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The Deltas of the east coast of India are highly susceptible to vertical deformations due to various factors like hydrological changes, reduced sediment inflow from rivers, groundwater exploitation, hydrocarbon extraction, sea-level variations, and neotectonics. This study focuses on the LOS (Line of Sight) deformation in the Krishna Delta, India, during the years 2006–2011 and 2016–2018, observed using L- Band [Advanced Land Observation Satellite (ALOS)-1] and C-Band (Sentinel-1 A) satellite data, respectively. The displacement rates were determined through time series Interferometric Synthetic Aperture Radar (InSAR) analysis using the Small Baseline Subset (SBAS) approach. A total of 49 interferograms from ALOS-1 and 241 from Sentinel-1 A, corresponding to 19 ALOS and 70 Sentinel-1 A images, were selected based on geometrical and temporal baselines. Along the coastal region of the Krishna Delta, 25 transects were analyzed for two different periods to understand the LOS deformation rate. Among these evaluated transects, 11 showed uplift, 11 exhibited subsidence, and 2 remained stable. Transect No 12 which falls within a no-data zone due to coastal inundation yielded null results. The observed subsidence (5–6 mm/yr) was predominantly in regions associated with aquaculture activities. We hypothesize that the groundwater imbalance resulting from aquaculture activities is a prevailing factor in the observed ground deformation in this region. Various factors such as accretion, mangrove cover, and spit morphology were also explored to understand uplift in the study area. The InSAR technique demonstrated its capability for estimating land deformation, establishing it as one of the best methods in coastal delta studies.
Introduction
Deltas have always been major centres of human habitation and agriculture due to their fertile plains (Islam, 2016). A combination of deltaic and tidal plains, bounded by mangrove swamps, marshes, coarse sand, and mud deposits, forms the coastal delta. Sand, mudflats, lagoon clays, and peats are the typical sediment ranges found in rivers (Jelgersma, 1996). These deltas are dynamic and vulnerable environments that sustain coastal hydrodynamics and sediment availability. The Intergovernmental Panel on Climate Change reported that the coastal areas are at risk of sea-level rise due to the projected global warming scenario of 1.5 °C and 2 °C (Hoegh-Guldberg et al., 2018). This could have a massive impact on metropolises around the world located within 100 km of the coast (Nicholls et al., 2007).
Studies on major deltas of the world show that an area of 96,000 km2 is within 2 m above the current sea level. Additionally, the vertical deformations of low-lying deltas might result in a massive impact due to the rising sea-level trend (Syvitski et al., 2009). Natural factors in the delta, coupled with anthropogenic pressures, result in subsidence (sinking) or uplift (rising), or both, at various rates and times (Brown & Nicholls, 2015). Some of the main factors that cause subsidence are groundwater exploitation, hydrocarbon extraction through mining or oil rigs, and tectonic processes (Overeem & Syvitski, 2009; Strozzi et al.,2001). Accelerated compaction and reduced aggradation of sediments (Syvitski et al., 2009) due to the construction of reservoirs and alteration of river channels (Nilsson et al., 2005), isostatic adjustment (Higgins, 2016), and deviant agricultural practices (Sharma et al., 2016) has resulted in more than 200 events of land subsidence worldwide, as reported by the USGS International Survey of Land Subsidence Database(Ortega-Guerrero, 2012). Delta subsidence will lead to saltwater intrusion, wetland loss, infrastructure damage, increased vulnerability, and coastal inundation (Higgins, 2016; Brown & Nicholls, 2015).
Advances in remote sensing methods based on Interferometric Synthetic Aperture Radar (InSAR) have surpassed reliable ground-based traditional subsidence measurements using borehole extensometers, tidal stations, and geodetic surveying. Ground-based methods have limitations in the number of locations (Teatini et al., 2011) and face challenges in managing levelling benchmarks in the deltaic environment (Zhang et al., 2015). Instruments like the Global Positioning System (GPS) are accurate and reliable for deformation studies but involve high maintenance and installation costs (Solari et al., 2018). Considering day and night operability, weather and solar illumination independence, frequent revisit capability, and wide spatial coverage, InSAR proves to be an improved alternative technique for studying ground deformation with millimeter-level accuracy (Zhang et al., 2015; Zhang et al., 2019a).
A study carried out by Lian et al. (2021) reported improved accuracy with Sentinel data fusion, using Sentinel-1 A and Sentinel-1B C-band data based on the variation law of subsidence velocity of ground levelling monitoring points. Sentinel-1 A-derived deformation and GPS values in the sedimentary and metamorphic terrain of the Kathmandu basin showed a linear fit (Krishnan et al., 2018). This region is composed of lacustrine and fluvial deposits, with groundwater as the major water source, standing as one of the validated results in similar terrains without GPS stations. Studies on the rate of compaction field measurements and InSAR-based subsidence due to groundwater pumping in the Mekong Delta, Vietnam prove the consistency of both methods (Erban et al., 2014). A subsidence of 18 cm/yr due to aquifer depletion was documented using [Advanced Land Observation Satellite ) ALOS and ENVISAT satellites in an agricultural region of Al Wagan, UAE, which has stabilized and reduced to 10 cm/yr, as observed from Sentinel-1(Liosis et al., 2018). Gudao and GuDong oilfield areas of the Yellow River Delta, China, experienced high subsidence (30 and 20 mm/yr) due to oil extraction (Liu et al., 2015) and urbanization (Lu et al., 2022). The rate at aquaculture facilities (> 250 mm/yr) exceeds the average sea-level rise and indicates that subsidence and its relative sea-level rise may endanger Asian mega-deltas (Higgins et al., 2013). Land subsidence of 10–22 cm was found in Jakarta, Indonesia, through Differential SAR Interferometry (Bayuaji et al., 2010). Complex patterns of deformations and reversal in trends were detected in the coal fields through time series analyses (Li et al., 2021). The subsidence in the Ganges-Brahmaputra Delta, Bangladesh, varies with lithology and is controlled by the local stratigraphy (Higgins et al., 2014).
Earlier studies on deformations were carried out in many parts of India through DInSAR (Alam et al., 2018) and a time-series InSAR approach. The deformations caused by groundwater withdrawal (Chatterjee et al., 2007; Suganthi et al., 2017; Malik et al., 2019; Tripathi et al., 2018), nuclear explosions (Sreejith et al., 2017), seasonal loading of the reservoir (Gahalaut et al., 2017) coal fires and mining (Chatterjee et al., 2015a; Gupta et al., 2013; Ishwar & Kumar, 2017), disasters such as landslides, earthquakes (Saraf et al., 2012; Sharma et al., 2020; Sreejith et al., 2016, 2021; Tiwari et al., 2018; Yadav et al., 2020), volcanic deformation (Sreejith et al., 2020), tectonics (Kothyari et al., 2019; Yhokha et al., 2015, Sreejith et al., 2018) subsidence in coastal cities and the implications of poor ground water management and seismic hazards (Shastri et al., 2023; Sudha Rani Nalakurthi et al., 2024) etc. were among them. Little or no studies about deformation were carried out in coastal areas or delta regions of India except Godavari delta (Murali et al., 2023, Tripathi et al., 2023).
