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

Author(s)

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]}

Affiliation

^[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

• Document type: Journal article
• Source: REMOTE SENSING; v. 11, n. 7 APR 1 2019.
• Web of Science Citations: 1

Abstract

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)

• FAPESP's process: 16/17652-9 - Functional diversity of intact and regenerating Amazon, Atlantic Forest and Cerrado systems using hyperspectral imagery
• Grantee: Fabien Hubert Wagner
• Support Opportunities: Scholarships in Brazil - Young Researchers
• FAPESP's process: 17/15257-8 - Detection of tree mortality using high resolution optical imagery for assessment of spatial and temporal patterns of forest dynamics and carbon in Amazon forest
• Grantee: Ricardo Dal'Agnol da Silva
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 15/50484-0 - Functional diversity of intact and regenerating Amazon, Atlantic Forest, and Cerrado systems using hyperspectral imagery
• Grantee: Fabien Hubert Wagner
• Support Opportunities: Research Grants - Young Investigators Grants
• FAPESP's process: 15/22987-7 - Assessment of climate change impacts on the biomass and carbon dynamics in the Amazon
• Grantee: Ricardo Dal'Agnol da Silva
• Support Opportunities: Scholarships in Brazil - Doctorate