A method to produce a flexible and customized fuel models dataset

Area of Science:

• Environmental Science
• Geospatial Analysis
• Fire Ecology

Background:

• Effective vegetation fire simulation relies on accurate fuel models, yet data availability and quality pose significant challenges for researchers and fire managers.
• Existing fuel model datasets often lack the necessary detail or customization for specific regional applications, hindering precise fire behavior prediction.

Purpose of the Study:

• To develop and present a novel methodology for generating customized, high-resolution fuel model maps by integrating multiple data sources and expert knowledge.
• To create a flexible and reproducible toolbox (FUMOD) that addresses the limitations of current fuel model data for vegetation fire research and management.

Main Methods:

• A hybrid approach combining expert knowledge, research-based insights, satellite data, and fieldwork to create a basemap of fuel model classes assigned to land cover types.
• Implementation of empirical and user-defined rules to refine the basemap, resulting in detailed surface fuel model maps.
• Development of the FUMOD toolbox within ModelBuilder/ArcGIS, integrating ten distinct sub-models for automated map generation.

Main Results:

• Successful generation of detailed, customized fuel model maps for Portugal since 2019 using the FUMOD toolbox.
• Demonstrated flexibility and reproducibility of the FUMOD method through the integration of independent spatial datasets of varying quality and availability.
• The FUMOD toolbox provides updated Portuguese fuel model grids, directly supporting regional fire risk assessments and suppression strategies.

Conclusions:

• The FUMOD toolbox offers a robust and adaptable solution for creating essential fuel model inputs for vegetation fire simulations.
• This methodology enhances the accuracy and detail of fuel mapping, thereby improving the reliability of fire behavior models and supporting informed decision-making in fire management.
• The open availability of datasets and models facilitates wider adoption and further research in fuel modeling for fire science.