The east coast of India is enriched with deltas and wetlands. The world’s largest, including the Ganges-Brahmaputra, Krishna, Godavari, and Cauvery, are significant among them. The Krishna, Ganges-Brahmaputra Delta, and Godavari Delta are at risk due to the increased rate of relative sea-level rise and socioeconomic vulnerability (Tessler et al., 2015). Satellite altimeter studies revealed a higher sea-level rise trend (5 mm/yr) than normal along the northern and eastern coast of the Bay of Bengal (Unnikrishnan et al., 2015). Significant areas of some major deltas worldwide, including the Ganges, Brahmani, Krishna, Godavari, and Mahanadi, were flooded. The lives of more than 100,000 people were lost, and more than a million inhabitants were shifted due to flooding during 2007–2008 (Syvitski et al., 2009). The Krishna Delta, a unique coastal region in India, is a critical study area due to its ecological significance, socio-economic importance, and environmental challenges. Despite existing research, gaps remain in understanding climate change impacts, coastal erosion, coastal subsidence, groundwater exploitation and community-based mitigation in this region. It was intended to study the deltas of India because of their uniqueness and importance and the state of unknown extent of their deformation. This particular study pertains to the Krishna delta region addressing the vertical land deformation and its potential causes.
It aims to assess the deformation rates in the Krishna Delta, India, using a satellite-based technique called InSAR (Interferometric Synthetic Aperture Radar). The study focuses on the period between 2006 and 2011 and 2016–2018 and aims to determine the level and extent of vertical land movement within this delta. This information is crucial for understanding the impact of factors like groundwater withdrawal, resource extraction, and natural processes on the delta’s stability, particularly in rising sea levels.
Study Area
The study area, Krishna Delta is formed by the perennial river Krishna, in the state of Andhra Pradesh and lies within 10 m above mean sea level. The study is delineated by the geographic coordinates ranging from 80°10’ to 81°25’ East longitude and 15°27’ to 16°39’ North latitude within the delta region. Vijayawada is the commercial centre of the Krishna district and is situated at the head of the Delta. Guntur, Tenali, Machilipatnam, Repalle, Nagayalanka, and Nizampatnam are the other major towns and cities in this region (Fig. 1). The district population is 4,517,398 with a population density of 518 persons per km (District survey report, 2018). Geomorphic features such as ancient channels, beach ridges, and mangrove swamps with three distributaries constitute the major Krishna Delta (Nageswara Rao, 1985). The rapid expansion of irrigation and construction of dams resulted in the reduction of annual average discharge from 56 km3 to less than 13 km3 by 1990–2001 (Trent et al., 2007). The Krishna Delta has the highest groundwater potential due to high precipitation and recharge rates (Trent et al., 2007). An average of 26.4 km3 of restorable groundwater is available in the basin (Kumar et al., 2005). 95% of the total annual flow and suspended sediment load marked highest in monsoon, shows the system is driven by the monsoon. At the same time, the annual average sediment load showed a maximum of 16.69 million tons in 1983 and a minimum of 0.007 million tons in 2004 over the period from 1966 to 2005 (Nageswara Rao et al., 2013).
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Fig. 1
Map showing the study area–Krishna Delta of Andhra Pradesh, India. The green dot represents the cities and towns of Krishna delta
The East Coast of India is prone to severe cyclones and floods. Due to its gentle gradient nature, storm surges reach kilometres inland during cyclones (Kallepalli et al., 2017). November 1977 cyclone was the most devastating with tidal waves of 6 m and inundation of 15 km inland in the Krishna district (Ramanamurty et al., 1993). Tectonic elements of the study area show a series of horst and graben successions towards the ocean (Chatterjee et al., 2015b; Kumar et al., 2014). This area is situated on a pericratonic rift basin over the Bapatla ridge and Nizampatnam sub-basin in its upper and lower part having a cross-trend fault, which resulted in the bulge of the delta. The sediments aged from the Cretaceous to the Recent with varying thicknesses of 2–7 km (Nageswara Rao et al., 2013).
Data Used and Methods
Selection of Data
A decadal study was conducted to observe subsidence in the deltaic system of the Krishna through SAR interferometry to estimate changes at the millimetre level. In this study, two different radar datasets were selected; ALOS-1 and Sentinel-1 A covering the periods 2006–2011 and 2016–2018, respectively, for the Krishna Delta region.
ALOS-1 PALSAR (wavelength = 23.6 cm), developed by the Japan Aerospace Exploration Agency (JAXA), has a spatial resolution of 10 m and an orbit repeat cycle of 46 days. ALOS-1 scenes (19 in total) were acquired in the single-polarization (VV) Interferometric Wide (IW) mode. Despite the limitations of L-band data, it has been widely used by researchers for ground deformation studies (Abe et al., 2020; Chen et al., 2021). Sentinel-1 A equipped with a C-band (wavelength = 5.6 cm) SAR sensor, was developed by the European Space Agency(ESA), with a temporal resolution of 12 days and a ground resolution of 5 m × 20 m. Single polarization and IW mode were chosen for the selection of datasets. A total of 70 Sentinel-1 A scenes were acquired from the Copernicus open-access hub.
Interferogram Generation and Rate Estimation
The InSAR technique is employed to detect the LOS deformation rate using the phase differences obtained from two or more SAR images captured at different times or orbital positions (Bamler and Philipp Hartl, 1998). The processing of SAR data (ALOS-1 and Sentinel-1 A) and the generation of interferograms were carried out using the GMTSAR InSAR processing tool (https://topex.ucsd.edu/gmtsar/) (Sandwell et al., 2011, 2016). A total of 49 interferogram pairs were chosen for ALOS, and 241 for Sentinel in the Krishna (Fig. 2). The network is chosen based on the small geometrical and temporal baselines. We considered 90 days and 700 m for the ALOS and 50 days and 100 m for the sentinel baseline pairs. The orbital parameters of the SAR satellite, along with an external Digital Elevation Model (DEM), are employed to calculate phase components within an interferogram. These components are associated with SAR imaging geometry and surface topography. Subtracting these phase components from the interferometric phase yields the differential phase, ideally depicting ground displacement between each Synthetic Aperture Radar (SAR) acquisition in Single-Look Complex (SLC) data. The topographic contributions were removed using the Shuttle Radar Topography Mission (SRTM) digital elevation model (30 m) (Farr et al., 2007). A Gaussian filter with a wavelength of 200 was used for filtering the interferograms. The interferograms were unwrapped through an algorithm called the statistical-cost Network-flow Algorithm for Phase Unwrapping (SNAPHU) (C. W. Chen & Zebker, 2001) with a coherence threshold of 0.12.
