# Zone adaptive fuel mapping for high resolution wildfire spread forecasting

## TL;DR

A new method called ZAFM improves wildfire spread forecasts by creating high-resolution fuel maps from land cover data, leading to better predictions during extreme wildfire events.

## Contribution

The novel zone-adaptive fuel mapping (ZAFM) method automatically generates high-resolution fuel maps from land cover data using open-access resources.

## Key findings

- ZAFM achieved an F2-score of approximately 0.90 in predicting fire spread during an extreme wildfire event in Spain.
- Other fuel maps derived from land cover data had F2-scores ranging from 0.78 to 0.89.
- ZAFM outperformed publicly available fuel maps in the case study simulations.

## Abstract

Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map.