Mapping individual trees from airborne multi-sensor imagery
Mapping individual trees from airborne multi-sensor imagery
Individual tree species mapping is important to understand forest dynamics and species distribution patterns. Airborne LiDAR with hyperspectral imaging has been extensively used to extract biophysical traits of vegetation and detect species. However, its application for individual tree mapping is limited due to technical problems. To address the problems, this paper presents effective and efficient algorithms in terms of tackling co-alingment of LiDAR and hyperspectral datasets, classifying individual trees, thus detecting tree species and leaf chemistry from the tree mapping.
Hyperspectral imaging, LiDAR, image registration, image segmentation, spectral analysis
5411-5414
Lee, Juheon
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Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Schonlieb, Carola Bibiane
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Coomes, David
4e3d573c-fda0-4ddc-a621-6a682ff615ca
10 November 2015
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Schonlieb, Carola Bibiane
a42e0ee1-9df4-41b3-ae0e-adab80249811
Coomes, David
4e3d573c-fda0-4ddc-a621-6a682ff615ca
Lee, Juheon, Cai, Xiaohao, Schonlieb, Carola Bibiane and Coomes, David
(2015)
Mapping individual trees from airborne multi-sensor imagery.
In,
2015 International Geoscience and Remote Sensing Symposium (IGARSS).
IEEE, .
(doi:10.1109/IGARSS.2015.7327059).
Record type:
Book Section
Abstract
Individual tree species mapping is important to understand forest dynamics and species distribution patterns. Airborne LiDAR with hyperspectral imaging has been extensively used to extract biophysical traits of vegetation and detect species. However, its application for individual tree mapping is limited due to technical problems. To address the problems, this paper presents effective and efficient algorithms in terms of tackling co-alingment of LiDAR and hyperspectral datasets, classifying individual trees, thus detecting tree species and leaf chemistry from the tree mapping.
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Published date: 10 November 2015
Keywords:
Hyperspectral imaging, LiDAR, image registration, image segmentation, spectral analysis
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Local EPrints ID: 438747
URI: http://eprints.soton.ac.uk/id/eprint/438747
PURE UUID: 18d3b3de-745f-457b-b16e-a7fdcad2fac8
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Date deposited: 23 Mar 2020 18:43
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
Juheon Lee
Author:
Xiaohao Cai
Author:
Carola Bibiane Schonlieb
Author:
David Coomes
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