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Sketch map of the study area. (a-d) respectively represent the location sketch map, elevation sketch map, visible light image, and surface classification results of the study area. The five coal fire areas are shown in (b), they are Wugonggou coal fire (I), Sigonghe coal fire area (II), Dingjiawan coal fire area (III), Wujiaquan coal fire area (VI) and, Laobatai coal fire area (V). In addition, the red box indicates the detection range of ground deformation in (b).
Source publication
Coal fire is a global catastrophe that causes resource waste, economic loss, and environmental pollution. Accurate detection of surface abnormalities induced by coal fires (such as thermal anomalies and land deformation) is critical for coal fire monitoring and extinguishment. However, the conventional thermal anomaly detection methods do not consi...
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... Fukang coalfield is located in the north-central part of Xinjiang, China, and belongs to Fukang City, Changji Prefecture (Fig. 1). The study coal fire area is situated on the south of the Junggar Basin, north of Peak Bogda on the East Tianshan Mountains, which encompasses 285.87km 2 , with a huge height variation, an average altitude of 762 m, complicated land cover types, and abundant coal deposits. The coal of this area is characterized by low coal ...
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... study area mainly includes five coal fire areas (18 in situ coal fire points), specifically, the Wugonggou coal fire area, Laobatai coal fire area, Wujiaquan coal fire area, Sigonghe coal fire area, and Dingjiawan coal fire area (Fig. 1). Among them, the Wugonggou fire area (7 in situ coal fire points) and Laobatai fire area (6 in situ coal fire points) are more serious. The burning position of the coal fire is generally 50 m to 300 m underground, some even reach 600 m ...
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... analyze the performance of the proposed method on coal fire detection over different subareas, we selected nine feature points (Fig. 8a) within the different subareas for time series analysis. According to the survey data, the feature points are divided into three categories: the big deformation feature points (D1, D2, D3), the thermal anomaly feature points (T1, T2, T3), and the coal fire feature points (F1, F2, F3). We have plotted the time series diagrams of these feature points (Fig. 9), including the time-series temperature (the red line), the difference between the LST and temperature threshold (the green dotted line), and the time-series deformation (the blue line). ...
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... addition, we calculated the minimum and maximum values of all temperature images used, the temperature threshold based on the proposed STTBT mothed, the area of thermal anomalies, and the average LST of the coal fire point (CFPlst), as shown in Fig. 10. It can be found that CFPlst is much lower than the maximum value of the LST, especially in summer, which is the reason why there will be more commission errors using summer data. These indicate that the upper and lower limits of the temperature threshold may exist simultaneously under certain conditions to accurately extract the coal ...
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... on coal fire detection was discussed in this section. Different phase offsets, the best value minus or adds 10, 20, 30, (i.e., 22.39, 12.39, 2.39, 7.61, 17.61, 27.61, and 37.61) were used for coal fire detection. The results of thermal anomalies delineation, subsidence filtering, and subsidence coal fire detection are shown in Fig.S6, Fig.S7, and Fig. 11a-g, a-g, respectively. At the same time, we calculated the area of thermal anomalies, the area of suspected coal fire, the number of detected fire points marked, and the accuracy rate ( Fig. 11h and Table ...
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... fire detection. The results of thermal anomalies delineation, subsidence filtering, and subsidence coal fire detection are shown in Fig.S6, Fig.S7, and Fig. 11a-g, a-g, respectively. At the same time, we calculated the area of thermal anomalies, the area of suspected coal fire, the number of detected fire points marked, and the accuracy rate ( Fig. 11h and Table ...
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... Fig. 11h, it can be found that with the increase of phase offset, the area of thermal anomalies increases exponentially, but the suspected coal fire areas first increase and then decrease. That is because the thermal anomaly areas always appear as subregions, and the land deformation areas do not change when phase offset varies. This means that ...
Citations
... It offers the capability to acquire ground deformation information under all weather conditions, with high precision and across large spatial extents. This efficiency notably reduces the observation period, widens the spatial scope, and enhances monitoring accuracy [9][10][11], and is widely used for deformation monitoring, such as the deformation monitoring of landslides, volcanic activity, land subsidence, and other geological disasters [12,13]. ...
