Monitoring sustainable forest management plans in the Amazon: Integrating LiDAR data and PlanetScope imagery

Abstract

Selective logging monitoring has traditionally relied on either medium-resolution optical imagery or LiDAR data alone, limiting the detection of both spectral and structural changes in forest cover. This study proposes a integrated analytical approach in parallel of LiDAR data and PlanetScope imagery to enhance monitoring of forest disturbances caused by selective logging in the Amazon. Notably, the correlation between the volume of wood extracted and LiDAR-detected areas is high (r2 = 0.9), demonstrating the accuracy of this method in detecting logging-impacted areas. In contrast, the correlation between wood volume and PlanetScope-based mapping is moderate (r2 = 0.7), indicating that while this approach effectively detects logging-related disturbances, its accuracy is influenced by factors such as canopy structure and image resolution. LiDAR mapping detected 15.5 % of the total impacted area, compared to 13.7 % detected by PlanetScope. LiDAR achieved higher accuracy in detecting subtle structural changes, such as small clearings (<0.2 ha). Globally, PlanetScope mapping underestimated the total area of clearings, identifying 63.3 ha, whereas LiDAR detected 113.8 ha. The global accuracy of PlanetScope mapping was moderate (P = 0.62) with low recall (R = 0.41), indicating significant underestimation of disturbed forest areas. Metrics such as the global F1-Score (0.50), IoU (0.33), and relatively high RMSE (50.51) further highlight the differences between the two methods. Despite these limitations, PlanetScope mapping was more effective than systems like DETER and SAD in detecting clearings smaller than 1 ha. The integration of these technologies provides more precise and reliable data, strengthening sustainable forest management monitoring and offering critical insights to inform public policies for the Amazon forest sector.