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Remote Sens. 2017, 9(2), 148; doi:10.3390/rs9020148

Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data

1
,
2,3
and
4,*
1
School of Information Science and Technology, East China Normal University, Shanghai 200241, China
2
School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China
3
East China Forest Inventory and Planning Institute, State Forestry Administration, Hangzhou 310019, China
4
College of Communication Engineering, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Received: 30 August 2016 / Accepted: 3 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Abstract

Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift—a non-parametric probability density-based clustering technique—to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees (“recall”) and 92 percent of the identified trees are correct (“precision”), showing good potential for use in the area of forest inventory. View Full-Text
Keywords: mean shift; adaptive; bandwidth calibration; LiDAR;airborne laser scanning; point cloud; individual trees; identification; segmentation; forest mean shift; adaptive; bandwidth calibration; LiDAR;airborne laser scanning; point cloud; individual trees; identification; segmentation; forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hu, X.; Chen, W.; Xu, W. Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data. Remote Sens. 2017, 9, 148.

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