Deformation monitoring plays a pivotal role in assessing dam safety. Interferometric Synthetic Aperture Radar (InSAR) has the advantage of obtaining an extensive range of deformation, regardless of weather conditions. The Datengxia Water Conservancy Hub is the largest in-construction dam in China. To effectively assess the in-construction dam safety, the SBAS-InSAR (Small Baseline Subset-InSAR) technique and 86 Sentinel-1 images (from 11 February 2020, to 16 January 2023) have been employed in this study to monitor the deformation over the reservoir and its surrounding areas. The reliability of the SBAS-InSAR monitoring results over the study area was demonstrated by the in situ monitoring results. And the InSAR results show that the central section of the left dam exhibits the most substantial cumulative deformation, attributed to the maximal water pressure. This is closely followed by the left end of the dam, which reflects a similar but smaller deformation. However, the in-construction cofferdam facilities make the right-end section of the left dam more robust, and the deformation is the most stable. Additionally, significant deformation of the auxiliary dam slope has been identified. Moreover, the analysis indicated that the deformation of the four upstream slopes is closely related to the precipitation, which potentially poses a threat to the safety of the Datengxia Dam.
... Non-contact measurement, low cost, as well as time-saving, and safe implementation of measurements, especially in the case of largescale sites, are unquestionable benefits of remote sensing methods. The advantage over direct methods is also the wide range of imaging, which ensures simultaneous data acquisition on entire objects (Wang et al., 2022). ...
... Satellites that have a sensor capable of thermal imaging are Landsat 4-5 TM, Landsat 7 ETM+, Landsat 8-9 OLI TIRS, Terra ASTER, Terra MODIS, NOAA-AVHRR, or Sentinel-3 (NASA, 2023a;United States Geological Survey, 2023e;NASA, 2023b;Copernicus, 2023;NOAA, 2023). In studies of the temperature of coal mining tailings ponds and heaps, thermal data are primarily from Landsat or ASTER systems (Chatterjee, 2006;Martha et al., 2010;Nádudvari, 2014;Huo et al., 2015;Mishra et al., 2020;Nádudvari et al., 2020;Nádudvari et al., 2021a;Wang et al., 2022). Thermal channels of satellites such as NOAA-AVHRR, Terra MODIS, and Sentinel-3 have a spatial resolution of 1 km. ...
Underground hard coal mining activity in southern Poland has lasted more than 200 years. Among many factors related to mining and influencing the natural environment, the longest-active are coal waste heaps and tailings ponds. Several hundred objects are inventoried in Lower and Upper Silesia, of which 109 are located in the Lower Silesian Coal Basin (LSCB). These remnants of mining activity are built of gangue, waste coal, and coal silt (post-mining wastes). They cause environmental hazards, i.e., soil, air, groundwater, and surface water pollution in the storage area. They also tend to combust spontaneously, emitting enormous amounts of greenhouse gases into the atmosphere and increasing their neighborhood’s air, soil, and water temperature. Indigenous fires occur more than 20 years after the end of the waste disposal phase. The post-mining heat island (PMHI) phenomena, related to thermal activity development of the post-coal mining heaps and tailings ponds, is still under-recognition and research. Therefore, our study aims to improve and develop a methodology for remote detection and monitoring of heat islands resulting from coal mining operations to track the thermal activity of heaps and tailings ponds in LSCB from mines closure to 2023. The study used open satellite data from the Landsat program to identify and track post-mining heat islands over 23 years within the former mining area and verify the results within the borders of the inventoried heaps and tailings ponds. As a result, geospatial analysis on a time scale was carried out to identify post-mining hot spots. The self-heating intensity index (SHII) and the air temperature thermal indicator (ATTI) were calculated for identified and confirmed objects. SHII ranged between 0.00 and 10.07, and ATTI, on the other hand, varied from −12.68 to 25.18. Moreover, maps of the thermal activity of selected heaps were developed, the characteristics of the self-combustion phenomena were identified, and the remote detection of PMHI and its monitoring methodology was developed. The provided method can be used in the future to regularly monitor coal mining areas to prevent and identify hazardous hot spots and verify the maturity stage of the self-combustion processes.
