Avaliação de recursos para o mapeamento de cobertura do solo em sub-bacia do rio Capibaribe-PE usando imagem Kompsat-2

Land use and land cover mapping is an important factor in geospatial analysis in watershed management. The integration of remote sensing images and Machine Learning classification techniques enable the identification and environmental monitoring of landscape elements. The MSC (MultiSpectral Camara) sensor on the Kompsat-2 satellite captures images of high spatial resolution, which allows the identification of terrestrial resources on a local scale. Six data models were developed for classifying land use and land cover by Random Forest in a Capibaribe River sub-basin. These models were created based on spectral indices and ranking of variable importance. The evaluation of the results was done through spatial quantification and accuracy analysis. Products based on bands and spectral indices showed global accuracy ranging between 94 and 98%, where the classes of Arboreal and Shrubby Vegetation stood out with estimates of accuracy of the producer and user above 80%. Products with the lowest data resources showed poor accuracy performance with overall accuracy values clustered below 60%. This study is the first to use adaptations of Kompsat-2 spectral data and computer learning methods to demonstrate the application of high-performance land cover mapping. Thus, this article contributed to the monitoring of the soil surface in urban sub-basins that need precise spatial information about the state of environmental conversation.