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Article: Automated methods for measuring DBH and tree heights with a commercial scanning lidar

TitleAutomated methods for measuring DBH and tree heights with a commercial scanning lidar
Authors
Issue Date2011
Citation
Photogrammetric Engineering and Remote Sensing, 2011, v. 77, n. 3, p. 219-227 How to Cite?
AbstractAccurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory. © 2011 American Society for Photogrammetry and Remote Sensing.
Persistent Identifierhttp://hdl.handle.net/10722/296683
ISSN
2020 Impact Factor: 1.083
2020 SCImago Journal Rankings: 0.483
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Huabing-
dc.contributor.authorLi, Zhan-
dc.contributor.authorGong, Peng-
dc.contributor.authorCheng, Xiao-
dc.contributor.authorClinton, Nick-
dc.contributor.authorCao, Chunxiang-
dc.contributor.authorNi, Wenjian-
dc.contributor.authorWang, Lei-
dc.date.accessioned2021-02-25T15:16:26Z-
dc.date.available2021-02-25T15:16:26Z-
dc.date.issued2011-
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 2011, v. 77, n. 3, p. 219-227-
dc.identifier.issn0099-1112-
dc.identifier.urihttp://hdl.handle.net/10722/296683-
dc.description.abstractAccurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory. © 2011 American Society for Photogrammetry and Remote Sensing.-
dc.languageeng-
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensing-
dc.titleAutomated methods for measuring DBH and tree heights with a commercial scanning lidar-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.14358/PERS.77.3.219-
dc.identifier.scopuseid_2-s2.0-79960342179-
dc.identifier.volume77-
dc.identifier.issue3-
dc.identifier.spage219-
dc.identifier.epage227-
dc.identifier.isiWOS:000288052100005-
dc.identifier.issnl0099-1112-

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