Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

Kaisen Ma,Wanli Tian,Hua Sun,Jing Yi,Fugen Jiang,Liyong Fu,Zhenxiong Chen,Zhi Du

doi:10.3390/rs14020298

Abstract

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.

Highlights

The normalized point cloud (NPC) and canopy height model (CHM) obtained from the unmanned aerial vehicles (UAV)-light detection and ranging (LiDAR) point cloud are shown for plot 6 in panels (a)–(e) plot the results at raster resolutions of 0.1 m × 0.1 m, 0.2 m × 0.2 m, 0.3 m × 0.3 m, 0.4 m × 0.4 m, and 0.5 m × 0.5 m, respectively, and (f) is the NPC result
We found that the segmentation results of the CHM and NPC data models are characterized by similar detection rates (r), with no obvious difference
In terms of the accuracy rate (p), the individual tree segmentation methods based on NPCs perform better than those based on CHMs, and the differences between the two approaches were obvious in a difficult broad-leaved heterogeneous forest

Summary

Introduction

Traditional forest resource investigation methods explore individual trees in a sample plot with simple and convenient instruments through scientific sampling [7,8]. While this kind of survey method is highly precise and is broadly implemented by forestry departments worldwide, it is time-consuming, laborious, and destructive to the surveyed vegetation to a certain extent. The survey results may not accurately reflect the current state of forest resources across a large area [9]. The reflectance in each band of an optical remote sensing image can indicate the chlorophyll content and growth status of a stand, and these features are closely related to forest parameters [10]

Methods

Results

Discussion

Conclusion