Application of remote sensing to identify coalfires in the Raniganj Coalbelt, India

Gangopadhyay, P.K.; Lahiri‐Dutt, Kuntala; Saha, Kanika

doi:10.1016/j.jag.2005.09.001

Cited by 49 publications

(16 citation statements)

References 17 publications

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“…Conventionally, these LSTs can be achieved from ground-based handheld thermal infrared imagery (Kuenzer and Dech, 2014) and by drilling holes for temperature measurements (Huijun et al, 2018). Using these methods, LST measurements are done very close to the fire, but they are nearly impossible to gather enough data over large areas (Gangopadhyay et al, 2006) or in inaccessible areas. To overcome this limitation, airborne thermal infrared data acquired during the daytime and nighttime have been used to identify hightemperature targets against low-temperature backgrounds (Bhattacharya et al, 1991;Greene et al, 1969).…”

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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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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“…Radiance balance and transfer is under the control of ɛ and the prelaunch calibration constants used to obtain the T using the inverse of Planck function as shown below [Gangopadhyay et al, 2006;Chander et al, 2009;Fornaciai et al, 2009;Coll et al, 2010;YCEO, 2010;NASA, 2011;Tarantino, 2012].…”

Section: Remote Sensing Data Processingmentioning

confidence: 99%

Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data

Özelkan

Bagis

Özelkan

et al. 2014

European Journal of Remote Sensing

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The aim of this study is to demonstrate the relationship between the long years' monthly average (LYMA) land surface temperature (LST) and the LYMA air temperature (T a ), the total precipitation (P t ), and the relative humidity (RH). Data from 27 meteorological stations in the Eastern Thrace region and corresponding thermal infrared images from Landsat-5 (TM) and Landsat-7 (ETM+) were used in this study. Simple regression models were developed for each meteorological station to predict the LYMA T a , P t and RH based on the LST values. The resulting LST-based prediction models were judged based on the correlation coefficient (r) and root mean square (RMSE). The average correlation and RMSE for the LST-based T a were r = 0.959 and RMSE = 1.771 o C. The average correlation and RMSE for the LST-based P t were r = -0.863 and RMSE = 10.098 mm. The average correlation and RMSE for the LST-based RH were r = -0.932 and RMSE = 1.875%. The results indicate that LST can be a good estimator for LYMA T a , P t and RH, and LYMA T a is positively, LYMA P t and LYMA RH are negatively correlated with LYMA LST.

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“…The Jharia fires are of special concern, because the region is densely settled, and the local population is endangered by sudden bedrock collapse. Other coal fires are burning in the Ray coalmining area (Kuenzer, 2005), the Ranigani area (Gangopadhyay, 2006), and the Singareni coal belt.…”

Section: Indiamentioning

confidence: 99%

Geomorphology of coal seam fires

2012

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Application of remote sensing to identify coalfires in the Raniganj Coalbelt, India

Cited by 49 publications

References 17 publications

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 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

Land Surface Temperature Retrieval for Climate Analysis and Association with Climate Data

Geomorphology of coal seam fires

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