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(a) Longitudinal river profiles of Raunthi River plotted against upslope area of the drainage basin. The upslope area is shown by step like pattern (red lines) and the river longitudinal profile is marked by black line. (b) Longitudinal river profile of Raunthi River plotted against the normalized steepness (k sn ) values is highlighted by red histograms and the river profile is highlighted by blue colour. (c) Spatial distribution of normalized steepness (ksn) values shows variation within the basin. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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Active surface deformation, displacement pattern, and erosional variability is estimated using the geomorphologically sensitive morphometry along with the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique using the SENTINEL-1A data (119 images) acquired between 07-02-2017 and 10-02-2021. The average velocities for th...
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... convexity of the Rishi Ganga basin is similar to that of the Raunthi River basin along the entire glaciated as well as fluvially dominated zone (Fig. 6a). The mainstem Rishi Ganga is 37.5 km long, fed by approximately five major tributaries (Fig. 6). It accommodates a flow accumulation of approximately 3.11km 3 in the upstream which drastically increases to 603 km 3 in the downstream regions (Fig. 6b). This anomalous downstream increase is maxi- ...
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... convexity of the Rishi Ganga basin is similar to that of the Raunthi River basin along the entire glaciated as well as fluvially dominated zone (Fig. 6a). The mainstem Rishi Ganga is 37.5 km long, fed by approximately five major tributaries (Fig. 6). It accommodates a flow accumulation of approximately 3.11km 3 in the upstream which drastically increases to 603 km 3 in the downstream regions (Fig. 6b). This anomalous downstream increase is maxi- ...
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... to that of the Raunthi River basin along the entire glaciated as well as fluvially dominated zone (Fig. 6a). The mainstem Rishi Ganga is 37.5 km long, fed by approximately five major tributaries (Fig. 6). It accommodates a flow accumulation of approximately 3.11km 3 in the upstream which drastically increases to 603 km 3 in the downstream regions (Fig. 6b). This anomalous downstream increase is maxi- ...
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... at an upstream distance of 17.5 km where it signifies at least the glacio-fluvial transition. The change from glacial to fluvial action on rocks downstream of snout led to sudden increase in the flow accumulation (Fig. 6a). Further, at an upstream distance of 5 km one can observe another anomalous increase in the flow accumulation. This vertical step lies exactly where Rishi Ganga mainstem intersect with the VT zone (Fig. 6c). Nevertheless, this vertical increment is significantly less than the one observed at the glacio-fluvial transition upstream of ...
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... transition. The change from glacial to fluvial action on rocks downstream of snout led to sudden increase in the flow accumulation (Fig. 6a). Further, at an upstream distance of 5 km one can observe another anomalous increase in the flow accumulation. This vertical step lies exactly where Rishi Ganga mainstem intersect with the VT zone (Fig. 6c). Nevertheless, this vertical increment is significantly less than the one observed at the glacio-fluvial transition upstream of mainstem Rishi Ganga (Fig. 6a). Such comparison along the single profile view presents how two different environment and processes can cause significant difference into contributing accumulation. The ...
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... at an upstream distance of 5 km one can observe another anomalous increase in the flow accumulation. This vertical step lies exactly where Rishi Ganga mainstem intersect with the VT zone (Fig. 6c). Nevertheless, this vertical increment is significantly less than the one observed at the glacio-fluvial transition upstream of mainstem Rishi Ganga (Fig. 6a). Such comparison along the single profile view presents how two different environment and processes can cause significant difference into contributing accumulation. The longitudinal variation of normalised steepness values along the Rishi Ganga shows abrupt transition increase (2000 to 8000) where river enters into the fluvial domain. ...
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... can be delineated into glaciated and no-glaciated zone. In glaciated zone the steepness values range between 0 and 2000 while in the downstream non-glaciated zone the values rise to as high as 8000. The planar view sows the spatial distribution of the steepness values within the basin, which are useful while correlating to the causative factors (Fig. 6c). The map view clearly provide evidence for the low values less than or equal to 500 in the glaciated zone of valleys and higher values >500 in the non-glaciated zones (Fig. ...
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... to as high as 8000. The planar view sows the spatial distribution of the steepness values within the basin, which are useful while correlating to the causative factors (Fig. 6c). The map view clearly provide evidence for the low values less than or equal to 500 in the glaciated zone of valleys and higher values >500 in the non-glaciated zones (Fig. ...
