Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data

Abstract

Accurate characterization of variability and trends in forest biomass at local to national scales is required for accounting of global carbon sources and sinks and monitoring their dynamics. Here we present a new remote sensing based approach for estimating live forest aboveground biomass (AGB) based on a simple parametric model that combines high-resolution estimates of leaf area index (LAI) from the Landsat Thematic Mapper sensor and canopy maximum height from the Geoscience Laser Altimeter System (GLAS) sensor onboard ICESat, the Ice, Cloud, and land Elevation Satellite. We tested our approach with a preliminary uncertainty assessment over the forested areas of California spanning a broad range of climatic and land-use conditions and find our AGB estimates to be comparable to estimates of AGB from inventory records and other available satellite-estimated AGB maps at aggregated scales. Our study offers a high-resolution approach to map forest aboveground biomass at regional-to-continental scales and assess sources of uncertainties in the estimates.