-shore areas in Massachusetts were mapped with a combination of aerial photography, digital imagery, and ground truth verification. In the open-water areas, 9,477.31 ha of seagrass were identified, slightly more than twice the 4,846.2 ha detected in the 46 coastal embayments. A subset of the 46 embayments, including all regions of the state were remapped in 2000, 2001, and 2002 and again in 2006 and 2007. We detected a wide range of changes from increases as high as 29% y−1 in Boston Harbor to declines as large as −33% y−1 in Salem Harbor. One embayment, Waquoit Bay, lost all of its seagrass during the mapping period. For the 12-year change analysis representing all geographic regions of the state, only three embayments exhibited increases in seagrass coverage while 30 of the original 46 embayments showed some indication of decline. For the decadal period, rates of decline in the individual embayments ranged from −0.06% y−1 to as high as −14.81% y−1. The median rate of decline by region ranged from −2.21% y−1 to −3.51% y−1 and was slightly less than the recently reported global rate of decline for seagrasses (−3.7% y−1). Accounting for the gains in three of the embayments, 755.16 ha (20.6%) of seagrass area originally detected was lost during the mapping interval. The results affirm that previously reported losses in a few embayments were symptomatic of more widespread seagrass declines in Massachusetts. State and Federal programs designed to improve environmental quality for conservation and restoration of seagrasses in Massachusetts should continue to be a priority for coastal managers.
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paper_title: Potential uses of TerraSAR-X for mapping herbaceous halophytes over salt marsh and tidal flats

paper_content:

Abstract This study presents a method and application results of mapping different halophytes over tidal flats and salt marshes using high resolution space-borne X-band synthetic aperture radar (SAR) that has been rarely used for salt marsh mapping. Halophytes in a salt marshes are sensitive to sea-level changes, sedimentation, and anthropogenic modifications. The alteration of the demarcations among halophyte species is an indicator of sea level and environmental changes within a salt marsh. The boundary of an herbaceous halophyte patch is, however, difficult to determine using remotely sensed data because of its sparseness. We examined the ecological status of the halophytes and their distribution changes using TerraSAR-X and optical data. We also determined the optimum season for halophyte mapping. An annual plant, Suaeda japonica ( S. japonica ), and a typical perennial salt marsh grass, Phragmites australis ( P. australis ), were selected for halophyte analysis. S. japonica is particularly sensitive to sea level fluctuation. Seasonal variation for the annual plant was more significant (1.47 dB standard deviation) than that for the perennial grass, with a pattern of lower backscattering in winter and a peak in the summer. The border between S. japonica and P. australis was successfully determined based on the distinctive X-band radar backscattering features. Winter is the best season to distinguish between the two different species, while summer is ideal for analyzing the distribution changes of annual plants in salt marshes. For a single polarization, we recommend using HH polarization, because it produces maximum backscattering on tidal flats and salt marshes. Our results show that high resolution SAR, such as TerraSAR-X and Cosmo-SkyMed, is an effective tool for mapping halophyte species in tidal flats and monitoring their seasonal variations.
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paper_title: Application of Landsat images to seagrass areal cover change analysis for Lawas, Terengganu and Kelantan of Malaysia

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Seagrass mapping and monitoring using remote sensing has been established using various imageries and mapping methods, ranging from complex optical modeling to manual interpretations. Few attempts have been made to find relations between spatiotemporal changes within seagrass habitats and their surrounding environment in the mapping efforts. In this study, issues of spatiotemporal changes in seagrass habitat including water quality, and causes and processes of seagrass degradation were addressed through assessing the ability of integrated manual enhancement and seed pixel growing techniques on multi-date Landsat images for mapping and monitoring seagrass resources. This integrated approach was tested on a total of forty-nine Landsat 5, 7 and 8 images of the seagrass meadow spatial distribution and cover change analysis for Punag-Sari estuary (Lawas), Pengkalan Nangka (Kelantan) lagoon, and Paka (Terengganu) lagoon of Malaysia. The resulted maps demonstrated the ability of Landsat images for producing spatially extensive maps and allowed quantitative estimation of seagrass coverage, accretion/erosion, and describe changes in noticeable areas. The findings indicated that a noticeable loss of seagrass habitats at varying magnitude occurred between 2000 and 2014 for Punang-Sari, between 1998 and 2014 for Pegkalan Nangka, and between 1988 and 2014 for Paka. The natural event mainly sand shifting was the main cause of seagrass habitat loss for Punang-Sari Lawas. Coastline change was identified as the most significant factor that caused seagrass spatial cover loss of the Pengkalan Nangka lagoon. The seagrass meadows of Pengkalan Nangka and Paka were greatly affected by natural events including floods, and local human-induced interferences such as dredging, and sand mining. The mapping approach and the map products presented in this paper will be a useful information source for the coastal management agencies involved in monitoring and management programs.
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paper_title: Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

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Abstract Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.
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paper_title: Multi-temporal change detection of seagrass beds using integrated Landsat TM/ETM +/OLI imageries in Cam Ranh Bay, Vietnam

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Abstract Seagrass beds comprise a unique marine ecosystem that acts as a biofilter in marine environments and serves as a spawning ground and nursery for various species of fish. Long-term monitoring of seagrass beds is critical to understanding the dynamic relationships between the ecosystems and the stresses from natural systems and society. This study investigated temporal changes of seagrass beds in Cam Ranh Bay (CRB), Vietnam using multi-temporal Landsat data from 1996 to 2015. The data were processed through 5 main steps including: (1) image preprocessing to convert Landsat data to the top of atmosphere reflectance (TOA) and to correct atmospheric effects, (2) water column correction to eliminate effects on remotely sensed data of aquatic environments, (3) image classification using a linear mixed model, (4) accuracy assessment using the ground reference data, and (5) change detection of seagrass beds. The classification results compared with the ground reference data indicated that the overall accuracies and Kappa coefficients were higher than 91.7% and 0.8, respectively, in all cases. From 1996 to 2015, the total area of seagrass beds had declined by approximately 25% (66 ha), mainly attributed to coastal development and infrastructure construction.
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paper_title: Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR