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Fig. 2
Baseline (m)- Time Plot for a ALOS and b Sentinel-1 A, illustrating the geometric position of the satellites over the acquisition periods of a 2006–2011 and b 2016–2018. Interferogram pairs are highlighted in green
SBAS InSAR algorithm (Berardino et al., 2002) was executed to understand the LOS deformation from a series of interferograms over the Krishna Delta for ALOS and Sentinel-1 A. The pairs of interferograms were chosen based on the spatial and temporal baselines. The Atmospheric Phase Screen (APS) is a major element which gives noise or additional fringes compromising the accuracy of the deformation results. In order to avoid or correct these errors, the generated interferograms are atmospherically corrected using ECMWF or ERA5 data (Hu & Mallorquí, 2019). Also, another method used to estimate the time series InSAR is Common Scene Stacking (CSS) (Zhang et al., 2023) which helps in removing the errors due to APS for non-linear deformation retrieval. This could help in reducing the error in the phase obtained due to ionosphere delay or atmospheric interventions. Quality control was assured for all the images. Suitable pairs, selected from the generated interferograms were considered for the small baseline approach system to evaluate the time series deformation in the Generic InSAR Analysis Toolbox (GIAnT), which works in the Python framework (Agram et al., 2013). The phase unwrapping does not proceed from a certain location, i.e. stable point. However, the SBAS-based approach is used for the estimation of the time series analysis (Harishankar et al.,2023), which utilizes the co-registered unwrapped interferogram for the overlapped stack and the locations having good coherence are processed for the estimation of the surface displacements in the study region. The temporal reference date for ALOS is 24/01/2008 and 09/01/2026 for Sentinel I. The methodology flowchart employed in this study is based on Mani et al. (2023).
The acquisition of SAR images from marginally different locations results in a phase offset in the interferograms due to its orbital drift. Hence, calibration of the GPS data with the SAR-derived values is essential to obtain the absolute deformation of the area (Higgins et al., 2014). Calibration with real-time data is not possible in this work when the study area does not have any GPS stations. However, a few studies confirm that the subsidence derived using the SBAS time series method is almost closer to the field observed values (Zhang et al., 2019b; Krishnan et al., 2018), which gives confidence and motivation for this present work.
Land Use Land Cover Classification (LULC)
Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) satellite data from United States Geological Survey (USGS) Earth Explorer were utilized for the years 2011 and 2019 (19/10/2011&15/03/2019) to comprehend the land use and land cover pattern of the Krishna Delta. The Normalized Difference Water Index (NDWI) was applied for the same years using the Near Infrared (NIR) and Shortwave Infrared (SWIR) bands to identify the expansion in aquaculture. A supervised classification technique was employed for the LULC classification. Seven classes were delineated, including cropland/vegetation, mudflats/marshy/swampy land, urban land, mangroves, aquaculture/waterbodies, fallow/barren/open spaces and sandy areas. To validate the accuracy of the supervised classification, a kappa coefficient accuracy assessment was conducted, comparing the classified results with ground truth information. For a more extended temporal perspective, the study includes area analyses of mangroves for the years 2006, 2010, 2016, and 2017. This analysis aims to provide insights into the changes in mangrove cover during the ALOS and Sentinel satellite periods.
Beach Profiling and Volumetric Analyses
Beach profiling and volumetric analysis were conducted at the Hamsaladevi location (K1) between transects 21 and 22 during 2017–2020 seasonally (December and February), as depicted in Fig. 11. Profiling was carried out using the Pentax AP-230 X auto-leveller, equipped with a 30x magnifying capacity, and a levelling staff. Measurements were conducted consistently during low tide, maintaining an equal interval of 6 m across the entire stretch of beaches.
Results
Time Series and Rate of LOS Displacement along the Coastal Area
The InSAR-derived LOS deformation over five years (2006–2011) and two years (2016–2018) using the SBAS time-series approach is illustrated in Fig. 3. Positive values indicate uplift or rising land, while negative values represent subsidence or
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Fig. 3
LOS deformation rates in Krishna Delta during two distinct periods: a 2006–2011 and b 2016–2018. The colour gradient from blue to red represents the deformation ranging from − 20 to 20 mm/yr. Twenty-five transects were plotted along the coast to compare the rate of LOS displacement
sinking. The LOS deformation rate ranges from + 20 mm/yr to − 20 mm/yr across the entire study area. Sentinel-1 A-derived time series rates exhibited better performance due to its shorter temporal baseline (every 12 days), whereas ALOS experienced a few discontinuities in the dataset.
Moreover, 25 transects were assessed from the coastline (0 km) to 5 km inland at every 5 km interval to comprehend the variations in the LOS deformation rate at each point from 2006 to 2018. The numbers in black, blue, red and yellow colours signify the stable, subsidence, uplift and no-data zones, respectively.
To further analyze the trend of the LOS deformation rates during 2006–2011 and 2016–2018, a stack profile for all the transects was computed. The transects were subsequently classified into four groups: Group I-Subsidence to Subsidence, Group II-Stable/Uplift to Subsidence, Group III- Subsidence/Stable to Uplift, and Group IV - Stable to Stable (Table 1). Notably, Transect 12, a special case with no-data value, is considered to demonstrate the inundation of the area.
Table 1. Transects categorized into four groups, their nature of deformation and average LOS rate during 2006–2011 and 2016–2018
Transect Numbers | 2006–2011 | Average Rate | 2016–2018 | Average Rate |
|---|---|---|---|---|
Group I: Subsidence to Subsidence | ||||
24 | Subsidence | -5 mm/yr | Subsidence | − 5 mm/yr |
Group II: Stable or Upliftment to Subsidence | ||||
2 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
3 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
4 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
8 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
9 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
10 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
11 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
13 | Upliftment | 3.5 mm/yr | Subsidence | − 2 mm/yr |
25 | Stable | − 2 mm/yr | Subsidence | − 9 mm/yr |
Group III: Stable or Subsidence to Upliftment | ||||
1 | Stable | 0–5 mm/yr | Upliftment | 6–12 mm/yr |
6 | Stable | 0–5 mm/yr | Upliftment | 6–12 mm/yr |
18 | Stable | 0–5 mm/yr | Upliftment | 6–12 mm/yr |
15 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
16 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
17 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
19 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
20 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
21 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
22 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
23 | Subsidence | − 3.5 mm/yr | Upliftment | 3 mm/yr |
Group IV: Stable to Stable | ||||
5 | Stable | 2–5 mm/yr | Stable | 2–5 mm/yr |
7 | Stable | 2–5 mm/yr | Stable | 2–5 mm/yr |
14 | Stable | 2–5 mm/yr | Stable | 2–5 mm/yr |
Group I Subsidence to Subsidence
Transect 24 exhibited a consistent subsidence trend during both periods, with an average rate of − 5 mm/yr (Fig. 4). Out of the 25 transects evaluated, this specific transect consistently displayed subsidence. Extensive aquacultural activities were observed along this transect, aligning with previous studies (Higgins et al., 2013; Du et al., 2017; Hung et al., 2018), indicating that aquaculture ponds likely must have contributed to the subsidence in the region. The average LOS deformation rate was recorded at − 5 mm/yr during the study period. Continued monitoring is essential to comprehensively understand this region’s behaviour, especially in areas with similar aquaculture trends. Seasonal variations in seawater levels within the ponds were identified as the primary contributing factor to the deformation, with excessive groundwater withdrawal to maintain the salinity in the ponds, exacerbating subsidence. As all the aquaculture ponds are not connected to canals and waterways, groundwater is heavily pumped into these ponds to compensate for the evaporation and to keep the required salinity. The LOS deformation rate at this transect exceeds the projected sea level rise (3.3 mm/yr) (Unnikrishnan et al., 2015), and is a potential site for coastal erosion soon.
