Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms

Marcos Roberto Martines,Ricardo Vicente Ferreira,Matheus Oliveira Alves,Rogério Hartung Toppa,Alexandre D M Cavagis,Marcel Fantin,Mariana De Paula Garcia Lúcio

doi:10.4236/jgis.2019.115035

Abstract

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 Ipero River, tributary of the Ipero-Mirim stream, Sarapui River basin, in Aracoiaba da Serra, State of Sao 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.

Highlights

Mapping the Brazilian Atlantic Forest remnants and their stages of succession is a pivotal step for the implementation of several studies, environmental control and management actions [1]
These natural forest patches are constantly pressured by a land cover dynamics, related to expansion and retraction of land uses that modify the landscape by forming a mosaic of forest patches with different sizes and in several stages of succession, isolated by an anthropized matrix [3]
Forest) and forestry areas (Eucalyptus spp. (Myrtaceae) or Pinus spp. (Pinaceae); 2) an image obtained from the Sentinel-2 Satellite of the GMES Program, with bands 02 to 12 was used as material; 3) the selection of samples for the classes: forest patches, forestry and other elements of the landscape was carried out; 4) these samples were used in the Maximum Likelihood Classification (MLC), Suport Vector Machine (SVM) and Random Trees (RT); 5) the test of accuracy of the classifiers was carried out from ground truth collected and application of statistical indices; 6) the result is a land cover map of each classification algorithm, regarding the “forest patches”, “forestry” and “other uses” categories

Summary

Introduction

Mapping the Brazilian Atlantic Forest remnants and their stages of succession is a pivotal step for the implementation of several studies, environmental control and management actions [1]. The classification of natural patches from the Atlantic Forest biome is fundamental for a wide range of studies, considering that the majority of natural forest remnants are in form of small patches, highly disturbed, isolated, little known and poorly protected [2]. These natural forest patches are constantly pressured by a land cover dynamics, related to expansion and retraction of land uses that modify the landscape by forming a mosaic of forest patches with different sizes and in several stages of succession, isolated by an anthropized matrix [3]. The application of image classification by remote sensing involves a complex process that encompasses many factors, including the choice of the classification method, which requires checking the accuracy of the results [5]

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Discussion

Conclusion