Ignoring variation in wood density drives substantial bias in biomass estimates across spatial scales
Bibliographic Data
| ID | 15544209 |
|---|---|
| Authors | Jørgen Sand Sæbø (0000-0001-7658-4290, Norwegian University of Life Sciences, corresponding author), Jacob B Socolar (0000-0002-9126-9093, Norwegian University of Life Sciences), Edicson Parra-Sanchez (0000-0003-2670-3882, University of Sheffield), Paul Woodcock (0000-0002-2178-5447, Joint Nature Conservation Committee), Christopher G Bousfield (0000-0003-3576-9779, University of Sheffield), Claudia A Medina Uribe (0000-0002-7714-9220, Alexander von Humboldt Biological Resources Research Institute), David P Edwards (0000-0001-8562-3853, University of Sheffield), Torbjørn Haugaasen (0000-0003-0901-5324, Norwegian University of Life Sciences) |
| Year | 2022 |
| Volume | 17 |
| Issue | 5 |
| Pages | 054002-054002 |
| Publication date | 2022-03-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac62ae |
| OpenAlex | W4220657805 |
| Language | EN |
| References cited | 58 |
Rapid development of remote sensing and Light Detection and Ranging (LiDAR) technology has refined estimates of tree architecture and extrapolation of biomass across large spatial scales. Yet, current biomass maps show significant discrepancies and mismatch to independent ground data. A potential obstacle to accurate biomass estimation is the loss of information on wood density, which can vary at local and regional scales, in the extrapolation process. Here we investigate if variation in wood specific gravity (WSG) substantially impacts the distribution of above-ground biomass (AGB) across a range of scales from local plots to large regions. We collected wood cores and measured tree volume in 341 forest sites across large altitudinal and climatic gradients in Colombia. At all spatial scales, variation in WSG was substantial compared to variation in volume. Imputing study-wide average values of WSG induced regional biases in AGB estimates of almost 30%, consequently undervaluing the difference between forest areas of low and high average wood density. Further, neither stem size nor climate usefully predicted WSG when accounting for spatial dependencies among our sampling plots. These results suggest that remote sensing- and LiDAR-based projections to biomass estimates can be considerably improved by explicitly accounting for spatial variation in WSG, necessitating further research on the spatial distribution of WSG and potential environmental predictors to advance efficient and accurate large-scale mapping of biomass
Atmospheric sciences · Biology · Biomass (ecology · Extrapolation · Geography · Lidar · Physical geography · Range (aeronautics · Remote sensing · Sampling (signal processing · Spatial distribution · Spatial ecology · Spatial heterogeneity · Spatial variability · Statistics · Environmental Science · Forest ecology and management · Mathematics · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Ecology · Geology · Soil Science
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Global Carbon Budget 2019
WorldClim 2
Inference from Iterative Simulation Using Multiple Sequences
| Citation velocity | historical |
|---|---|
| Highly cited | No |