Assessing the impacts of selective logging on the forest canopy in the Amazon using airborne LiDAR

Abstract

Monitoring the impacts of selective logging in tropical forests remains challenging due to the reliance on labor intensive field surveys. This study relies on the use of pre- and post-logging airborne LiDAR data to provide a precise and scalable method for quantifying canopy disturbances, carried out within the Sustainable Management Plan for the Jamari National Forest in Rondônia. The analysis of the airborne LiDAR data revealed a significant increase in canopy gaps after logging ($F=63.5$, $p<0.001$), with canopy gaps corresponding to an average increase of 3.9 ± 0.4% relative to the total plot area due to logging activities. The mean canopy gap area per felled tree was 158.29 m² ($\pm$ 35.7). A strong positive correlation was found between canopy gaps that emerged after logging and the logged AGB (18.4 ± 1.7 $\mathrm{Mg}{\mathrm{ha}}^{-1}$). A significant reduction in mean canopy height was also observed, decreasing from 26.26 ± 0.40 m before logging to 24.62 ± 0.33 m after logging ($F=9.86$, $p=0.005$). The mean canopy gap area shifted from 40.68 ± 2.30 m² to 77.07 ± 2.82 m². Furthermore, there was an increase of 14.6% in the total number of gaps. The average Gini coefficient was 0.50 ± 0.02 before logging and 0.64 ± 0.01 in the post-logging areas and the average total impact on the canopy was 16.6 ± 1.5% of the selectively logged area. The results obtained using the proposed methodology were consistent with field observations, demonstrating high accuracy of LiDAR-detected impacts when compared with inventory and GNSS data. This high detection rate highlights the sensitivity of LiDAR point cloud data in capturing small structural changes. Compared to pre-logging conditions, the observed alterations demonstrate that LiDAR provides a more precise and scalable approach for quantifying the impact of selective logging on forest structure.