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Zone adaptive fuel mapping for high resolution wildfire spread forecasting.

Paula Sánchez1, Irene González2, Carlos Carrillo3

  • 1Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain. paula.sanchez.gayet@uab.cat.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

Accurate fuel maps are vital for predicting extreme wildfire events. A new zone-adaptive fuel mapping method (ZAFM) uses land cover data to create high-resolution fuel maps, significantly improving wildfire spread forecasts.

Keywords:
Forest firesHigh-resolution fuel mappingLand cover map

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Area of Science:

  • Earth and Environmental Sciences
  • Geospatial Analysis
  • Wildfire Science

Background:

  • Extreme wildfire events (EWE) pose significant global damage, necessitating improved prediction and mitigation strategies.
  • Wildfire spread simulators require accurate fuel maps, but these are often unavailable, outdated, or low-resolution.
  • Existing methods lack a standardized approach for converting widely available land cover maps into reliable fuel maps.

Purpose of the Study:

  • To develop and validate an automatic methodology for generating high-resolution fuel maps from land cover data.
  • To introduce the zone-adaptive fuel mapping (ZAFM) approach for consistent, local fuel map creation using public resources.
  • To enhance the accuracy of wildfire spread forecasts by utilizing improved fuel mapping.

Main Methods:

  • Proposed the zone-adaptive fuel mapping (ZAFM) methodology, an automatic process converting land cover maps to fuel maps.
  • Applied ZAFM to an extreme wildfire event in northeastern Spain (summer 2022) as a case study.
  • Assessed accuracy by comparing ZAFM-derived fuel map forecasts against simulations using other fuel maps and real fire evolution data, employing the F2-score metric.

Main Results:

  • ZAFM achieved the highest F2-score of approximately 0.90 in predicting wildfire spread, outperforming other fuel maps (F2-scores 0.78–0.89).
  • Simulations using ZAFM demonstrated superior predictive performance compared to forecasts based on alternative fuel maps derived from the same land cover data.
  • Validation against publicly available fuel maps in Catalonia confirmed ZAFM's higher F2-scores in case study simulations.

Conclusions:

  • The zone-adaptive fuel mapping (ZAFM) method significantly improves wildfire spread forecast accuracy.
  • Utilizing up-to-date, high-resolution land cover data through ZAFM is crucial for enhancing predictive capabilities in wildfire management.
  • ZAFM's reliance on open-access data enables its global application for creating fuel maps from any available high-resolution land cover map.