LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest - Alexandra Stefanidou (Remote Sensing, 2020)
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 ( <semantics> R 2 = 0 . 15 </semantics> , <semantics> r R M S E = 42 . 36 % </semantics> ) while most CBH values were overestimated resulting in an <semantics> r b i a s </semantics> of <semantics> − 17 . 52 % </semantics> . On the contrary, cross-validation of the developed regression model showcased an <semantics> R 2 </semantics> , <semantics> r R M S E </semantics> and <semantics> r b i a s </semantics> of <semantics> 0 . 61 </semantics> , <semantics> 18 . 19 % </semantics> and <semantics> − 0 . 09 % </semantics> 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.