Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data

Haijian Liu,Changshan Wu

doi:10.3390/f11010028

Abstract

LiDAR (Light Detection and Ranging)-based individual tree crown reconstruction is a challenge task due to the variable canopy morphologies and the penetrating properties of LiDAR to tree crown surfaces. Traditional methods, including LiDAR-derived rasterization, low-pass filtering smooth algorithm, and original triangular irregular network (TIN) model, have difficulties in balancing morphological accuracy and model smoothness. To address this issue, a scene-based TIN was generated with three steps based on the local scene principle. First, local Delaunay triangles were formed through connecting neighboring point sets. Second, key control points within each local Delaunay triangle, including steeple, inverted tip, ridge, saddle, and horseshoe shape control points, were extracted by analyzing multiple local scenes. These key points were derived to determine the fluctuations of forest canopies. Third, the scene-based TIN model was generated using the control points as nodes. Visual analysis indicates the new model can accurately reconstruct different canopy shapes with a relatively smooth surface, and statistical analysis of individual trees confirms that the overall error of the new model is smaller than others. Especially, the scene-based TIN derived raster reduced the average error to 0.18 m, with a standard deviation of 0.41, while the average errors of LiDAR-derived raster, low-pass filtered smooth raster, and original TIN derived raster have average errors of 0.96, 2.05, and 1.00 m, respectively. The local scene-based control point extraction also reduces data storage due to the elimination of redundant points, and furthermore the different point densities on different objects are beneficial for canopy segmentation.

Highlights

Individual tree crown reconstruction is essential for forest quality assessment, health supervision, and planning management of forest resources [1]
In the forest application, various algorithms have been proposed for canopy model creation using LiDAR data, which can be grouped into three major categories: rasterized canopy height model (CHM), geometric model, and vectorized mesh model
As the identification of these points depends on the spatial relationship with their neighbors, the model can be constructed with three steps: (1) determining the set of neighbors of each point using Delaunay triangulation, (2) extracting control points based on local scenes, and (3) creating the canopy model with the triangular irregular network (TIN) algorithm

Summary

Introduction

Individual tree crown reconstruction is essential for forest quality assessment, health supervision, and planning management of forest resources [1]. A three-dimensional (3D) digitization of tree crowns can create virtual models through 3D tools to present more direct spatial information. Similar to 3D buildings, 3D canopy models can be applied to visually represent the morphological structure of forests, as well as quantitatively describe the characteristic parameters of forest topologies [2]. LiDAR (Light Detection and Ranging) has proven to be an effective and increasingly popular technology for 3D model reconstruction of terrains, buildings, transportation facilities, pipelines, and vegetation [11,12]. LiDAR has the advantage of extracting accurate terrain and semantic information [14] and generating forest canopy models from the individual tree level to global level [15]. In the forest application, various algorithms have been proposed for canopy model creation using LiDAR data, which can be grouped into three major categories: rasterized canopy height model (CHM), geometric model, and vectorized mesh model

Methods

Results

Discussion

Conclusion