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) techniqu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1521 |
| _version_ | 1797412148789379072 |
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| author | Moidu Jameela Riyas Tajdarul Hassan Syed Hrishikesh Kumar Claudia Kuenzer |
| author_facet | Moidu Jameela Riyas Tajdarul Hassan Syed Hrishikesh Kumar Claudia Kuenzer |
| author_sort | Moidu Jameela Riyas |
| collection | DOAJ |
| description | 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 km<sup>2</sup> 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. |
| first_indexed | 2024-03-09T04:57:56Z |
| format | Article |
| id | doaj.art-31f43a75b2ee479aaedccef8f880ab5b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:57:56Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-31f43a75b2ee479aaedccef8f880ab5b2023-12-03T13:03:34ZengMDPI AGRemote Sensing2072-42922021-04-01138152110.3390/rs13081521Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR DataMoidu Jameela Riyas0Tajdarul Hassan Syed1Hrishikesh Kumar2Claudia Kuenzer3Department of Applied Geology, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, IndiaDepartment of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208016, IndiaGeosciences Division, ISRO—Space Application Center, Ahmedabad 380015, IndiaEarth Observation Center (EOC), German Aerospace Center (DLR), 82234 Oberpfaffenhofen, GermanyPublic 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 km<sup>2</sup> 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.https://www.mdpi.com/2072-4292/13/8/1521coal fireInSARsubsidenceremote sensingcoalinterferometry |
| spellingShingle | Moidu Jameela Riyas Tajdarul Hassan Syed Hrishikesh Kumar Claudia Kuenzer Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data Remote Sensing coal fire InSAR subsidence remote sensing coal interferometry |
| title | Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data |
| title_full | Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data |
| title_fullStr | Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data |
| title_full_unstemmed | Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data |
| title_short | Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data |
| title_sort | detecting and analyzing the evolution of subsidence due to coal fires in jharia coalfield india using sentinel 1 sar data |
| topic | coal fire InSAR subsidence remote sensing coal interferometry |
| url | https://www.mdpi.com/2072-4292/13/8/1521 |
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