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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

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Pinage, Ekena Rangel [1, 2] ; Keller, Michael [3, 4, 5] ; Duffy, Paul [6] ; Longo, Marcos [3, 4] ; dos-Santos, Maiza Nara [3] ; Morton, Douglas C. [7]
Total Authors: 6
[1] Oregon State Univ, Coll Forestry, Corvallis, OR 97331 - USA
[2] Univ Technol Sydney, Sch Life Sci, Sydney, NSW 2007 - Australia
[3] Embrapa Agr Informat, BR-13083886 Campinas, SP - Brazil
[4] NASA, Jet Prop Lab, CALTECH, Pasadena, CA 91109 - USA
[5] US Forest Serv, USDA, Int Inst Trop Forestry, San Juan, PR 00926 - USA
[6] Neptune Inc, Lakewood, CO 80215 - USA
[7] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 - USA
Total Affiliations: 7
Document type: Journal article
Source: REMOTE SENSING; v. 11, n. 6 MAR 24 2019.
Web of Science Citations: 1

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6-7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of -4.14 +/- 0.76 MgC ha(-1) y(-1). In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data. (AU)

FAPESP's process: 15/07227-6 - Long term effects of forest degradation on carbon cycling in Amazonia
Grantee:Marcos Longo
Support type: Scholarships in Brazil - Post-Doctorate