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Published VersionCC BY V4.0, Open Access
Journal article
A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis
Remote Sensing , Vol.15(10), pp.1-28
2023
Abstract
Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations.
Details
- Title
- A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis
- Authors
- Kim Penglase (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringTom Lewis (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringSanjeev Srivastava (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Remote Sensing , Vol.15(10), pp.1-28
- Publisher
- MDPI AG
- Date published
- 2023
- DOI
- 10.3390/rs15102621
- ISSN
- 2072-4292
- Copyright note
- © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Grant note
- The sole funding contribution was a scholarship awarded to K.P. by the Forest Industries Research Centre, an entity within the University of the Sunshine Coast; therefore, this research received no external funding.
- Organisation Unit
- School of Science, Technology and Engineering; Forest Research Institute; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99728398002621
- Output Type
- Journal article
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- Web Of Science research areas
- Environmental Sciences
- Geosciences, Multidisciplinary
- Imaging Science & Photographic Technology
- Remote Sensing
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