The aim of this work was to differentiate Atlantic Forest patches, as well as their spatial distribution, from other tree covers that compose the landscape, by comparing three methods of digital images classification,...The aim of this work was to differentiate Atlantic Forest patches, as well as their spatial distribution, from other tree covers that compose the landscape, by comparing three methods of digital images classification, using techniques of geoprocessing and remote sensing. The study area was a sub-basin of the Iperó River, tributary of the Iperó-Mirim stream, Sarapuí River basin, in Araçoiaba da Serra, State of São Paulo, Brazil. This research has been developed on a Geographic Information System environment platform, using medium resolution images from Sentinel-2 Satellite. Three image classification algorithms: Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and Random Tree (RT) were applied to verify the separability of forest patches, forestry and other uses. The results were analyzed by means of a confusion matrix, accuracy and kappa index, thus showing that the three algorithms were able to successfully differentiate the targets, with the higher efficiency attributed to MLC and the lowest to RT. Overall, the three classifiers presented errors, but specifically for the forest patches, the highest accuracy was obtained from SVM.展开更多
文摘The aim of this work was to differentiate Atlantic Forest patches, as well as their spatial distribution, from other tree covers that compose the landscape, by comparing three methods of digital images classification, using techniques of geoprocessing and remote sensing. The study area was a sub-basin of the Iperó River, tributary of the Iperó-Mirim stream, Sarapuí River basin, in Araçoiaba da Serra, State of São Paulo, Brazil. This research has been developed on a Geographic Information System environment platform, using medium resolution images from Sentinel-2 Satellite. Three image classification algorithms: Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and Random Tree (RT) were applied to verify the separability of forest patches, forestry and other uses. The results were analyzed by means of a confusion matrix, accuracy and kappa index, thus showing that the three algorithms were able to successfully differentiate the targets, with the higher efficiency attributed to MLC and the lowest to RT. Overall, the three classifiers presented errors, but specifically for the forest patches, the highest accuracy was obtained from SVM.
