Spatiotemporal problems with detecting and mapping mosaic fire regimes with coarse-resolution satellite data in savanna environments

Fire is a prominent disturbance factor and a major force of environmental change especially in the African savannas. The development of an accurate system to map and monitor fires on the African continent is a priority of numerous international research centers and programs. This effort has produced a flurry of research projects in recent years to detect and map areas affected by fires at the continental scale using coarse-resolution satellite imagery. The end product of these projects consists of weekly or monthly maps of burned area, several of which are available to the user community on the internet. It is argued here that the algorithms used to generate these products are designed to capture relatively large and contiguously burned areas and that the heterogeneous patterns of burn scars created by mosaic burning regimes pose a problem for current detection methodologies. Fine-scale burned area maps are generated using a series of Landsat ETM+imagery covering the 2002-2003 fire season for the study area in the savanna of southern Mali. These maps document a seasonal-mosaic pattern of burning in which burning begins early in the dry season and continues for several months ultimately affecting over 50% of the landscape. The majority of these fires burn relatively small areas producing a highly fragmented landscape pattern. A comparison of the fine scale maps with those from two widely available coarse-resolution products finds that the latter fail to detect approximately 90% of the burned area. A general argument is developed which suggests that the documented bias in the coarse resolution products is a function of low-resolution bias which derives from the fine-scale spatiotemporal pattern of burning not uncommon to savanna and other frequently burned environments. The study demonstrates how low-resolution bias can result in a significant underestimation of burned areas and/or a shift in the seasonal burned area profile in areas where mosaic burning occurs. The findings have implications for the development of broad-scale burned area detection algorithms as well as their applications to natural resource management and global environmental change research.