Publications

Tang, H; Armston, J; Hancock, S; Marselis, S; Goetz, S; Dubayah, R (2019). Characterizing global forest canopy cover distribution using spaceborne lidar. REMOTE SENSING OF ENVIRONMENT, 231, UNSP 111262.

Abstract
Detailed characterization of global forest dynamics requires accurate measurements of canopy cover beyond estimating the extent of forested area. Passive-optical remote sensing techniques, despite remarkable success in identifying global hotspots of forest cover loss, cannot fully satisfy observation requirements at the plot or canopy crown level. Critical issues including signal saturation and algorithm uncertainty impose limitations on capturing subtle canopy cover changes using standard products generated from satellite imagery, particularly over largely intact dense tropical forests. Spaceborne lidar remote sensing can fill this gap in contemporary Earth observation networks by providing direct measurements of 3-D canopy structure. Here we analyze global canopy cover distributions using observations from the Geoscience Laser Altimetry System (GLAS) onboard of NASA's Ice, Cloud, and land Elevation Satellite (ICESat-1). We found ICESat-based cover estimates were sensitive to canopy cover dynamics even over dense forests with cover exceeding 80% and were able to better characterize biome-level gradients and canopy cover distributions than the existing products derived from conventional optical remote sensing. At the footprint level, ICESat-1 produced almost no bias when compared with airborne estimates, and had RMSE values on the order of similar to 20% cover. Improved cover products based on lidar should allow comprehensive analysis of subtle forest structure changes at landscape scales, and provide unique information for biophysical stratification of forests and changes in vertical canopy structure. This is particularly true given the Global Ecosystem Dynamics Investigation (GEDI) lidar recently installed on the International Space Station, which will acquire higher resolution lidar data at greater sampling densities than has been available to date.

DOI:
10.1016/j.rse.2019.111262

ISSN:
0034-4257