Integration of Remote Sensing and Deep Learning for Forest Fire Risk Mapping

Abstract

Forest fires have emerged as one of the most critical environmental hazards, causing extensive ecological degradation, biodiversity loss, carbon emissions, and socioeconomic disruptions across forested landscapes worldwide. Accurate forest fire risk mapping is therefore essential for supporting early warning systems, resource allocation, and sustainable forest management. Recent advances in Earth observation technologies have enabled the continuous acquisition of multisource remote sensing data, while deep learning techniques have demonstrated superior capabilities in extracting complex spatial and temporal patterns from large geospatial datasets. This paper investigates the integration of remote sensing and deep learning methodologies for forest fire risk mapping by utilizing satellite-derived environmental variables, including vegetation indices, land surface temperature, topography, meteorological parameters, and land-cover characteristics. The study evaluates the effectiveness of deep neural architectures in modeling nonlinear relationships between fire occurrence and contributing factors. Furthermore, a comprehensive framework is proposed to enhance prediction accuracy, spatial generalization, and operational applicability for wildfire management. The findings highlight the potential of synergizing remote sensing observations with advanced deep learning models to improve fire susceptibility assessment, support decision-making processes, and strengthen disaster preparedness strategies under changing climatic conditions.