... Thermal infrared (TIR) remote sensing products collected from a range of satellite, airborne, and unmanned aerial vehicle (UAV) based platforms have contributed significantly to understanding the complex interactions between components of the surface-vegetationatmosphere continuum [1]. Land-surface temperature (LST) represents one of the most informative variables in fields such as fire detection [2], hydrology [3] and precision agriculture [4,5]. In the agricultural sector, TIR data have gained considerable attention as an indicator of vegetation stress and plant water use [6,7]. ...
Land Surface Temperature (LST) is a key variable used across various applications, including irrigation monitoring, vegetation health assessment and urban heat island studies. While satellites offer moderate-resolution LST data, unmanned aerial vehicles (UAVs) provide high-resolution thermal infrared measurements. However, the continuous and rapid variation in LST makes the production of orthomosaics from UAV-based image collections challenging. Understanding the environmental and meteorological factors that amplify this variation is necessary to select the most suitable conditions for collecting UAV-based thermal data. Here, we capture variations in LST while hovering for 15–20 min over diverse surfaces, covering sand, water, grass, and an olive tree orchard. The impact of different flying heights and times of the day was examined, with all collected thermal data evaluated against calibrated field-based Apogee SI-111 sensors. The evaluation showed a significant error in UAV-based data associated with wind speed, which increased the bias from −1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. Different surfaces, albeit under varying ambient conditions, showed temperature variations ranging from 1.4 to 6 °C during the flights. The temperature variations observed while hovering were linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. Irrigation and atmospheric conditions (i.e., thin clouds) also contributed to observed temperature variations. This research offers valuable insights into LST variations during standard 15–20 min UAV flights under diverse environmental conditions. Understanding these factors is essential for developing correction procedures and considering data inconsistencies when processing and interpreting UAV-based thermal infrared data and derived orthomosaics.
... These studies are mainly based on optical multispectral [8], [9], thermal infrared (TIR), and radar data, most of which are used for surface feature extraction, thermal analysis [10]- [12], and land subsidence accompanying underground coal fires [13], [14]. Besides, some studies combine various RS technologies to monitor coal fire areas [15]- [18]. Among the RS coal fire detection techniques, Satellite TIR RS (SAT TIR RS) is the one mostly employed, which detects land surface temperature (LST) thermal anomalies that are the most direct surface feature related with underground coal fires. ...
Underground coal fires are global catastrophes that result in energy waste, carbon emission and eco-environment pollution. Remote sensing (RS) detection is essential for underground coal fire extinguishing engineering, and the mostly used is thermal infrared (TIR) RS. It can well obtain the thermal anomalies of land surface temperature (LST) which is the most direct surface feature of underground coal fires. However, most studies using TIR RS simply delineate underground fire sources vertically according to LST anomalies, which has relatively little impact when initially determining coal fire area locations on the large scale. As for the precise location of small-scale subsurface fire sources, the deviation between subsurface fire source locations inferred and real locations could lead to errors or even mistakes to fire extinguishing engineering. There is a lack of subsurface fire source evolution model reconstruction method, and the spatiotemporal correlations characteristic of LST thermal anomalies and underground fire sources have not yet been discussed. To this end, taking Miquan coalfield (western China) as an example, a 3D Empirical Bayesian Kriging (EBK3D) method is firstly proposed to reconstruct the 4D temperature fields of underground fire sources. Then the feasibility of the vertical correspondence approach to inferring small-scale subsurface fire sources through LST thermal anomalies detected by unmanned aerial vehicle TIR RS and satellite TIR RS is analyzed. Finally, the spatiotemporal correlation characteristic of LST thermal anomalies and subsurface fire sources is analyzed. As the results show that, it is feasible to reconstruct the underground fire source evolution model by EBK3D method. The reconstructed 4D temperature fields can dynamically reflect the evolutionary states of underground fire sources in three time periods, with cross-validated root mean square errors of 52.2°C, 49.6°C, and 37.1°C and the R of linear regressions of 0.925, 0.9145, and 0.8429, respectively. The LST thermal anomalies show a significant spatiotemporal delay with respect to the subsurface fire source evolution. This makes the locations of the underground fire sources traced by the vertical correspondence method deviate from the real ones. The offsets of underground fire sources relative to surface thermal anomalies in the coal seam strike and dip directions for different time periods at depths of (T1: -44.43 m, T2: -27.72 m, T3: -20.04 m) are (T1: 73.80 m, T2: 52.33 m, T3: 45.06 m) and (T1: 16.79 m, T2: 17.27 m, T3: 24.82 m), respectively. The R for linear regression model of the offset averages in three directions versus time and fire source size are (0.9247, 0.7949, and 0.9564), (0.8739, 0.85 and 0.9152) respectively.
