Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Chen Chen,Xiang Xiang,Moriya Moriya

doi:10.3390/rs12030571

Abstract

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

Highlights

Forest ecosystems are one of the three surface ecosystems which act as the terrestrial biosphere, and play a crucial role in global and regional carbon cycles [1,2]
The results indicated that the trend surface interpolation results were on the high side, and the error was the largest; the thin spline interpolation algorithm has the lowest error, followed by the Kriging algorithm
According to the analysis of individual tree parameter inversion results, it was found that the local maximum algorithm can effectively extract the treetop, which can be used to determine the position of individual trees, but can extract data for too many treetops; the watershed algorithm can effectively estimate the height and maximum crown width of individual tree, but it was necessary to remove the impact of some non-canopy areas

Summary

Introduction

Forest ecosystems are one of the three surface ecosystems which act as the terrestrial biosphere, and play a crucial role in global and regional carbon cycles [1,2]. An accurate estimate of the forest canopy structure is necessary for the monitoring and investigation of forest resources, and it will improve the accuracy of aboveground biomass calculation, further contributing to a more accurate estimation of forest carbon sequestration [5,7,8]. Light Detection and Ranging (LiDAR) has been increasingly utilized to retrieve information on forest canopy structure in recent years [9,10,11]. It is difficult to separate topographic components from waveform LiDAR data, and the separation accuracy remains to be improved. Scientists have done a large number of studies on inversion of forest canopy structural parameters based on point cloud data [10,12,13,14,15]

Methods

Results

Discussion

Conclusion