Coal fire identification and state assessment by integrating multitemporal thermal infrared and InSAR remote sensing data: A case study of Midong District, Urumqi, China

Abstract

Coal fire disasters generally occur in major coal-producing countries. They destroyed many coal resources and triggered various environmental problems. The identification and state assessment of coal fire areas are paramount to coal fire management. In this paper, a method for identifying coal fires by integrating land surface temperatures (LSTs) from multitemporal thermal infrared remote sensing and subsidence information from multitemporal interferometric synthetic aperture radar (MT-InSAR) is proposed. The method primarily consists of estimating the LSTs and subsidence values, normalizing and equidistantly partitioning the LSTs, estimating the thermal anomaly frequency (TAF), extracting high-subsidence areas, determining the thermal anomaly frequency threshold (TAFT), and identifying coal fire areas according to the TAFT. The coal fire ratio index (CRI) was developed to quantitatively evaluate the severity of coal fires and its variations. Midong District, Urumqi, Xinjiang, China, was selected as the study area. Forty-five Landsat 8 images and sixty-one Sentinel-1 SAR images were used to retrieve LST and subsidence time series, respectively. The proposed method and CRI were applied to identify and evaluate coal fires in the study area. The results demonstrate the satisfactory reliability of these methods compared with field surveys. The maximum CRI was 6.145 × 10-4 (July 28, 2019), and the minimum CRI was 2.685 × 10-5 (January 13, 2020). Coal fires in summer were more severe than those in winter. The CRI profile presented seasonal and annual periodicities and was affected by the local climate. The soil cohesion and humidity are higher in winter due to possible snowmelt, which weakens the interaction between oxygen and coal seams and the subsequent combustion of coal. The LST, thermal anomaly, and deformation information were combined in a time series overlay analysis to reveal the differences in the LSTs and subsidence values among mining areas, coal fire areas, and areas with both mining and coal fires. The results verify the applicability and reliability of the proposed method.