The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 303–308, 2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 303–308, 2020

  06 Nov 2020

06 Nov 2020


N. V. Estrabis1, L. Osco1, A. P. Ramos2, W. N. Gonçalves1, V. Liesenberg3, H. Pistori4, and J. Marcato Junior1 N. V. Estrabis et al.
  • 1Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Mato Grosso do Sul, Brazil
  • 2Environmental and Regional Development, University of Western São Paulo, Presidente Prudente, Brazil
  • 3Department of Forest Engineering, Santa Catarina State University, Santa Catarina, Brazil
  • 4Department of Computer Engineering, Dom Bosco Catholic University, Mato Grosso do Sul, Brazil

Keywords: Native Vegetation, Landsat 8 OLI, Google Earth Engine, Vegetation Indices, Atlantic Forest, Random Forest

Abstract. Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and non-native-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); III- mNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.