A Novel Method Based on Kernel Density for Estimating Crown Base Height Using UAV-Borne LiDAR Data

As an essential parameter in forestry, crown base height (CBH) faces many tasks. The methods are still developing for estimating it. Unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) supplies new, massive, and high-density data for estimating CBH. Many methods had been generated to compute CBH indirectly using regression-based ways or directly using geometric/statistical LiDAR-based ways. However, there were few methods to deal with the problem of understory, trunk, and noise points caused by high-density UAV data. A robust method was first proposed in this study to directly estimate CBH from LiDAR data, which contained two significant skills: 1) understory vegetation removal for each tree using a polynomial curve and 2) computing CBH by kernel densification of the elevation frequency histogram of LiDAR data. It could tolerate the understory and trunk points better through kernel convolution. The method proposed in this study and a previous simple model were applied in a crabapple plot in the Huailai Remote Sensing Comprehensive Experimental Station, Hebei, China, and verified by field-measured data. It was inspiring that our method is slightly better, and the mean CBH of LiDAR-derived trees was only 1.60 cm higher than that of field-measured trees. The mean absolute error (MAE) of CBH was 4.91 cm, ${R}^{2}$ was 0.73, the root-mean-squared error (RMSE) was 8.29 cm, and the bias was 2.68% for these trees. Generally, this method showed strong usability for high-density UAV LiDAR data and high precision for measuring CBH of low trees.