INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the...

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Main Authors: A. Moradi, M. Satari, M. Momeni
Format: Article
Language:English
Published: Copernicus Publications 2016-06-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/337/2016/isprs-archives-XLI-B3-337-2016.pdf
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spelling doaj-3842440027114cada4ab45447d75d5082020-11-25T02:31:38ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B333734310.5194/isprs-archives-XLI-B3-337-2016INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATAA. Moradi0M. Satari1M. Satari2M. Momeni3M. Momeni4Dept. of Geomatic Engineering, University of Isfahan, Hezarjarib Street, Isfahan, IranDept. of Geomatic Engineering, University of Isfahan, Hezarjarib Street, Isfahan, IranInstitute of Remote Sensing, University of Isfahan, Hezarjarib Street, Isfahan, IranDept. of Geomatic Engineering, University of Isfahan, Hezarjarib Street, Isfahan, IranInstitute of Remote Sensing, University of Isfahan, Hezarjarib Street, Isfahan, IranAirborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm 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 (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/337/2016/isprs-archives-XLI-B3-337-2016.pdf
collection DOAJ
language English
format Article
sources DOAJ
author A. Moradi
M. Satari
M. Satari
M. Momeni
M. Momeni
spellingShingle A. Moradi
M. Satari
M. Satari
M. Momeni
M. Momeni
INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet A. Moradi
M. Satari
M. Satari
M. Momeni
M. Momeni
author_sort A. Moradi
title INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
title_short INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
title_full INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
title_fullStr INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
title_full_unstemmed INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA
title_sort individual tree of urban forest extraction from very high density lidar data
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2016-06-01
description Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm 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 (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/337/2016/isprs-archives-XLI-B3-337-2016.pdf
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AT msatari individualtreeofurbanforestextractionfromveryhighdensitylidardata
AT msatari individualtreeofurbanforestextractionfromveryhighdensitylidardata
AT mmomeni individualtreeofurbanforestextractionfromveryhighdensitylidardata
AT mmomeni individualtreeofurbanforestextractionfromveryhighdensitylidardata
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