New Metrics and the Combinations for Estimating Forest Biomass from GLAS Data

Abstract

Geoscience laser altimeter system (GLAS) data have been widely used for forest aboveground biomass (AGB) estimation, but there is no consensus on the optimal metrics. To explore whether a few optimal GLAS metrics could generate accurate AGB estimates, we proposed five metrics and explored their combinations with ten existing ones. The importance of these metrics was measured according to their contributions to changes in the cross-validated mean-squared error. The two to eight most important metrics were then selected to develop AGB models, and their performances were evaluated using field AGB. The optimal combination of GLAS metrics was finally used for AGB estimation at GLAS footprints from 2004 to 2007 within a 2°×2° spatial extent in Tahe and Changbai Mountain, China. The results showed that four GLAS metrics, including our proposed metric CRH25 (25th percentile of canopy reflection heights) combined with Lead, quadratic mean canopy height, and H75, yield the best AGB estimates, with an R2 of 0.61±0.15 and RMSE of 52.20±23.50 Mg/ha, and the inclusion of more GLAS metrics did not improve the results. The estimated forest AGB in Tahe was 89.03±19.16 Mg/ha and 103.07±23.42 Mg/ha in Changbai Mountain. In both regions, the annual average forest AGB estimates for 2005 were higher than the AGB estimates for 2004, 2006, and 2007. The results of this study suggested that a few waveform parameters could enable the accurate estimation of forest AGB. Moreover, this study indicated that GLAS data might be able to monitor forest AGB changes, which require further investigation.