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

Elcio Hideiti Shiguemori,Paulo Fernando Ferreira Silva Filho,Eraldo Aparecido Trondoli Matricardi,Edson Eyji Sano,Polyanna Da Conceição Bispo,Tahisa Neitzel Kuck

doi:10.3390/rs13173341

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.

Highlights

Anthropogenic activities are responsible for the current global temperature increase of about 1.0 ◦ C and for an expected increase of 1.5 ◦ C sometime between 2030 and 2052.Potential impacts and risks associated with increasing temperature include elevation of the sea level, higher frequency and intensity of extreme temperatures, storms, and droughts, loss of biodiversity, reduction in oxygen concentration in the oceans, and shortage of food production [1]
This study aimed to compare the performance of the machine-learning-based random forest, AdaBoost, and multilayer perceptron artificial neural network (MLP-Artificial neural networks (ANNs)) classification techniques in identifying selective logging activities in a forest concession site located in the Jamari National Forest, Rondônia State, Brazil
By overlaying the samples selected as timber extraction in the LiDAR images, the coordinates of extracted trees provided by the SFB, and the polygons generated by the thresholding of the coefficients of variation (CV), we found 849 trees extracted in the UPA

Summary

Introduction

Anthropogenic activities are responsible for the current global temperature increase of about 1.0 ◦ C and for an expected increase of 1.5 ◦ C sometime between 2030 and 2052.Potential impacts and risks associated with increasing temperature include elevation of the sea level, higher frequency and intensity of extreme temperatures, storms, and droughts, loss of biodiversity, reduction in oxygen concentration in the oceans, and shortage of food production [1]. Anthropogenic activities are responsible for the current global temperature increase of about 1.0 ◦ C and for an expected increase of 1.5 ◦ C sometime between 2030 and 2052. A reduction in anthropogenic CO2 emissions is mandatory to control the global rise in temperature. Deforestation and forest degradation are the second largest anthropogenic sources of CO2 emissions into the atmosphere, since they are related to the combustion of forest biomass, as well as to the decomposition of remaining plant materials. According to Qin et al [3], forest degradation contributes three times more to the aboveground gross biomass loss than deforestation in the Brazilian Amazon.

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