摘要
Xinjiang in China is one of the areas worst affected by coal fires.Coal fires cannot only waste a large amount of natural resources and cause serious economic losses,but they also cause huge damage to the atmosphere,the soil,the surrounding geology,and the environment.Therefore,there is an urgent need to effectively explore remote sensing based detection of coal fires for timely understanding of their latest development trend.In this study,in order to investigate the distribution of coal fires in an accurate and reliable manner,we exploited both Landsat-8 optical data and Sentinel-1A synthetic aperture radar(SAR)images,using the generalized single-channel algorithm and the InSAR time-series analysis approach,respectively,for coal fire detection in the southern part of the Fukang region of Xinjiang,China.The generalized single-channel algorithm was used for land surface temperature information extraction.Meanwhile,the timeseries InSAR analysis technology was employed for estimating the surface micro deformation information,which was then used for building a band-pass filter.The suspected coal fire locations could then be established by a band-pass filtering operation on the obtained surface temperature map.Finally,the locations of the suspected coal fires were validated by the use of field survey data.The results indicate that the integration of thermal infrared remote sensing and radar interferometry technologies is an efficient investigation approach for coal fire detection in a large-scale region,which would provide the necessary spatial information support for the survey and control of coal fires.
基金
This work was supported by the National Natural Science Foundation of China(No.41874044)
the Jiangsu Province Foundation of Brand Specialty Construction Projects in College and University(No.PPZY2015B144).The authors also gratefully acknowledge the European Space Agency for providing the Sentinel-1A SAR data and the US Geological Survey for providing the Landsat-8 data and the DEM data.The authors would also like to thank NASA for the auxiliary atmospheric data.
