Coal fire mapping of East Basuria Colliery, Jharia coalfield using vertical derivative technique of magnetic data

Pal, Sanjit Kumar; Vaish, Jitendra; Kumar, Sahadev; Bharti, Abhay Kumar

doi:10.1007/s12040-016-0655-4

Cited by 51 publications

(23 citation statements)

References 57 publications

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“…Such an abnormal value may be contributed due to the presence of dyke intrusion near the well. The low and negative values of residual magnetic anomalies have also been reported by Vaish and Pal (2015) and Pal et al (2016) for a fire delineation study at East Basuria Colliery of Jharia Coal Fields, India. The possible reason for drying up of the well can be linked to hindrance created by dyke on recharge of water from the surroundings.…”

Section: Resultssupporting

confidence: 69%

Detection and delineation of coal mine fire in Jharia coal field, India using geophysical approach: A case study

et al. 2018

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Coal mine fire is a serious problem in Jharia coal field, India. The coal mine fire can be detected with different techniques such as borehole temperature measurement, thermo-compositional analysis, remote sensing techniques, thermo-graphic measurement and geophysical methods. In this study, various geophysical methods were used to detect the surface and subsurface coal mine fires. Geophysical techniques used in the present study are apparent resistivity, self-potential (SP), magnetic method and thermography. Geophysical anomalies such as low SP value of −60 mV, high negative magnetic response and low apparent resistivity value helped us to detect and delineate the fire and non-fire areas laterally as well as depthwise. Furthermore, the thermography survey was carried out in the coal field using thermal imaging camera in order to substantiate the geophysical methods. This integrated approach was found to be more advantageous for the detection and delineation of surface and subsurface fire with respect to use of any specific techniques. Moreover, the level of threat towards the locality, national railway line was also assessed unambiguously using the above techniques. Hence, proper planning and implementation towards the mitigation of hazard can be achieved on the basis of the reported results.

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Section: Resultssupporting

confidence: 69%

Detection and delineation of coal mine fire in Jharia coal field, India using geophysical approach: A case study

et al. 2018

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“…Further research work is needed on the use of remotely sensed images acquired in different seasons such as summer and spring. Moreover, more experiments on typical coal fields presented in previous studies such as India's Jharia coal field (Pal et al, 2016;Roy et al, 2015a;Roy et al, 2015b;Singh et al, 2017) and China's Wuda (Song et al, 2015) and Rujigou coal fields (Huo et al, 2014a;Huo et al, 2014b) are necessary to test the effectiveness of HSA in UCF detection.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the high cost involved in data acquisition by these airborne scanners, available free orbital images such as Landsat-5 TM, -7 ETM+, -8 TIRS, ASTER and NOAA-9 AVHRR have been widely used to detect coal fires by many authors (Abbas et al, 2015;Du et al, 2015a;Du et al, 2015b;Jiang et al, 2011;Singh et al, 2017;Song and Kuenzer, 2017). Among these images, Landsat-8 TIRS is a high resolution thermal infrared data which has been successfully used to extract surface and UCF information in many coal fields around the world such as China's Wuda (Song et al, 2015) and Rujigou (Huo et al, 2014a;Huo et al, 2014b) and India's Jharia (Pal et al, 2016;Roy et al, 2015a;Roy et al, 2015b;Singh et al, 2017). Therefore, the Landsat-8 TIRS data was used to detect UCFs in this study.…”

Section: Introductionmentioning

confidence: 99%

Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Nguyễn¹,

Vu²

2019

Environ. Nat. Resour. J.

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Underground coal fire (UCF) detection from remotely sensed data plays an important role in controlling and preventing the effects of coal fires and their environmental impact. The limitation of commonly used methods does not take into account spatial autocorrelation among observations. For solving this limitation, a method for UCF detection was proposed using hot spot analysis (HSA). Based on the radiative transfer equation (RTE), land surface temperatures (LSTs) were firstly retrieved from the Landsat-8 TIRS data. The degree of spatial clustering among these LSTs was measured using HSA. UCF areas were then delineated based on 99 percent confidence level of hot spot areas. These fires were finally validated using known UCF sites and cross-validated with the results extracted from the ASTER TIR image. It was found from a case study in the Khanh Hoa coal field (North-East of Vietnam); (i) UCFs were strongly correlated with known coal fires and were highly consistent with those obtained from the ASTER TIR data; (ii) a total fire area of 197 hectares was detected, of which the fire areas of low, medium, high and extremely high levels were 37.3, 47.3, 53.2 and 59.3 hectares respectively; (iii) these fires were mainly detected in the central area and at coal ash dump sites of the southern coal field. The results show HSA can be used to effectively detect UCFs.

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“…Wang et al (2016) established a foundation for prevention and control of spontaneous combustion using simultaneous thermal analysis-Fourier transform infrared spectroscopy. Pal et al (2016) dealt with the coal fire mapping of using the magnetic method which is based on the fact that rise in temperature would result significant changes in magnetic susceptibility and thermoremanent magnetization (TRM) of the overlying rocks. Hu and Xia (2017) developed a new methodology for monitoring spontaneous combustion of coal waste dumps based on a 3D temperature distribution model.…”

Section: Introductionmentioning

confidence: 99%

Use of Landsat thermal imagery for dynamically monitoring spontaneous combustion of Datong Jurassic coalfields in China

Xue

Li²,

Shang³

et al. 2018

J Earth Syst Sci

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It is highly helpful and necessary to investigate and monitor the status of coal seam. Fortunately, remote sensing has facilitated the identification and dynamical monitoring of spontaneous combustion for a large area coal mining area, especially using the time series remotely-sensed datasets. In this paper, Datong Jurassic coal mining area is used as the study area, China, and an exclusion method and a multiplefactor analysis method are jointly used to identify the spontaneous combustion, including land surface temperature (LST), burnt rocks, and land use and land cover change (LUCC). The LST is firstly retrieved using a single-window algorithm due to a thermal infrared band of Landsat-5 TM (Thematic Mapper). Burnt rocks is then extracted using a decision-tree classification method based on a high-resolution SPOT-5 image. The thermal anomaly areas are identified and refined by the spatial overlay analysis of the above affecting factors. Three-period maps of coal fire areas are obtained and dynamically analyzed in 2007, 2009 and 2010. The results show that a total of 12 coal fire areas have been identified, which account for more than 1% of the total area of the study area. In general, there is an increasing trend yearly and a total of 771,970 m 2 is increased. The average annual increase is 257,320 m 2 , the average annual growth rate is 3.78%, and the dynamic degree is 11.29%.

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Coal fire mapping of East Basuria Colliery, Jharia coalfield using vertical derivative technique of magnetic data

Cited by 51 publications

References 57 publications

Detection and delineation of coal mine fire in Jharia coal field, India using geophysical approach: A case study

Detection and delineation of coal mine fire in Jharia coal field, India using geophysical approach: A case study

Use of Hot Spot Analysis to Detect Underground Coal Fires from Landsat-8 TIRS Data: A Case Study in the Khanh Hoa Coal Field, North-East of Vietnam

Use of Landsat thermal imagery for dynamically monitoring spontaneous combustion of Datong Jurassic coalfields in China

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