Abstract
Estimating forest aboveground biomass (AGB) is a crucial step to better understand the carbon sequestration capacity of forest ecosystems and their interactions with climate change. TheLight detection and ranging (Lidar) derived three dimensional (3-D) structural information makes it possible to accurately estimate forest AGB based on allometric growth relationships. In this study, we propose a novel physical-based parameter named “Lidar Biomass Index (LBI)” based on the lidar equation using point cloud data. Both terrestrial laser scanning (TLS) data and reconstructed point cloud data of analytical trees were used. By comparing lidar-based AGB with field-based deconstructed measurements of 57 trees (including 40 coniferous and 17 broadleaf trees) in Northeast China, our results showed that the LBI-HCmean-based tree-level AGB better explained variations in the field data obtained for coniferous species (Larix kaempferi) (R2 = 0.948, RMSE = 23.301 kg) than that of broadleaf species (Fraxinus mandshurica) (R2 = 0.881, RMSE = 19.428 kg). The LBI provides an effective solution for estimating tree-level AGB from a 3-D perspective.
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