Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations

Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experie...

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Main Authors: Luke Wallace, Bryan Hally, Samuel Hillman, Simon D. Jones, Karin Reinke
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Fire
Subjects:
Online Access:https://www.mdpi.com/2571-6255/3/4/59
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spelling doaj-1cb762303983415c8d030ff40f35a2182020-11-25T04:00:57ZengMDPI AGFire2571-62552020-10-013595910.3390/fire3040059Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and LimitationsLuke Wallace0Bryan Hally1Samuel Hillman2Simon D. Jones3Karin Reinke4School of Science, RMIT University, Melbourne 3000, AustraliaSchool of Science, RMIT University, Melbourne 3000, AustraliaSchool of Science, RMIT University, Melbourne 3000, AustraliaSchool of Science, RMIT University, Melbourne 3000, AustraliaSchool of Science, RMIT University, Melbourne 3000, AustraliaSite-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generated from imagery captured using a low-cost compact camera for describing the fuel hazard within the surface and near-surface layers. Terrestrial imagery was obtained at three distances for five target plots. Subsets of these images were then processed to determine the effect of varying overlap and distribution of image captures. The majority of the point clouds produced using this image-based technique provide an accurate representation of the 3D structure of the surface and near-surface fuels. Results indicate that high image overlap and pixel size are critical; multi-angle image capture is shown to be crucial in providing a representation of the vertical stratification of fuel. Terrestrial image-based point clouds represent a viable technique for low cost and rapid assessment of fuel structure.https://www.mdpi.com/2571-6255/3/4/59Structure from Motionvegetation structurefuel hazardTerrestrial Laser Scanning
collection DOAJ
language English
format Article
sources DOAJ
author Luke Wallace
Bryan Hally
Samuel Hillman
Simon D. Jones
Karin Reinke
spellingShingle Luke Wallace
Bryan Hally
Samuel Hillman
Simon D. Jones
Karin Reinke
Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
Fire
Structure from Motion
vegetation structure
fuel hazard
Terrestrial Laser Scanning
author_facet Luke Wallace
Bryan Hally
Samuel Hillman
Simon D. Jones
Karin Reinke
author_sort Luke Wallace
title Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
title_short Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
title_full Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
title_fullStr Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
title_full_unstemmed Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations
title_sort terrestrial image-based point clouds for mapping near-ground vegetation structure: potential and limitations
publisher MDPI AG
series Fire
issn 2571-6255
publishDate 2020-10-01
description Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generated from imagery captured using a low-cost compact camera for describing the fuel hazard within the surface and near-surface layers. Terrestrial imagery was obtained at three distances for five target plots. Subsets of these images were then processed to determine the effect of varying overlap and distribution of image captures. The majority of the point clouds produced using this image-based technique provide an accurate representation of the 3D structure of the surface and near-surface fuels. Results indicate that high image overlap and pixel size are critical; multi-angle image capture is shown to be crucial in providing a representation of the vertical stratification of fuel. Terrestrial image-based point clouds represent a viable technique for low cost and rapid assessment of fuel structure.
topic Structure from Motion
vegetation structure
fuel hazard
Terrestrial Laser Scanning
url https://www.mdpi.com/2571-6255/3/4/59
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AT simondjones terrestrialimagebasedpointcloudsformappingneargroundvegetationstructurepotentialandlimitations
AT karinreinke terrestrialimagebasedpointcloudsformappingneargroundvegetationstructurepotentialandlimitations
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