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Tytuł pozycji:

Individual Carpinus betulus and Acer velutinum tree species delineation by geometrical and statistical characteristics derived from airborne LiDAR data

Tytuł:
Individual Carpinus betulus and Acer velutinum tree species delineation by geometrical and statistical characteristics derived from airborne LiDAR data
Autorzy:
Remazan Ali Khorrami
Ali Asghar Darvishsefat
Masoud Tabari Kochaksaraei
Shaban Shataee Jouibary
Temat:
Acer velutinum
discriminant analysis
identification of individual tree species
LiDAR
Carpinus betulus
Forestry
SD1-669.5
Źródło:
تحقیقات جنگل و صنوبر ایران, Vol 23, Iss 2, Pp 269-278 (2015)
Wydawca:
Research Institute of Forests and Rangelands of Iran, 2015.
Rok publikacji:
2015
Kolekcja:
LCC:Forestry
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
Persian
ISSN:
1735-0883
2383-1146
Relacje:
http://ijfpr.areeo.ac.ir/article_103011_aac7d2f1620b9324bffb76d553cc84d9.pdf; https://doaj.org/toc/1735-0883; https://doaj.org/toc/2383-1146
DOI:
10.22092/ijfpr.2015.103011
Dostęp URL:
https://doaj.org/article/c21d96e3f40b447f88213a10d836d0c9  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.21d96e3f40b447f88213a10d836d0c9
Czasopismo naukowe
In this research, the possibility of application of LiDAR-derived characteristics has been studied for discrimination amongst common hornbeam (Carpinus betulus) and Persian maple (Acer velutinum) trees. LiDAR data of individual sample trees were separated in laser point clouds using their measured center coordinates and crown diameter in the field. In district 1 of Shast-Kolate Education and Research Forest in Gorgan, 80 individual A. velutinum and C. betulus tree samples were selected. The trees were either located in dominant storey or were not overlaid by adjacent tree crowns. Tree heights were measured using Vertex 1V GPS device. Crown diameter was measured in four cardinal directions using Leica Disto lasermeter. Center coordinates of the sample trees were determined using both DGPS and distance/azimuth measurement by Total Station device. Different geometrical and statistical metrics of sample trees were extracted from LiDAR data. The results of discriminant analysis suggested the height standard deviation of laser points over 80% of tree height and crown slope as the selected metrics to differentiate the tree species (accuracy=80.3%). The mean of those two metrics showed larger values for hornbeam than maple. The selected LiDAR metrics were ascribed to represent the shape and height variation of returned crown laser points. It is therefore concluded that geometrical LiDAR-derived attributes could successfully helo by differentiating between the two tested tree species.

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