In various regions worldwide, people rely heavily on groundwater as a significant water source for daily usage. The resulting large-scale depletion of groundwater has triggered surface deformation in densely populated urban areas. This paper aims to employ Persistent Scattered Interferometry Synthetic Aperture Radar (PS-InSAR) techniques to monitor and quantify the land surface deformation (LSD), assess the relationships between LSD and groundwater levels (GWL), and provide insights for urban planning in Lahore, Pakistan, as the research area. A series of Sentinel-1 images from the ascending track between 2017 and 2020 were analyzed. Moreover, the Mann–Kendall (MK) test and coefficient of determination were computed to analyze the long-term trends and spatial relationships between GWL depletion and line of sight (LOS) displacement. Our findings reveal significant increases in land subsidence (LS) and GWL from 2017 to 2020, particularly in the city center of Lahore. Notably, the annual mean subsidence during this period rose from −27 mm/year to −106 mm/year, indicating an accelerating trend with an average subsidence of −20 mm/year. Furthermore, the MK test indicated a declining trend in GWL, averaging 0.49 m/year from 2003 to 2020, exacerbating LS. Regions with significant groundwater discharge are particularly susceptible to subsidence rates up to −100 mm. The LS variation was positively correlated with the GWL at a significant level (p < 0.05) and accounted for a high positive correlation at the center of the city, where the urban load was high. Overall, the adopted methodology effectively detects, maps, and monitors land surfaces vulnerable to subsidence, offering valuable insights into efficient sustainable urban planning, surface infrastructure design, and subsidence-induced hazard mitigation in large urban areas.

Surface deformation caused by an earthquake is very important to study for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. In this study, we estimated the surface deformation due to an earthquake along an active blind fault using Sentinel-1 SAR data. On 24 September 2019, an earthquake with 5.6 Mw and 10 km depth stroke near Mirpur, Pakistan. The Mirpur area was highly affected by this earthquake with a huge collapse and the death of 34 people. This study aims to estimate the surface deformation associated with this earthquake in Mirpur and adjacent areas. The interferometric synthetic aperture radar (InSAR) technique was applied to study earthquake-induced surface motion. InSAR data consisting of nine Sentinel-1A SAR images from 11 August 2019 to 22 October 2019 was used to investigate the pre-, co- and post-seismic deformation trends. Time series investigation revealed that there was no significant deformation in the pre-seismic time. In the co-seismic time, strong displacement was observed and in post-seismic results, small displacements were seen due to 4.4 and 3.2 Mw aftershocks. Burst overlap interferometry and offset-tracking analysis were used for more sensitive measurements in the along-track direction. Comprehensive 3D displacement was mapped with the combination of LOS and along-track offset deformation. The major outcome of our results was the confirmation of the existence of a previously unpublished blind fault in Mirpur. Previously, this fault line was triggered during the 2005 earthquake and then it was activated on 24 September 2019. Additionally, we presented the co-seismically induced rockslides and some secondary faulting evidence, most of which occurred along or close to the pre-existing blind faults. The study area already faces many problems due to natural hazards where additional surface deformations, particularly because of the earthquake with activated blind fault, have increased its vulnerability.

Coastal areas are highly dynamic regions where surface deformation due to natural and anthropogenic activities poses significant challenges. Synthetic Aperture Radar (SAR) interferometry techniques, such as Persistent Scatterer Interferometry (PSInSAR), provide advanced capabilities to monitor surface deformation with high precision. This study applies PSInSAR techniques to estimate surface deformation along coastal zones from 2017 to 2020 using Sentinel-1 data. In the densely populated areas of Pasni, an annual subsidence rate of 130 mm is observed, while the northern, less populated region experiences an uplift of 70 mm per year. Seawater intrusion is an emerging issue causing surface deformation in Pasni’s coastal areas. It infiltrates freshwater aquifers, primarily due to excessive groundwater extraction and rising sea levels. Over time, seawater intrusion destabilizes the underlying soil and rock structures, leading to subsidence or gradual sinking of the ground surface. This form of surface deformation poses significant risks to infrastructure, agriculture, and the local ecosystem. Land deformation varies along the study area’s coastline. The eastern region, which is highly reclaimed, is particularly affected by erosion. The results derived from Sentinel-1 SAR data indicate significant subsidence in major urban districts. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region.

Interferometric Synthetic Aperture Radar (InSAR) is crucial for monitoring ground displacement, particularly in Pakistan’s capital area, where urban expansion and active geotectonics converge. This study introduces the Consecutive Interferogram Stacking Approach (CISA), a processing framework optimized for near-real-time deformation monitoring using full-resolution Sentinel-1 data from adjacent acquisition pairs. Unlike conventional InSAR techniques that rely on spatial multilooking to suppress phase noise—which sacrifices spatial resolution for computational efficiency—CISA preserves native resolution through sequential interferogram stacking, accepting that short-interval interferograms retain geophysical phase instabilities (including fading signals) inherent to scatterer decorrelation. By minimizing temporal decorrelation through consecutive pairing, CISA enhances interferogram coherence (6–14% improvement) and reduces Root Mean Square Error (RMSE) by approximately 25% compared to conventional multilooked time series, while enabling the computational efficiency critical for operational applications. The framework’s incremental architecture allows velocity updates within hours of new image acquisition—requiring only single interferogram addition rather than complete network reprocessing—making it suitable for rapid-response hazard assessment where latency constraints outweigh the need for long-baseline phase filtering. CISA reveals spatiotemporal subsidence patterns potentially reflecting the influence of fault zone geometry, groundwater fluctuation, and urbanization, with full-resolution analysis delineating linear deformation patterns spatially consistent with blind fault traces through multi-directional displacement modeling. These findings demonstrate that operational monitoring of geohazards can be achieved through strategic trade-offs between processing latency and geophysical noise suppression, providing actionable intelligence for infrastructure risk management in tectonically active urban environments.

Remote Sensing and GIS techniques employed in artificial groundwater recharge proved to be an effective and economic tool among the scientific community. Studies revealed that thematic layers with simple mathematical logics and weighted indexing techniques are used in the delineation of groundwater potential zone on several horizon conditions. In this study, the weighted modelling on hard rock terrain for groundwater recharge in Quetta valley is evaluated for its effectiveness due to its varying geological structure, considering parameters that include lithology, lineament density, slope, drainage density, and land cover. Imageries data of 30m resolution obtained from Landsat 8 and ASTER. Moreover, ground data of observation wells obtained from WAPDA, Pakistan. Our analysis identified the artificial replenishment sites for groundwater recharge which are correlated with ground data from water wells using Pearson’s correlation. Pearson’s value found 0.574 shows a positive correlation. Furthermore, categorizes the suitability of sites for groundwater recharge into four classes: most suitable, moderate, less, and not suitable. The output is evaluated against the structural density layer, hence, found the effectiveness of weighted modelling in groundwater recharge analysis in hard rock terrain for Quetta valley.