Geospatial Analysis of Wildfire Impact and Predictive Modeling of Susceptibility: A Case Study of Maui, Hawaii and California

Abstract

Catastrophic wildfires pose a growing global threat. This study analyzes their impacts through a comparative analysis, developing a predictive risk model that integrates satellite remote sensing within a cloud-based Geographic Information Systems (GIS) framework. On the Google Earth Engine platform, the differenced Normalized Burn Ratio (dNBR) and Normalized Difference Vegetation Index (NDVI) were applied to assess burn severity and vegetation health for the 2023 Maui wildfire and the early 2025 California wildfire. Despite stronger resilience, California's fires cause a heavier overall economic impact, while Maui's smaller fire delivered a more concentrated, catastrophic blow to its community. The predictive risk model demonstrated high accuracy when validated against historical fire data, successfully identifying low fuel moisture, topography, land cover and human factors as key drivers of susceptibility. This research underscores the need for context-specific management and shows that GIS and cloud-based analysis are powerful tools for enhancing wildfire resilience, response, and planning.