Application of remote sensing and geospatial techniques for forest fire monitoring and air quality impact assessment

Abstract

Wildfires are among the most significant environmental disturbances worldwide, with substantial impacts on ecosystems and air quality. Remote sensing techniques have advanced considerably on detection, characterization, and monitoring of wildfire and their atmospheric effects. This study presents a structured literature review between 2019 and 2026 using the ProKnow-C methodology, resulting in a portfolio of 154 peer-reviewed articles. The results reveal growing research on emission estimation and air quality assessment, particularly on atmospheric impacts, burned area, fire severity, and fire detection. Machine and deep learning approaches emerge as dominant techniques, reflecting a shift toward data-driven methodologies. Additionally, integrated multisensor data improve detection accuracy and reduce uncertainties associated with atmospheric interference. This review proposes a coherent analytical chain that integrates fire detection, burned area mapping, fire severity assessment, emission estimation, and air quality evaluation. This study provides an understanding and monitoring of wildfire impacts, their current limitations and future research directions.