Remote Sensing AI for Crop Planting in Wildfire Fuel Mapping

Abstract

Accurate wildfire prediction requires updated, high-resolution fuel maps that account for seasonal vegetation variations. The flammability of crops varies by season, affecting the behavior of wildfires. This study combines remote sensing indices and machine learning to dynamically update fuel models in cropland zones. Using Sentinel-2 data, the status of the cropland is classified as “planted” or “unplanted”, achieving 80% accuracy. Applied to a 2019 wildfire in Catalonia (Spain), the updated fuel map closely matched the observed fire spread. The methodology outperforms traditional approaches and is efficient, allowing for real-time updates based on seasonal changes.