The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 639–644, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-639-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 639–644, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-639-2021

  29 Jun 2021

29 Jun 2021

MAPPING COASTAL AND WETLAND VEGETATION COMMUNITIES USING MULTI-TEMPORAL SENTINEL-2 DATA

P. Villa1, C. Giardino1, S. Mantovani2, D. Tapete3, A. Vecoli2, and F. Braga4 P. Villa et al.
  • 1Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), Milan, Italy
  • 2Meteorological and Environmental Earth Observation (MEEO), Ferrara, Italy
  • 3Italian Space Agency (ASI), Rome, Italy
  • 4Institute of Marine Sciences, National Research Council (CNR-ISMAR), Venice, Italy

Keywords: Salt marsh vegetation, Coastal vegetation, Classification, Decision trees, Synoptic seasonal features

Abstract. Operational monitoring of complex vegetation communities, such as the ones growing in coastal and wetland areas, can be effectively supported by satellite remote sensing, providing quantitative spatialized information on vegetation parameters, as well as on their temporal evolution. With this work, we explored and evaluated the potential of Sentinel-2 data for assessing the status and evolution of coastal vegetation as the primary indicator of ecosystem conditions, by mapping the different plant communities of Venice lagoon (Northeast Italy) via a rule-based classification approach exploiting synoptic seasonal features of spectral indices and multispectral reflectance. The results demonstrated that coastal and wetland vegetation community type maps derived for two different years scored a good overall accuracy around 80%, with some misclassification in the coastal areas and overestimation of salt marsh communities coverage, and that virtual collaborative environments can facilitate the use of Sentinel-2 data and products to multidisciplinary users.