Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

Late Cenozoic Coal Fires in the Liuhuanggou Area (Xinjiang, Northwestern China): Ages, Controlling Factors and Evolution

—Coal fires are a phenomenon that can be observed worldwide in areas where rocks containing coal seams are exposed and can pose major environmental threats. A coal fire can begin through spontaneous combustion when coals are exposed to dry and oxygen-rich near-surface conditions. Burning, depending on the temperature of heating, causes baking or even melting of the surrounding rocks and the formation of different types of combustion metamorphic rocks. In Northwestern China, coal fire occurrences are concentrated at the edges of the sedimentary basins or at the margins of orogenic belts, where coalrich units were exposed owing to the Indo-Eurasian collision. On the northern margin of the Tianshan range, evidence of coal fires is widespread in the Jurassic sedimentary units containing coal seams which outcrop along the Central Asian Orogenic Belt. In some cases, coal fires are active and can be linked to ongoing mining activity, but outcrops of combustion metamorphic rocks not associated with fires are also found and are indicative of past burning events. We examine combustion metamorphic rocks outcropping in the Toutunhe River valley (Liuhuangou area, Xinjiang, Northwestern China). Combustion metamorphic rocks in the study area were mapped and classified according to their morphological and mineralogical characteristics. Outcrops are exposed at various heights on the valley flanks, which are characterized by the presence of multiple levels of fluvial terraces. These terraces are indicative of the phases of erosion and deposition of the Toutunhe River and testify to tectonic uplift. The investigation of the stratigraphic and crosscutting relationship of combustion metamorphic rocks with terrace deposits and apatite fissiontrack dating made it possible to determine that at least four phases of coal fire activity occurred from late Miocene to Quaternary. The first and oldest burning phase dates back to 10 ± 1.3 Ma and terminated prior to 2–3 Ma; the second was active before ~550 ka; the third had terminated by ~140 ka; the fourth began later than ~5.7 ka. The relationships between combustion metamorphic rocks and fluvial terraces further suggest that coal fire ignition/extinction in the area since the Miocene have been linked to the interplay between the uplift of the Central Asian Orogenic Belt and the phases of fluvial erosion and deposition in interglacial periods.

Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data

Public safety and socio-economic development of the Jharia coalfield (JCF) in India is critically dependent on precise monitoring and comprehensive understanding of coal fires, which have been burning underneath for more than a century. This study utilizes New-Small BAseline Subset (N-SBAS) technique to compute surface deformation time series for 2017–2020 to characterize the spatiotemporal dynamics of coal fires in JCF. The line-of-sight (LOS) surface deformation estimated from ascending and descending Sentinel-1 SAR data are subsequently decomposed to derive precise vertical subsidence estimates. The most prominent subsidence (~22 cm) is observed in Kusunda colliery. The subsidence regions also correspond well with the Landsat-8 based thermal anomaly map and field evidence. Subsequently, the vertical surface deformation time-series is analyzed to characterize temporal variations within the 9.5 km2 area of coal fires. Results reveal that nearly 10% of the coal fire area is newly formed, while 73% persisted throughout the study period. Vulnerability analyses performed in terms of the susceptibility of the population to land surface collapse demonstrate that Tisra, Chhatatanr, and Sijua are the most vulnerable towns. Our results provide critical information for developing early warning systems and remediation strategies.

Integrated geophysical approach for Coal mine fire in Jharia coalfield, India

Jharia coalfield is one of India's largest and most productive coal fields, home of the oldest-burning coal seam fires in the world. This paper highlights the combined study comprising Surface Temperature measurement, Magnetic, and Electrical Resistivity Tomography (ERT) methods for delineation and mapping of coal fires with their depths, extension and propagation direction. Surface temperature mapping allows straight forward demarcation of coal fire areas. Magnetic data provide a map showing the earlier burned, currently burning and unburned patches based on the contrast between magnetic effects of the subsurface and fluctuations with temperatures exceeding or under the Curie temperature. The pyrolysis of coal is one of the main processes in coal fires, which results in very high conductivity. Thus, electrical resistivity tomography (ERT) is also a promising technique for the characterization of a coal fire-affected area. Most of the features of low and high resistivity associated with active fires and voids have been delimited well in the ERT sections that are generated from combine inversion of all arrays. These are also delineated well by the surface temperature mapping, and Magnetic methods. The field photographs, available coal seam and borehole lithology clearly show the suitability of the combined study for characterization of coal seam fire. A model of the coal fire, char formation and void formation resulting from coal seam fire has been established. Finally, a fire propagation model of the study area has been established based on the combined geophysical data that can be effectively used for mine hazard mitigation.

Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

The Risk of Coal Fires And Land Subsidence in Jharia Coalfields, India, Analysed Using Remote Sensing Techniques

<p>Coal fires, land subsidence, roof collapse, and other life-threatening risks are a predictable phenomenon for the mineworkers and the neighbourhood population in coalfields. Jharia Coalfields in India are suffered heavily from land subsidence and coal fires for over a century. In addition to the loss of precious coal reserves, this has led to severe damage to the environment, livelihood, transportation, and precious lives.</p><p>Such incidents highlight the dire need for a well-defined methodology for risk analysis for the coalfield. In this study, we regenerated a Land Use Land Cover map prepared using Indian Remote Sensing satellite imagery and ground survey. Persistent Scatterer Interferometry analysis using Sentinel -1 images was carried out to study the land subsidence phenomenon between Nov 2018 and Apr 2019. For the same study period, coal fire zones were identified with Landsat – 8 thermal band imagery. Integration of coal fire maps, subsidence velocity maps, and land use maps was further implemented in a geographical information background environment to extract the high-risk zones. These high-risk areas include residential areas, railways, and mining sites, requiring immediate attention.</p><p>The results show that the coal mines are affected by subsidence of up to 20 cm/yr and a temperature anomaly of nearly 20<sup>o</sup>C is noticed. A high-risk zone of almost 18 sq. km. was demarcated with Kusunda, Gaslitand, and West Mudidih collieries being the most critically affected zones in the Coal mines. The study demonstrates the potential to combine data from multiple satellite sensors to build a safer ecosystem around the coal mines.  </p>

Fossil Fuel Fires: A Forgotten Factor of Air Quality

Spontaneous fossil fuel fires, especially coal fires, are known worldwide. They occur in numerous sites, both completely natural (coal seam outcrops) and anthropogenic (burning mining waste heaps, or BMWHs). Coal and waste/barren rock fires produce gaseous emanations, acting within exhalative processes. This factor is rarely being considered as influencing quality of the atmospheric air. The paper shortly discusses most important available methods for field gas analysis, with an emphasis on a portable FTIR spectrometer. It summarizes results of gas analyses from Polish BMWHs, using a multi-tool approach. It also lists a number of additional analyses from 53 vents of these environmentally important objects, with the main purpose of enlarging the knowledge of the span of concentrations of the particular compounds. This is especially true for formaldehyde, pyridine, CO, 1,1,1-trichloroethene, 1,1-dichloroethene, cumene, SO2, and, to a lesser extent, NO2, CCl4, ethane, propane, ethene, and thiophene. The latter, and DMS, are confirmed as gaseous S source more frequent and rich than SO2.