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Description

As wildfires in the wildland-urban interface (WUI) of the Southern Gulf Islands region are becoming more frequent and intense, fire managers need up-to-date, detailed fuel maps to support fire behavior modelling and operational suppression planning. However, accurately characterizing the various fuel types at a fine spatial scale remains a significant challenge. This study developed a classification framework that combined remote sensing and deep learning to generate a detailed fuel-type map for Mudge Island, British Columbia. Specifically, a one-dimensional convolutional neural network (1D-CNN) was applied to Sentinel-2 multispectral imagery, integrating 12 spectral bands and three vegetation indices: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI) to classify land cover into seven categories. Additional biophysical features, including above-ground biomass and aridity, were incorporated to refine fuel characterization. The final output is a 28-class fuel map that aligned with the Scott and Burgan (2005) Fire Behavior Fuel Model (FBFM). Model performance differed across evaluation approaches. Internal validation against a high-confidence reference layer indicated robust classification performance, achieving an overall accuracy of 86.9%, a mean producer's accuracy of 87.9%, a mean user's accuracy of 86.9%, and an F1 score (a balance between precision and recall) of 87.3%. In contrast, external validation using four independent datasets showed lower levels of agreement, with most accuracy metrics falling below 70%. Comparing the model with the baseline fuel map revealed significant differences: conifer fuels achieved an accuracy rate of 97.9%, while the accuracy rate for other classes ranged from 0% to 18.2%. Despite these discrepancies, the framework demonstrated the potential of multispectral data and deep learning for scalable fuel mapping, offering invaluable insights to inform community-scale, small-island wildfire risk assessment, fuel treatment prioritization, and fire management planning.