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Citations
... Radar feature analysis. Synthetic-aperture radar (SAR) sensors can provide information in different polarizations and microwave channels, aiding in identifying landslides, assessing slope stability, and reconstructing active surface deformation (Kothyari et al., 2022). In addition to the geospatial layers, we included Sentinel 1 VV and VH polarizations as core radar features for the LSM. ...
Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.
... Late Paleocene-Eocene inter-continental docking of the Indian and Eurasian plates resulted the occurrence of a large-magnitude earthquakes along 2500 km long strike distance of the lofty Himalayan terrain (Molnar and Tapponnier 1975;Patriat and Achache The Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) is an advanced InSAR technique and is an effective tool that is capable of monitoring and estimating the earth's surface deformation up to millimeter scale precision (Crosetto et al. 2016). The most important aspect of the InSAR is to estimate the ground deformation over a larger areal extent without physical access in compression to other geodetic measurement techniques (Kandregula et al. 2021;Kothyari et al. 2021;Dumka et al. 2022;Joshi et al. 2022;Suribabu et al. 2022;and references therein). The InSAR technique is successfully implemented in various fields of geosciences viz., subsidence monitoring (Ferretti et al. 2000), landslide monitoring (Ferretti et al. 2001;Hilley et al. 2004), seismology (Massonnet et al. 1993;Massonnet et al. 1994;Dixon 1995;Peltzer et al. 1996Peltzer et al. , 1999Massonnet et al. 1996;Carnec and Delacourt 2000), glaciology (Mohr and Madsen 2000), hydrology (Borgeaud and Wegm€ uller 1997;Rodriguez and Martin 1992;Lakhote et al. 2023), ecology (Dixon 1995;Ludwig et al. 2000) and volcanology (Massonnet et al. 1995;Salvi et al. 2004). ...
... Occurrences of low to moderate-intensity earthquakes are partially releasing accumulated strain beneath the central Himalayan region (Bilham et al. 1998;Mahesh et al. 2015;Kothyari et al. 2021). About 30% of the convergence rates are consumed by central Himalaya (Molnar and Tapponnier 1975) and � 17.7 ± 2 mm/yr is being accommodated by slip rates of large magnitude earthquakes (Molnar 1990). ...
The Kumaun Himalaya is considered as the most active part of the Central Seismic gap in the Indian Sub-continent. In this paper, we presented active surface deformation rates of the Kumaun region from February 2017 to February 2021 using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique. The cumulative displacement that occurred during the span of 4 years is ±55 mm, whereas the Line of Sight (LOS) deformation velocity rate ranges is ±7 mm/yr. Apart from PSI, we also estimated the b-value of the Kumaun region from 1803 to 2021 (399 events) and its value is 0.54 ± 0.03. A distinct NE-SW trend of b-value is observed where earthquakes with M > 6 occurred towards NE of this trend. The PSI-derived deformation reveals that the central part of the Inner Lesser Himalaya along with the Main Central Thrust (MCT) zone is dominated by uplift. The zone between the Munsiari Thrust (MT) and MCT in the central region shows the maximum uplift ranging 5–7 mm/yr which exactly lies above the mid-crustal ramp of the Main Himalayan Thrust (MHT). Our results are well corroborated with available observations of geodetic strain and peak ground acceleration values. However, the deformation patterns and high-velocity rates in the central part of the study area between MT and MCT indicate the accumulation of high stress.
... In the steady state conditions the variability in uplift of longitudinal river channel can be conventionally evaluated using the standard stream power incision model i.e. steepness index (K sn ) (Whipple and Tucker, 1999;Kothyari et al., 2022;Joshi et al., 2021) is one of the most used proxies based on stream power model which evaluate variability in rock upliftment in different catchment, when topography is in steady state. Erodibility (k), drainage area (A), and slope of the drainage basin (S) of a drainage basin are the product of river incision rate (E) and can be represented as follows (Whipple and Tucker, 1999). ...