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article i nfo Linking intertidal processes to their natural patterns within a framework of coastal erosion requires monitoring techniques providing high-resolution spatio-temporal data from the scale of processes to this of patterns. The Scanning Hydrographic Operational Airborne LiDAR Survey (SHOALS) consists of a ubiquitous topographic and bathymetric LiDAR (Light Detection And Ranging) system that has become an important technology for generating high-resolution Digital Terrain Models (DTM) and Digital Surface Models (DSM) over intertidal landscapes. The objectives of this project are i) to highlight the capacity of SHOALS Topography and intensity data (Red and Near-InfraRed) to detect intertidal vegetation, ii) to assess the salt-marsh zonation, and iii) to map intertidal habitats and its adjacent coastal areas (Gulf of St. Lawrence, Canada). The study area was selected based on the spectrum of land cover types, encompassing beach, salt-marsh, arable farm and urban coastal environments. Surfaces constructed from the LiDAR survey included DSM, DTM, Normalized Surface Model (NSM), Digital Intensity Model for InfraRed (DIMI), Digital Intensity Model for Red (DIMR), and Normalized Difference LiDAR Vegetation Index Model (NDLVIM), derived from the two previous models. The correlation between the so-called NDLVI and the amount of salt-marsh vegetation, measured in situ, was 0.87 (pb 0.01). Then, LiDAR-assessed salt-marsh ecological zonation allowed finding out intermediate and strong relationships between NDLVI and Topography (r 2 = 0.89, pb 0.038) and Topographic heterogeneity (r 2 = 0.54, pb 0.1394), respectively. Finally, NDLVI and Topography surfaces were classified using maximum likelihood algorithm into 17 classes, whose overall accuracy and kappa coefficient were 91.89% and 0.9088, respectively. These results support that (1) intertidal vegetation can be discriminated by NDLVI, (2) salt-marsh ecological zonation pattern, and (3) accurate coastal land cover maps can be satisfactorily generated from a single LiDAR survey using the NDLVIM and DTM approach.
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paper_title: The application of remote sensing to seagrass ecosystems: an overview and future research prospects

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This review evaluates various methods employed to produce seagrass habitat maps using optical and acoustic remote-sensing (RS) techniques coupled with in situ sampling to highlight recent advances and to define areas where potential future research should be focused in the application of RS technologies. A critical review of 195 studies revealed that, in the past four decades, advances in the application of RS methods, notably using Landsat imagery, are identified for seagrass detection, assessment of areal coverage, distribution and abundance mapping, and the detection of extent and biomass changes, as illustrated in peer-reviewed literature. Rapid technological and methodological advances have occurred in the acquisition and interpretation of optical and acoustic data for the mapping of seagrass habitats. The methods have been tested to segment, classify, and combine RS data with biological field or ground truth sample data. There is no single technology or approach that is suitable for and capable of measuring all seagrass parameters (presence/absence, cover, species, and biomass) and assessing change. Integration of field, imagery, and mapping approaches is therefore required. Further research is required for continued improvements in understanding of theoretical and methodological aspects of seagrass RS.
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paper_title: Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery

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article i nfo An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30+ years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegeta- tion mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrim- ination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object- based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to ac- curate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity.
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paper_title: Observation of typhoon-induced seagrass die-off using remote sensing

paper_content:

Abstract Satellite remote sensing has been used as an effective tool to monitor and manage seagrass beds, but this tool is not appropriate to use in highly turbid waters along the coast of Korea. In this study, satellite data were used to identify the method for detecting seagrass beds in turbid bay waters and describing temporal changes over the past 24 years. Moreover, we found the cause of seagrass die-off phenomenon by observing the long-term satellite images and evaluating the impact of the typhoon on seagrass beds. All seagrass spectra from different sensors (Landsat TM/ETM+, Aster, Spot-4, and Kompsat-2) have low reflectance in green and high in NIR regions, whereas unvegetated seawater has the opposite spectrum features. A training area in the green band and Mahalanobis Distance Classification algorithm was adopted for classification and mapping of seagrass beds. The error matrix using the in situ reference data for Kompsat-2 image classification was 72.9% accurate. The average area of seagrass beds was 4.6 km 2 from May 1990 to January 2012, and it was consistently >4.0 km 2 . A die-off of seagrass beds was observed in September 2012 after Typhoon Bolaven, Tembin, and Sanba consecutively passed the study site. When Typhoon Sanba passed the study site, it had sustained maximum wind speed (147.6 km h −1 ) during low tide, unlike the other typhoons. We found that the water column buffer did not have any effect on resistance of seagrass bed to disturbance, which led to greater damage. Additionally, the three sequential typhoons that passed through during the short time probably produced a cumulative effect. These results allow an understanding of past changes, and reveal the sudden and influential changes in seagrass distribution. As seagrass removal might have negative effect on other associated communities, comprehensive monitoring is required to ensure that disturbed seagrass habitats is recovered naturally.
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paper_title: Using MODIS data for understanding changes in seagrass meadow health: a case study in the Great Barrier Reef (Australia)

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Stretching more than 2000 km along the Queensland coast, the Great Barrier Reef Marine Park (GBR) shelters over 43,000 square km of seagrass meadows. Despite the status of marine protected area and World Heritage listing of the GBR, local seagrass meadows are under stress from reduced water quality levels; with reduction in the amount of light available for seagrass photosynthesis defined as the primary cause of seagrass loss throughout the GBR. Methods have been developed to map GBR plume water types by using MODIS quasi-true colour (hereafter true colour) images reclassified in function of their dominant colour. These data can be used as an interpretative tool for understanding changes in seagrass meadow health (as defined in this study by the seagrass area and abundance) at different spatial and temporal scales. We tested this method in Cleveland Bay, in the northern GBR, where substantial loss in seagrass area and biomass was detected by annual monitoring from 2007 to 2011. A strong correlation was found between bay-wide seagrass meadow area and biomass and exposure to turbid Primary (sediment-dominated) water type. There was also a strong correlation between the changes of biomass and area of individual meadows and exposure of seagrass ecosystems to Primary water type over the 5-year period. Seagrass meadows were also grouped according to the dominant species within each meadow, irrespective of location within Cleveland Bay. These consolidated community types did not correlate well with the exposure to Primary water type, and this is likely to be due to local environmental conditions with the individual meadows that comprise these groupings. This study proved that remote sensing data provide the synoptic window and repetitivity required to investigate changes in water quality conditions over time. Remote sensing data provide an opportunity to investigate the risk of marine-coastal ecosystems to light limitation due to increased water turbidity when in situ water quality data is not available or is insufficient.
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paper_title: Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA

paper_content:

Multiple endmember spectral mixture analysis (MESMA) was applied to the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) imagery of a salt marsh in China Camp at San Pablo Bay, California. A nine‐endmember set representing materials within the scene was used including: two Salicornia and two soils, and Grindelia, Spartina, dry grass, water and shade. The resultant abundance maps were used to investigate the spatial distribution of the marsh vegetation species, Salicornia virginica, Grindelia Stricta and Spartina foliosa. The Spartina abundance map exhibited a well‐defined zone bordering the water and the lower marsh, which is in good agreement with the field observations made in 2002. Comparison of the Salicornia map with all six field global positional system (GPS) polygons indicates Salicornia was classified with high accuracy. The proposed approach did a good job in classifying Spartina and Salicornia which cover 93.81% of the total marsh. The Grindelia fraction image underestimates in some ...
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paper_title: Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series

