Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images

Abstract

Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements, necessitating accurate and timely detection of burned areas for post-fire management. This study focused on the immediate assessment of a recent major forest fire that occurred on March 15, 2024, in southwestern China. We comprehensively utilized high temporal resolution MODIS and Black Marble nighttime light images to monitor the fire’s development and introduced a novel method for detecting burned forest areas using a new Shadow-Enhanced Vegetation Index (SEVI) coupling with a machine learning technique. The SEVI effectively enhances the vegetation index (VI) values on shaded slopes and hence reduces the VI disparity between shaded and sunlit areas, which is critical for accurately extracting fire scars in such terrain. While SEVI primarily identifies burned forest areas, the Random Forest (RF) technique detects all burned areas, including both forested and non-forested regions. Consequently, the total burned area of the Yajiang forest fire was estimated at 23,588 ha, with the burned forest area covering 19,266 ha. The combination of SEVI and RF algorithms provided a comprehensive and efficient tool for identifying burned areas. Additionally, our study employed the Remote Sensing-based Ecological Index (RSEI) to assess the ecological impact of the fire on the region, uncovering an immediate 15 % decline in regional ecological conditions following the fire. The usage of RSEI has the potential to quantitatively understand ecological responses to the fire. The findings achieved in this study underscore the significance of precise fire-burned area extraction techniques for enhancing forest fire management and ecosystem recovery strategies, while also highlighting the broader ecological implications of such events.