Forest attributes such as tree heights, diameter distribution, volumes, and biomass can be modeled utilizing the relationship between remotely sensed metrics as predictor variables, and measurements of forest attributes on the ground. The quality of the models relies on the actual relationship between the forest attributes and the remotely sensed metrics. The processing of airborne laser scanning (ALS) point clouds acquired under heterogeneous terrain conditions introduces a distortion of the three-dimensional shape and structure of the ALS data for tree crowns and thus errors in the derived metrics. In the present study, Procrustean transformation and histogram matching were proposed as a means of countering the distortion of the ALS data. The transformations were tested on a dataset consisting of 192 field plots of 250 m2
in size located on a gradient from gentle to steep terrain slopes in western Norway. Regression models with predictor variables derived from (1) Procrustean transformed- and (2) histogram matched point clouds were compared to models with variables derived from untransformed point clouds. Models for timber volume, basal area, dominant height, Lorey’s mean height, basal area weighted mean diameter, and number of stems were assessed. The results indicate that both (1) Procrustean transformation and (2) histogram matching can be used to counter crown distortion in ALS point clouds. Furthermore, both techniques are simple and can easily be implemented in the traditional processing chain of ALS metrics extraction.
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