Spatial Distribution of Grassland Fires at the Regional Scale Based on the MODIS Active Fire Products

Grassland fires are major disturbances to ecosystems and economies around the world. Therefore, research on the spatial patterns of grassland fires is important for understanding the dynamics of fire occurrence and providing evidence for fire prevention and management. One of the problems in grassland fire risk analysis is that historically observed fire data are generally in the point format, with imprecise positions, whereas other influencing factors are often expressed in continuous areal units. To minimize the influences of inaccurate locations and grid size, kernel density estimation, a non-parametric statistical method for estimating probability densities, can be used to produce density estimates. This method has been widely used to convert historical fire data into continuous surfaces. In this study, kernel density estimation was applied to grassland fire
events in the eastern Inner Mongolia of China, based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily active fire data from 2001 to 2014. The bandwidth choice was based on the mean random distance method. Annual and seasonal kernel density maps were produced, showing that the spatial patterns of grassland fire events remained temporally consistent. These results were used to create grassland fire risk zones on the basis of
the mean density values in the study area. Grassland fire prevention and planning may focus on high-risk areas identified using this method.