155517 - Up-scaling fuel hazard metrics derived from terrestrial laser scanning.pdf (15.02 MB)
Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model
journal contributionposted on 2023-05-21, 16:38 authored by Taneja, T, Luke WallaceLuke Wallace, Hillman, S, Reinke, K, Hilton, J, Jones, S, Hally, B
The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R2 = 0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R2 = 0.23) and canopy layers (R2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions.
Publication titleRemote Sensing
Department/SchoolSchool of Geography, Planning and Spatial Sciences
Place of publicationSwitzerland
Rights statementCopyright: © 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 4.0 International (CC BY 4.0) license, (https:// creativecommons.org/licenses/by/4.0/).