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Fig. 4
Profile plot of transect 24 showing subsidence trend during 2006–2011 and 2016–2018
Group II: Stable or Uplift to Subsidence
Nine transects, including transects 2–4, transects 8–11, 13, and 25, were categorized in this group. Transect 25, which remained stable during 2006–2011, exhibited a subsidence trend during 2016–2018, with the average LOS deformation rate increasing from − 2 mm/yr to − 9 mm/yr. Similar to transect 24, regions with high subsidence were linked to aquaculture activities (Fig. Fig. 5). Other transects, which previously showed uplift (3.5 mm/yr), have now transitioned to subsidence (− 4 mm/yr) in recent times (Fig. 6). The region encompassing transect numbers 2, 3,4, 8, 9, 10,11, and 13 is dominated by mangroves and spits close to the coast, with aquaculture activities extending inland. Transects 4, 8 and 9 exhibited a reversal trend, transforming from uplift from the coast to inland during 2006–2011 to subsidence in recent times. At a distance of 4–5 km from the coast, transects 4, 8 and 9 have shifted from 5 mm/yr to − 5 mm/yr, 3 mm/yr to − 4 mm/yr and 10 mm/yr to − 2 mm/yr, respectively. These transects, located in aquaculture regions, continuously displayed subsidence, with aquaculture practices exacerbating the respective locations to higher deformation rates. Transect 10 underwent an average LOS deformation rate from 8 mm/yr to 3 mm/yr and is located in a mangrove restoration area. This contributes to the region remaining stable compared to nearby transects experiencing subsidence. Additionally, transect 10 indicated an uplift of 5 mm/yr in 2016–2018 near the coast, attributed to the accretion pattern of the spit and growth of mangroves.
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Fig. 5
Profile plot of transect 25 showing stable during 2006–2011 and subsidence during 2016–2018
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Fig. 6
Profile plots of transect 2–4, 8–11 and 13 showing upliftment during 2006–2011 and subsidence in the recent period
Group III: Stable or Subsidence to Uplift
Out of 11 transects in this group, Transects 1, 6, and 18 were found to be undergoing uplift (6–12 mm/yr) from a stable condition (0–3 mm/yr) (Fig. 7). Transect 1 passes through mangroves, a river channel network, open spaces, and agricultural land. A high rate of uplift was observed at the open space/ barren land located between 2 and 2.5 km along this transect in both periods. In contrast to transect 2, transect 6 traverses open spaces and inactive aquaculture ponds in recent times, exhibiting higher permeability. This uplift can be attributed to the rebound of the land at these sites. Transect 18, running along the beach, shows an uplift trend up to 3 km from the coast. Additionally, a higher uplift rate of 8 mm/yr (2006–2011) and 13 mm/yr (2016–2018) is observed at a distance of 1.5–2 km. The presence of dunes and vegetation may account for the enhanced uplift at this location.
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Fig. 7
Profile plots of transects 1,6 and 18 undergoing upliftment from stable during 2016–2018
Transects 15–17 and 19–23 have transitioned from subsidence (− 4 mm/yr) to uplift (3 mm/yr) (Fig. 8). Transects 15,16 and 17 are located in spits and mangrove-dominated regions. The average LOS deformation rate at these spits was − 3 mm/yr in 2006–2011, which has uplifted to 5 mm/yr due to sediment deposition. These dynamic spits were found to be accreting due to the sediment deposits, with thick mangrove cover identified further from the coast in all three transects. Transects 19, 20, 21, and 22 traverse from the beach to aquaculture inland, similar to transect 18. These transects depicted an average LOS deformation rate of − 6 mm/yr in 2006–2011, transitioning to 5 mm/yr in recent times. Inactive/abandoned aquaculture ponds are also observed along these transects further inland. This stretch is undergoing an accretion pattern, as observed during in-situ beach profile measurements. Transect 23, a dominant mangrove region in a rural area, has shown an uplift in the recent period. The average LOS deformation rate was − 5 mm/yr during 2006–2011, transforming to 5 mm/yr during 2016–2018.
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Fig. 8
Profile plots of transects 15–17,19–21 and 23 showing subsidence trend in 2006–2011 and upliftment in recent times
Group IV: Stable to Stable
A total of three transects (5,7 and 14) were grouped in this category (Fig. 9). These three transects, dominated by mangroves, remained in a similar trend during both periods. The average LOS deformation rate ranged between 2 and 5 mm/yr during 2006–2011 and 2016–2018.
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Fig. 9
Profile plots of Transects 5, 7 and 14 show stable conditions during both the periods
Discussion
The LOS deformation rate reveals an uneven trend in the coastal regions of the Krishna Delta with a high LOS deformation rate in the aquaculture regions and the coasts. These changes can be attributed to the various parameters such as morphology, land use types and mangrove cover. All these parameters play a major role in the Krishna Delta. This is the maiden attempt to identify the deformation rates in the Krishna delta of the east coast of India.
Coastal Morphology
Geomorphological landforms, such as spits and bars at the mouth of all distributaries, indicate the nature and quantity of deposition in the delta. An increase in the area (5 km2) of the spit near Lankevanidibba mangroves was computed between 1973 and 2014. It was observed that accretion is higher at the headward portion of the spit rather than at the foot (Reshma & Murali, 2018). The morphology and pattern of sedimentation along the coast control the growth of mangrove forests (Nageswara Rao & Hema Malini, 1986). Additionally, the redistribution of river-borne sediments regenerated into wave-built landforms due to the effect of wave action was reported in the Krishna Delta (Nageswara Rao, 1985). Transect 10, crossing the bottom portion of the spit and extending to the mangroves, exhibits an uplift (3.5 mm/yr) to subsidence (− 2 mm/yr) trend during the observation period. The erosion of the foot of the spit contributed to the subsidence along transect 10.
