Coal fire mapping from satellite thermal IR data – A case example in Jharia Coalfield, Jharkhand, India

Chatterjee, R. S.

doi:10.1016/j.isprsjprs.2005.12.002

Cited by 130 publications

(45 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SAGBT can mask subtle thermal anomalies associated with slow combustion in coal waste piles or coal storages, as shown by Figure 10a. Due to the lack of studies providing accuracy assessments, we make no claim that SAGBT was more accurate than the Moving Window and the field-based pixel-integrated methods [19]. For improving SAGBT results, we suggest partitioning the large area into relatively homogeneous small regions, considering their geological setting, geomorphology and land cover/use.…”

Section: Comparison With Other Non-interactive and In-situ Based Methodsmentioning

confidence: 99%

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

Bernardes

Cao

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Abstract:The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work "Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data-Part 1, Methodology". The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by OPEN ACCESSRemote Sens. 2015, 7 2603 using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%-85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site.

show abstract

Section: Comparison With Other Non-interactive and In-situ Based Methodsmentioning

confidence: 99%

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

Bernardes

Cao

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Nighttime data are ideal for this study because solar heating of the Earth's surface contributes significantly to the total radiant energy flux during the daytime. In addition, topographic unevenness and land cover/land use differences may cause differential solar heating during the daytime [2]. Therefore, the nighttime thermal infrared images can decrease the impact of differential solar heating and differences in topography and land cover, such as bare soil and desert [39].…”

Section: Remote Sensing Data For Algorithm Developmentmentioning

confidence: 99%

“…By summarizing multiple coal fire studies, the temperatures around the fire sources have been determined to range from 150 °C (423 K) to 250 °C (523 K) [2]. The coal fire in the Wuda Coalfield initiated from spontaneous combustion spots, propagated along the cracks, and merged into a plane [31].…”

Section: Thermal Anomalies and The Definition Of Coal Fire Areasmentioning

confidence: 99%

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Cao

Mishra

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

Coal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire's boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value OPEN ACCESSRemote Sens. 2015, 7 6577 reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled "Part 2, Validation and Sensitivity Analysis," we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires' decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during [2...

show abstract

“…Furthermore, the nighttime satellite SWIR data avoids the effects of solar irradiance on the land surface. According to the literature and previous field measurements, the temperatures of coal fires in most coalfields in China range from 150 °C to 250 °C [6,38,51], which is the same as other coalfields in other coal-producing countries, such as the Jharia coalfield in India, where the surface coal fire temperatures also range from 150 °C to 250 °C [35,51]. The temperature sensitivity of the ETM+ SWIR band 7 data (~160 °C-277 °C) matches very well with the range of temperatures of surface coal fires [18].…”

Section: Fixed Thresholding Technique For Surface Coal Fires Detectiomentioning

confidence: 99%

Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

et al. 2014

View full text Add to dashboard Cite

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results OPEN ACCESSRemote Sens. 2014, 6 1235 from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km 2 ; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east.

show abstract

Coal fire mapping from satellite thermal IR data – A case example in Jharia Coalfield, Jharkhand, India

Cited by 130 publications

References 19 publications

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Using ASTER Thermal Infrared Data, Part I: Methodology and Decadal Change Detection

Detection of Coal Fire Dynamics and Propagation Direction from Multi-Temporal Nighttime Landsat SWIR and TIR Data: A Case Study on the Rujigou Coalfield, Northwest (NW) China

Contact Info

Product

Resources

About