An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai mountains, China

Abstract Light Detection And Ranging (LiDAR) has a unique capability for estimating forest canopy height, which has a direct relationship with, and can provide better understanding of the aboveground forest carbon storage. The full waveform data of the large-footprint LiDAR Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat), combined with field measurements of forest canopy height, were employed to achieve improved estimates of forest canopy height over sloping terrain in the Changbai mountains region, China. With analyzing ground-truth experiments, the study proposed an improved model over Lefsky's model to predict maximum canopy height using the logarithmic transformation of waveform extent and elevation change as independent variables. While Lefsky's model explained 8–89% of maximum canopy height variation in the study area, the improved model explained 56–92% of variation within the 0–30° terrain slope category. The results reveal that the improved model can reduce the mixed effects caused by both sloping terrain and rough land surface, and make a significant improvement for accurately estimating maximum canopy height over sloping terrain.

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