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Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

Effect of fuel spatial resolution on predictive wildfire models

Ritu Taneja A B D , James Hilton https://orcid.org/0000-0003-3676-0880 B , Luke Wallace C , Karin Reinke A and Simon Jones A
+ Author Affiliations
- Author Affiliations

A Geospatial Science, RMIT University, Melbourne, Vic. 3001, Australia.

B CSIRO Data61, Private Bag 10, Clayton South, Vic. 3169, Australia.

C School of Geography, Planning and Spatial Sciences, University of Tasmania, Hobart, Tas. 7015, Australia.

D Corresponding author. Email: s3704716@student.rmit.edu.au

International Journal of Wildland Fire 30(10) 776-789 https://doi.org/10.1071/WF20192
Submitted: 3 January 2021  Accepted: 26 July 2021   Published: 26 August 2021

Journal Compilation © IAWF 2021 Open Access CC BY-NC-ND

Abstract

Computational models of wildfires are necessary for operational prediction and risk assessment. These models require accurate spatial fuel data and remote sensing techniques have ability to provide high spatial resolution raster data for landscapes. We modelled a series of fires to understand and quantify the impact of the spatial resolution of fuel data on the behaviour of fire predictive model. Airborne laser scanning data was used to derive canopy height models and percentage cover grids at spatial resolutions ranging from 2 m to 50 m for Mallee heath fire spread model. The shape, unburnt area within the fire extent and extent of fire areas were compared over time. These model outputs were strongly affected by the spatial resolution of input data when the length scale of the fuel data is smaller than connectivity length scale of the fuel. At higher spatial resolutions breaks in the fuel were well resolved often resulting in a significant reduction in the predicted size of the fire. Our findings provide information for practitioners for wildfire modelling where local features may be important, such as operational predictions incorporating fire and fuel breaks, and risk modelling of peri-urban edges or assessment of potential fuel reduction mitigations.

Keywords: wildfire modelling, vegetation structure, airborne laser scanning, fuel sampling, Spark, Spatial resolution.


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