Logging, including selective and illegal activities, is widespread, affecting the carbon cycle and the biodiversity of tropical forests. However, automated approaches using very high resolution (VHR) satellite data (≤1 m spatial resolution) to accurately track these small-scale human disturbances over large and remote areas are not readily available. The main constraint for performing this type of analysis is the lack of spatially accurate tree-scale validation data. In this study, we assessed the potential of VHR satellite imagery to detect canopy tree loss related to selective logging in closed-canopy tropical forests. To do this, we compared the tree loss detection capability of WorldView-2 and GeoEye-1 satellites with airborne LiDAR, which acquired pre-and post-logging data at the Jamari National Forest in the Brazilian Amazon. We found that logging drove changes in canopy height ranging from −5.6 to −42.2 m, with a mean reduction of −23.5 m. A simple LiDAR height difference threshold of −10 m was enough to map 97% of the logged trees. Compared to LiDAR, tree losses can be detected using VHR satellite imagery and a random forest (RF) model with an average precision of 64%, while mapping 60% of the total tree loss. Tree losses associated with large gap openings or tall trees were more successfully detected. In general, the most important remote sensing metrics for the RF model were standard deviation statistics, especially those extracted from the reflectance of the visible bands (R, G, B), and the shadow fraction. While most small canopy gaps closed within~2 years, larger gaps could still be observed over a longer time. Nevertheless, the use of annual imagery is advised to reach acceptable detectability. Our study shows that VHR satellite imagery has the potential for monitoring the logging in tropical forests and detecting hotspots of natural disturbance with a low cost at the regional scale.
As atividades de manejo florestal são consideradas importantes para o desenvolvimento sustentável para a Amazônia. Tais atividades exigem, entretanto, monitoramento rigoroso que muitas vezes são de difícil operacionalização. O mapeamento das áreas afetadas pela exploração seletiva de madeira e a mensuração dos danos florestais decorrentes da exploração florestal ainda são dependentes de extensos e onerosos levantamentos de campo. Neste estudo foi utilizada a tecnologia Light Detection And Ranging (LiDAR) aerotransportada para realização dos impactos causados pela extração seletiva de madeiras em 21 Unidades de Produção Anual na Amazônia. As áreas de estudo estão localizadas nos estados de Rondônia e do Pará, em Florestas Nacionais sob regime de concessão florestal federal. Foram utilizadas duas métricas derivadas da nuvem de pontos LiDAR para o mapeamento dos impactos nas florestas: a Canopy Height Model (CHM) como métrica do dossel e a Relative Density Model (RDM) como métrica do sub-bosque. Os resultados da detecção dos impactos florestais obtidos com o mapeamento com dados do LiDAR são compatíveis com o mapeamento realizado em campo. Observou-se que as práticas de extração florestal de impacto reduzido causaram danos no sub-bosque na ordem de 6,8% ±1,3 % da área total das UPA (Unidade de Produção Anual) avaliadas e 4,9 ±0,9 % de abertura de clareiras. A tecnologia LiDAR demonstrou ser efetiva para o monitoramento dos impactos da extração seletiva de madeiras em áreas sob concessão florestal federal na Amazônia.
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