Individual Tree Identification in ULS Point Clouds Using a Crown Width Mixed-Effects Model Based on NFI Data

Abstract

A linear mixed-effects model was used to relate crown width to height using an inventory plot as a random effect for trees in Czechia based on data from the National Forest Inventory (NFI). This model was used to estimate window size for a local maximum filter procedure (LMF) to detect individual tree tops in unmanned aerial laser scanning (ULS) point clouds of mixed species forest stands with diverse structures. Random model parameters were estimated for the study site based on several sample trees. Models calibrated with five or more samples achieved significantly better results (mean percentage error; MPE −0.17 for 5 samples) compared to when a fixed-effects model (MPE −0.62) was used. Lower performance was observed in dense stands with trees that were between 5 and 10 m in height. It was concluded that locally calibrated models predicting crown widths from tree heights might serve as a universal point of departure when searching for an optimal window size setting in LMF procedures.