Monitoring sustanaible forest management in the Amazon: strategies, challen ges, and advanced remote sensing technologies

Abstract

The Amazon rainforest is one of the most biodiverse and essential ecosystems on the planet, playing a crucial role in climate regulation and ecosystem services. However, human pressures such as deforestation and degradation have significantly impacted its structure and functioning. Forest degradation, characterized by repeated interventions like fires, illegal logging, and edge effects, differs from disturbances, which are abrupt and localized events, such as treefalls, storms, and legal selective logging. While disturbances do not necessarily lead to long-term degradation, these processes are often conflated. The lack of clear differentiation highlights the need for the development of robust methodologies to distinguish between these processes. Advancing these methodologies, particularly through remote sensing technologies, is critical for accurately assessing the extent and impact of disturbances and degradation, enabling more targeted management strategies and conservation efforts in the Amazon. In Chapter 1, 80 scientific articles on anthropogenic impacts on the Amazon using remote sensing were analyzed. Although methodological advances were evident, gaps in monitoring less-studied regions, such as the central and western Amazon, highlighted the need for advanced tools in sustainable forest management (SFM). Chapter 2 focused on the Saracá-Taquera National Forest, examining the effects of logging intensity on volumetric stock, commercial species abundance, and forest cover. Using forest inventory data, elevation, and PlanetScope images, analyses structured in 1-hectare cells for pre-, during, and post-logging periods (t0, t1, t2) revealed that higher commercial stocks were located farther from water bodies. Logging was concentrated on a few trees per hectare, intensifying in areas with higher timber volumes. In Chapter 3, the integration of LiDAR data and PlanetScope images improved the detection of disturbances caused by selective logging. LiDAR identified 15.48% of impacted areas, surpassing PlanetScope's 13.69% sensitivity, particularly for subtle changes like clearings smaller than 0.2 hectares. PlanetScope, however, outperformed systems like DETER and SAD in identifying clearings under 1 hectare. The combined use of these technologies proved critical for impact monitoring, strengthening sustainable management, and supporting public policies. A key aspect of SFM is its socio environmental dimension. The Amazon is home to many traditional and indigenous communities whose livelihoods depend on the forest. Ensuring resource sustainability requires their inclusion in management processes, valuing traditional knowledge, and addressing equitable benefit-sharing and economic opportunities. Strategies must prioritize long-term resilience by balancing environmental conservation with the well being of local populations. This study underscores the importance of integrating advanced technologies and adaptive strategies to balance economic exploitation and environmental conservation. These initiatives are essential for promoting sustainable management and protecting Amazonian forest resources.