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      Multitemporal optical and radar metrics for wetland mapping at national level in Albania

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          Abstract

          Wetlands are highly dynamic, with many natural and anthropogenic drivers causing seasonal, periodic or permanent changes in their structure and composition. Thus, it is necessary to use time series of images for accurate classifications and monitoring. We used all available Sentinel-1 and Sentinel-2 images to produce a national wetlands map for Albania. We derived different indices and temporal metrics and investigated their impacts and synergies in terms of mapping accuracy. Best results were achieved when combining Sentinel-1 with Sentinel-2 and its derived indices. We reduced systematic errors and increased the thematic resolution using morphometric characteristics and knowledge-based rules, achieving an overall accuracy of 82%. Results were also validated against field inventories. This methodology can be reproducible to other countries and can be made operational for an integrated planning that considers the food, water, and energy nexus.

          Abstract

          Environmental science, Geography, Earth Sciences, wetlands, Random Forest, data fusion, Sentinel.

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          Most cited references41

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          Use of normalized difference built-up index in automatically mapping urban areas from TM imagery

          S. Ni, Y Zha, J. Gao (2003)
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            Continuous change detection and classification of land cover using all available Landsat data

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              The value of wetlands: importance of scale and landscape setting

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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                19 August 2020
                August 2020
                19 August 2020
                : 6
                : 8
                : e04496
                Affiliations
                [a ]Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, Bonn, 53113, Germany
                [b ]Tour du Valat Research Centre for the Conservation of Mediterranean Wetlands, Le Sambuc, 13200, Arles, France
                [c ]The Greek Biotope/Wetland Centre (EKBY), Greece
                [d ]Department of Geography, University of Bonn, Bonn, 53113, Germany
                [e ]Remote Sensing and Digital Image Processing Group at the University of Osnabrück, Osnabrück, 49074, Germany
                [f ]German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Münchener Straße 20, 82234, Weßling, Germany
                [g ]Department of Remote Sensing, University of Würzburg, Oswald-Külpe-Str.86, 97074, Würzburg, Germany
                Author notes
                []Corresponding author. jmuro@ 123456uni-bonn.de
                Article
                S2405-8440(20)31340-2 e04496
                10.1016/j.heliyon.2020.e04496
                7452495
                c6f3d5b9-0030-4b59-b8ea-ff73d8d67313
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 19 July 2019
                : 3 February 2020
                : 15 July 2020
                Categories
                Article

                environmental science,geography,earth sciences,wetlands,random forest,data fusion,sentinel

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