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Up-scaling fuel hazard metrics derived from terrestrial laser scanning using a machine learning model
Citation
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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Abstract
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 |
| Downloads: | 0 |
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