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Canopy Base Height Estimates From Lidar

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Canopy Base Height Estimates From Lidar

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.

Given the above mentioned conclusions, further research requires the investigation of the potential of full-waveform or discrete-return LiDAR data of higher point density to estimate plot-level canopy base height over the same study area, which could not be achieved in the framework of this research due to legal restrictions. Such LiDAR data would probably provide more structural information from the low parts of the canopy resulting in more reliable CBH predictions. Moreover, the probable acquisition of spaceborne LiDAR data would provide a cost-effective alternative and the examination of their potential capability to accurately predict CBH in complex forests would be of high interest and importance

For the complete paper CLICK HERE.

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