Change of land surface temperatures (LSTs) is indicative of underground coal fires (UCFs). The limitation of commonly used methods for UCF detection is that spatial pattern of LSTs is not taken into account. This study aims to identify spatial pattern of LSTs retrieved from remotely sensed data and its relation to UCFs. LSTs were firstly retrieved from the Landsat-8 TIRS data using the radiative transfer equation (RTE). The local Moran’s I statistics was then used to identify the spatial pattern of these LSTs. Different degrees of spatial pattern (low, medium, high, extreme high, lower, and upper outliers) were then identified by setting the first, second, third, and fourth quartiles and the MEDIAN + 1.5 × IQR (IQR is the interquartile range) and MEDIAN + 3 × IQR formulas as thresholds. The relation of LST spatial pattern to UCFs was finally identified by overlapping spatial pattern layers on known active UCF sites and compared with those reported previously. Results obtained from 2nd December 2013 Landsat-8 TIRS data in the Khanh Hoa coal field (north-east of Vietnam) showed that LSTs followed a pattern of spatial clustering of high values in the coal field. The areas with the degrees of spatial correlation in the quartiles of 0–25%, 25–50%, 50–75%, and above 75% were 66.5, 66.9, 34.1, and 71.1 ha, respectively. Lower and upper outliers were detected at positions of 12.38 and 21.31 corresponding to the areas of 23.4 and 4.6 ha, respectively. These outliers were mainly concentrated around eight active UCF sites and were highly consistent with those obtained from previous studies. The results of this investigation show that the closer the UCF sites, the higher the spatial autocorrelation level. There exists a strong degree of positive correlation between the distribution of LST spatial pattern with active UCFs. These findings suggest that high degree of autocorrelation of LSTs can be used to effectively detect UCFs.

地表温度（LST）的变化指示地下燃煤（UCF）。UCF检测常用方法的局限性在于未考虑LST的空间模式。这项研究旨在确定从遥感数据中检索到的LST的空间格局及其与UCF的关系。首先使用辐射转移方程（RTE）从Landsat-8 TIRS数据中检索LST。当地的莫兰一世然后使用统计数据来识别这些LST的空间格局。然后通过设置第一，第二，第三和第四四分位数以及MEDIAN + 1.5×IQR（IQR是四分位数范围）来识别不同程度的空间格局（低，中，高，极高，更低和上离群）和MEDIAN + 3×IQR公式作为阈值。LST空间模式与UCF的关系最终通过在已知的活动UCF站点上重叠空间模式层来确定，并与先前报道的那些进行了比较。从2013年12月2日Khanh Hoa煤田（越南东北部）的Landsat-8 TIRS数据获得的结果表明，LST遵循煤田中高价值的空间聚类模式。四分位数中具有空间相关度的区域分别为6-25、66.9、25％，50％，50％，75％和75％以上，34.1和71.1公顷。在对应于23.4和4.6公顷面积的12.38和21.31位置分别检测到较低和较高的异常值。这些离群值主要集中在八个活动的UCF站点附近，与先前的研究高度一致。这项研究的结果表明，UCF站点越近，空间自相关水平越高。LST空间模式的分布与活动UCF之间存在高度正相关。这些发现表明，LST的高度自相关可用于有效检测UCF。这些离群值主要集中在八个活动的UCF站点附近，与先前的研究高度一致。这项研究的结果表明，UCF站点越近，空间自相关水平越高。LST空间模式的分布与活动UCF之间存在高度正相关。这些发现表明，LST的高度自相关可用于有效检测UCF。这些离群值主要集中在八个活动的UCF站点附近，与先前的研究高度一致。这项研究的结果表明，UCF站点越近，空间自相关水平越高。LST空间模式的分布与活动UCF之间存在高度正相关。这些发现表明，LST的高度自相关可用于有效检测UCF。