Objective Point cloud density is an important parameter of light detection and ranging(LiDAR) technology. Point cloud density has an important impact on the extraction of remote sensing retrieval index for forest.

Methods The experimental data, sized by 1 600 m×1 450 m, had been obtained by unmanned aerial vehicle (UAV) LiDAR and thinned by the graded random thinning method in order to simulate different point cloud density during actual operation, which was used to extract the remote sensing retrieval index for forest such as canopy closure, gap fraction, leaf area index, height quantile variables and density quantile variables. Then these parameters were used to make difference comparison with the indexes extracted through raw data.

Results (1) The lower the point cloud density is, the lower the extracted canopy closure is slightly, while the extracted gap fraction is slightly increased. The point cloud density has little influence on the extracted canopy closure and gap fraction. (2) When the point cloud density is high, it has little impact on leaf area index, but when the point cloud density is small, it has a great impact on leaf area index, and some areas may have sudden changes on leaf area index. (3) When the point cloud density is large, the effect of point cloud density on height and density quantile variables is not obvious, but when the point cloud density drops to 3.6 point/m², there may be sudden changes in density and height density quantile variables in some areas.

Conclusions In short, the point cloud density has an important impact on the description of forest structural characteristics. The appropriate point cloud density is conducive to describe the forest structure morphology more accurately, but the low point cloud density affects the extraction of remote sensing retrieval index for forest. This study has certain guidance and reference for selection of point cloud density to estimate the remote sensing retrieval index with UAV LiDAR on forestry.