Extracting the individual trees of urban forests from high density airborne LiDAR data
Airborne LiDAR (Light Detection and Ranging) has a high potential to provide 3D data for research and operational applications in a wide range of disciplines related to management of forest ecosystems and urban trees. Most proposed methods for extracting the individual trees first detect the points...
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Iranian Society of Forestry
2018-06-01
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doaj-832da68517a1498cb19c96a20187182a2021-06-26T06:56:18ZfasIranian Society of Forestryمجله جنگل ایران2008-61132423-44352018-06-01101274263497Extracting the individual trees of urban forests from high density airborne LiDAR dataA Moradi0M. Satari1M. Momeni2MS. student of Geomatics Eng., Department of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. IranAssistant Prof., Dept. of Geomatics Eng., Dept. of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. IranAssociate Prof., Dept. of Geomatics Eng., Dept. of Civil Eng. and Transportation, University of Isfahan, Isfahan, I. R. IranAirborne LiDAR (Light Detection and Ranging) has a high potential to provide 3D data for research and operational applications in a wide range of disciplines related to management of forest ecosystems and urban trees. Most proposed methods for extracting the individual trees first detect the points of tree top or bottom and then use them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points effect on the process of detecting individual trees heavily. In this study, a new method is presented to extract the individual tree segments using LiDAR points with 10 cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium. The accuracy assessment of this method showed that with the increasing detection rate of young trees, it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors, respectively.http://www.ijf-isaforestry.ir/article_63497_0e92d34f7e91036972abf084668570f5.pdfoptics clusteringpoint cloudprincipal components analysistree extraction |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
A Moradi M. Satari M. Momeni |
spellingShingle |
A Moradi M. Satari M. Momeni Extracting the individual trees of urban forests from high density airborne LiDAR data مجله جنگل ایران optics clustering point cloud principal components analysis tree extraction |
author_facet |
A Moradi M. Satari M. Momeni |
author_sort |
A Moradi |
title |
Extracting the individual trees of urban forests from high density airborne LiDAR data |
title_short |
Extracting the individual trees of urban forests from high density airborne LiDAR data |
title_full |
Extracting the individual trees of urban forests from high density airborne LiDAR data |
title_fullStr |
Extracting the individual trees of urban forests from high density airborne LiDAR data |
title_full_unstemmed |
Extracting the individual trees of urban forests from high density airborne LiDAR data |
title_sort |
extracting the individual trees of urban forests from high density airborne lidar data |
publisher |
Iranian Society of Forestry |
series |
مجله جنگل ایران |
issn |
2008-6113 2423-4435 |
publishDate |
2018-06-01 |
description |
Airborne LiDAR (Light Detection and Ranging) has a high potential to provide 3D data for research and operational applications in a wide range of disciplines related to management of forest ecosystems and urban trees. Most proposed methods for extracting the individual trees first detect the points of tree top or bottom and then use them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points effect on the process of detecting individual trees heavily. In this study, a new method is presented to extract the individual tree segments using LiDAR points with 10 cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium. The accuracy assessment of this method showed that with the increasing detection rate of young trees, it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors, respectively. |
topic |
optics clustering point cloud principal components analysis tree extraction |
url |
http://www.ijf-isaforestry.ir/article_63497_0e92d34f7e91036972abf084668570f5.pdf |
work_keys_str_mv |
AT amoradi extractingtheindividualtreesofurbanforestsfromhighdensityairbornelidardata AT msatari extractingtheindividualtreesofurbanforestsfromhighdensityairbornelidardata AT mmomeni extractingtheindividualtreesofurbanforestsfromhighdensityairbornelidardata |
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1721359131544125440 |