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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Quantifying Canopy Tree Loss and Gap Recovery in Tropical Forests under Low-Intensity Logging Using VHR Satellite Imagery and Airborne LiDAR

Texto completo
Autor(es):
Dalagnol, Ricardo [1, 2] ; Phillips, Oliver L. [1] ; Gloor, Emanuel [1] ; Galvao, Lenio S. [2] ; Wagner, Fabien H. [2] ; Locks, Charton J. [3] ; Aragao, Luiz E. O. C. [2, 4]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] Univ Leeds, Sch Geog, Leeds LS2 9JT, W Yorkshire - England
[2] Natl Inst Space Res INPE, Remote Sensing Div, BR-12227010 Sao Jose Dos Campos, SP - Brazil
[3] Brazilian Forest Serv, BR-70818900 Brasilia, DF - Brazil
[4] Univ Exeter, Geog, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon - England
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: REMOTE SENSING; v. 11, n. 7 APR 1 2019.
Citações Web of Science: 1
Resumo

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 similar to 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. (AU)

Processo FAPESP: 17/15257-8 - Detecção da mortalidade florestal usando imagens ópticas de alta resolução espacial para avaliação dos padrões espaciais e temporais da dinâmica da floresta e carbono na Floresta Amazônica
Beneficiário:Ricardo Dal'Agnol da Silva
Linha de fomento: Bolsas no Exterior - Estágio de Pesquisa - Doutorado
Processo FAPESP: 16/17652-9 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
Beneficiário:Fabien Hubert Wagner
Linha de fomento: Bolsas no Brasil - Apoio a Jovens Pesquisadores
Processo FAPESP: 15/50484-0 - Diversidade funcional dos biomas Amazônia, Mata Atlântica e Cerrado nos ambientes intactos e em regeneração por meio de imagens hiperspectrais
Beneficiário:Fabien Hubert Wagner
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
Processo FAPESP: 15/22987-7 - Avaliação do impacto de mudanças climáticas sobre a dinâmica de biomassa e carbono na Amazônia
Beneficiário:Ricardo Dal'Agnol da Silva
Linha de fomento: Bolsas no Brasil - Doutorado