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Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, resulting in a wealth of spaceborne image time series. Here, we exploit recent advances in high spatial resolution sensors and machine learning to study Mediterranean seagrasses. We process a multispectral RapidEye time series between 2011 and 2016 to detect interannual seagrass dynamics in 888 submerged hectares of the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean Sea). We assess the extent change of two Mediterranean seagrass species, the dominant Posidonia oceanica and Cymodocea nodosa, following atmospheric and analytical water column correction, as well as machine learning classification, using Random Forests, of the RapidEye time series. Prior corrections are necessary to untangle the initially weak signal of the submerged seagrass habitats from satellite imagery. The central results of this study show that P. oceanica seagrass area has declined by 4.1%, with a trend of -11.2 ha/yr, while C. nodosa seagrass area has increased by 17.7% with a trend of +18 ha/yr throughout the 5-year study period. Trends of change in spatial distribution of seagrasses in the Thermaikos Gulf site are in line with reported trends in the Mediterranean. Our presented methodology could be a time- and cost-effective method toward the quantitative ecological assessment of seagrass dynamics elsewhere in the future. From small meadows to whole coastlines, knowledge of aquatic plant dynamics could resolve decline or growth trends and accurately highlight key units for future restoration, management, and conservation.
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paper_title: Multi-temporal mapping of seagrass cover, species and biomass: A semi-automated object based image analysis approach

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The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100m2) scales. However, landscape scale (>100km2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142km2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.
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paper_title: Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

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We used LiDAR topographic data, AVIRIS hyperspectral data, and locally measured tidal fluctuations to characterize patterns of plant distribution within a southern California salt marsh (Carpinteria Salt Marsh (CSM)). LiDAR data required ground truthing and correction before they were suitable for use. Twenty to forty percent of the uncertainty associated with LiDAR was due to variance in the elevation of the target surface, the balance was attributed to error inherent in the LiDAR system. The incidence of LiDAR penetration of plant canopy cover (i.e., registration of ground elevation) was only three percent. The depth of LiDAR penetration into the plant canopy varied according to plant species composition; plant species-specific corrections significantly improved LiDAR accuracy (58% reduction in overall uncertainty) and with the use of ground-based surveys, reduced overall RMSE to an average of 6.3 cm in vegetated areas. A supervised classification of AVIRIS data was used to generate a vegetation map with six classification types; overall classification accuracy averaged 59% with a kappa coefficient of 0.40. The vegetation classification map was overlaid with a LiDAR-based digital elevation model (DEM) to compute elevation distributions and frequencies of tidal inundation. The average elevations of the dominant plant classifications found in CSM (e.g., Salicornia virginica, Jaumea carnosa, and salt-grass mix, a mixture of multiple marsh plant species) occurred within a 17 cm range, a vertical change that resulted in a 7% difference in the period of tidal inundation.
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paper_title: Historical changes in seagrass coverage on the Mississippi barrier islands, northern Gulf of Mexico, determined from vertical aerial imagery (1940–2007)

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Vertical aerial image data were used with an edge-detection procedure and visual image interpretation to determine yearly to decadal changes in seagrass (predominantly Halodule wrightii Ascherson) coverage on the Mississippi barrier islands. On Horn Island, seagrass coverage declined from 77 ha in 1940 to 19 ha in 1971, but returned to its 1940 value by 2006. Coverage on Petit Bois declined from 54 ha in 1940 to 8–19 ha from 1952 through 2007. On East Ship, seagrass coverage varied at 2–19 ha from 1963 to 2007. On West Ship, coverage dropped to zero in 2003, but by 2007 it had increased to approximate its 1975 value of 1.8 ha. On Cat Island, coverage increased from 22 ha in 2003 to 71 ha in 2007. There was no apparent negative impact of Hurricane Camille or Hurricane Katrina on seagrass coverage, which could vary annually by a factor of two or more.
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paper_title: The Tidal Marsh Inundation Index (TMII): An inundation filter to flag flooded pixels and improve MODIS tidal marsh vegetation time-series analysis

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Abstract Remote sensing in tidal marshes can provide regional assessments of wetland extent, phenology, primary production, and carbon sequestration. However, periodic tidal flooding reduces spectral reflectance, especially in the near and short-wave infrared wavelengths. Consequently, marsh vegetation time-series products that lack tidal filtering, such as those provided by MODIS (Moderate Resolution Imaging Spectroradiometer), may not reflect true vegetation trends. We created a new Tidal Marsh Inundation Index (TMII) for processing daily 500-m MODIS surface reflectance data and calibrated it with a Spartina alterniflora salt marsh pixel on Sapelo Island, GA. Ground-truth data for TMII was extracted from a PhenoCam, which collected high frequency digital photography of the TMII calibration pixel. To identify the best wavelengths to include in the TMII, we compared goodness of fit metrics from generalized linear models (GLMs). Predictors for these GLMs included suites of normalized difference indices from the literature as well as other band combinations. We also explored including a phenology parameter that could scale TMII relative to vegetation development. Ultimately, TMII was based on the normalized difference of green and shortwave infrared reflectance in combination with a phenology parameter composed of the moving average of the normalized difference of near infrared and shortwave infrared reflectance. This final index allowed a single optimized decision boundary to identify flooding across the annual growth cycle. When compared to ground-truth data from the PhenoCam, the TMII classified flooded conditions with 67–82% and dry conditions with 75–81% accuracy, respectively, across training, testing and validation datasets. We applied TMII to new S. alterniflora marsh MODIS pixels on Sapelo Island, GA as well as on Plum Island, MA. For these new pixels, TMII classified marsh flooding with 77–80% overall accuracy. We also demonstrated how users can apply TMII filtering in a MODIS workflow to create vegetation time-series composites within S. alterniflora , Spartina patens and Juncus roemerianus marshes. We showed how a new user can validate and optimize TMII in their application, either by comparing it to inundation data or by validating the filtered vegetation time series against field data. We also compared TMII-filtered composites to the existing MODIS MOD13 16-d Normalized Difference Vegetation Index (NDVI) product. TMII-filtered composites generated less noisy time-series that fit field data better than MOD13. TMII filtering was most important on Sapelo Island, where the tide range was high and vegetation was sparse. Results were less dramatic when TMII was applied to different marsh species within the Gulf Coast sites with lower tidal ranges, but TMII-filtering still improved vegetation time series. Thus, preprocessing MODIS imagery with the TMII effectively identified most inundated pixels. The TMII represents a step forward for wetland remote sensing that will be useful for improved estimation of phenology, biomass and carbon storage in coastal marshes.
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paper_title: Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia

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Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972–2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland’s urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.
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paper_title: Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests

