A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

Abstract

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.

Forest Change Detection and Prediction using the Crow Chicken Swarm Optimization based Deep LSTM Classifier

“…In 2023, Affonso et al [35] have executed a remote sensing tool called Volta Grande Do Xingi (VGX). Initially, the images are tested with two different methods.…”

Identifikasi Sebaran Spasial dan Kerapatan Mangrove Gili Lawang menggunakan Citra Landsat 9 OLI-2/TIRS-2

“…Algoritma SVM memiliki kemampuan yang baik dalam menemukan hyperplane pemisah dan titik maksimal sehingga menghasilkan nilai akurasi klasifikasi objek yang tinggi (Firmansyah et al, 2019). Algoritma SVM banyak digunakan dalam proses klasifikasi objek atau tutupan lahan dikarenakan keandalannya dibandingkan beberapa algoritma lain yang tersedia seperti maximum likelihood dan distance mahalanobis (Affonso et al, 2023;Karan & Samadder, 2018). Tabel 2 menjelaskan sebaran spasial objek-objek yang berhasil diidentifikasi oleh algoritma SVM.…”