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标题：Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+
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作者：Veronika Leitold ; Michael Keller ; Douglas C Morton 等
期刊名称：Carbon Balance and Management
印刷版ISSN：1750-0680
电子版ISSN：1750-0680
出版年度：2015
卷号：10
期号：1
页码：3-14
DOI：10.1186/s13021-015-0013-x
出版社：BioMed Central
摘要：Background Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m−2) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m−2, 4 m−2, 2 m−2 and 1 m−2) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m−2, the bias in height estimates translated into errors of 80–125 Mg ha−1 in predicted aboveground biomass. Conclusions Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.
关键词：Tropical montane forest; Airborne lidar; Digital Terrain Model; Elevation accuracy; Data thinning; Canopy height; Biomass estimation; REDD+