Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data

Forest biomass is a key biophysical parameter for climate change, ecological modeling and forest management. Compared with discrete-return LiDAR data, full-waveform LiDAR data can provide more accurate and abundant vertical structure information on vegetation and thus have been increasingly applied to the estimation of forest aboveground biomass (AGB). The main objective of this research is to estimate forest AGB using full waveform airborne LiDAR data. In this study, we constructed voxel-based waveforms (0.5 x 0.5 m) using small footprint full waveform LiDAR data, and then aggregated voxel based waveforms into pseudo large footprint waveforms with a plot size of 20 x 20 m. We extracted a range of waveform metrics from voxel-based waveforms and pseudo-large-footprint waveforms (FWm), respectively, and then calculated the mean of the voxel-based waveform metrics within a plot (FW mu). Based on the Random Forest (RF) regression, the forest biomasses were estimated using two types of waveform metrics: FWm (R-2 = 0.84, RMSE% = 21.4%, bias = -0.11 Mg ha(-1)) and FW mu (R-2 = 0.81, RMSE% = 23.3%, bias = 0.13 Mg ha(-1)). We found that slightly higher biomass estimation accuracy was obtained with FW(m )than with FW mu. In addition, a comparison between the biomasses predicted by the waveform metrics and by the traditional discrete-return metrics (R-2 = 0.80, RMSE% = 23.4%, bias = 0.20 Mg ha(-1)) was performed to explore the potential to improve biomass estimates using the waveform metrics, and the results showed that both waveform metrics and discrete-return metrics could accurately predict forest biomass. However, the biomass estimations from the waveform metrics were more accurate than those from the traditional discrete-return metrics. We concluded that the method proposed in this study has the potential to estimate vegetation structure parameters using full-waveform LiDAR data.