Beach profiling and volumetric analysis at a location between transects 21 and 22 (Hamsaladevi) was carried out from 2017 to 2020 (Fig. 10). These observations indicated an accretion phase throughout the study period, gaining a volume of 8–10 m3/m from December 2017-February 2020. These transects reflected an uplift trend (3.5 mm/yr) in recent times due to deposition. The observed uplift of 3.5 mm per year observed at transects 21 and 22 aligns with the volume during the period from 2016 to 2018. This concurrence suggests a correlation between the LOS deformation rate and coastal accretion.
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Fig. 10
Accretion in Hamsaladevi Beach (a) Observation location on Google Earth (b) beach profile plot from 2017 to 2020 (c) and corresponding volumetric change
Land Use Types
The role of different types of land use/land cover in this region was determined through LULC mapping (Fig. 11a, b). The analysis of LULC revealed that the economy of the Krishna Delta region is dependent on agriculture and aquaculture. This indicates that most parts of the delta are utilized for agricultural purposes, relying on rainfall during monsoon periods and groundwater withdrawal (both monsoon and non-monsoon periods). The expansion of aquaculture ponds was observed as tidewater reached much farther inland through the river. To examine the expansion of aquaculture ponds during both study periods, the Normalized Difference Water Index (NDWI) was carried out. The blue and green shaded areas represent the aquaculture ponds in 2011 and 2019 (Fig. 12). The NDWI of both 2011 and 2019 was overlaid to observe newly formed aquaculture ponds. It was observed that new aquaculture ponds had formed in transects 2–4, which showed subsidence in recent times. Similarly; expansion along previously constructed ponds was also revealed by this analysis. Most of these aquaculture practices have rendered those regions prone to deformation. Transects 2–5 and 25, situated on the aquaculture ponds, exhibit a high subsidence of − 4 to − 6 mm/yr. These deformations are likely due to extensive groundwater pumping in those blocks, making the land more susceptible to subsidence. The groundwater level in these regions has changed from 0.74 to 3.25 m below ground level at station Vadarevu which is between transects 2 and 3. A recent study on role of groundwater extraction and its impact on land deformation revealed high subsidence in the Machilipatnam region of Krishna delta (Subham Rajewar et al., 2024). Similar studies in Taiwan and Leizhou Peninsula, China, reported significant subsidence of 4.5 cm/yr and 20 mm/yr in aquaculture locations due to excessive groundwater pumping (Hung et al., 2018; Du et al., 2017). We also hypothesize here that the groundwater imbalance resulting from aquaculture activities must be a prevailing factor for the observed ground deformation in this region.
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Fig. 11
LULC Map of Krishna Delta for the years a 2011 and b 2019. The map illustrates seven distinct classes identified through a supervised classification technique
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Fig. 12
NDWI of Krishna Delta, highlighting aquaculture areas in a 2011 (depicted in blue) and b 2019 (depicted in green)
Mangrove Cover
Extensive mangroves are identified along the coastal regions of the Krishna Delta. Mangroves play a crucial role in trapping sediments and contributing to the land-building process (Chaudhuri et al., 2019). Area analyses estimated a 9% increase in mangrove cover from 2006 to 2017 (Fig. 13a, b). There was an increase of 16 km2 and 35 km2 in the mangrove area observed between 2006 and 2010 and 2010–2016, along the coastal stretch of the delta respectively. The uplift trend was noticed along transects 14 (2–5 mm/yr), 22(3 mm/yr), and 23(3 mm/yr). The increase in the mangrove cover is one of the contributing factors to the stabilization of the coastal area. Furthermore, construction failures at high subsidence locations, marked in red in Fig. 14a, were noted during field investigations ( Fig. 14b, c, d).
[See PDF for image]
Fig. 13
a Mangrove change analysis during 2006 and 2017 b Percentage change in mangrove cover during 2006, 2010, 2016 and 2017
[See PDF for image]
Fig. 14
Field photographs a Locations b, c and d at high subsidence regions
Conclusion
This study utilized the InSAR technique to evaluate vertical deformation in the coastal delta of Krishna. The analysis covered two distinct periods, 2006–2011 using ALOS-1 satellite data and 2016–2018 using Sentinel-1 A satellite data. The LOS deformation rates, determined through the SBAS time series approach was found to be efficient in estimating deformation at a finer level. The results revealed that while most parts of the Krishna delta are stable and several locations reported subsidence. The stability of the land during the study period is supported by beach morphology and mangrove growth in the coastal area. Out of the 25 transects analyzed along the 125 km coastal stretch, a majority experienced moderate ground deformation, indicating disturbances in the stability of the coast and its depositional nature. Despite this overall trend, locations where aquaculture activities are carried out exhibited subsidence signatures in 2016–2018. This subsidence is attributed to excessive groundwater exploitation in those regions for maintaining the ponds. The proliferation of aquaculture ponds poses a risk to this region and requires continuous monitoring. A comprehensive groundwater exploration policy should be implemented to prevent further deformation. Additionally, this delta region should be explored considering aspects such as hydrocarbon exploration, future sea-level trends, and deformation caused by the coastal inundation during storm surges. Comprehensive studies in these areas will contribute to a more holistic understanding of the dynamics and challenges faced by the Krishna Delta.
Acknowledgements
The authors thank the Director, CSIR-National Institute of Oceanography, to carry out this study. Director, SAC, Ahmedabad is acknowledged for the support of this work. Sentinel data was downloaded through the Sentinel Scientific hub (https://scihub.copernicus.eu/dhus/#/home). Space Applications Centre have acquired ALOS PALSAR data from JAXA through RA-6, PINo.3047. This work is part of the first author’s Ph.D. thesis registered at Bharathidasan University, Tiruchirappalli, India. The first author acknowledges the DST-INSPIRE fellowship for her Ph.D. GMTSAR was used in this research which is open source (GNU General Public License) software. It was supported by ConocoPhillips, the National Science Foundation Geoinformatics Program, Scripps Institution of Oceanography, and San Diego State University. The NIO contribution No is 7298.
