Evaluating uncertainty in mapping forest carbon with airborne LiDAR

Abstract

Airborne LiDAR is increasingly used to map carbon stocks in tropical forests, but our understanding of mapping errors is constrained by the spatial resolution (i.e., plot size) used to calibrate LiDAR with field data (typically 0.1-0.36 ha). Reported LiDAR errors range from 17 to 40 Mg C ha- 1, but should be lower at coarser resolutions because relative errors are expected to scale with (plot area)-1/2. We tested this prediction empirically using a 50-ha plot with mapped trees, allowing an assessment of LiDAR prediction errors at multiple spatial resolutions. We found that errors scaled approximately as expected, declining by 38% (compared to 40% predicted from theory) from 0.36- to 1-ha resolution. We further reduced errors at all spatial resolutions by accounting for tree crowns that are bisected by plot edges (not typically done in forestry), and collectively show that airborne LiDAR can map carbon stocks with 10% error at 1-ha resolution -- a level comparable to the use of field plots alone.