Assessing and Predicting Forest Fires Burn Severity: A High-Resolution Approach Using the Global Forest Burn Severity Dataset

Forest fires play a crucial role in the functioning and renewal of ecosystems.  Over the past two decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify, necessitating advanced tools for accurate severity assessment and predictive analysis. This study details the development of the Global Forest Burn Severity (GFBS) dataset, derived from Landsat imagery, providing global 30-meter resolution data spanning multiple years (2003 – 2016), which bridges the existing gap in high-resolution global assessments of forest burn severity, enabling researchers and policymakers to implement more effective forest conservation and fire management strategies. The trends of forest fires across different ecoregions are further analyzed based on the developed dataset, exploring the complex interactions between fire behavior and weather variables. This kind of analysis helps identify key drivers influencing burn severity, which vary significantly across different ecological zones. The integration of these findings with our GFBS dataset allows for the exploration of spatial and temporal patterns in burn severity on a global scale. Additionally, this study tries to develop an ecoregion-specific burn severity model that utilizes the GFBS dataset to predict future forest fires under various climate change scenarios. This model enhances our understanding of how changing climatic conditions could impact fire severity and frequency, providing essential insights for policymakers and conservation efforts aimed at mitigating the effects of wildfires.