Vegetation density is among the important parameters required for determination of hydrodynamic roughness over vegetated areas. High density airborne light detection and ranging (LiDAR) data offer several potentials to improve estimation of vegetation density. Available methods in estimating vegetation density based on regression models did not take into account understorey vegetation and were not tested under different forest conditions. We present a method to develop and validate a generic regression model by using simulations of airborne laser scanning. The results show that available indices failed to produce good estimation which leads to a new predictor called low points index (LP). The vegetation density of trees is estimated using the FLI-MAP 400 data based on a regression model and estimated tree diameter at breast height. Finally, vegetation density is estimated at different spatial resolutions, which is useful for the estimation of multi-resolution and spatially distributed hydrodynamic roughness.

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