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Abstract Remote-sensing based cover mapping has a long history of use in natural resource management for a wide-range of applications. In order to be effectively employed, remote-sensing based cover maps must be accurate and meet the spatial scale inherent to the phenomena of interest. In this study, we employed the Random Forests algorithm in a supervised classification approach to construct a fine-scale (i.e., 1.0 meter pixel resolution) remote-sensing based cover map of coastal dune and salt marsh ecosystems at Cape Cod National Seashore, USA. We achieved high overall classification accuracies (i.e., 75.1% and 86.8% correct classification rate for the dune and salt marsh study areas, respectively) with a large proportion of the misclassified holdout validation assessment points being errors of commission between overlapping and/or ecologically similar cover classes. In addition, we were able to considerably reduce the predictor variable set by using a forward selection variable reduction method while achieving modestly higher classification accuracies compared to the global model. Our results suggest that coastal dune and salt marsh ecosystems can be mapped at a fine spatial resolution with reasonably high classification accuracy using a supervised Random Forests-based approach such as ours.
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paper_title: A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants

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article i nfo Article history: Object-oriented classification (OOC) has shown many significant advantages over other methods for classification of urban or forest ecosystems. However, it remains unclear if this technology could exhibit these advantages on mapping monospecific plant stands in herbaceous plant dominated ecosystems (e.g. saltmarshes). In this study, we compared the effectiveness of OOC and pixel-based classification (PBC) methods for mapping plants in a saltmarsh ecosystem. QuickBird was selected for very high resolution (VHR) imagery. Eleven models defined by classification types, feature spaces, classifiers, and hierarchical approaches with multi-scale segmentation were built for comparison. The results showed that the QuickBird imagery efficiently discriminated saltmarsh monospecific vegetation stands and that OOC performed better than PBC in terms of accuracy. We also found that the improvement of OOC was primarily due to employing membership functions and a hierarchical approach with multi-scale segmentation. Although texture and shape features have been deemed as two major advantages of OOC, enhanced performance was not observed in this study. The results of this study demonstrated that OOC would be superior to PBC for classifying herbaceous plant species in terms of accuracy. To improve the classification accuracy, greater concern should be given to exploration of the relationships between features of both objects and classes and to combining information from different object scales, while shape and texture features can be a minor consideration due to their intricately high spatial variability.
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paper_title: Classification mapping of salt marsh vegetation by flexible monthly NDVI time-series using Landsat imagery

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Abstract Salt marshes are deemed as one of the most dynamic and valuable ecosystems on Earth. Recently, salt marsh deterioration and loss have become widespread because of anthropogenic stressors and sea level rise. Long-term acquisition of spatial information on salt marsh vegetation communities is thus critical to detect the general evolutionary trend of marsh ecosystems before irreversible change occurs. Medium resolution imagery organized in inter-annual time series has been proven suitable for large-scale mapping of salt marsh vegetation. For long-term monitoring purpose, the challenge still lies in developing time series based on data with sparse and uneven temporal distribution. This paper proposes a flexible Monthly NDVI Time-Series (MNTS) approach to achieve multi-temporal classification maps of salt marsh vegetation communities in the Virginia Coast Reserve, USA, by utilizing all viable Landsat TM/ETM + images during the period 1984–2011. Salt marsh vegetation communities are identified on a reference MNTS spanning 12 months with an overall accuracy of 0.898, approximately 0.107 higher than classifications using single images. Utilizing a flexible selection process based on the reference MNTS, a significant inverse hyperbolic relationship emerges between overall accuracy and average length of the time series. Based on these results, eight classification maps with average accuracy of 0.844 and time interval of 2–5 years are acquired. A spatio-temporal analysis of the maps indicates that the upper low marsh vegetation community has diminished by 19.4% in the study period, with a recent acceleration of losses. The conversion of marsh area to vegetation communities typical of low elevations (37.7 km2) is more than twice the conversion to vegetation communities typical of high elevations (18.3 km2), suggesting that salt marsh ecosystems at the Virginia Coast Reserve are affected by sea level rise.
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paper_title: The impacts of land-use changes on the recovery of saltmarshes in Portugal

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Human-induced land-use changes have resulted in loss and degradation of intertidal environments worldwide. Saltmarsh ecosystem dynamics in Portugal are greatly influenced by historic uses and consequent habitat degradation. This study uses an original approach combining vegetation surveys and spatial analysis of historic maps and aerial photographs to assess the effects of land use changes on saltmarshes in two areas in the Algarve, southern Portugal. Historical maps from c. 1800 and aerial photographs from 1958 to 2010 were analyzed to map saltmarsh ecosystems and quantify land-use changes in the Alvor estuary and Arade River. Between c. 1800 and 2010 more than half of saltmarshes were lost due to dyke building and saltmarsh reclamation for agriculture. In mid-1960s, the abandonment of reclaimed agricultural areas resulted in the recolonization of saltmarsh vegetation, which developed physically separated from natural marshes. In the study area, these saltmarshes naturally evolved into two distinct typologies: (1) enclosed mixed marshes, formed by patches of brackish, freshwater and some invasive species developing due to saline intrusion in areas where dykes have not been breached; and (2) tidally-restored saltmarshes, formed in areas where dyke breaching allows incursion of tides and development of a vegetation structure similar to natural saltmarshes. In Europe, passive (without human intervention) and active (artificially planned) saltmarsh restoration are important mechanisms for voluntary or statutory re-creation of intertidal habitats. Improved understanding of the factors influencing the development of distinct saltmarsh typologies through passive ecosystem recovery can provide new insights to support decision-making concerning intertidal habitat restoration.
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paper_title: Classification of Salt Marsh Vegetation Using Edaphic and Remote Sensing-Derived Variables

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Abstract Salt marsh plant communities are known for their striking patterns of vertical zonation. Two of the most important edaphic parameters that affect species distribution patterns are soil salinity and waterlogging, both of which are related to topographical variations and distance to the water. The primary objective of this study was to evaluate whether information on elevation and distance derived through remote sensing could be used to predict plant distributions in a southeastern United States salt marsh. We classified four marsh vegetation classes (tall Spartina alterniflora , medium S. alterniflora /short S. alterniflora , marsh meadow, and Borrichia frutescens / Juncus roemerianus ) based on landscape metrics obtained from a light detection and ranging (LIDAR)-derived digital elevation model (DEM) and compared results to a classification based on field-collected edaphic variables. Our secondary objective was to compare the performance of linear discriminant analysis (LDA) with non-parametric classification and regression trees (CART) for these classifications. Models based on the edaphic variables soil water content, salinity, and redox potential attained accuracies of 0.62 and 0.71 with LDA and CART, respectively. When the remote sensing-derived variables DEM elevation, slope, distance to the mean high water line, and distance to upland area were used, classification accuracies improved to 0.78 for LDA and 0.79 for CART. Our results suggest that remote sensing-derived metrics can capture edaphic gradients effectively, which makes them especially suited to landscape level analyses of salt marsh plant habitats, with potential application for predicting the effects of sea level rise on salt marsh plant distribution.
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paper_title: Mapping changes in tidal wetland vegetation composition and pattern across a salinity gradient using high spatial resolution imagery

