Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

Wei Yao,Jan Krull,Peter Krzystek,Marco Heurich

doi:10.3390/f5061122

Abstract

Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m2, 10 pts/m2, 15 pts/m2 and 20 pts/m2, respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures.

Highlights

Airborne laser scanning (ALS) or Light detection and ranging (LiDAR) has been widely used in mapping the Earth’s surface including urban and forested areas in 3D
Since the sensitivity analysis is performed based on varying one control parameter at each time, those parameters which did not vary during the sensitivity analysis should be assigned with constant values: NCutThre = 0.16, Vsize = 0.5, λ = 1.5
It is necessary to characterize and even quantify the influence of uncertainty associated with various control parameters for single tree detection based on 3D segmentation of LiDAR point clouds, since the performance of the forest characterization based on individual tree method (ITD) strategy is directly related to the accuracy of tree detection results

Summary

Introduction

Airborne laser scanning (ALS) or Light detection and ranging (LiDAR) has been widely used in mapping the Earth’s surface including urban and forested areas in 3D. LiDAR sampling or full-area coverage is deemed as favorable means to achieve timely and robust large-area characterizations of vertically and horizontal distributed forest structures. In the ABA, ALS point cloud data are aggregated at the inventory plot level by describing them based on the height distribution and canopy density metrics [6,7,8], such as echo ratio, height percentiles and canopy cover percentiles. These metrics can be imported into regression models as independent variables. The response is an aggregation of single tree measurements on sample plots and could be as in the case of mean tree height, stem volume, biomass or forest fuel parameters [9,10,11,12,13]

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