多光谱遥感影像煤火监测新方法

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。

A Novel Method to Monitor Coal Fires Based on Multi-Spectral Landsat Images

Coal fires pose a serious threat to the environment worldwide,and they are responsible for atmos-pheric pollution,water contamination,land subsidence and the safety of miners.The multi-spectral Landsat images offer the possibility of detecting and monitoring coal fires at large scales.In this study,the thermal in-frared spectral is extracted,and a mono-window algorithm is used for retrieving coal fire temperature.Howev-er,the surface emissivity and atmospheric water vapor content play important roles in determining the temper-ature for this algorithm.The surface emissivity is particularly difficult to obtain with satellite overpasses be-cause it is affected by a variety of factors.In general,an average emissivity value is assigned to represent all land cover categories,which leads to a big error for retrieving coal fire temperature.Meanwhile,atmospheric water vapor content is calculated by simulating atmospheric profile through standard atmosphere models. However,it is difficult to obtain real water vapor content and atmospheric profile is affected by many factors with each satellite pass.The lack of knowledge of the real atmospheric profile is a large constraint,and inaccu-rate simulation can introduce big errors.Aiming at overcoming drawbacks mentioned above and increasing the accuracy for this algorithm,the NDVI threshold method is applied to estimate surface emissivity.The NDVI threshold method separates different land cover categories,and different emissivity is assigned to different land cover classifications.Based on the ground meteorological parameters'relationship between atmospheric water vapor and atmospheric water vapor pressure,an empirical relationship is found to estimate atmospheric water vapor content.For this method,the ground meteorological parameters are easily obtained from meteorological observation stations and it is convenient to estimate water vapor content.The mono-window algorithm is im-proved and coal fire temperature is retrieved.This methodology was applied to the Wuda coalfield,in China, and coal fire temperatures were retrieved and extracted from the background from 1 988 to 201 5 in the study ar-ea.A thorough inventory of coal fire areas and locations is annually presented,and area changes are qualita-tively analyzed during the observation period.This method is considered as feasible and effective for retrieving and monitoring coal fires based on multi-spectral Landsat images in comparison to other techniques in the Wuda coalfield,China.