Author Contributions
Conceptualization: Mani Murali R. Data curation: Ratheesh Ramakrishnan. Formal analysis: Reshma K N, Santhosh Kumar S, Ritesh Agrawal and Ratheesh Ramakrishnan. Methodology: Mani Murali R, Ritesh Agrawal, Ratheesh Ramakrishnan, and Rajawat A.S. Software: Reshma K N, Santhosh Kumar S and Ritesh Agrawal. Supervision: Mani Murali R and Rajawat A.S. Writing – Original draft: Reshma K N and Santhosh Kumar S. Writing – review & editing: Mani Murali R
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declarations
Conflict of interest
The authors declared that they have no conflict of interest.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
Abe, T; Iwahana, G; Efremov, PV; Desyatkin, AR; Kawamura, T; Fedorov, A et al. Surface displacement revealed by L-band InSAR analysis in the Mayya area, Central Yakutia, underlain by continuous permafrost. Earth Planets and Space; 2020; 72,
Agram, PS; Jolivet, R; Riel, B; Lin, Simons, M; Hetland, E et al. Multiscale InSAR Time Series (MInTS) analysis of surface deformation. Eos Transactions American Geophysical Union; 2013; [DOI: https://dx.doi.org/10.1029/2011JB008731]
Alam, MS; Kumar, D; Chatterjee, RS; Upreti, V.
APSAC, & ITE&C Department Govt.of Andhra Pradesh (2018). District Survey Report - Krishna District.
Bamler, R Philipp Hartl. Synthetic aperture radar interferometry. Inverse Problems; 1998; 14, pp. 1-54. [DOI: https://dx.doi.org/10.1088/0266-5611/14/4/001]
Bayuaji, L; Sumantyo, JTS; Kuze, H. ALOS PALSAR D-InSAR for land subsidence mapping in Jakarta, Indonesia. Canadian Journal of Remote Sensing; 2010; 36,
Berardino, P; Fornaro, G; Lanari, R; Sansosti, E. A new algorithm for monitoring localized deformation phenomena based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing; 2002; 40,
Brown, S; Nicholls, RJ. Subsidence and human influences in mega deltas: The case of the ganges-Brahmaputra-Meghna. Science of the Total Environment; 2015; 527–528, pp. 362-374.1:CAS:528:DC%2BC2MXosVagtrw%3D [DOI: https://dx.doi.org/10.1016/j.scitotenv.2015.04.124]
Chatterjee, RS; Roy, PS; Dadhwal, VK; Lakhera, RC; Quang, TX; Saha, R. Assessment of land subsidence phenomenon in Kolkata City, India using satellite-based D-InSAR technique. Current Science; 2007; 93,
Chatterjee, R., Paul, S., Singha, D. K., & Mukhopadhyay, M. (2015a). Overpressure zones in relation to in situ stress for the krishna-godavari basin, eastern continental margin of India: Implications for hydrocarbon prospectivity. Petroleum Geosciences: Indian Contexts, 127–142. https://doi.org/10.1007/978-3-319-03119-4_5
Chatterjee, RS; Thapa, S; Singh, KB; Varunakumar, G; Raju, EVR. Detecting, mapping and monitoring of land subsidence in Jharia Coalfield, Jharkhand, India by spaceborne differential interferometric SAR, GPS and precision levelling techniques. Journal of Earth System Science; 2015; 124,
Chaudhuri, P., Chaudhuri, S., & Ghosh, R. (2019). The role of Mangroves in coastal and estuarine sedimentary accretion in Southeast Asia. In Sedimentation Engineering (pp. 1–23). https://doi.org/10.5772/intechopen.85591
Chen, CW; Zebker, HA. Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization. Journal of Optical Society of America; 2001; 18,
Chen, Z; Montpetit, B; Banks, S; White, L; Behnamian, A; Duffe, J; Pasher, J. InSAR monitoring of arctic landfast sea ice deformation using L-Band ALOS-2, C-band radarsat-2 and sentinel-1. Remote Sensing; 2021; 13,
Du, Y; Feng, G; Peng, X; Li, Z. Subsidence evolution of the Leizhou Peninsula, China, based on inSAR observation from 1992 to 2010. Applied Sciences (Switzerland); 2017; 7,
Erban, LE; Gorelick, SM; Zebker, H. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. Environmental Research Letters; 2014; 9,
Farr, TG; Rosen, PA; Caro, E; Crippen, R; Duren, R; Hensley, S. The shuttle radar topography mission. Reviews of Geophysics; 2007; 45, pp. 1-33. [DOI: https://dx.doi.org/10.1029/2005RG000183]
Gahalaut, VK; Yadav, RK; Sreejith, KM; Gahalaut, K; Bürgmann, R; Agrawa, R et al. InSAR and GPS measurements of crustal deformation due to seasonal loading of Tehri reservoir in Garhwal Himalaya, India. Geophysical Journal International; 2017; 209,
Gupta, N; Syed, TH; Athiphro, A. Monitoring subsurface coal fires in Jharia Coalfield using observations of land subsidence from differential interferometric synthetic aperture radar (DInSAR). Journal of Earth System Science; 2013; 122,
Hari, S; Prakash, C; Dharmendra, S; Ravi, Bhandari., CM; Arijit, B; Roy, Suresh, K; Raghavendra, PS. Multi-temporal InSAR and Sentinel-1 for assessing land surface movement of Joshimath town, India. Geomatics, Natural Hazards and Risk; 2023; 14, 2253972. [DOI: https://dx.doi.org/10.1080/19475705.2023.2253972]
Higgins, S. Review: Advances in delta-subsidence research using satellite methods. Hydrogeology Journal; 2016; 24,
Higgins, S; Overeem, I; Tanaka, A; Syvitski, JPM. Land subsidence at aquaculture facilities in the Yellow River delta, China. Geophysical Research Letters; 2013; 40,
Higgins, SA; Overeem, I; Steckler, MS; Syvitski, JPM; Seeber, L; Akhter,. InSAR measurements of compaction and subsidence in the Ganges-Brahmputra Delta, Bangladesh. Journal of Geophysical Research: Earth Surface; 2014; 119, pp. 1768-1781. [DOI: https://dx.doi.org/10.1002/2014JF003117]
Hoegh-Guldberg, O., Jacob, D., Taylor, M., Bindi, M., Brown, S., Camilloni, I. (2018). Impacts of 1.5°C Global Warming on Natural and Human Systems. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, 179–311. https://doi.org/10.1002/ejoc.201200111
Hu, Z; Mallorquí, JJ. An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model. Remote Sensing; 2019; 11,
Hung, WC; Hwang, C; Chen, YA; Zhang, L; Chen, KH; Wei, SH et al. Land subsidence in Chiayi, Taiwan, from compaction well, leveling and ALOS/PALSAR: Aquaculture-induced relative sea level rise. Remote Sensing; 2018; 10,
Ishwar, SG; Kumar, D. Application of DInSAR in mine surface subsidence monitoring and prediction. Current Science; 2017; 112,
Islam, SN. Deltaic floodplains development and wetland ecosystems management in the Ganges – Brahmaputra – Meghna Rivers Delta in Bangladesh. Sustainable Water Resources Management; 2016; 2,
Jelgersma, S. (1996). Land subsidence in coastal lowlands. In Sea Level Rise and Coastal Subsidence: Causes, consequences, and strategies (pp. 47–62). Dordrecht: Springer Netherlands.
