A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds

Guangpeng Fan,Danyu Chen,Liangliang Nan,Zhiming Wang,Feixiang Chen,Hao Li,Yanqi Dong

doi:10.3390/rs12111779

Abstract

Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.

Highlights

light detection and ranging (LiDAR) technology is widely used to extract tree location, tree height, diameter at breast height (DBH), tree crown and tree species classification, etc. [1,2]
A common approach is to input tree attributes (DBH, tree height and specific wood density) into an allometric model established by destructive methods to estimate tree volume or above-ground biomass (AGB) [3,4,5,6]
The most important was that this study provides a complete modeling and calculation method for tree quantitative analysis and builds a new tree quantitative structure model for forestry LiDAR data

Summary

Introduction

LiDAR technology is widely used to extract tree location, tree height, diameter at breast height (DBH), tree crown and tree species classification, etc. [1,2]. A common approach is to input tree attributes (DBH, tree height and specific wood density) into an allometric model established by destructive methods to estimate tree volume or above-ground biomass (AGB) [3,4,5,6]. These indirect methods are often based on different assumptions, which makes it difficult to carry out any meaningful verification of the measured results. Higher order branch structures and topologies are important for estimating tree volume, AGB, carbon stock, canopy gap, canopy size and other more general ecological characteristics [17,18,19]. Estimation of carbon cycle, stand stock, biomass, tree mass and decline time of branches all require accurate estimation of branch size and tree attributes [22,23]

Methods

Results

Conclusion