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Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model


Taneja, T and Wallace, L and Hillman, S and Reinke, K and Hilton, J and Jones, S and Hally, B, Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model, Remote Sensing, 15, (5) Article 1273. ISSN 2072-4292 (2023) [Refereed Article]

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DOI: doi:10.3390/rs15051273


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.

Item Details

Item Type:Refereed Article
Keywords:up-scaling, fuel metrics, fuel hazard, random forest, visual assessments, field data, fuel layers, near-surface, cover, height, elevated, canopy, ALS
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Natural hazards
Objective Field:Climatological hazards (e.g. extreme temperatures, drought and wildfires)
UTAS Author:Wallace, L (Dr Luke Wallace)
ID Code:155517
Year Published:2023
Deposited By:Geography and Spatial Science
Deposited On:2023-02-27
Last Modified:2023-02-27

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