Effect of layer thickness and voxel size inversion on leaf area density based on the voxel-based canopy profiling method

Voxel-based canopy profiling is commonly used to determine small-scale leaf area. Layer thickness and voxel size impact accuracy when using this method. Here, we determined the optimal combination of layer thickness and voxel size to estimate leaf area density accurately. Terrestrial LiDAR Stonex X300 was used to generate point cloud data for Masson pines ( Pinus massoniana ). The canopy layer was stratified into 0.10–1.00-m-thick layers, while voxel size was 0.01–0.10 m. The leaf area density of individual trees was estimated using leaf area indices for the upper, middle, and lower canopy and the overall canopy. The true leaf area index, obtained by layered harvesting, was used to verify the inversion results. Leaf area density was inverted by nine combinations of layer thickness and voxel size. The average relative accuracy and mean estimated accuracy of these combined inversion results exceeded 80%. When layer thickness was 1.00 m and voxel size 0.05 m, inversion was closest to the true value. The average relative accuracy was 92.58%, mean estimated accuracy 98.00%, and root mean square error 0.17. The combination of leaf area density and index was accurately retrieved. In conclusion, nondestructive voxel-based canopy profiling proved suitable for inverting the leaf area density of Masson pine in Hetian Town, Fujian Province.