Spatial modelling of wildfire hotspots and their key drivers across districts of Zimbabwe, Southern Africa

Abstract

Understanding spatial patterns of wildfire hotspots and cold spots as well as their key drivers is important for designing appropriate fire management strategies. This study tested the extent to which wildfires cluster in Zimbabwe before assessing important determinants of wildfire clusters. Optimised Hotspot analysis was applied to detect significant wildfire hotspots and cold spots in Zimbabwe. Key determinants of wildfire hotspots were determined using spatial lag regression. Results show that wildfire hotspots are concentrated in the northern districts of the country while cold spots are prevalent in the central, eastern, southern as well as western districts. The study identified distance from settlements, dry matter productivity, mean annual temperature and slope as key drivers of wildfire hotspots and cold spots. Our results underscore the importance of adopting spatial analytical techniques in modelling wildfire hotspots as a first step towards developing sustainable wildfire management strategies and policies.

Keywords:

• Optimised hotspot analysis
• wildfire
• environmental variables
• spatial lag model
• spatial autocorrelation

Disclosure statement

No potential conflict of interest was reported by the authors.