Forest Carbon Mapping with Airborne LiDAR

Check out these two new papers on forest carbon mapping with airborne LiDAR.

- Joseph Mascaro, Matteo Detto, Gregory P. Asner, Helene C. Muller-Landau. Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sensing of Environment (2011), doi:10.1016/j.rse.2011.07.019

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.

- Gregory P. Asner, Joseph Mascaro, Helene C. Muller-Landau, Ghislain Vieilledent, Romuald Vaudry, Maminiaina Rasamoelina, Jefferson S. Hall, Michiel van Breugel (2011). A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia DOI 10.1007/s00442-011-2165-z

Abstract
Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height–diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r 2 = 0.80, RMSE = 27.6 Mg C ha−1). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy. Download free PDF.