Remote Sensing, Volume 17, Issue 3
February-1 2025 - 215 articles
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Cover Story: Fuel load is crucial in wildfire behavior models, affecting fire severity, flame length, and fuel consumption. Accurate spatiotemporal predictions are vital for wildfire management, yet field surveys are labor-intensive and limited in scope. Remote sensing offers a scalable alternative, enabling near-real-time fuel load assessments from local to global scales. This study provides a critical review on remote sensing applications for fuel load estimation, assessing sensor capabilities and limitations. While technological constraints persist, advancements in remote sensing, machine learning, and UAV-based photogrammetry show promise for accurate mapping. Challenges include data availability for calibration and validation and achieving high accuracy for surface fuels. Future innovations aim to refine predictive models and mapping precision. View this paper 
