Spatial pattern of land surface temperatures and its relation to underground coal fires in the Khanh Hoa Coal Field, North-East of Vietnam

Abstract

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.