Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)

Williams, Jonathan, Schönlieb, Carola-Bibiane, Swinfield, Tom , Lee, Juheon, Cai, Xiaohao, Qie, Lan and Coomes, David A. (2019) Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC). IEEE Transactions on Geoscience and Remote Sensing . pp. 1-23. ISSN 0196-2892

Full content URL: https://doi.org/10.1109/TGRS.2019.2940146

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Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
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Abstract

Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning datasets 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 Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from airborne LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these threedimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to 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.

Keywords:Biomass, light detection and ranging (LiDAR), remote sensing, vegetation mapping
Subjects:G Mathematical and Computer Sciences > G120 Applied Mathematics
H Engineering > H610 Electronic Engineering
G Mathematical and Computer Sciences > G400 Computer Science
C Biological Sciences > C180 Ecology
H Engineering > H240 Surveying Science
Divisions:College of Science > School of Life Sciences
ID Code:38237
Deposited On:04 Nov 2019 10:45

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