Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: a support vector approach.

Autor: Tripathi A; Department of Civil & Environmental Engineering, Indian Institute of Technology (IIT) Patna, Patna, India., Malik K; Department of Mining Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, India. kapilmalik4u@gmail.com., Reshi AR; Department of Civil Engineering, Indian Institute of Technology (IIT) Madras, Madras, India., Moniruzzaman M; Department of Geography, St. Mary's University, Halifax, NS, Canada., Tiwari RK; Department of Civil Engineering, Indian Institute of Technology (IIT) Ropar, Rupnagar, Punjab, India.
Jazyk: angličtina
Zdroj: Environmental monitoring and assessment [Environ Monit Assess] 2023 Oct 12; Vol. 195 (11), pp. 1298. Date of Electronic Publication: 2023 Oct 12.
DOI: 10.1007/s10661-023-11896-1
Abstrakt: The surface subsidence in the Krishna Godavari (KG) basin in India has increased with the discovery of crude oil and natural gas reserves since 1983. With private players coming up to bag the exploration and refining contracts, there must be timely monitoring of the surface subsidence of the region so that remedial measures for the resettlement of the populations can be taken promptly. Regular monitoring is necessary since the region is fertile and any seawater ingress results in the loss of valuable cultivable land. Multi-temporal SAR Interferometry (MTInSAR) technique has been applied successfully all over the world for the study and regular monitoring of land surface subsidence scenarios. This study utilizes data from Sentinel-1 C-band SAR sensor for MTInSAR-based surface subsidence and RADAR Vegetation Index (RVI)-based vegetation loss for the same season estimation between 2017 and 2022 for the KG basin region. It is inferred from the study that the region has shown surface subsidence of 80 mm/year between April 2020 and June 2022. This study uses support vector regressor (SVR) to predict the loss in forest cover in terms of RVI using MTInSAR-based surface subsidence, VH, and VV backscatter as parameters. It is observed that the SVR gave R 2 -statistics of 0.89 and 0.873 in the training and testing phases with a mean absolute error (MAE) and root mean squared error (RMSE) of 0.08 and 0.02, respectively. It is also observed that the region showed a loss of 3.21 km 2 of cultivable land between 2020 and 2022.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
Externí odkaz: