A SPATIAL DISTRIBUTIONMAP OF THE WILDFIRE RISK IN MONGOLIA USING DECISION SUPPORT SYSTEM
- 1National Remote Sensing Center, Juulchiny street-5, Ulaanbaatar 15160, Mongolia
- 2Mongolian Geospatial Association, P.O. Box 24/38, Post office-38, Ulaanbaatar 15141, Mongolia
- 3Information and Research Institute of Meteorology Hydrology and Environment, Juulchiny street-5, Ulaanbaatar 15160, Mongolia
- 4The Ministry of Environment and Tourism, Government building II, United Nations Street–5/2, Chingeltei District, Ulaanbaatar 15160, Mongolia
- 5Disaster Research Institute under national Emergency Agency, Partizani street-6, Ulaanbaatar 210644, Mongolia
Keywords: MODIS, burnt area, fire hotspot, land cover, Multi-Criteria Evaluation Analysis, SPI
Abstract. Wildfire is a cause of major disturbance to ecosystems and economies throughout the world. Hence, the wildfire is a vital issue of environment for creating natural disaster and socio-economic damage to affect in ecosystem and human lives. Moreover, the forest and steppe fire are one of natural risks in issues of Mongolian economy. This paper attempted to identify a spatial distribution of both risk and damage cost of the wildfire in Mongolia. The variables are to affect in the forest and steppe fire such as a biophysical parameters, field and statistical yearbook were integrated by climatic data to apply all into GIS application. These analyses are applied to the approach of decision-making support system, particularly as a Multi-Criteria Evaluation Analysis (MCEA). In addition, an evaluation of data could be divided into three group factors namely; environmental, social economic and fire damage including seventeen input parameters. These factors are based on important criteria and rank of the wildfire risk levels. All initial parameters were integrated into spatial model and used to estimate the wildfire risk index. This index was divided into five categories: very high, high, moderate, low and non-risk. Results show that a percentage of the study area was predicted by wildfire risk index in each category as follows. About 6–8 % of the study area identified as categories of the moderate and low risk; but 5–16 % of it was estimated as very high and high risk. In remaining category, 65 % of the study area was occupied as a maximum value in the non-risk. Moreover, these results demonstrated that a spatial distribution map of wildfire risk was predicted well including five categories for the study period. The integration of these factors in GIS could be useful to identify risk area and to make the strategy and prevention of wildfire hazard for the stakeholder, government and local of decision makers. Furthermore, it could be helped to improve the management of the forest, ecological and biodiversity conservation.