Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach
Ahmadi, Seyed Ali ; Ghorbanian, Arsalan LU ; Golparvar, Farshad ; Mohammadzadeh, Ali and Jamali, Sadegh LU
(2022)
In European Journal of Remote Sensing
55(1).
p.520-539
- Abstract
- Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the... (More)
- Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the potential of the proposed graph-based method for individual tree detection in a complex forest region in Mazandaran, Iran. In particular, the graph-based method obtained Precision, Recall, and F1-score values of 0.64, 0.73, and 0.68, respectively. Furthermore, the intercomparison with the well-known and most used Local Maximum (LM) suggested the applicability of the proposed method. After point cloud generation, the proposed method was implemented entirely in Python using open-source packages, which increases its applicability for other scholars and managers. The source code of the proposed algorithm can be found at https://github.com/Seyed-Ali-Ahmadi/Graph-based_ITCD.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/76fe08b0-ad6b-4557-a0ca-dbbcd9fa77bf
- author
- Ahmadi, Seyed Ali
; Ghorbanian, Arsalan
LU
; Golparvar, Farshad
; Mohammadzadeh, Ali
and Jamali, Sadegh
LU
- organization
- publishing date
- 2022-10-17
- type
- Contribution to journal
- publication status
- published
- subject
- in
- European Journal of Remote Sensing
- volume
- 55
- issue
- 1
- pages
- 520 - 539
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85140838910
- ISSN
- 2279-7254
- DOI
- 10.1080/22797254.2022.2129095
- language
- English
- LU publication?
- yes
- id
- 76fe08b0-ad6b-4557-a0ca-dbbcd9fa77bf
- date added to LUP
- 2022-10-18 08:47:12
- date last changed
- 2023-05-15 13:16:36
@article{76fe08b0-ad6b-4557-a0ca-dbbcd9fa77bf,
abstract = {{Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the potential of the proposed graph-based method for individual tree detection in a complex forest region in Mazandaran, Iran. In particular, the graph-based method obtained Precision, Recall, and F1-score values of 0.64, 0.73, and 0.68, respectively. Furthermore, the intercomparison with the well-known and most used Local Maximum (LM) suggested the applicability of the proposed method. After point cloud generation, the proposed method was implemented entirely in Python using open-source packages, which increases its applicability for other scholars and managers. The source code of the proposed algorithm can be found at https://github.com/Seyed-Ali-Ahmadi/Graph-based_ITCD.<br/><br/>}},
author = {{Ahmadi, Seyed Ali and Ghorbanian, Arsalan and Golparvar, Farshad and Mohammadzadeh, Ali and Jamali, Sadegh}},
issn = {{2279-7254}},
language = {{eng}},
month = {{10}},
number = {{1}},
pages = {{520--539}},
publisher = {{Taylor & Francis}},
series = {{European Journal of Remote Sensing}},
title = {{Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach}},
url = {{http://dx.doi.org/10.1080/22797254.2022.2129095}},
doi = {{10.1080/22797254.2022.2129095}},
volume = {{55}},
year = {{2022}},
}