The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 237–242, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-237-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 237–242, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-237-2020

  06 Nov 2020

06 Nov 2020

WETLAND MAPPING WITH MULTITEMPORAL SENTINEL RADAR REMOTE SENSING IN THE SOUTHEAST REGION OF BRAZIL

J. B. G. Salinas1, M. K. P. Eggerth2, M. E. Miller3, R. R. B. Meza1, J. T. A. Chacaltana2, J. R. Acuña1, and G. F. Barroso3 J. B. G. Salinas et al.
  • 1Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima, Peru
  • 2PPGEA, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
  • 3PPGOAM, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil

Keywords: Wetlands, SAR, Multitemporal, Classification, Sentinel-1

Abstract. A classification method with multi-temporal images of synthetic aperture radar (SAR) combined with Geographic information system, geoinformation data, and field validation, was applied for wetland mapping accuracy and typology. Wetland mapping is vital for management and conservation, particularly under environmental pressures such as wetland drainage and land reclamation. The aim of this study is to develop an accurate mapping of wetlands and open water systems of the Lower Doce River Valley - LDRV (Southeastern Brazil) with Synthetic Aperture Radar (SAR) imagery, using multitemporal classification techniques and ground truth validation. Sentinel-1B SAR imagery from 2016 and 2019 was processed with Google Earth Engine (GEE). Monthly median imagery condition for the rainy season was obtained and K-means unsupervised classification was applied. The study yields 4,157 wetlands, 262.27 km2 with predominant small patches. Fieldwork revealed three main wetlands categories: coastal wetlands, inland wetlands and artificial wetlands. The results have shown an overall accuracy of 81.9% and a Kappa coefficient of 0.71. Wetlands, non-wetlands, and open waters classes present accuracy of 50, 80 and 95%, respectively.