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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

Details

Title
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images
Author
Tahisa Neitzel Kuck 1   VIAFID ORCID Logo  ; Sano, Edson Eyji 2   VIAFID ORCID Logo  ; Polyanna da Conceição Bispo 3   VIAFID ORCID Logo  ; Shiguemori, Elcio Hideiti 4   VIAFID ORCID Logo  ; Paulo Fernando Ferreira Silva Filho 4   VIAFID ORCID Logo  ; Eraldo Aparecido Trondoli Matricardi 5 

 Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), São José dos Campos 12228-001, Brazil; [email protected] (E.H.S.); [email protected] (P.F.F.S.F.); Geoscience Institute, Universidade de Brasília (UnB), Brasília 70910-900, Brazil; [email protected] 
 Geoscience Institute, Universidade de Brasília (UnB), Brasília 70910-900, Brazil; [email protected]; Embrapa Cerrados, Planaltina 73310-970, Brazil 
 Department of Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 9PL, UK; [email protected] 
 Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance Division, Institute for Advanced Studies (IEAv), São José dos Campos 12228-001, Brazil; [email protected] (E.H.S.); [email protected] (P.F.F.S.F.) 
 Forestry Department, Universidade de Brasília (UnB), Brasília 70919-970, Brazil; [email protected] 
First page
3341
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2571500056
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.