Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data

Shaobo Xia,Dong Chen,Jiju Peethambaran,Pu Wang,Sheng Xu

doi:10.3390/rs13030338

Abstract

Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incompleteness and complex tree structures. Furthermore, these methods often require lots of preprocessing such as ground filtering and noise removal. The fast and easy-to-use top-based methods that are widely applied in processing ALS point clouds are not applicable in localizing trees in TLS point clouds due to the data incompleteness and complex canopy structures. The objective of this study is to make the top-based methods applicable to TLS forest point clouds. To this end, a novel point cloud transformation is presented, which enhances the visual salience of tree instances and makes the top-based methods adapting to TLS forest scenes. The input for the proposed method is the raw point clouds and no other pre-processing steps are needed. The new method is tested on an international benchmark and the experimental results demonstrate its necessity and effectiveness. Finally, the proposed method has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.

Highlights

Terrestrial Laser Scanning (TLS) has become popular in plot-level tree mapping due to its ability in penetrating canopies and acquiring 3D fine-grained structures of vegetation [1,2]
As tree instances form the basis of many applications such as stem mapping [3,4], tree volume and diameter at breast height (DBH) measurement [5,6], reconstruction [7,8], data registration [9], and biophysical parameter retrieval [10], it is necessary to extract tree instances from point clouds in the preprocessing
We focus on the tree localization which is a sub-step in the tree instance mapping, aiming at localizing trees in point clouds

Summary

Introduction

Terrestrial Laser Scanning (TLS) has become popular in plot-level tree mapping due to its ability in penetrating canopies and acquiring 3D fine-grained structures of vegetation [1,2]. Most of the previous tree localization methods for TLS point clouds can be categorized into two classes: model-fitting methods and feature-based methods. The model-fitting methods treat the tree localization as the detection of stems from point clouds [11,12,13]. Maas et al [14] divide tree point clouds into slices along the vertical direction and the circle fitting is applied to the sliced points, model inliers of fitted circles will be recognized as stems. Liang et al [6] use a cylinder fitting strategy to find stems in point clouds. The feature-based methods utilize spatial and local geometric features of point clouds to identify stems [15,16]. Tao et al [18] use DBSCAN

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Conclusion