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Individual Tree Crown Methods for 3D Data from Remote Sensing

Lindberg, Eva and Holmgren, Johan (2017). Individual Tree Crown Methods for 3D Data from Remote Sensing. Current Forestry Reports. 3 , 19 - 31
[Article Review/Survey]

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Purpose of Review: The rapid development of remote sensing technology has made dense 3D data available from airborne laser scanning and recently also photogrammetric point clouds. This paper reviews methods for extraction of individual trees from 3D data and their applications in forestry and ecology.Recent Findings: Methods for analysis of 3D data at tree level have been developed since the turn of the century. The first algorithms were based on 2D surface models of the upper contours of tree crowns. These methods are robust and provide information about the trees in the top-most canopy. There are also methods that use the complete 3D data. However, development of these 3D methods is still needed to include use of geometric properties. To detect a large fraction of the tallest trees, a surface model method generally gives the best results, but detection of smaller trees below the top-most canopy requires methods utilizing the whole point cloud. Several new sensors are now available with capability to describe the upper part of the canopy, which can be used to frequently update vegetation maps. Highly sensitive laser photo detectors have become available for civilian applications, which will enable acquisition of high-resolution 3D laser data for large areas to much lower costs.Summary: Methods for ITC delineation from 3D data provide information about a large fraction of the trees, but there is still a challenge to make optimal use of the information from the whole point cloud. Newly developed sensors might make ITC methods cheaper and feasible for large areas.

Authors/Creators:Lindberg, Eva and Holmgren, Johan
Title:Individual Tree Crown Methods for 3D Data from Remote Sensing
Series Name/Journal:Current Forestry Reports
Year of publishing :2017
Page range:19 - 31
Number of Pages:13
Publication Type:Article Review/Survey
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Geosciences, Multidisciplinary
(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
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Additional ID:
Type of IDID
Web of Science (WoS)000399162400002
ID Code:23364
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:27 Apr 2021 11:45
Metadata Last Modified:23 Feb 2022 21:30

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