Kallepalli, A; Kakani, NR; James, DB; Richardson, MA. Digital shoreline analysis system-based change detection along the highly eroding Krishna – Godavari delta front. Journal of Applied Remote Sensing; 2017; 11,
Kothyari, GC; Joshi, N; Taloor, AK; Kandregula, RS; Kotlia, BS; Pant, CC; Singh, RK. Landscape evolution and deduction of surface deformation in the Soan Dun, NW Himalaya, India. Quaternary International; 2019; 507, pp. 302-323. [DOI: https://dx.doi.org/10.1016/j.quaint.2019.02.016]
Krishnan, S; Kim, DJ; Jung, J. Subsidence in the Kathmandu Basin, before and after the 2015 mw 7.8 Gorkha Earthquake, Nepal revealed from small baseline Subset-DInSAR analysis. GIScience and Remote Sensing; 2018; 55,
Kumar, R; Singh, D; Sharma, D. Water resources of India. Current Science; 2005; 89, pp. 794-811.
Kumar, P; Collett, TS; Boswell, R; Cochran, JR; Lall, M; Mazumdar, A et al. Geologic implications of gas hydrates in the offshore of India: Krishna-Godavari Basin, Mahanadi Basin, Andaman Sea, Kerala-Konkan Basin. Marine and Petroleum Geology; 2014; 58, pp. 29-98.1:CAS:528:DC%2BC2cXhsFygurnF [DOI: https://dx.doi.org/10.1016/j.marpetgeo.2014.07.031]
Li, L; Zhao, C; Huang, J; Huan, C; Wu, H; Xu, P et al. Complex surface deformation of the Coalfield in the Northwest Suburbs of Xuzhou from 2015 to 2020 revealed by Time Series InSAR. Canadian Journal of Remote Sensing; 2021; 0,
Lian, X., Wu, Y., Ge, L., Du, Z., & Liu, X. (2021). DInSAR monitoring of surface subsidence by fusing Sentinel-1A and -1B data to improve time resolution in a mining area. Canadian Journal of Remote Sensing,47(4), 596–606. https://doi.org/10.1080/07038992.2021.1952554
Liosis, N; Marpu, PR; Pavlopoulos, K; Ouarda, TBMJ. Ground subsidence monitoring with SAR interferometry techniques in the rural area of Al Wagan, UAE. Remote Sensing of Environment; 2018; 216,
Liu, P; Li, Q; Li, Z; Hoey, T; Liu, Y; Wang, C. Land subsidence over oilfields in the Yellow River Delta. Remote Sensing; 2015; 7,
Lu, Y; Chen, D; Chen, Y. Analysis of spatiotemporal land subsidence patterns of Suzhou City, China, over the past 15 years based on multisource SAR data. Journal of the Indian Society of Remote Sensing; 2022; 50,
Malik, K; Kumar, D; Perissin, D. Assessment of subsidence in Delhi NCR due to groundwater depletion using TerraSAR-X and persistent scatterers interferometry. Imaging Science Journal; 2019; 67,
Mani, MR; Reshma, KN; Santhosh, KS; Ritesh, A; Ramakrishnan, R; Sreejith, KM; Rajawat, AS. Land subsidence studies in the Godavari Delta regions of the East coast of India using ALOS and Sentinel 1 data. Ecological Informatics; 2023; 78, 102373. [DOI: https://dx.doi.org/10.1016/j.ecoinf.2023.102373]
Nageswara Rao, K. Evolution and dynamics of the Krishna delta, India. The National Geographical Journal of India; 1985; 31, pp. 1-9.
Nageswara Rao, K; Hema Malini, B. Mangrove environment along the east coast of Krishna and Godavari deltas. Indian Journal of Landscape Systems and Ecological Studies; 1986; 9,
Nageswara Rao, K; Subraelu, P; Nagakumar, KCV; Demudu, G; Hema Malini, B; Rajawat, S; Ajai,. Geomorphological implications of the basement structure in the Krishna-Godavari deltas, India. Zeitschrift fur Geomorphologie; 2013; 57,
Nicholls, R. J., Wong, P. P., Burkett, V. R., Codignotto, J. O., Hay, J. E., McLean, R. F., Coastal systems and low-lying areas. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution ofWorking Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (P. J. van der L. M.L., Parry, J. P. (2007). Palutikof & C. E. Hanson, Eds.). Cambridge, UK, 315–356: Cambridge University Press.
Nilsson, C; Reidy, C; Dynesius, M; Revenga, C. Fragmentation and flow regulation of the world’s large river systems. Science; 2005; 308, pp. 405-408.1:CAS:528:DC%2BD2MXjtFOnt7g%3D [DOI: https://dx.doi.org/10.1126/science.1107887]
Ortega-Guerrero, MA. Land Subsidence in Urban Environment. Encyclopedia of Sustainability Science and Technology (In: Meyers); 2012; Springer: [DOI: https://dx.doi.org/10.1007/978-1-4419-0851-3]
Overeem, I., & Syvitski, J. P. (2009). Dynamics and vulnerability of Delta Systems. LOICZ Reports & Studies No.35.GKSS Research Center.
