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In recent years, the increasing frequency and severity of wildfire have caused widespread devastation to infrastructure, communities, and ecosystems. Effective wildfire risk mitigation strategies require accurate and up-to-date information about the spatiotemporal distribution of vegetation, which serves as the primary fuel for wildfires. However, current approaches to fuel mapping typically rely on static datasets like LANDFIRE, which often lack the spatial precision and temporal relevance needed for accurate wildfire risk assessment. This study, therefore, presents an application-driven evaluation of a dynamic fuel mapping generation model using satellite imagery. Specifically, a K-nearest neighbors (KNN) algorithm is adapted and evaluated for pixel-wise fuel mapping, incorporating enhancements such as active learning and systematic sampling to improve classification robustness. Model performance is assessed across three sources of satellite imagery with different spatial resolutions: Harmonized Landsat and Sentinel-2 (HLS), Sentinel-2, and SPOT-6. Results show that classification success depends not only on resolution but also on the compatibility between input data and ground truth labels. Moreover, findings indicate that different satellite imagery resolutions are better suited for different mapping objectives, with high-resolution imagery offering advantages for fine-scale analysis, while lower-resolution data remain effective for regional assessments, highlighting the need for evaluation metrics and modeling approaches that account for both pixel-level accuracy and broader spatial patterns. This work contributes a scalable, interpretable, and dynamic fuel type classification approach and offers practical insights for sensor selection and data preparation in dynamic fuel mapping. 

Wildfire poses an escalating threat to communities in the wildland-urban interface (WUI), where both government efforts and homeowner actions are critical for risk reduction. While actions such as home hardening, defensible space creation, and vegetation management are recognized as effective, homeowner adoption rates remain suboptimal. This highlights the need to better understand the drivers of homeowner mitigation behavior, the dynamic interactions with neighbors, and strategies to incentivize broader participation. Existing research largely examines individual decision-making in isolation, often neglecting the interdependence among homeowners inherent in wildfire mitigation as a collective action problem. Moreover, the role of government subsidies in promoting mitigation actions remains underexplored. This study develops a utility-based model that captures homeowners’ mitigation decisions by incorporating income, homeownership tenure, wildfire risk, mitigation costs and benefits, and neighbor behaviors. Using Nash equilibrium analysis, we examine how homeowner strategies evolve under different levels of government subsidy and assess the collective utility outcomes for the community. Our results demonstrate the existence of a critical subsidy threshold necessary for wildfire mitigation to become the dominant homeowner strategy and highlight the importance of early-stage interventions and personal risk framing to overcome free-riding behavior and coordination failures. These results also underscore the importance of dynamic, targeted subsidy policies to foster collaborative mitigation efforts and strengthen WUI community resilience.