Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

摘要

BackgroundForest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR (Light Detection and Ranging), small-footprint full-waveform airborne LiDAR (FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.MethodsA range of voxel sizes (from 10.0?m to 40.0?m interval of 2?m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxel-based LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest (RF) regression method.Results and conclusionsThe results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies ( R sup2/sup ranged from 0.625 to 0.832) of forest canopy height. However, the R sup2/sup values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18?m ( R sup2/sup?=?0.832, RMSE?=?2.57?m, RMSE%?=?20.6%). Compared with the lowest estimation accuracy, the R sup2/sup value had a significant improvement (33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.