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Open AccessArticle

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

by Haijian Liu 1,2 and Changshan Wu 3,*
1
Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou 311121, China
2
Zhejiang Provincial/Key Laboratory of Urban Wetlands and Regional Change, Hangzhou 311121, China
3
Department of Geography, University of Wisconsin-Milwaukee, 3210 N. Maryland Ave, Milwaukee, WI 53211, USA
*
Author to whom correspondence should be addressed.
Forests 2020, 11(1), 28; https://doi.org/10.3390/f11010028
Received: 20 November 2019 / Revised: 17 December 2019 / Accepted: 20 December 2019 / Published: 23 December 2019
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. View Full-Text
Keywords: scene-based tin; individual tree crown reconstruction; control points; lidar scene-based tin; individual tree crown reconstruction; control points; lidar
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Liu, H.; Wu, C. Developing a Scene-Based Triangulated Irregular Network (TIN) Technique for Individual Tree Crown Reconstruction with LiDAR Data. Forests 2020, 11, 28.

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