... Mountains and glaciers in the Himalayas show the effects of climate change and global warming (Bhambri and Bolch 2009;Kulkarni et al. 2011Kulkarni et al. , 2013. Recently, some important studies have been conducted addressing the Chamoli disaster event including its preliminary assessment (Rana et al. 2021;Martha et al. 2021), plausible causes Mehta et al. 2021;Sati 2022;Tiwari et al. 2022;Mao et al. 2022), damage assessment (Jiang et al. 2021;Rana et al. 2021;Rautela et al. 2021;Vangla 2022;Sati 2022), hydrological characteristics assessment (Rana et al. 2021), seismic assessment (Meena et al. 2021b;Cook et al. 2021;Kothyari et al. 2022;Tiwari et al. 2022), pre-and post-event assessment , event reconstruction analysis (Bhardwaj and Sam 2022), and detailed disaster report (NDMA, 2022). Raunthi Gadhera catchment and the two Himalayan river valleys of Rishiganga and Dhauliganga experienced unprecedented destruction due to the flash flood. ...
... Various studies have been published in the recent past confirming the massive destruction along the channel Martha et al. 2021;Rautela et al. 2021;Mehta et al. 2021;Kothyari et al. 2022). Additionally, avalanche models (Jiang et al. 2021;Shugar et al. 2021) and a debris flow model (Bhardwaj and Sam 2022) were developed to simulate this event. ...
The Indian Himalayan Region features climatically sensitive and extremely rugged topography. In recent years, risks to human settlements, infrastructure, and the mountain ecosystem have increased manifold because of frequent occurrences of natural hazards. In this context, the Chamoli flash flood event that occurred on February 7, 2021, in Uttarakhand was an extraordinary event causing unprecedented destruction in Rishiganga and Dhauliganga river valleys. This study attempts to simulate the event from the source up to 40.8 km downstream using a hydrodynamic modeling approach. HEC-RAS software was employed to model a hypothesized storage breach of 26.4 × 10⁶ m³ at the source location, generating a peak inflow of 12,761.88 m³/s. The breach was simulated as an unsteady flow with a computational interval of 5 s and a mapping interval of 2 min for 6 h. The model-generated peak discharge values range between 7908.8 and 7975.26 m³s⁻¹ near Rishiganga HEP and between 5779.53 and 5957.46 m³s⁻¹ near Tapovan HEP. Also, flow depths at the above locations were 19.85 and 18.15 m, respectively. The flow velocities were 6.92 and 3.86 m/s, respectively. The model output shows good agreement with the extent and height of actual debris assessed using systematic pre- and post-event analysis of high-resolution satellite datasets. The presented modeling and damage assessment approach may be applied in dynamic mountain ecosystems where population and infrastructure growth require continuous evaluation of hazards. Furthermore, this study proposes implementable recommendations highlighting key issues of specific inadequacies and unpreparedness in the remote high mountain regions of Uttarakhand and elsewhere.
... The flash floods that occurred in the Uttarakhand Himalayan region on the Dhauliganga catchment on February 7, 2021 was one of the most disastrous events to have occurred since the Kedarnath floods of 2013 (Sati & Kumar, 2022). The sudden break of a huge mass of glacier from the upper reaches of the mountains and the sudden rise in the water level and flow speed washed away many buildings and hydro-electric facilities along the Rishi Ganga hydro power facility, besides killing many people (Pandey et al., 2021;Kothyari et al., 2022). The Tapovan village near Joshimath town in Chamoli district was the focal point of the disaster. ...
... This event was widely assessed by researchers globally using satellite data available in optical wavelength (Pandey et al., 2021;Shugar et al., 2021;Meena et al., 2021;Verma et al., 2022). However, this event has not been much assessed using SAR data except one study by Kothyari et al. (2022). Hence this study employs Sentinel-1 C-band (VV and VH polarization) SAR (5.405 GHz Frequency and 5.6 cm wavelength) Interferometry data operating in microwave wavelength to assess the impact of February 7, 2021 event. ...
