LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest.

Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and BEHAVE. The present study focused on the potential of airborne LiDAR data analysis to estimate plot-level CBH in a dense uneven-aged structured forest on complex terrain. A comparative study of two widely employed methods was performed, namely the voxel-based approach and regression analysis, which revealed a clear outperformance of the latter. More specifically, the voxel-based CBH estimates were found to lack correlation with the reference data (R2=0.15, rRMSE=42.36%) while most CBH values were overestimated resulting in an rbias of -17.52%. On the contrary, cross-validation of the developed regression model showcased an R2, rRMSE and rbias of 0.61, 18.19% and -0.09% respectively. Overall analysis of the results proved the voxel-based approach incapable of accurately estimating plot-level CBH due to vegetation and topographic heterogeneity of the forest environment, which however didn't affect the regression analysis performance.