ABSTRACT Remote sensing data used in this study included MSI (Multispectral Instrument) Sentinel-2, SAR (Synthetic Aperture Radar) Sentinel-1, GLCM (Grey Level Co-Occurrence Matrix) texture data derived from Sentinel-1, and geomorphometric data derived from SRTM (Shuttle Radar Topography Mission) images. The input data was divided into separate groups for machine learning algorithms, including Support Vector Machine (SVM), Classification and Regression Tree (CART), and Random Forest (RF), which were implemented on the Google Earth Engine platform. RF showed the highest overall accuracies (93 to 97%), regardless of the dataset used as input, with the Kappa index ranging from 0.89 (optical and SAR data) to 0.95 (optical, SAR, and geomorphometric data). CART showed identical overall accuracy values (92.5%) except for the dataset supplemented with SAR texture data, which showed slightly lower accuracy (91.7%), with the Kappa index ranging from 0.89 to 0.91. The worst performance was classifying optical data by SVM, resulting in 59% accuracy and a Kappa index of 0.37. However, the synergy of optical, SAR, and geomorphometric data classified by SVM achieved 75% accuracy. Multispectral Instrument Sentinel2, Sentinel2 Sentinel 2, 2 Sentinel-2 Synthetic Sentinel1, Sentinel1 1, 1 Sentinel-1 Grey CoOccurrence Co Occurrence Matrix Shuttle Mission images algorithms , (SVM) CART, (CART) RF, (RF) platform 93 (9 97%, 97 97% 97%) 089 0 89 0.8 095 95 0.9 data. . 92.5% 925 92 5 (92.5% 91.7%, 917 91.7% 91 7 (91.7%) 091 0.91 59 037 37 0.37 However 75 Sentinel- (SVM (CART (RF 9 ( 08 8 0. 09 92.5 (92.5 91.7 (91.7% 03 3 0.3 92. (92. 91. (91.7 (92 (91. (91
RESUMO Foram utilizados dados de sensoriamento remoto adquiridos pelos sensores MSI (Multispetral Instrument) do satélite Sentinel-2 e SAR (Synthetic Aperture Radar) Sentinel-1, dados de textura GLCM (Grey Level Co-Ocurrence Matrix) derivados das imagens Sentinel-1 e dados geomorfométricos derivados de imagens SRTM (Shuttle Radar Topography Mission). Os dados compuseram diferentes grupos de entrada para os classificadores de aprendizagem de máquina Support Vector Machine (SVM), Classification and Regression Tree (CART) e Random Forest (RF), implementados na plataforma Google Earth Engine. O RF apresentou as maiores exatidões globais (93 a 97%), independente do conjunto de dados utilizados como entrada, com o índice Kappa variando de 0,89 (dados ópticos e SAR) a 0,95 (dados ópticos, SAR e geomorfométricos). O CART apresentou valores idênticos de exatidão global (92,5%) exceto para o conjunto de dados acrescido dos dados de textura SAR, que apresentou exatidão ligeiramente mais baixa (91,7%), com índice Kappa variando de 0,89 a 0,91. O pior desempenho foi o da classificação de dados ópticos por SVM, resultando em 59% de exatidão e 0,37 de índice Kappa. Todavia, a sinergia de dados ópticos, SAR e geomorfométricos classificados por SVM atingiu 75% de exatidão. Multispetral Instrument Sentinel2 Sentinel 2 Sentinel- Synthetic Sentinel1, Sentinel1 1, 1 Grey CoOcurrence Co Ocurrence Matrix Shuttle Mission. Mission . Mission) , (SVM) (CART RF, (RF) Engine 93 (9 97%, 97 97% 97%) 089 0 89 0,8 095 95 0,9 geomorfométricos. geomorfométricos) 92,5% 925 92 5 (92,5% 91,7%, 917 91,7% 91 7 (91,7%) 091 0,91 59 037 37 0,3 Todavia 75 (SVM (RF 9 ( 08 8 0, 09 92,5 (92,5 91,7 (91,7% 03 3 92, (92, 91, (91,7 (92 (91, (91