The 7th of February 2021, Joshimath flood scenario was one such event that caused widespread damage and led to complete washout of the many hydroelectric power projects located on the course of Dhauliganga River. The physical monitoring and mapping of such events is a difficult task that often involves deployment of labour force in inhospitable terrains. Therefore, remote sensing techniques are used for the mapping and machine learning model-based predictions for future scenarios. Synthetic Aperture RADAR (SAR) remote sensing has been widely used over the years for accurate estimation of many natural and anthropogenic disaster events. This study utilizes Persistent Scatterer SAR interferometry (PSInSAR) technique to map the surface displacement of the 2021 flood scenario and make predictions for future displacement using a Deep Learning Neural Network (DLNN) model. 16 images of both ascending and descending pass were taken for the estimation of Line of Sight (LOS) displacement velocity mapping between January 2020 and April 2021 for Tapovan area. Further, 36 images from January 2020 to November 2022, in ascending and descending passes were used for prediction of future LOS surface displacement using a DLNN model for Joshimath town to see the possible impact of February 7, 2021 event. The PSInSAR LOS displacements were found to be −1.2 cm–1.2 cm between January 1, 2020 and April 16, 2021, for Tapovan region where the floods had occurred on February 7, 2021. The predicted LOS displacement was observed to be −10 cm–10 cm for December 2022 for Joshimath town. These observations clearly indicate the impact of the event to Joshimath town and as one of the causative factors of recent observations of widespread cracks in the buildings in the region.
... The epicenter lies within the regional micro-seismicity belt located south of the Vaikrita Thrust in the Main Central Thrust (MCT) zone. Displacements may be controlled by tectonically enhanced erosion balanced by displacement within the hanging wall of the Vaikrita Thrust/MCT [68]. The aftershocks recorded by the WIHG network lie below the MCT at 7-17 km depth over the inferred ramp region [66]. ...
Tectonic geomorphology has been evidenced as an essential tool to define and measure recent tectonic deformation. The Nandakini River originates from Nanda Ghunti glacier at 6886 m in Lesser Himalaya. It covers about ~551 km2 basin area, with a fourth-order stream. It confluences with the Alaknanda River near Nand Prayag (at elevation 852 m), Chamoli district of Uttarakhand. The drainage pattern is predominantly dendritic in the study area. The morphotectonic indices are measured to know the tectonic activity of the drainage basin. Morphometric indices reflecting hypsometric Integral is 1.07, indicating a deeper incision and a slight erosion. The basin elongation ratio is 0.17, suggesting that the area is tectonically active. The drainage basin asymmetric factor (44) suggest tilting. In addition, the drainage basin shape (3.27) indicates the basin is tectonically strong, the transverse topographic symmetry factor (0.31) indicates the asymmetric nature, the valley floor width to valley height ratio is 0.54 shows the deep, narrow valleys, and Active V-shaped incision. Stream longitudinal profile showing the area tectonically influenced. The Sinuosity Index of 1.12 suggests that low sinuous nature. The Stream length-gradient Index is 10.64, indicating high tectonic activity in valleys and basin areas. The transverse profile helps to understand the tilting of the basin. The Surface profile shows irregularity, and the linear trend of the profile shows the tilting of the basin. River carrying capacity with large boulder-size sediment indicates youth stage and tectonically active environment. The morphotectonic indices suggest that the drainage basin was affected by the regional structures and the present tectonics.
... remote sensing works as a valuable tool due to its spatial and temporal and repeated coverage (Mersha and Meten, 2020;Taloor et al., 2021aTaloor et al., , 2021bTaloor et al., , 2021cTaloor et al., , 2022. In geographic information systems (GIS), it enables us to gather georeferenced data from remote sensing and other resources for use in analysis, modelling, simulations, and visualization, and it assists us in making knowledgeable results (Roy and Saha, 2019;Li et al., 2021;Kothyari et al., 2021Kothyari et al., , 2022. Now, Remote sensing and GIS are used to create a landslide inventory map as well as thematic maps relating to landslide occurrences in the study region. ...
Sikkim Himalaya is located in the North-Eastern Himalaya and is prone to landslides caused by rainfall, anthropogenic factors, earthquakes, and other natural disasters. This study aims to identify landslide susceptibility map for the Sikkim Himalaya, India, utilizing an integrated methodology of Geographic Information System (GIS), Remote Sensing (RS), and the Frequency Ratio (FR) method. Identification of landslide susceptibility map in this region has used multiple datasets as rainfall, earthquake's magnitude, slope angle, altitude, distance to Drainages, topographic roughness index, geomorphology, geology, soil, gravity anomaly, distance to faults, stream transportation index, topographic witness index, stream power index, distance to roads, LULC, and NDVI. All the above-mentioned factor/thematic layers were generated using remotely sensed as well as ground data with the help of Arc GIS software. Therefore, the weights of these all-thematic layers were calculated using the FR model for the occurrence of landslides prone in the area. After that, calculated weights of all factor/thematic layers were integrated with Arc GIS software to generate a landslide susceptibility map. The landslide susceptibility zone of the study area has been divided into 5 different classes, namely ‘Very High (11.88%)’, ‘High (15.75%)’, ‘Medium (25.88%)’, ‘Low (25.30%)’, and ‘Very low (21.19%)’. The accuracy assessment of the study area was 87.8, which was done by the Receiver Operating Characteristic (ROC) curve method. This research investigates the possibility of using broader approaches to determine landslide-prone areas.
