Density-Based Spatial Clustering of Vegetation Fire Points Based on Genetic Optimization of Threshold Values

Vegetation fires are among the most common natural disasters, posing significant threats to people and the natural environment worldwide. Density-based clustering methods can be used to identify geospatial clustering patterns of fire points. It further helps reveal the spatial distribution characteristics of wildfires, which are crucial for regional-specific fire mapping, prediction, mitigation, and protection. DBSCAN (density-based spatial clustering of applications with noise) is widely used for clustering spatial objects. It needs two user-determined threshold values: the local radius and the minimum number of neighboring points for core points, which require user expertise and background information. This work proposes a dual-population genetic optimization to determine threshold values of DBSCAN for clustering vegetation fire points in western China. By constructing randomly generated threshold populations, optimized threshold values are obtained through crossover, mutation, and inter-population exchange, measured by multiple clustering metrics. Focusing on vegetation wildfires in western China during 2016–2022, the results reveal that vegetation wildfires can be divided into eight regions, each exhibiting distinct spatiotemporal patterns and geographic contexts.