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Remote Sens. 2015, 7(6), 7892-7913; doi:10.3390/rs70607892

Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory

1
Department of Geosciences, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
2
Department of Geospatial Information Science , University of Texas at Dallas, 800 W Campbell Road, GR31, Richardson, TX 75080, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä and Prasad S. Thenkabail
Received: 10 April 2015 / Accepted: 10 June 2015 / Published: 16 June 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
View Full-Text   |   Download PDF [6716 KB, uploaded 16 June 2015]   |  

Abstract

The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques. View Full-Text
Keywords: LiDAR; individual tree extraction; tree metrics estimation LiDAR; individual tree extraction; tree metrics estimation
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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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MDPI and ACS Style

Zhang, C.; Zhou, Y.; Qiu, F. Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory. Remote Sens. 2015, 7, 7892-7913.

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