YaraEzAl Deen Sultan ^1* Kanni Raj Arumugam Pillai ¹ Archana Sharma ^2*

• ¹ Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
• ² Marwadi University, Rajkot, Gujarat, India

Forest fires significantly impact ecosystems; thus, identifying characteristics that increase the danger of fires is critical to mitigating their negative impacts. This study examines the parameters contributing to wildfires in the Vellore Reserve Forest (VRF) to develop GIS-based risk maps and models to enhance fire protection, fuel mitigation strategies, and land use decisions by improving wildfire risk recognition and prediction. The Vellore district, covering 6077 square kilometers, has a significant 27% forest cover, covering 162,286 hectares. This forest is primarily found between latitudinal and longitudinal coordinates in the calm taluks of Gudiyatham, Tirupattur, and Vellore-the Vellore Reserve Forest Report 2023 highlights this ecological diversity. Geographic information systems (GIS) based analysis of forest fire was done using normalized difference vegetation index, normalized difference moisture index, fuel danger index, (human) activity danger index, weather danger index, topographic danger index, normalized burn ratio index, and differenced NBR. Real-time maps were photographed by MODIS and Landsat 9 satellites to obtain a normalized difference in vegetation and moisture index. Initially, data are converted to digital maps. The most helpful fuel, activity, weather, and topography danger indexes are calculated using the Raster Calculator utility, Euclidean Distance tool, Kriging tool, and Digital Elevation Model, respectively. In the Vellore district, the calculated activity danger index ranges from 0 to 12000, showing that the high risk emanates from human activities. The climate is dry from May to July, and the weather danger index is 345-348. In other seasons, the weather index is 338-341, indicating a low-risk level. In Vellore, low to medium-risk values for the topography index are 56.5-933, and high-risk values are 934-1690. Fire severity is indexed in terms of both NBR and dNBR. NBR and dNBR are calculated from the NIR-SWIR ratio. The innovative elements of this study are characterized by a comprehensive, integrated strategy that employs GIS technology, providing an understanding of localized factors influencing wildfire ignition. The significant insights regarding the metrics that govern wildfire dynamics, serve as a vital resource for wildfire management efforts in the region and the models would help predict the future wildfire risk under climate change and land use conditions.