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Detailed vegetation mapping of wetlands, both natural and restored, can offer valuable infor- mation about vegetation diversity and community structure and provides the means for examining vegetation change over time. We mapped vegetation at six tidal marshes (two natural, four restored) in the San Francisco Estuary, CA, USA, between 2003 and 2004 using detailed vegetation field surveys and high spatial-resolution color-infrared aerial photography. Vegetation classes were determined by performing hierarchical agglomerative clustering on the field data collected from each tidal marsh. Supervised classifi- cation of the CIR photography resulted in vegetation class mapping accuracies ranging from 70 to 92%; 10 out of 12 classification accuracies were above 80%, demonstrating the potential to map emergent wetland vegetation. The number of vegetation classes decreased with salinity, and increased with size and age. In general, landscape diversity, as measured by the Shannon's diversity index, also decreased with salinity, with an exception for the most saline site, a newly restored marsh. Vegetation change between years is evident, but the differences across sites in composition and pattern were larger than change within sites over two growing seasons.
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paper_title: Land cover changes in tidal salt marshes of the Bahía Blanca estuary (Argentina) during the past 40 years

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The present work describes land cover changes in the inner section of Canal Principal, in the Bahia Blanca estuary. The study area is subjected to a rising relative sea level, large interanual variations in rainfall, and recent changes human in land use. We used historical aerial photographs, high resolution satellite images, and GIS to quantify changes in land cover for the years 1967, 1996, and 2005. The replacement of Sarcocornia perennis marshes and halophytic shrub-like steppes by mudflats is a recurring pattern through the area. We estimated a total loss to mudflats of 33 and 6% of the area of marshes and steppes, respectively, and it may reflect increased erosion of relict Holocene coastal terraces in response to a rising sea level. Human activities have played a significant role in reshaping coastal landscape, particularly in the harbor area. Fifty percent of the area originally covered by shrub-like steppes and 33% of the Sarcocornia perennis marshes were replaced by human land uses. Major changes correspond to dredged spoil deposition and landfilling. One of the most striking changes observed is the increase of the area covered by Spartina alterniflora marshes. This type of replacement suggests the occurrence of depositional environments that, at least locally, allow bed elevation and vegetation growth. In the harbor area, an enhanced sedimentation may result from maintenance dredging. At the mouth of Maldonado channel, sediment deposition may occur during extraordinary heavy rainfall associated to El Nino.
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paper_title: Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data

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Abstract Wetlands rank among the most diverse ecosystems on earth and function as important ecosystem service providers. Pressures on wetland ecosystems caused by human activities, such as land use transformations or agricultural intensification, lead to strong wetland degradation. Satellite-based wetland mapping still bears the most uncertainties compared to other land cover types mapping. Image classification techniques have to better adapt to specific wetland characteristics, such as spatial heterogeneity, seasonal dynamics and fuzzy transitions between different land cover classes. For this purpose, a pixel-based method for wetland delineation based on multi-temporal Landsat data in West Turkey was developed and analyzed. In addition to common vegetation indices and texture measures, the usefulness of seasonal indices was tested. Multi-temporal Landsat imagery was combined with high resolution satellite data to extract sub-pixel information of coastal and inland wetland classes based on a random forest regression algorithm. The classification achieved an overall accuracy of 79.02%. In addition to the hard wetland classification the mapping framework provides a map of fractional cover information of different wetland classes including information about fuzzy spatial transitions of highly heterogeneous distribution patterns of wetland habitats and related intra-annual seasonal dynamics. Mapping spatio-temporal wetland dynamics at continuous field scales increases the applicability of Landsat-derived maps for local-scale ecosystem monitoring and environmental management on habitat level.
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paper_title: Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR

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article i nfo Linking intertidal processes to their natural patterns within a framework of coastal erosion requires monitoring techniques providing high-resolution spatio-temporal data from the scale of processes to this of patterns. The Scanning Hydrographic Operational Airborne LiDAR Survey (SHOALS) consists of a ubiquitous topographic and bathymetric LiDAR (Light Detection And Ranging) system that has become an important technology for generating high-resolution Digital Terrain Models (DTM) and Digital Surface Models (DSM) over intertidal landscapes. The objectives of this project are i) to highlight the capacity of SHOALS Topography and intensity data (Red and Near-InfraRed) to detect intertidal vegetation, ii) to assess the salt-marsh zonation, and iii) to map intertidal habitats and its adjacent coastal areas (Gulf of St. Lawrence, Canada). The study area was selected based on the spectrum of land cover types, encompassing beach, salt-marsh, arable farm and urban coastal environments. Surfaces constructed from the LiDAR survey included DSM, DTM, Normalized Surface Model (NSM), Digital Intensity Model for InfraRed (DIMI), Digital Intensity Model for Red (DIMR), and Normalized Difference LiDAR Vegetation Index Model (NDLVIM), derived from the two previous models. The correlation between the so-called NDLVI and the amount of salt-marsh vegetation, measured in situ, was 0.87 (pb 0.01). Then, LiDAR-assessed salt-marsh ecological zonation allowed finding out intermediate and strong relationships between NDLVI and Topography (r 2 = 0.89, pb 0.038) and Topographic heterogeneity (r 2 = 0.54, pb 0.1394), respectively. Finally, NDLVI and Topography surfaces were classified using maximum likelihood algorithm into 17 classes, whose overall accuracy and kappa coefficient were 91.89% and 0.9088, respectively. These results support that (1) intertidal vegetation can be discriminated by NDLVI, (2) salt-marsh ecological zonation pattern, and (3) accurate coastal land cover maps can be satisfactorily generated from a single LiDAR survey using the NDLVIM and DTM approach.
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paper_title: Coastal wetland mapping combining multi-date SAR and LiDAR

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Inventorying and monitoring are imperative to management of vulnerable coastal wetlands. Multi-date and multi-sensor remote sensing offer new capabilities to wetland programmes such as the US National Wetland Inventory. This pilot study focuses on swamp forests and pocosins, marshes, shrub–scrub and invasive Phragmites australis. Combinations of spaceborne multi-date Synthetic Aperture Radar (SAR) imagery and airborne light detection and ranging (LiDAR) elevation (bare earth elevation and vegetation height) were evaluated. Multi-date SAR data (horizontal-horizontal and horizontal-vertical dual polarizations) highlighted physiognomic dynamics, with LiDAR vegetation canopy discerning selected classes. The highest overall accuracy used SAR, LiDAR canopy and digital elevation model (DEM) data (81% κ = 0.744), but not significantly different from the SAR-only classification (81% κ = 0.742). Both classifications exceeded the data combination using SAR data and DEM (66% κ = 0.521) and SAR data with vegetation ca...
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paper_title: Large-scale mapping of the riverbanks, mud flats and salt marshes of the Scheldt basin, using airborne imaging spectroscopy and LiDAR

