Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds – ScienceOpen
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      Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

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      Remote Sensing
      MDPI AG

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          Abstract

          Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.

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          Most cited references99

          • Record: found
          • Abstract: not found
          • Article: not found

          Red and photographic infrared linear combinations for monitoring vegetation

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            • Record: found
            • Abstract: not found
            • Article: not found

            Random forest in remote sensing: A review of applications and future directions

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery

              Hanqiu Xu (2006)
                Bookmark

                Author and article information

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                Journal
                Remote Sensing
                Remote Sensing
                MDPI AG
                2072-4292
                July 2022
                July 06 2022
                : 14
                : 14
                : 3260
                Article
                10.3390/rs14143260
                fa9d7294-5d7b-4bff-b4c1-c00dc0cbad07
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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