Spatial distribution of grassland fires at the regional scale based on the MODIS active fire products
Zhengxiang Zhang A , Zhiqiang Feng B , Hongyan Zhang A , Jianjun Zhao A , Shan Yu C and Wala Du D E
+ Author Affiliations
- Author Affiliations
A Provincial Laboratory of Resources and Environmental Research for Northeast China, Northeast Normal University, Changchun 130024, China.
B School of Geosciences, University of Edinburgh, Edinburgh, EH8 9XP, Scotland, UK.
C Inner Mongolia Autonomous Region Key Laboratory of Remote Sensing & Geography Information System, Inner Mongolia, Huhhot 010022, China.
D Ecological and Agricultural Meteorology Centre of Inner Mongolia Autonomous Region, Inner Mongolia, Huhhot 010051, China.
E Corresponding author. Email: dwlrsgis@163.com
International Journal of Wildland Fire 26(3) 209-218 https://doi.org/10.1071/WF16026
Submitted: 15 February 2016 Accepted: 5 January 2017 Published: 23 February 2017
Abstract
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 minimise the influences of inaccurate locations and grid size, density estimates can be produced using kernel density estimation (KDE) – a nonparametric statistical method for estimating probability densities. This method has been widely used to convert historical fire data into continuous surfaces. In this study, KDE 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.
Additional keywords: bandwidth, fire risk zone, kernel density estimation, spatial pattern.
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