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For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m2 and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation. Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.
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paper_title: Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data

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The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.
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paper_title: Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: A case study within a Basque estuary

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Abstract The bathymetric LiDAR system is an airborne laser that detects sea bottom at high vertical and horizontal resolutions in shallow coastal waters. This study assesses the capabilities of the airborne bathymetric LiDAR sensor (Hawk Eye system) for coastal habitat mapping in the Oka estuary (within the Biosphere Reserve of Urdaibai, SE Bay of Biscay, northern Spain), where water conditions are moderately turbid. Three specific objectives were addressed: 1) to assess the data quality of the Hawk Eye LiDAR, both for terrestrial and subtidal zones, in terms of height measurement density, coverage, and vertical accuracy; 2) to compare bathymetric LiDAR with a ship-borne multibeam echosounder (MBES) for different bottom types and depth ranges; and 3) to test the discrimination potential of LiDAR height and reflectance information, together with multi-spectral imagery (three visible and near infrared bands), for the classification of 22 salt marsh and rocky shore habitats, covering supralittoral, intertidal and subtidal zones. The bathymetric LiDAR Hawk Eye data enabled the generation of a digital elevation model (DEM) of the Oka estuary, at 2 m of horizontal spatial resolution in the terrestrial zone (with a vertical accuracy of 0.15 m) and at 4 m within the subtidal, extending a water depth of 21 m. Data gaps occurred in 14.4% of the area surveyed with the LiDAR (13.69 km 2 ). Comparison of the LiDAR system and the MBES showed no significant mean difference in depth. However, the Root Mean Square error of the former was high (0.84 m), especially concentrated upon rocky (0.55–1.77 m) rather than in sediment bottoms (0.38–0.62 m). The potential of LiDAR topographic variables and reflectance alone for discriminating 15 intertidal and submerged habitats was low (with overall classification accuracy between 52.4 and 65.4%). In particular, reflectance retrieved for this case study has been found to be not particularly useful for classification purposes. The combination of the LiDAR-based DEM and derived topographical features with the near infrared and visible bands has permitted the mapping of 22 supralittoral, intertidal and subtidal habitats of the Oka estuary, with high overall classification accuracies of between 84.5% and 92.1%, using the maximum likelihood algorithm. The airborne bathymetric Hawk Eye LiDAR, although somewhat limited by water turbidity and wave breaking, provides unique height information obscured from topographic LiDAR and acoustic systems, together with an improvement of the habitat mapping reliability in the complex and dynamic coastal fringe.
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paper_title: Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data

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Abstract This research investigated the use of multi-source remote sensing data to map natural coastal salt marsh vegetation habitats. Coastal zones are very dynamic and provide a number of critical ecosystem services, particularly in relation to flood mitigation but they have been found to be difficult to monitor using remotely sensed data. This research analysed combinations of S-band and X-band quad-polarimetric airborne SAR, elevation data and optical remotely sensed imagery. In total 30 variables were analysed. The SAR inputs included backscatter intensity channels and Cloude-Pottier, Freeman-Durden and Van Zyl decomposition SAR descriptors. Classification was carried out using Random Forest classifiers at two thematic resolutions which generated a general mapping of salt marsh vegetation and a high-resolution mapping of thematically detailed salt marsh vegetation habitats. The results indicate that Random Forest models are able to handle multi-source datasets and generate high classification accuracies. Models based on either SAR or optical RS variables alone were found to be less accurate than models that combining variables from multiple sources. The results show that X-band SAR data provided the best information to map vegetation extent and analysis showed that S-band SAR data was better able to differentiate between different vegetation habitats. The methods and analyses suggested in this paper extend previous research into remote monitoring of costal zones and illustrate the opportunities for mapping natural coastal areas afforded through combinations of radar and optical remote sensing data.
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paper_title: Fusion of lidar and multispectral data to quantify salt marsh carbon stocks

paper_content:

Abstract Herbaceous salt marshes are among the most productive ecosystems on earth. Unfortunately, quantification of the above-ground portion of biomass using passive optical remote sensing is constrained by the complexities of mixed spectral appearance in the water–land environment. Lidar remote sensing, on the other hand, has been extensively used to estimate forest biomass, and a few studies have reported their use in characterizing short or herbaceous plants. However, no empirical studies have demonstrated the combined use of lidar and spectral data to quantify above-ground biomass in herbaceous environments, including salt marshes. Thus, the findings of this study will contribute substantially to the understanding of potentials and limitations of using lidar and multi-spectral data for vegetation characterization and biomass estimates in salt marshes and other similar herbaceous environments. In this study, we evaluate the increased capability of a data fusion approach using small footprint discrete return lidar and multispectral data to quantify above-ground biomass and thus, carbon stocks in salt marshes. The specific objectives of our study were the following: 1) to understand the interaction between discrete-return airborne lidar and marsh vegetation; 2) to determine the appropriate grid size/s of lidar-derived datasets for characterizing marsh terrain and vegetation; 3) to investigate the applicability of a number of lidar metrics to predict salt marsh vegetation height and above-ground biomass; and 4) to evaluate the utility of integrating multispectral imagery with lidar to improve the predictability of regression models for quantifying above-ground biomass and carbon. Our results showed that salt marsh Digital Terrain Models (DTMs) derived using local minima in a grid spacing of 5 m × 5 m provided the best accuracy in terrain elevation estimates with an RMSE of less than 10 cm. Lidar-derived maximum vegetation heights (Lmax) provided the best agreement with field height measurements, but explained only 41% of the variance in vegetation height measurements (RMSE = 5.85 cm). Regardless of the metrics used, lidar-measured heights underestimated the field vegetation height, which is consistent with the findings of previous studies in short or herbaceous vegetation. The fusion of lidar with multispectral data improved model predictions of live, dead, and total biomass quantities. The improvement provided by the fusion over the use of lidar or multispectral data alone was marginal; the combination explained 47% of the variance, whereas the best models using lidar and multispectral data separately explained 37% and 28% of variances in live biomass measurements, respectively. However, the best biomass prediction models reported considerably low RMSEs and % root square errors (%RSEs). For example, %RSE for the biomass prediction model using lidar-derived maximum vegetation height (Lmax) was closer to 20%, which is the recommended error threshold for remote sensing based forest biomass prediction models that can be repeatedly applicable for estimating forest carbon stock change. Our findings also demonstrate that lidar as compared to spectral data can provide better estimates of above-ground biomass and carbon, even in the herbaceous and low-relief context of a salt marsh.
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paper_title: Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery

paper_content:

