Spatio-temporal distribution pattern of forest,shrub and grass fire spots in China based on MODIS data
[Objective]This paper is aim to have a clear understanding of the dynamic changes of forest fire occurrence in China from space and time scale,which provides reference for forest fire prevention decision.[Method]Taking the national land area as the study area,MODIS fire point data products as satellite hot spots(SHS)and land cover data products in 2003-2022 were selected to explore the spatial distribution characteristics of forest and shrub fire spots in 2003-2022 by using optimized hot spot analysis method.Statistical methods were adopted to analyze interannual,monthly and traditional festivals'characteristic.Mann-Kendall trend analysis method was used to study the trend of distribution of fire spots in forest,shrub and grass.[Result](1)Dense regions of SHS were mainly aggregated in Guangdong province,Yunnan province,Guangxi Zhuang Autonomous Region and Heilongjiang province.Sparse regions of SHS were mainly concentrated in Henan,Hebei and Shandong province.(2)The number of SHS in grass was the highest frequency,which were mainly distributed in spring and winter,while the number of SHS in shrub was the lowest,which were significantly distributed in summer.(3)The rank of the top three of the seven traditional festivals in the number of SHS were as follows:Spring Festival,Tomb-sweeping Day and Labor Day.(4)According to the inter-annual and monthly statistics of the number of forest shrub fire spots from 2003 to 2022,the number of SHS showed a fluctuating trend.In terms of inter-annual changes,2015 was the year of abrupt change in the number of SHS.For monthly,May was the month of abrupt change in the number of SHS.[Conclusion](1)SHS are clustered in the south and are sparse in the central and eastern regions,and no obvious migration occurs in the gathering regions from 2003 to 2022.Therefore,differentiated management can be carried out when implementing fire prevention deployment.(2)The distribution areas of grassland in spring and winter and broad-leaved forests in spring and summer need to strengthen fire prevention management.
forest fire preventionMODISMann-Kendall trend analysisoptimized hot spot analysisspatio-temporal distribution pattern
万方数据
维普
