Assessing ALOS-2/PALSAR-2 Data's Potential in Detecting Forest Volume Losses from Selective Logging in a Section of the Tapajós National Forest

This study focuses on evaluating the unique capabilities of ALOS-2/PALSAR-2 (ALOS2) polarimetric images for detecting forest volume losses resulting from the selective logging process within a sustainable framework in the Tapajós National Forest (TNF), situated in the heart of the Brazilian Amazon. Specifically, two areas within TNF characterized by intensive logging activities, ranging between 27 m³ ha⁻¹ and 29 m³ ha⁻¹, were chosen as Annual Production Units (APUs). Each APU was logged during a distinct year: APU 2016 and APU 2017. Extracting attributes from ALOS2 images, encompassing backscatter properties (including algebraic calculations, band ratios, SAR vegetation indices, and texture measurements) and phase information (comprising entropy and alpha angle), this investigation aims to detect forest volume losses. This involves evaluating the disparities in pixel values between logged and unlogged regions. The analysis employs Wilcoxon's nonparametric test at a 95% confidence level to determine the statistical significance of the observed differences. The findings gleaned from ALOS2 data demonstrate robust performance. Among the considered attributes, the Radar Normalized Difference Vegetation Index (RNDVI) emerges as the most promising indicator for detecting forest volume losses attributed to degradation through selective logging. Notably, this effectiveness is consistent across both investigated areas, with a p-value of 0.003 for APU 2016 and 0.037 for APU 2017. Additionally, the cross-polarization ratio and the texture measure known as Contrast in HV polarization display significant potential. This study underscores ALOS2's efficacy in identifying forest volume losses arising from selective logging. The insights gained, particularly the prominence of RNDVI in degradation detection, offer valuable perspectives for monitoring and mitigating ecological impacts stemming from logging activities within intricate forest ecosystems.