Evaluation of Water Level Behavior in Coal-Mining Area, Adjacent Township, and District Areas of Jharkhand State, India

Singh, Prasoon Kumar; Tiwari, Ashwani Kumar; Verma, Poornima

doi:10.1007/978-981-10-4552-3_17

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the outline of the subsidence zones does not follow the characteristic circular shapes [71,72]. Furthermore, the aquifers in the region are mostly unconfined, with shallow groundwater levels ranging from 1.4 to 11.1 m below ground level (mbgl) [73]. Since there are no other probable causes, subsidence regions identified in JCF are either due to coal fires or UG mining activities.…”

Section: Temporal Variationsmentioning

confidence: 99%

Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data

et al. 2021

View full text Add to dashboard Cite

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 km2 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.

show abstract

Section: Temporal Variationsmentioning

confidence: 99%