Wildfire Early Warning Systems: A Multisensor and Predictive Modelling Comparison Across Countries with a Canadian Perspective

Abstract. This study evaluates wildfire early warning systems (EWS) across fifteen countries spanning four World Bank income tiers (high, upper-middle, lower-middle, and low) using a two-parameter framework: P1 (multi-sensor satellite integration) and P2 (hotspot detection and predictive modelling). The assessment examines how geostationary (GEO) and low Earth orbit (LEO) satellites are integrated with temporal resolution, spectral capability, forecast horizon, and operational deployment.
The United States and Australia score 5/5 through continuous GEO/LEO fusion and validated multi-day predictive models. Canada, Spain, and Greece score 4/5 with operational multi-sensor systems still improving high-frequency integration. Chile scores about 3.5/5, lower than expected for a high-income country due to incomplete sensor integration. Upper-middle-income countries (Brazil, Mexico, South Africa) score around 3/5, while lower-middle-income countries (India, Nepal) score 2 to 2.5/5, both groups showing only partial predictive model integration. Uganda, Mozambique, Madagascar, and Niger (1 to 1.5/5) depend on global satellite products with minimal predictive capability.
The results show a clear maturity gradient tied to income level, driven by GEO/LEO fusion, predictive modelling development, and operational readiness. These findings highlight the need for international support to help lower-capacity countries reduce wildfire risk.