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煤火在世界各地均有不同程度的发生,严重威胁生态环境,煤火燃烧释放大量的有毒有害气体,造成大气污染,同时煤火燃烧形成地下空洞,导致地表塌陷,直接威胁着矿区人员的生命安全。遥感技术的迅猛发展使大尺度反演与监测煤火温度变为可能。单窗算法是一个简单可行且精度较高的煤火温度反演方法,该方法需要两个大气参数进行温度反演,即大气水分含量和地表比辐射率。由于该算法需要卫星影像获取瞬间时的大气水分含量,而卫星过境瞬间的大气水分含量受多种因素影响难以获得;单窗算法对不同类型的地物均采用统一的地表比辐射率,这会导致反演温度不精确,误差较大。针对上述存在的问题,采用基于地面湿度参量建立起的大气可降水量与地面水汽压间的经验关系,计算大气水分含量,同时,采用NDVI阈值法计算不同地物类型的地表比辐射率,对单窗算法中的两个参数进行精确估计,从而改进提高该算法的精度及可操作性。将改进的算法应用于内蒙古乌达矿区,反演从1988年到2015年间研究区的煤火温度,提取每年研究区的温度异常区域,对比分析煤火区域分布、面积变化情况。本文提出的改进算法能够快速、高效的反演煤火温度,对掌握煤火异常区域变化情况提供技术支持,具有可操作性及现实意义。
Coal fires all over the world have varying degrees of occurrence, a serious threat to the ecological environment, burning coal fire release a large number of toxic and harmful gases, causing air pollution, while burning coal to form underground voids, leading to surface subsidence, a direct threat to the lives of mine workers Safety. The rapid development of remote sensing technology makes large-scale inversion and monitoring of coal fire temperature possible. The single-window algorithm is a simple and feasible method of temperature inversion for coal-fired fire with high accuracy. The method requires two atmospheric parameters for temperature inversion, namely, the atmospheric moisture content and the surface emissivity. Because this algorithm needs the atmospheric moisture content at the moment of satellite image acquisition, the atmospheric moisture content at the moment of satellite transit is difficult to obtain due to many factors. The single-window algorithm uses uniform surface emissivity for different types of ground objects, Lead to inaccurate inversion temperature, the error is larger. In view of the problems mentioned above, the atmospheric moisture content is calculated based on the empirical relationship between the precipitable water vapor and groundwater vapor pressure established based on the ground humidity parameters. At the same time, the NDVI threshold method is used to calculate the surface specific emissivity of different landform types. The two parameters in the single-window algorithm are estimated accurately, so as to improve and improve the accuracy and operability of the algorithm. The improved algorithm was applied to the Wuda mining area in Inner Mongolia. The temperature of coal fire in the study area was retrieved from 1988 to 2015, and the temperature anomaly area of the study area was extracted. The distribution and area change of coal fire area were compared. The improved algorithm proposed in this paper can quickly and efficiently reverse the temperature of coal-fired fires and provide technical support for understanding the changes of coal-fire anomalies. It is of practical and practical significance.