... The ground deformation (uplift/subsidence) in the active thrust/ fault/fold belt is the most owing to the movement along active faults (Dumka et al., 2018;Kandregula et al., 2021;Kothyari et al., 2021;Lakhote et al., 2020). The active ground subsidence in rapidly growing urban regions is also associated with the over-extraction of subsurface water Perissin, 2015;. ...
... Doon gravels, 2. Bansi and Subathu, 3. Mandhali Formation, 4. Chandpur Formation, 5. Nagthat Formation, 6. Bliani Formation, 7. Krol Formation, 8. Tal Formation, 9. Almora group, 10. Ramgarh group) Kandregula et al., 2021;Kothyari et al., 2021). Globally such active features can be estimated from the interferometric synthetic aperture radar (InSAR) measurements (Abidin et al., 2016;Chen et al., 2018;Dumka et al., 2018;He et al., 2020;Lakhote et al., 2020;Malik et al., 2021) because of its larger coverage and meticulous mm level accuracy (Abidin et al., 2016;Chaussard et al., 2014;Galloway & Burbey, 2011;Wasowski & Bovenga, 2014). ...
... Globally such active features can be estimated from the interferometric synthetic aperture radar (InSAR) measurements (Abidin et al., 2016;Chen et al., 2018;Dumka et al., 2018;He et al., 2020;Lakhote et al., 2020;Malik et al., 2021) because of its larger coverage and meticulous mm level accuracy (Abidin et al., 2016;Chaussard et al., 2014;Galloway & Burbey, 2011;Wasowski & Bovenga, 2014). In the present study, we used Sentinel-1A data products whose strip or bandwidth is around 250 km, with a high spatial resolution and larger coverage, shorter period of observation points and capable, of sleuthing negligible ground fluctuations in different tectonically and climatically active regions (Chen et al., 2018;Di Traglia et al., 2018;Dumka et al., 2018;He et al., 2020;Kothyari et al., 2021;Malik et al., 2021;Wu et al., 2019). ...
... The ground deformation (uplift/subsidence) in the active thrust/ fault/fold belt is the most owing to the movement along active faults (Dumka et al., 2018;Kandregula et al., 2021;Kothyari et al., 2021;Lakhote et al., 2020). The active ground subsidence in rapidly growing urban regions is also associated with the over-extraction of subsurface water Perissin, 2015;. ...
... Doon gravels, 2. Bansi and Subathu, 3. Mandhali Formation, 4. Chandpur Formation, 5. Nagthat Formation, 6. Bliani Formation, 7. Krol Formation, 8. Tal Formation, 9. Almora group, 10. Ramgarh group) Kandregula et al., 2021;Kothyari et al., 2021). Globally such active features can be estimated from the interferometric synthetic aperture radar (InSAR) measurements (Abidin et al., 2016;Chen et al., 2018;Dumka et al., 2018;He et al., 2020;Lakhote et al., 2020;Malik et al., 2021) because of its larger coverage and meticulous mm level accuracy (Abidin et al., 2016;Chaussard et al., 2014;Galloway & Burbey, 2011;Wasowski & Bovenga, 2014). ...
... Globally such active features can be estimated from the interferometric synthetic aperture radar (InSAR) measurements (Abidin et al., 2016;Chen et al., 2018;Dumka et al., 2018;He et al., 2020;Lakhote et al., 2020;Malik et al., 2021) because of its larger coverage and meticulous mm level accuracy (Abidin et al., 2016;Chaussard et al., 2014;Galloway & Burbey, 2011;Wasowski & Bovenga, 2014). In the present study, we used Sentinel-1A data products whose strip or bandwidth is around 250 km, with a high spatial resolution and larger coverage, shorter period of observation points and capable, of sleuthing negligible ground fluctuations in different tectonically and climatically active regions (Chen et al., 2018;Di Traglia et al., 2018;Dumka et al., 2018;He et al., 2020;Kothyari et al., 2021;Malik et al., 2021;Wu et al., 2019). ...