Abstract Salt marshes are seen as the most dynamic and valuable ecosystems in coastal zones, and in these areas, it is crucial to obtain accurate remote sensing information on the spatial distributions of species over time. However, discriminating various types of salt marsh is rather difficult because of their strong spectral similarities. Previous salt marsh mapping studies have focused mainly on high spatial and spectral (i.e., hyperspectral) resolution images combined with auxiliary information; however, the results are often limited to small regions. With a high temporal and moderate spatial resolution, the Chinese HuanJing-1 (HJ-1) satellite optical imagery can be used not only to monitor phenological changes of salt marsh vegetation over short-time intervals, but also to obtain coverage of large areas. Here, we apply HJ-1 satellite imagery to the middle coast of Jiangsu in east China to monitor changes in saltmarsh vegetation cover. First, we constructed a monthly NDVI time-series to classify various types of salt marsh and then we tested the possibility of using compressed time-series continuously, to broaden the applicability of this particular approach. Our principal findings are as follows: (1) the overall accuracy of salt marsh mapping based on the monthly NDVI time-series was 90.3%, which was ∼16.0% higher than the single-phase classification strategy; (2) a compressed time-series, including NDVI from six key months (April, June–September, and November), demonstrated very little reduction (2.3%) in overall accuracy but led to obvious improvements in unstable regions; and (3) a simple rule for Spartina alterniflora identification was established using a scene solely from November, which may provide an effective way for regularly monitoring its distribution.
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paper_title: Detection of Salt Marsh Vegetation Stress and Recovery after the Deepwater Horizon Oil Spill in Barataria Bay, Gulf of Mexico Using AVIRIS Data

paper_content:

The British Petroleum Deepwater Horizon Oil Spill in the Gulf of Mexico was the biggest oil spill in US history. To assess the impact of the oil spill on the saltmarsh plant community, we examined Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data flown over Barataria Bay, Louisiana in September 2010 and August 2011. Oil contamination was mapped using oil absorption features in pixel spectra and used to examine impact of oil along the oiled shorelines. Results showed that vegetation stress was restricted to the tidal zone extending 14 m inland from the shoreline in September 2010. Four indexes of plant stress and three indexes of canopy water content all consistently showed that stress was highest in pixels next to the shoreline and decreased with increasing distance from the shoreline. Index values along the oiled shoreline were significantly lower than those along the oil-free shoreline. Regression of index values with respect to distance from oil showed that in 2011, index values were no longer correlated with proximity to oil suggesting that the marsh was on its way to recovery. Change detection between the two dates showed that areas denuded of vegetation after the oil impact experienced varying degrees of re-vegetation in the following year. This recovery was poorest in the first three pixels adjacent to the shoreline. This study illustrates the usefulness of high spatial resolution airborne imaging spectroscopy to map actual locations where oil from the spill reached the shore and then to assess its impacts on the plant community. We demonstrate that post-oiling trends in terms of plant health and mortality could be detected and monitored, including recovery of these saltmarsh meadows one year after the oil spill.
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paper_title: Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy

paper_content:

Abstract The April 2010 Deepwater Horizon (DWH) oil spill was the largest coastal spill in U.S. history. Monitoring subsequent change in marsh plant community distributions is critical to assess ecosystem impacts and to establish future coastal management priorities. Strategically deployed airborne imaging spectrometers, like the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), offer the spectral and spatial resolution needed to differentiate plant species. However, obtaining satisfactory and consistent classification accuracies over time is a major challenge, particularly in dynamic intertidal landscapes. Here, we develop and evaluate an image classification system for a time series of AVIRIS data for mapping dominant species in a heavily oiled salt marsh ecosystem. Using field-referenced image endmembers and canonical discriminant analysis (CDA), we classified 21 AVIRIS images acquired during the fall of 2010, 2011 and 2012. Classification results were evaluated using ground surveys that were conducted contemporaneously to AVIRIS collection dates. We analyzed changes in dominant species cover from 2010 to 2012 for oiled and non-oiled shorelines. CDA discriminated dominant species with a high level of accuracy (overall accuracy = 82%, kappa = 0.78) and consistency over three imaging dates (overall 2010 = 82%, overall 2011 = 82%, overall 2012 = 88%). Marshes dominated by Spartina alterniflora were the most spatially abundant in shoreline zones (≤ 28 m from shore) for all three dates (2010 = 79%, 2011 = 61%, 2012 = 63%), followed by Juncus roemerianus (2010 = 11%, 2011 = 19%, 2012 = 17%) and Distichlis spicata (2010 = 4%, 2011 = 10%, 2012 = 7%). Marshes that were heavily contaminated with oil exhibited variable responses from 2010 to 2012. Marsh vegetation classes converted to a subtidal, open water class along oiled and non-oiled shorelines that were similarly situated in the landscape. However, marsh loss along oil-contaminated shorelines doubled that of non-oiled shorelines. Only S. alterniflora dominated marshes were extensively degraded, losing 15% (354,604 m 2 ) cover in oiled shoreline zones, suggesting that S. alterniflora marshes may be more vulnerable to shoreline erosion following hydrocarbon stress, due to their landscape position.
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paper_title: Understanding land subsidence in salt marshes of the Venice Lagoon from SAR Interferometry and ground-based investigations

paper_content:

Abstract The existence of salt marshes and tidal morphologies is strictly connected to their elevation with respect to the mean sea level. Quantifying land subsidence of these high-valued transitional environments is therefore crucial to investigate their long-term possible survival, also in view of the expected climate changes. However, monitoring with a certain accuracy their movements has been challenging until now due to the peculiar features of these morphological forms: they are difficult to access, made of largely unconsolidated deposits, without anthropogenic structures, relatively far from anthropogenic facilities, and become submerged by the sea water twice a day. For these reasons, they have never be linked to traditional levelling and GPS networks, and also standard Interferometric SAR applications returned very poor results in terms of spatial and temporal coverage. An advanced Persistent Scatterer Interferometry (PSI) technique on a 5-year long stack of X-bandwidth SAR acquisitions of the Venice Lagoon is here presented. The regularity of the acquisitions, the short satellite revisiting time (11 days), the high image resolution (~ 3 × 3 m), and the strategies used in the PSI application have allowed us to detect thousands of measurable persistent targets (PTs) in the Venice Lagoon salt marshes. The measured displacements range from small uplifts to subsidence rates of more than 20 mm/yr. The analyses of the observed displacements point out that land subsidence is much larger on man-made than natural salt marshes, with a significant negative correlation with the marsh age. In addition, land subsidence with the presence of halophytic vegetation species is generally smaller than on unvegetated marshes. Finally, at a few selected sites, the integration of the PSI outcome with local ground-based measurements, such as multi-depth benchmarks, feldspar marker horizons and surface elevation tables, has allowed quantifying the displacement variability versus depth and therefore developing a first conceptual model of the salt marsh consolidation and accretion processes.
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paper_title: The WEKA data mining software: an update

paper_content:

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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paper_title: Remote sensing approaches for monitoring mangrove species, structure and biomass: opportunities and challenges

paper_content:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
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paper_title: Scikit-learn: Machine Learning in Python

paper_content:

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
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paper_title: Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area

paper_content:

