Spatiotemporal Analysis of Landscape Fire Activity and Aerosol Trends in the Context of Urbanization

Climate change is accelerating the frequency and magnitude of landscape fires, and the smoke they emit impacts both the rural areas where it is emitted and the urban areas where it disperses. This study explores the spatiotemporal trends of remotely sensed Aerosol Optical Depth (AOD) and burned areas (BA) across different levels of urbanization between 2000-2024 in the East African Community (Democratic Republic of the Congo, Burundi, Kenya, Rwanda, South Sudan, Tanzania, and Uganda). To characterize long-term spatiotemporal patterns in AOD and burned area, we assessed temporal trends using Mann–Kendall tests, examined the evolution of hotspots through space–time cubes and emerging hotspot analysis, and mapped hot and cold spot trends. We then used zonal statistics to quantify how these hotspot patterns vary across different degrees of urbanization (DoU), allowing us to evaluate whether wildfire smoke and burned-area dynamics disproportionately affect more urbanized landscapes. Our findings show that while BA has decreased, especially in urban settings, airborne aerosol concentrations have increased, particularly over urban areas. AOD increased over time across the region, with urban areas experiencing larger and more statistically significant increases than rural areas. AOD has increased significantly (p<0.05) over time in a greater number of urban DoUs (3 out of 4) compared to rural DoUs (1 out of 4). In contrast, the total burned area has generally decreased over time across all DoUs, being statistically significant (p<0.05) in urban centres and low-density rural areas. There was a weak correlation between the hotspot z-scores of AOD and BA (r=0.32), suggesting smoke transport and other sources of air pollution. These results align with the observation in other studies that global forest fire emissions have increased despite the decline in global BA. This research contributes a novel framework for tracking wildfire metrics across urban gradients using remotely sensed data, space-time cube modelling, trend detection, and hotspot analytics.