Ramanamurty, MV et al. Geomorphological studies for disaster mitigation—A case study of the Krishna. International Journal of Remote Sensing; 1993; 14, pp. 3269-3274. [DOI: https://dx.doi.org/10.1080/01431169308904441]
Reshma, KN; Murali, RM. Current status and decadal growth analysis of krishna-godavari delta regions using remote sensing. Journal of Coastal Research; 2018; 85, pp. 1416-1420. [DOI: https://dx.doi.org/10.2112/si85-284.1]
Sandwell, D; Mellors, R; Tong, X; Wei, M; Wessel, P. For Mapping Surface deformation. Eos Transactions American Geophysical Union; 2011; 92,
Sandwell, D. T., Mellors, R., Tong, X., Wei, M., & Wessel, P. (2016). GMTSAR: An InSAR Processing System based on generic mapping tools David (Second.). http://escholarship.org/uc/item/8zq2c02m.pdf
Saraf, AK; Das, J; Biswas, A; Rawat, V; Sharma, K; Suzat, Y. SAR interferometry in post-seismic ground deformation detection related to the 2001 Bhuj earthquake, India. International Journal of Remote Sensing; 2012; 33,
Sharma, P; Jones, CE; Dudas, J; Bawden, GW; Deverel, S. Monitoring of subsidence with UAVSAR on Sherman Island in California’s Sacramento-San Joaquin Delta. Remote Sensing of Environment; 2016; 181,
Sharma, V; Wadhawan, M; Rana, N; Sreejith, KM; Agrawal, R; Kamra, C et al. A long duration non-volcanic earthquake sequence in the stable continental region of India: The Palghar swarm. Tectonophysics; 2020; 779,
Shastri, A; Sreejith, KM; Rose, MS; Agrawal, R; Sunil, PS; Sunda, S; Chaudhary, BS. Two decades of land subsidence in Kolkata, India revealed by InSAR and GPS measurements: Implications for groundwater management and seismic hazard assessment. Natural Hazards; 2023; 118,
Solari, L; Soldato, M; Del, Bianchini, S; Ciampalini, A; Ezquerro, P; Montalti, R et al. From ERS 1 / 2 to Sentinel-1 : Subsidence monitoring in Italy in the last two decades. Frontiers in Earth Science; 2018; 6,
Sreejith, KM; Sunil, PS; Agrawal, R; Saji, AP; Ramesh, DS; Rajawat, S. Coseismic and early postseismic deformation due to the 25 April 2015, mw 7.8 Gorkha, Nepal, earthquake from InSAR and GPS measurements. Geophysical Research Letters; 2016; 43,
Sreejith, KM; Agrawal, R; Rajawat, AS. Constraints on the location, depth and yield of the 2017 September 3 North Korean nuclear test from InSAR measurements and modelling. Geophysical Journal International; 2017; 220,
Sreejith, KM; Sunil, PS; Agrawal, R; Saji, AP; Rajawat, S; Ramesh, DS. Audit of stored strain energy and extent of future earthquake rupture in central Himalaya. Scientific Reports; 2018; 8 Springer: pp. 85-96. [DOI: https://dx.doi.org/10.1038/s41598-018-35025-y]
Sreejith, KM; Agrawal, R; Agram, P; Rajawat, AS. Surface deformation of the Barren Island volcano, Andaman Sea (2007–2017) constrained by InSAR measurements: Evidence for shallow magma reservoir and lava field subsidence. Journal of Volcanology and Geothermal Research; 2020; 407, 107107.1:CAS:528:DC%2BB3cXisVOnsbvF [DOI: https://dx.doi.org/10.1016/j.jvolgeores.2020.107107]
Sreejith, KM; Jasir, MCM; Agrawal, R; Rajawat, AS. The 2019 September 24, mw = 6, Mirpur earthquake, NW Himalaya: Geodetic evidence for shallow, near-horizontal décollement rupture of the Main Himalayan Thrust. Tectonophysics; 2021; 816,
Strozzi, T; Wegmüller, U; Tosi, L; Bitelli, G; Spreckels, V. Land subsidence monitoring with differential SAR interferometry. Photogrammetric Engineering and Remote Sensing; 2001; 67,
Subham Rajewar, AA; Sreejith, KM; Agrawal, R; Puviarasan, N; Sai Krishna, KC; Mathur, M; Gahalaut, K; Vineet, K; Gahalaut,. Cause of ground subsidence in Machhlipatnam region. Current Science; 2024; 127,
Sudha Rani Nalakurthi, NV; Behera, MR; Bhaskaran, PK. Land subsidence detection using sentinel-1 interferometer and its relation with environmental drivers: A case study for coastal Mumbai city. Spatial Information Research; 2024; [DOI: https://dx.doi.org/10.1007/s41324-024-00588-8]
Suganthi, S; Elango, L; Subramanian, SK. Microwave D-InSAR technique for assessment of land subsidence in Kolkata city, India. Arabian Journal of Geosciences; 2017; 10, 458. [DOI: https://dx.doi.org/10.1007/s12517-017-3207-6]
Syvitski, JPM; Kettner, AJ; Overeem, I; Hutton, EWH; Hannon, MT; Brakenridge, GR et al. Sinking deltas due to human activities. Nature Geoscience; 2009; 2,
Teatini, P; Tosi, L; Strozzi, T. Quantitative evidence that compaction of Holocene sediments drives the present land subsidence of the Po Delta, Italy. Journal of Geophysical Research: Solid Earth; 2011; 116,
Tessler, ZD; Vorosmarty, CJ; Grossberg, M; Gladkova, I; Aizenman, H; Syvitski, JPM; Foufoula-Georgiou, E. Profiling risk and sustainability in coastal deltas of the world. Environmental Science; 2015; 349,
Tiwari, A; Narayan, AB; Dwivedi, R; Dikshit, O; Nagarajan, B. Monitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys. Geocarto International; 2018; Taylor & Francis: [DOI: https://dx.doi.org/10.1080/10106049.2018.1524516]
Trent, B. W., Gaur, A., Christopher, S. A., Prasad, T., Parthasaradhi, Rao, G., et al. (2007). Closing of the Krishna Basin. Irrigation, Streamflow Depletion and Macroscale Hydrology.
Tripathi, A., Maithani, S., & Kumar, S. (2018). X-band persistent SAR interferometry for surface subsidence detection in Rudrapur City, India (p. 28). https://doi.org/10.1117/12.2326267
Tripathi, A; Malik, K; Reshi, AR; Moniruzzaman, M; Tiwari, RK. Multi-temporal SAR Interferometry (MTInSAR)-based study of surface subsidence and its impact on Krishna Godavari (KG) basin in India: A support vector approach. Environmental Monitoring and Assessment; 2023; 195,
Unnikrishnan, S; Nidheesh, G; Lengaigne, M. Sea-level-rise trends off the Indian coasts during the last two decades. Current Science; 2015; 108,
Yadav, RK; Gahalaut, VK; Gautam, PK; Jayangondaperumal, R; Sreejith, KM; Singh, I et al. Geodetic monitoring of landslide movement at two sites in the garhwal himalaya. Himalayan Geology; 2020; 41,
Yhokha, A; Chang, CP; Goswami, PK; Yen, JY; Lee, SI. Surface deformation in the Himalaya and adjoining piedmont zone of the Ganga Plain, Uttarakhand, India: Determined by different radar interferometric techniques. Journal of Asian Earth Sciences; 2015; 106, pp. 119-129. [DOI: https://dx.doi.org/10.1016/j.jseaes.2015.02.032]
Zhang, J; Huang, H; Bi, HB. Land subsidence in the modern Yellow River Delta based on InSAR time series analysis. Natural Hazards; 2015; 75, pp. 2385-2397. [DOI: https://dx.doi.org/10.1007/s11069-014-1434-7]
Zhang, Q; Member, S; Wu, J; Li, Z. PFA for bistatic forward-looking SAR mounted on high-speed maneuvering platforms. IEEE Transactions on Geoscience and Remote Sensing; 2019; 57,
Zhang, Y; Liu, Y; Jin, M; Jing, Y; Liu, Y; Liu, Y et al. Monitoring land subsidence in wuhan city (China) using the SBAS-INSAR method with radarsat-2 imagery data. Sensors (Basel, Switzerland); 2019; 19,
Zhang, Z; Feng, W; Xu, X; Samsonov, S. Performance of common scene stacking atmospheric correction on nonlinear InSAR deformation retrieval. Remote Sensing; 2023; 15,
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