Author(s)

Marcos Roberto Martines¹, Mariana de Paula Garcia Lúcio¹, Alexandre D. M. Cavagis², Marcel Fantin³, Ricardo Vicente Ferreira⁴, Matheus Oliveira Alves⁵, Rogério Hartung Toppa¹

¹Federal University of São Carlos, UFSCar/NEEPC, São Carlos, São Paulo, Brazil.
²Federal University of São Carlos, UFSCar/DFQM, São Carlos, São Paulo, Brazil.
³University of São Paulo, USP, São Carlos, São Paulo, Brazil.
⁴Federal University of Triangulo Mineiro, Minas Gerais, Brazil.
⁵Faculdades Associadas de Uberaba, FAZU, Minas Gerais, Brazil.

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.

Atlantic Forest, Land Cover, Image Classification

Martines, M. , Lúcio, M. , Cavagis, A. , Fantin, M. , Ferreira, R. , Alves, M. and Toppa, R. (2019) Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms. Journal of Geographic Information System, 11, 567-578. doi: 10.4236/jgis.2019.115035.