Abstract Accurate information about nature and extent of land cover changes especially in rapidly growing and new industrial cites is essential. New Burg El-Arab city, located to the west of Alexandria, Egypt has developed a new industrial community with a corresponding increase in urbanization and population. The objective of this research is to assess, evaluate and monitor the nature and extent of land cover changes in New Burg El-Arab city through the period from 1990 to 2000 using remotely sensed Landsat multispectral images. Four change detection techniques namely; post-classification, image differencing, image rationing and principal component analysis were applied. The objective is extended to examine the effectiveness of each change detection technique regarding not only the ability to differentiate changed from unchanged areas, but also the ability to classify the changed areas according to the “from-to” identifiers. The results indicated that the post classification change detection technique provided the highest accuracy while the principal component analysis technique gave the least accuracy. It is also found that the soil area has decreased by 37.02% while the urban and vegetation areas have increased by 16.55% and 20.50% respectively through the study period.
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paper_title: Hyperspectral Remote Sensing Image Classification Based on Rotation Forest

paper_content:

In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral remote sensing image classification for the first time. The framework of Rotation Forest is to project the original data into a new feature space using transformation methods for each base classifier (decision tree), then the base classifier can train in different new spaces for the purpose of encouraging both individual accuracy and diversity within the ensemble simultaneously. Principal component analysis (PCA), maximum noise fraction, independent component analysis, and local Fisher discriminant analysis are introduced as feature transformation algorithms in the original Rotation Forest. The performance of Rotation Forest was evaluated based on several criteria: different data sets, sensitivity to the number of training samples, ensemble size and the number of features in a subset. Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest. They indicate that Rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.
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paper_title: ESA's sentinel missions in support of Earth system science

paper_content:

Abstract The spatial and temporal characteristics of the new Sentinel missions, primarily designed to provide routine multidisciplinary observations for operational services, are also very suitable for addressing some of the challenges associated with advancing Earth System sciences. The Sentinels are ensuring long-term observational commitment and will operate a range of instruments with different spectral bands and spatial resolutions with global coverage and high revisit times. The complexity of Earth System models has been increasing gradually and most simulations of future climate and Earth system evolution are based on coupled models that include aspects of physics, bio/geo-chemistry, anthropogenic impacts and even recently some elements of socioeconomic factors. Sentinels will provide unique observations to describe such coupled atmosphere, oceans, land and cryosphere and the exchanges among them into Earth System models. This paper emphasizes the indispensable value of the data provided by the family of Sentinel constellations in the context of the urgent need for improved process understanding of the Earth system.
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paper_title: Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification

paper_content:

Classification techniques for hyperspectral images based on random forest (RF) ensembles and extended multiextinction profiles (EMEPs) are proposed as a means of improving performance. To this end, five strategies—bagging, boosting, random subspace, rotation-based, and boosted rotation-based—are used to construct the RF ensembles. EPs, which are based on an extrema-oriented connected filtering technique, are applied to the images associated with the first informative components extracted by independent component analysis, leading to a set of EMEPs. The effectiveness of the proposed method is investigated on two benchmark hyperspectral images: the University of Pavia and Indian Pines. Comparative experimental evaluations reveal the superior performance of the proposed methods, especially those employing rotation-based and boosted rotation-based approaches. An additional advantage is that the CPU processing time is acceptable.
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paper_title: A critical synthesis of remotely sensed optical image change detection techniques

paper_content:

Abstract State of the art reviews of remote sensing change detection are becoming increasingly complicated and disparate due to an ever growing list of techniques, algorithms and methods. To provide a clearer, synoptic view of the field this review has organised the literature by the unit of analysis and the comparison method used to identify change. This significantly reduces the conceptual overlap present in previous reviews giving a succinct nomenclature with which to understand and apply change detection workflows. Under this framework, several decades of research have been summarised to provide an overview of current change detection approaches. Seven units of analysis and six comparison methods were identified and described highlighting the advantages and limitations of each within a change detection workflow. Of these, the pixel and post-classification change methods remain the most popular choices. In this review we extend previous summaries and provide an accessible description of the field. This supports future research by placing a clear separation between the analysis unit and the change classification method. This separation is then discussed, providing guidance for applied change detection research and future benchmarking experiments.
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paper_title: Remote sensing approaches for monitoring mangrove species, structure and biomass: opportunities and challenges

paper_content:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
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paper_title: Monitoring mangrove forests: Are we taking full advantage of technology?

paper_content:

Abstract Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.
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paper_title: Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach

paper_content:

This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL.
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paper_title: Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

paper_content:

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.
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Title: A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010–2018

Section 1: Introduction
Description 1: Brief overview of blue carbon ecosystems, their importance, and the motivation for studying remote sensing approaches for monitoring these ecosystems.

Section 2: Mangroves
Description 2: Discussion on the characteristics, significance, and geographic distribution of mangrove ecosystems, including the threats they face.

Section 3: Seagrasses
Description 3: Overview of seagrass ecosystems, their ecological roles, and the distribution and conservation challenges they encounter.

Section 4: Salt Marshes
Description 4: Description and ecological importance of salt marsh ecosystems, including a summary of their distribution and the threats they face.

Section 5: Summary of Inventory, Datasets, and Methods
Description 5: Inventory of studies from 2010 onwards, detailing the datasets and remote sensing methods used for monitoring blue carbon ecosystems.

Section 6: Mapping Mangrove Ecosystems
Description 6: Review of various remote sensing methods used for mapping mangrove ecosystems, including their advantages and disadvantages.

Section 7: Monitoring Mangrove Ecosystems
Description 7: Analysis of time-series data and methods for monitoring changes in mangrove ecosystems, with examples of recent studies.

Section 8: Mapping Seagrass Ecosystems
Description 8: Examination of remote sensing techniques for mapping seagrass meadows, focusing on the accuracy and challenges of different methods.

Section 9: Monitoring Seagrass Ecosystems
Description 9: Overview of methods and datasets used for monitoring seagrass ecosystems, including long-term change detection and recent technological advancements.

Section 10: Mapping Salt Marsh Ecosystems
Description 10: Detailed look at the methods and datasets used for mapping salt marsh ecosystems, including innovations and accuracy assessments.

Section 11: Monitoring Salt Marsh Ecosystems
Description 11: Review of techniques and studies related to monitoring changes in salt marsh ecosystems, focusing on long-term trends and implications.

Section 12: Future Trends in Mapping
Description 12: Discussion on emerging trends and future directions in the mapping of blue carbon ecosystems using advanced remote sensing technologies.

Section 13: Future Trends in Monitoring
Description 13: Exploration of future trends and challenges in monitoring blue carbon ecosystems, with a focus on innovations in remote sensing and data analysis.

Section 14: Concluding Remarks
Description 14: Summary of the key findings from the review, emphasizing the significance of remote sensing for the conservation and management of blue carbon ecosystems.