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

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Abstract
As an essential parameter in forestry, crown base height (CBH) faces many tasks. The methods are still developing for estimating it. Unmanned aerial vehicles (UAV)light detection and ranging (LiDAR) supplies new, massive and high-density data for estimating CBH. Many methods hadbeen generated to compute CBH indirectly using regression-based ways or directly using geometric/statistical LiDAR-based ways. However, there werefew methods to deal with the problem of understory, trunkand noise points caused by high-density UAV data. A robust method was firstlyproposed in this studyto directly estimate CBH from LiDAR data, which containedtwo significant skills: 1)understory vegetation removal for each tree using a polynomial curve,and 2) computing CBH by kernel densification ofthe elevation frequency histogram of LiDAR data. It could tolerate the understoryand trunk points better through kernel convolution. The method proposed in this study and a previous simple model were applied in a crabapple plot in Huailai Remote Sensing Comprehensive Experimental Station, Hebei Province, China and verified byfield-measured data. Itwasinspiringthat our method is slightly better, 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, R2 was 0.73, RMSE was 8.29 cm and bias was 2.68%for these trees. Generally, this method showedstrong usability for high-density UAV LiDAR data and high precision for measuringCBH of low trees.
Funding Information
  • National Natural Science Foundation of China (41871264)
  • Youth Innovation Promotion Association CAS (2019130)

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