Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data
Open Access
- 23 December 2019
- Vol. 11 (1), 28
- https://doi.org/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.Keywords
Funding Information
- Natural Science Foundation of Zhejiang Province (LY19D010005)
This publication has 38 references indexed in Scilit:
- Tree Crown Width Estimation, Using Discrete Airborne LiDAR DataCanadian Journal of Remote Sensing, 2016
- A new method for generating canopy height models from discrete-return LiDAR point cloudsRemote Sensing Letters, 2014
- A perspective on urban canopy layer modeling for weather, climate and air quality applicationsUrban Climate, 2013
- Development of a GIS Application for Urban Forestry Management PlanningProcedia Technology, 2013
- Estimating biomass of individual pine trees using airborne lidarBiomass and Bioenergy, 2007
- Detection of Individual Tree Crowns in Airborne Lidar DataPhotogrammetric Engineering & Remote Sensing, 2006
- Three-dimensional visualization of maize canopy based on crop growth model and spectrum dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial ImageryPhotogrammetric Engineering & Remote Sensing, 2004
- Combined high-density lidar and multispectral imagery for individual tree crown analysisCanadian Journal of Remote Sensing, 2003
- Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomassCanadian Journal of Remote Sensing, 2003