The Effects of Cell Size and Filtering Range on Automatically Extracted Tree Number and Average Tree Height Using Light Detection and Ranging Data in Fusion/LDV
Objective: The use of airborne light detection and ranging (LiDAR) data is common in forest structure analyses. This study investigated the differences in tree number extraction and tree height measurements obtained using LiDAR data, employing different parameters (cell size, filter type, and filter range) and local maxima (LM) method. Tree numbers were extracted and parameter combinations resulting in values closest to the observed data were identified.
Method: For the two study sites, a Japanese cedar forest and a Japanese cypress forest, digital elevation model has a resolution of 1 m, and Digital Canopy Height Model (DCHM) calculated the difference using the resolution with varying parameters. Different combinations of cell size, filter type, and filter range parameters were employed for tree number extraction using DCHM. A total of 108 combinations were investigated, including 9 cell sizes (0.1 m to 0.5 m, at 0.05 m intervals) for the digital surface model, 2 filter types (median and average) for the LM method, and 6 LM ranges (from 3×3 to 13×13). Average tree height was calculated concurrently with tree number extraction, and results were compared with those of the on-site survey.
Results: An exponential trend was observed for the extracted tree numbers, with the number of extracted trees increasing for smaller cell sizes and decreasing for larger cell sizes. No significant differences were observed between the filter types. Tree height tended to measured values was close to the actual values observed on-site.
Conclusion: Extracted tree numbers close to the actual values were obtained near the shortest distance point from the origin (i.e., near the concave point) on the exponential (inverse) graph, suggesting that tree numbers can be estimated using LiDAR data.