https://orcid.org/0000-0003-3091-6637
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ABSTRACT
Spontaneous combustion of underground coal seam leads to significant loss of coal resource while causing serious geological disasters, environmental degradation and social security problems. The Midong Coalfield in Urumqi was selected to conduct research on dynamic detection and assessment of underground coal fires from 2013 to 2020 based on time series satellite imagery of Landsat-8. The land surface temperature (LST) was retrieved using RTE. The hotspot SFSE was proposed to map the spatial-temporal distribution of coal fires. The CTIR was also established to solve how to comprehensively evaluate the intensity and spatial variations of coal fire thermal island effect. Based on Sequence Overlap Analysis the integrated dynamic interpretation of thermal anomaly intensity, area and migration of coal fire hazard has been provided. In addition, the TADC was proposed to effectively reflect the active fire centre and aggregation tendency. The results show that: The RTE inversion error is within 4.6 °C, verified with the surveyed LST. The overall accuracy of fire thermal anomaly extraction by HSA by field-tested exceeds 65%. A feature can be judged to be a fire pixel when its recurrence reaches 75% or more of the total number of observation frequency. The maximum and minimum extent extracted of the total fire area is 65.6×105 and 46.6×105 m2, respectively. And the overall expansion rate is 1.29×105 m2 year−1, in 82% agreement with the measurement. The burning intensity of CTIR first increases 0.024 year−1 averagely, but then decreases at a rate of 0.0055 year−1. Fractures and faults in the fire-affected area play a critical role in fire induction and distribution. Frequent mining and excavation activities are confirmed to accelerate the further fire production and propagation. After suppression, the relevant fire area has shrunken by up to 60%, while the overall CTIR is reduced by more than 70%.
Highlights
Coal fire severity and spatial variation are evaluated using time series satellite thermal infrared mapping
Hotspot analysis reliability for coal fire anomaly identification and extraction is up to 70%
Remote sensing thermal anomalies occurrence over 75% of the observation period can be considered as coal fire pixels
Coal fire Thermal Island effect is highly indicative in combustion assessment of intensity and distribution
The proposed Combustion Centroid and sequence overlap analysis provides efficient and economical application on fire-fighting priority, location and accessibility
Acronyms annotation
| CTIR | = | Coal-fire Thermal-island Intensity Ratio |
| HSA | = | Hot Spot Analysis |
| LST | = | Land Surface Temperature |
| MVF | = | Manual Visual Filtering |
| NDVI | = | Normalized Difference Vegetation Index |
| OLI | = | Operational Land Imager |
| RTE | = | Radiative Transfer Equation |
| SDEV | = | Standard Deviation |
| SFSE | = | Sequential Frequency Superposition Extraction |
| TADC | = | Thermal Anomaly Density Centre |
| TIRS | = | Thermal Infrared Sensor |
| UCF | = | Underground Coal Fire |
Acknowledgements
This research was funded by 111 Project (Grant No. B17041), National Natural Science Foundation of China (Grant No. 52074277, 41871260), Natural Science Foundation of Jiangsu Province (Grant No. BK20211585), and China Scholarship Council (Grant No. 202006420037). Field surveys supported by the Xinjiang Coal Field Fire-fighting Engineering Bureau. Landsat data courtesy of the U.S. Geological Survey. The authors would like to thank the anonymous peer reviewers/editors for the constructive comments and suggestions.
Disclosure statement
No potential conflict of interest was reported by the authors.