A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO<sub>2</sub> emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constraine...

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Bibliographic Details
Main Authors: Tahisa Neitzel Kuck, Edson Eyji Sano, Polyanna da Conceição Bispo, Elcio Hideiti Shiguemori, Paulo Fernando Ferreira Silva Filho, Eraldo Aparecido Trondoli Matricardi
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Remote Sensing
Subjects:
synthetic aperture radar
machine learning
random forest
AdaBoost
multilayer perceptron
Online Access:https://www.mdpi.com/2072-4292/13/17/3341

Internet

https://www.mdpi.com/2072-4292/13/17/3341

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