Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China

Abstract

Quantification of forest aboveground biomass density (AGB) is useful in forest carbon cycle studies, biodiversity protection and climate-change mitigation actions. However, a finer resolution and spatially continuous forest AGB map is inaccessible at national level in China. In this study, we developed forest type- and ecozone-specific allometric models based on 1607 field plots. The allometric models were applied to Geoscience Laser Altimeter System (GLAS) data to calculate AGB at the footprint level. We then mapped a 30 m resolution national forest AGB by relating the GLAS footprint AGB to various variables derived from Landsat images and Phased Array L-band Synthetic Aperture Radar (PALSAR) data. We estimated the average forest AGB to be 69.88 Mg/ha with a standard deviation of 35.38 Mg/ha and the total AGB carbon stock to be 5.44 PgC in China. Our AGB estimates corresponded reasonably well with AGB inventories from the top ten provinces in the forested area, and the coefficient of determination and root mean square error were 0.73 and 20.65 Mg/ha, respectively. We found that the main uncertainties for AGB estimation could be attributed to errors in allometric models and in height measurements by the GLAS. We also found that Landsat-derived variables outperform PALSAR-derived variables and that the textural features of PALSAR better support forest AGB estimates than backscattered intensity.