... It has the advantages of noncontact, high accuracy, and low cost. As a result, InSAR has evolved into a powerful tool for monitoring various geohazards, including earthquakes [17], volcanoes [18], landslides [19], coal mining [20], [21], and coal fires [22]. Moreover, InSAR is also used to locate underground goafs, which overcomes shortages of conventional geophysical approaches to some extent [6]. ...
Accurately locating goafs is critical for identifying illegal mining, preventing mining-related geohazards, and facilitating the development and utilization of underground spaces. Conventional methods for locating goafs with InSAR techniques primarily rely on the Probability Integral Model (PIM), which tends to overestimate the ground deformation under subcritical extraction. On the other hand, the number of subcritical extraction working faces significantly rises with mining depth. Under these circumstances, accurately locating subcritical underground goafs using existing methods becomes challenging. To this end, a novel method, which incorporates the improved probability integral model (IPIM) and InSAR technique for locating subcritical goafs, is proposed, named the locating goaf method based on IPIM (LGM-IPIM). Firstly, based on the IPIM, a model between the subcritical goaf parameters and InSAR-derived deformation is built. Then, to reduce the influence of surrounding mining, the goaf azimuth angle is determined with textures and patterns of the InSAR-derived deformation time series. Finally, the genetic algorithm-particle swarm optimization (GA-PSO) is employed to determine the goafs' parameters. The effectiveness of the proposed algorithm has been verified by simulation and real data. The results demonstrate that the proposed LGM-IPIM outperforms conventional methods, presenting the best performance and the highest accuracy. Specifically, compared to the locating goaf method based on PIM (LGM-PIM), the proposed LGM-IPIM improves the location accuracy of goaf boundary points by 28.90% and 86.23% in Areas A and B, respectively. In addition, the proposed LGM-IPIM has robustness against minor errors within the deformation monitoring and IPIM parameters.
... Spontaneous coal combustion is one of the five major disasters in coal mines [1][2][3], which restricts the safe and efficient exploitation and utilization of coal resources in our country [4][5][6]. With the continuous in-depth research on the mechanism of spontaneous coal combustion having been conducted for a long time, scholars have proposed various theories from different angles to explain the spontaneous coal combustion phenomenon, among which the coal-oxygen composite theory [7][8][9][10][11] has been widely recognized. ...
Carbon dioxide (CO2) is widely used in the prevention and control of spontaneous coal combustion. In this manuscript, three low-rank coals with different metamorphic degrees were selected as the research objects. The temperature-programmed experiments, in situ infrared cooling experiments, simulation of the competitive adsorption of CO2 and oxygen (O2) in coal pores, and simulation study of the CO2 inhibition of the coal oxygen composite reaction were used to obtain the role and effect of CO2 in preventing oxygen adsorption in coal at the low-temperature oxidation stage. It was concluded that CO2 can displace the O2 near the pore wall to physically prevent the adsorption of O2. Through the changing law of heating rate and a kinetics analysis, it was found that CO2 can increase its activation energy by 5.3–108.3% during the slow heating stage of coal and reduce its heat rate. At around 120 °C, coal loses the protective effect of CO2. From the changes in functional groups, it can be seen that when coal was cooled in the CO2 atmosphere, mainly pyrolysis and condensation reactions occurred due to the lack of O2. In addition, CO2 can also inhibit the chain reaction of the chemical adsorption of oxygen in coal. This work provides a theoretical basis for CO2 prevention and the control of spontaneous coal combustion.
... Coal spontaneous combustion is one of the five major disasters in coal mines [1][2][3] , which restricts the safe and efficient exploitation and utilization of coal resources in our country [4][5][6] . With the continuous in-depth research on the mechanism of coal spontaneous combustion for a long time, scholars have proposed various theories from different angles to explain the coal spontaneous combustion phenomenon, among which the coal-oxygen composite theory [7][8][9][10][11] has been widely recognized. ...
... The experimental instrument was a TENSOR27 Fourier transform infrared spectrometer from BLUKE, Germany. 4 ...
