Seasonality of vegetation fires in Africa from remote sensing data and application to a global chemistry model


  • W. F. Cooke,

  • B. Koffi,

  • J.-M. Grégoire


This paper sets out to show the potential use of remote sensing of active vegetation fires for continental- to global-scale modeling of biomass burning studies. It focuses on the analysis of the seasonality of vegetation fires for the African continent, as derived from NOAA-AVHRR-GAC-5km satellite data. These data are ideally suited for savanna fires, which constitute between 60 and 80% of the biomass burnt in Africa. Monthly counts of fire pixels, within 1° latitude × 1° longitude grid cells, over continental Africa have been calculated from November 1984 through October 1989. These 1° grid cells are summated to a 5° × 5° grid to enable comparison with previous studies and are analyzed at this resolution to show various features of the fire season. The analysis shows that previous attempts to characterize the seasonality of biomass burning have tended to underestimate the intensity of the peak months of burning or have predicted too long a fire season in certain areas. It also shows that there can be, for a given area, a temporal shift in the timing of the fire season from year to year. Such an interannual variability of fire seasonality makes satellite data more appropriate than statistical data for the modeling of atmospheric transport of vegetation fire products and the comparison with experimental measurements. Modeled values of black carbon mass concentration from a global transport model (MOGUNTIA), using the seasonality of biomass burning as an independent variable, are compared with measurements taken at Amsterdam Island (38°30′S, 77°30′E) and Lamto, Ivory Coast (6°N, 5°W). Although 5-year averaged satellite data were used, the seasonality as derived from satellite data determined in this paper gives modeled values of black carbon mass concentration that are in good agreement with the measurements.