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3D segmentation of trees through a flexible multiclass graph cut algorithm

3D segmentation of trees through a flexible multiclass graph cut algorithm
3D segmentation of trees through a flexible multiclass graph cut algorithm
Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.
Biomass, light detection and ranging (LiDAR), remote sensing, vegetation mapping
0196-2892
754-776
Williams, Jonathan
9ba8f687-3355-41e3-87c6-c3c3b6184c91
Schonlieb, Carola Bibiane
a42e0ee1-9df4-41b3-ae0e-adab80249811
Swinfield, Tom
d800043c-0fe2-46c3-8308-a3284d5b000c
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Qie, Lan
1891c2ed-303c-4986-82f5-6e3b577dcd03
Coomes, David A.
4e3d573c-fda0-4ddc-a621-6a682ff615ca
Williams, Jonathan
9ba8f687-3355-41e3-87c6-c3c3b6184c91
Schonlieb, Carola Bibiane
a42e0ee1-9df4-41b3-ae0e-adab80249811
Swinfield, Tom
d800043c-0fe2-46c3-8308-a3284d5b000c
Lee, Juheon
cd382ebf-0bcc-47b8-a60d-68c6540d31bb
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Qie, Lan
1891c2ed-303c-4986-82f5-6e3b577dcd03
Coomes, David A.
4e3d573c-fda0-4ddc-a621-6a682ff615ca

Williams, Jonathan, Schonlieb, Carola Bibiane, Swinfield, Tom, Lee, Juheon, Cai, Xiaohao, Qie, Lan and Coomes, David A. (2020) 3D segmentation of trees through a flexible multiclass graph cut algorithm. IEEE Transactions on Geoscience and Remote Sensing, 58 (2), 754-776, [8854321]. (doi:10.1109/TGRS.2019.2940146).

Record type: Article

Abstract

Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.

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More information

Accepted/In Press date: 20 August 2019
e-pub ahead of print date: 1 October 2019
Published date: 1 February 2020
Keywords: Biomass, light detection and ranging (LiDAR), remote sensing, vegetation mapping

Identifiers

Local EPrints ID: 439499
URI: http://eprints.soton.ac.uk/id/eprint/439499
ISSN: 0196-2892
PURE UUID: 2ab5124d-e5d5-48d1-9340-7ff7eb71ac0a

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Date deposited: 24 Apr 2020 16:30
Last modified: 19 Mar 2021 17:33

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Contributors

Author: Jonathan Williams
Author: Carola Bibiane Schonlieb
Author: Tom Swinfield
Author: Juheon Lee
Author: Xiaohao Cai
Author: Lan Qie
Author: David A. Coomes

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