CO2 is widely used in the prevention and control of coal spontaneous combustion. In this manuscript, three low-rank coals with different metamorphic degrees were selected as the research objects. The temperature-programmed experiments, in-situ infrared cooling experiments, simulation of competitive adsorption of CO2 and O2 in coal pores, and simulation study of CO2 inhibition of coal oxygen composite reaction were used to obtain the role and effect of CO2 in preventing oxygen adsorption in coal at low-temperature oxidation stage. It is concluded that CO2 can displace the O2 near the pore wall to physically prevent the adsorption of O2. Through the change law of heating rate and kinetic analysis, it is found that CO2 can increase its activation energy by 5.3%-108.3% during the slow heating stage of coal, and reduce its heating rate. At around 120°C, coal loses the protective effect of CO2. From the changes of functional groups, it can be seen that when coal is cooled in CO2 atmosphere, mainly pyrolysis and condensation reactions occur due to the lack of O2. In addition, CO2 can also inhibit the chain reaction of coal's chemical adsorption of oxygen. This work provides a theoretical basis for CO2 prevention and control of coal spontaneous combustion.
... The first four types can accurately detect coal fires in small areas but are unsuitable for multi-time synchronous identification and monitoring in large coal fire areas. Fortunately, remote sensing techniques can efficiently identify the range of coal fires in large areas by extracting and analyzing the land surface temperature anomalies, ground deformation and collapse, which makes up for the shortcomings of these detection methods [17][18][19][20]. ...
Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected stackingInSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results
were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.
... Methods commonly used for temperature anomaly extraction can be divided into two main categories: single-threshold algorithms and multi-threshold algorithms. Single-threshold algorithms mainly include the Fixed Threshold Algorithm (FTA) [23], Gradient Threshold Algorithm [24], Adaptive Gradient Threshold Algorithm [25], and Spatiotemporal Temperature-Based Thresholding algorithm [26]. The main multi-threshold algorithm is the Temperature Anomaly Extraction (TAE) algorithm [27]. ...
... Because of the influence of solar radiation and the poor spatial resolution of thermal infrared remote sensing images, temperature anomaly information is often insufficient for accurate identification of fire zones [28]. Therefore, some studies considered surface deformation information to identify coal fire zones using the feature that underground coal fires trigger surface deformation [12], [23], [24], [26], [29], [30]. However, surface deformation in coal fire zones is complex and often accompanied by different degrees of decoherence. ...
... However, surface deformation in coal fire zones is complex and often accompanied by different degrees of decoherence. Traditional time series Interferometric Synthetic Aperture Radar (InSAR) techniques sometimes fail to obtain comprehensive deformation information [12], [24], [26]. Therefore, this study adopted the Distributed Scatterer (DS)-InSAR technique to improve the coherent point density by means of homogeneous point identification and phase optimization. ...
Coal fire is a geological disaster that causes resource waste and environmental pollution globally. Accurate identification of the spatial location of coal fires is critical for effective coal fire governance. However, existing methods for identifying coal fire zones have problems such as a high omission and misclassification ratio and insufficient consideration of the temporal variation in temperature. Therefore, this study proposed a Temporal Temperature Anomaly Extraction algorithm based on Adaptive Windows (TTAE-AW) to extract temporal temperature anomaly information. Moreover, the spatial coverage of deformation monitoring points was improved using Distributed Scatterer Interferometric Synthetic Aperture Radar (DS-InSAR), and then a Double-Threshold Two-stage Filter method (DTTF) was proposed to accurately identify the spatial location of coal fire zones. The Rujigou mining area in Ningxia (China) was chosen as the region of study. Results showed that the temperature anomalies extracted using the TTAE-AW method are more concentrated in coal fire zones and that the amount in different seasons is more stable. The average accuracy and Kappa coefficient were improved by 15.5% and 0.345, respectively, over those of the conventional method. Compared with the Small Baselines Subset InSAR approach, the DS-InSAR technique has 158% higher spatial coverage for monitoring coal fire zones. Compared with in situ observations of coal fire points, the accuracy and Kappa coefficient of the spatial location of fire zones obtained using the DTTF method were 91% and 0.77, respectively, demonstrating that the proposed method can provide reliable technical support for coal fire monitoring and management.