In the Garhwal Himalaya, the steeply inclined Main Boundary Thrust (MBT) is characterized by landforms that indicate towards tectonic activity in the recent past. Linear active fault trace, sub‐recent fault scarps, strath terrace, uplifted terraces, deflected drainage, and triangular facet cones are some of the morphotectonic evidences of recent tectonic activity along the MBT. The MBT is defined by active fault trace in the form of linear depression and shows a prominent slope break across it. Thrusting of the Lesser Himalayan bedrocks over the colluvial deposits and back tilting of the colluvial deposits are also observed in the MBT zone. The formation of the sag pond and the deposition of the Quaternary lacustrine sediments followed by the deformation of the lacustrine deposits indicate phases of tectonic activities along the MBT. The later phase of deformation is represented by the folded lacustrine and colluvial deposits, where the amplitude of the fold measures about 3 m‐high and the deformed section measures about 23 m in length. The ongoing deformation patterns of the MBT zone have also been monitored with the help of Synthetic Aperture Radar‐based time series analysis. We used Sentinel‐1C dataset in ascending direction with polarization of the VV+ VH acquired between December 2, 2017 to September 7, 2021 to identify phase changes caused by ongoing active deformation. The Persistent Scatterer Interferometry (PSI) result based on Sentinel‐1A suggests that the Dehradun–Mussoorie and adjoining region has undergone an average deformation of 5.5 to −11 mm/year and the ground displacement of 1.5 to −3.8 cm. The time series analysis reveals that the Mussoorie hills in the MBT zone have higher rate of deformation.
... The satellite based remote sensing techniques are globally used by the geoscientists for different objectives such as, active fault mapping and associated land deformations, identification of active ground subsidence, archaeoseismological investigations and landslide monitoring (De Zan and Guarnieri, 2006;Saber et al., 2020;Lakhote et al., 2020;Kothyari et al., 2021;Kothyari et al., 2019Dumka et al., 2021a, Dumka et al., 2021bSuribabu et al., 2021;Malik et al., 2021;) etc. Furthermore, the satellite-based radar technique, e.g., InSAR (Interferometric Synthetic Aperture Radar) is widely used to monitor the co-seismic deformation (cm to mm level accuracy) in larger areas (Gabriel et al., 1989;Saber et al., 2020;Kandregula et al., 2021;Massonnet et al., 1993;Burgmannm et al., 2000;Sansoti et al., 2010). ...
... (Δφ topo = Residual topographic height, Δφ displ = estimated displacement, Δφ atmo = atmospheric phase disturbance, Δφ noise = phase disturbance). The detail steps of the radar technique as per Kothyari et al., 2021) is provided in Supplementary document. ...
The co-seismic deformation of the 28th April 2021 Assam earthquake with a magnitude of 6.4 has been investigated using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR). The results obtained from the PSInSAR are validated with the results of the Global Positioning System (GPS). It is observed that the co-seismic deformation of the earthquake is not visible because of the atmospheric noise. Therefore, we used the Sentinel-1 time-series data of the area to minimize the atmospheric noise effect the data using the Atmospheric Phase Screening (APS) processing of Sentinel-1 time series data. The time-series analysis shows minor changes that occurred during the earthquake. Firstly, the detection of coherence-based changes was carried out to identify the most affected areas, caused by the earthquake. The results of time series analysis suggest that the region located to the north of the Brahmaputra River is subsiding at a much faster rate (−25 to -75 mm/y) than its southern counterpart, where the southern part is subsiding with an average velocity of −0.1 to −15 mm/y. The subsided locations identified using the PSInSAR are further confirmed by the presence of liquefaction features, ground cracks, and tilted buildings related to post-liquefaction ground settlement. To see the deformation along the Bomdila Fault (BF), re-analysis of the available GPS results has been carried out and the derived results reveal a significant amount of deformation (~4 mm/y) associated with BF and Jorhat Fault (JF); whereas the deformation associated with Kopili Fault (KF) is 3.14 mm/y. Further, our results suggest the presence of compressional strain in the BF zone, while extensional strain in the KF zone. The earthquake focal mechanism analysis suggests that the event of the April 2021 was primarily associated with the BF rather than the KF.
