4.6 Article

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

期刊

ROYAL SOCIETY OPEN SCIENCE
卷 8, 期 2, 页码 -

出版社

ROYAL SOC
DOI: 10.1098/rsos.201458

关键词

tropical forests; tree structure; above-ground biomass; destructive harvest; terrestrial lidar; allometry

资金

  1. Natural Environment Research Council (NERC) [NE/N00373X/1]
  2. European Research Council [757526]
  3. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [457914/2013-0/MCTI/CNPq/FNDCT/LBA/ESECAFLOR]
  4. NERC [NE/N006852/1, NE/N014022/1]
  5. NERC National Centre for Earth Observation (NCEO) [NE/R016518/1]
  6. European Research Council (ERC) [757526] Funding Source: European Research Council (ERC)
  7. NERC [NE/P011780/1, nceo020002, NE/N00373X/1] Funding Source: UKRI

向作者/读者索取更多资源

In this study, four large tropical rainforest trees were harvested in East Amazonia to provide ecological insights and compare non-destructive methods for estimating above-ground biomass. The use of terrestrial lidar proved to be more accurate in estimating biomass compared to traditional allometric methods, potentially reducing uncertainty in carbon cycle estimates for tropical forests.
A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2 m, height: 30-46 m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

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