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

Jaroslav Kubišta,Peter Surový

doi:10.3390/rs14040926

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.

Highlights

Point Clouds Using a Crown WidthDue to mass production and increasing affordability, the use of unmanned aerial vehicles (UAV) for forest monitoring is no longer the prerogative of advanced scientific teams
The detection of individual tree tops from UAV-based photogrammetry point clouds is a frequently discussed procedure, and different methods have been reviewed by several authors, for example [1]
Correctly estimating the crown width based on tree height alone poses a significant challenge, as other influential factors, such as tree species, genetic variability, diameter at breast height (DBH), stand and site characteristics, etc. cannot be directly addressed in local maximum filter (LMF)

Summary

Introduction

Point Clouds Using a Crown WidthDue to mass production and increasing affordability, the use of unmanned aerial vehicles (UAV) for forest monitoring is no longer the prerogative of advanced scientific teams. A similar trend has been observed in the software used for processing the data acquired by UAVs into 3D point clouds representing forest trees during laser scanning (light detection and ranging-Lidar) operations or its surface in the case of photogrammetry. The detection of individual tree tops from UAV-based photogrammetry point clouds is a frequently discussed procedure, and different methods have been reviewed by several authors, for example [1]. Several studies suggest that using LMF with a variable window size produces more promising results, especially in more diverse conditions [2]. The correct size of the window, whether fixed or variable, is critical for the results, and an incorrect size can lead to serious errors. Determining the window size is often a trial-and-error process for particular stands or situations. The R-project [3] LidR package [4] allows for a variable

Methods

Discussion

Conclusion