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Article

Upstream River Erosion vis-a-vis Sediments Variability in Hugli Estuary, India: A Geospatial Approach

by
Anirban Mukhopadhyay
1,
Rituparna Acharyya
2,
Michał Habel
3,*,
Indrajit Pal
1,
Niloy Pramanick
1,
Jyoti Prakash Hati
2,
Manas Kumar Sanyal
4 and
Tuhin Ghosh
5
1
Disaster Preparedness, Mitigation and Management (DPMM), Asian Institute of Technology, Chang Wat Pathum, Pathum Thani 12120, Thailand
2
Science for Sustainability, Kolkata 700010, India
3
Faculty of Geographical Sciences, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
4
Department of Human Resource Management, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal 711103, India
5
School of Oceanographic Studies, Jadavpur University, Kolkata 70003, India
*
Author to whom correspondence should be addressed.
Water 2023, 15(7), 1285; https://doi.org/10.3390/w15071285
Submission received: 28 February 2023 / Revised: 17 March 2023 / Accepted: 20 March 2023 / Published: 24 March 2023
(This article belongs to the Special Issue Sediment Transport, Budgets and Quality in Riverine Environments)
Browse Figures
Review Reports Versions Notes

Abstract

:
Satellite data shows that the Bhagirathi-Hugli River’s riverbank has faced severe erosion during the last decades (1990 to 2020), with the middle stretch of the river being more prone to erosion. This huge sediment load derived from upstream erosion is coming to the estuary. The suspended sediment concentration dynamics of the Hugli estuary were calculated using in-situ data and remote sensing reflectance by establishing a linear regression. A continuous huge sediment load is found in the estuarine water. The sediment concentration was higher pre-monsoon than post-monsoon as the region is highly influenced by monsoonal rainfall and runoff. The sediment concentration was also higher in the estuary’s southwestern section than in the northern part. The impact of this high sediment load contributes to the deposition. This depositional area assessment was performed using an object-based classification approach called Support Vector Machine utilizing Grey Level Co-occurrence Matrix to create cluster textural indices. Despite the impact of continuous sea level rise in the estuary, the result shows that effective island and Chars areas have increased in the past decade due to the upstream erosion-driven sediments.

1. Introduction

The flood plains of the Bhagirathi-Hugli river are primarily characterised by flooding, erosion, sedimentation, and channel migration [1]. It has undergone rapid hydro-morphological changes due to environmental and anthropogenic factors [2,3,4]. The total amount of sediment in the Hugli estuary varies with the tidal influx [5], as a massive deposit accumulates during high tide and tidal bore. The above-mentioned tidal impacts can even be observed approximately 200 km upstream from the Hugli estuary [6]. Hence, the estuary of Bhagirathi-Hugli comprises vast quantities of fluvial-tidal and coastal marine sediments composed of sand, silt, or clay in various proportions [7]. There is also variation in the sediment texture; the sediments in the estuary are coarser across the upper reaches (D50 = 0.12 mm) and finer through the lower reaches (D50 = 0.09 mm) [5]. The coarser sediments settle across the bed, generating several submerged and exposed bars, sandbars [8]. Most sedimentation in this estuary occurs during the pre-and post-monsoon, and a portion of it is flushed out in the monsoon season’s high flow [9]. Then finer sediments contribute to the estuary’s suspended load, raising the water’s turbidity [10]. The mean freshwater discharge to the estuary is approximately 3000 m3·s−1 during the southwest monsoon and 1000 m3·s−1 during summer [5]. Hence, the lower river channel section gradually deteriorates due to sediment deposition during the dry season, when larger flood currents prevent river-driven sediments from being transported to the sea [11]. Intense sedimentation can cause channel instabilities by reducing its carrying capacity, resulting oscillation, flooding, avulsion, cut-off, or abandonment of channels [12].
Riverbank erosion in Bhagirathi-Hugli is also widespread downstream of the Farakka Barrage in West Bengal’s Murshidabad district [13]. Shantipur Block in Nadia District, West Bengal, located over 211 km downstream of Farakka Barrage, along the left bank of Bhagirathi-Hooghly, suffers from extensive bank erosion [2]. As per official figures, in West Bengal, the Bhagirathi-Hugli River engulfs an average of 8 km2 of land per year [2]. In the past 32 years, it has eroded approximately 245 km2 along its banks while only accumulating 105 km2 [1], resulting in land degradation and sedimentation. At the southern part of the Hugli estuary, erosion and deposition play a reversing role that denotes the stability of the small islands. For example, rapid erosion has been observed on Ghoramara Island, whereas Lohachara and Supribhanga islands have vanished due to extensive erosion [14]. It is most likely a result of the high sand content of the soil, which left them vulnerable to rapid erosion during tidal activities, regional sea level rise, and other localized effects. On the other hand, accretion in the western part of the estuary is also prominent, where the total area of Nayachar Island has been increasing for the last four decades [15].
Systematic evaluation of erosion-accretion along riverbanks is incredibly complex and challenging due to morphological conditions, changes within the river channel, lateral displacement, and riverbank instability [16]. Earth observation techniques such as Remote Sensing and GIS have become increasingly crucial [3,17] to facilitate updated channel location mapping and evaluating the fluvial processes occurring within a channel. Since the hydro-geomorphic data over a larger timescale are already available from satellite datasets, Digital Shoreline Analysis System (DSAS) has become a very acceptable approach. DSAS can precisely monitor and map the rate and predict various riverbank edge locations (right and left banks differently) [18,19,20]. In recent years, several techniques have been developed to delineate water lines, including the threshold approach [21], the inter-spectral relation technique [22], the decision tree, and the DNA exponential mode [23]. Recent studies have applied water indexing technologies such as NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index), WRI (Water Ratio Index), AWEI (Automated Water Extraction Index), and WI (Water Index) for retrieving water bodies [24,25]. Furthermore, the application of remote sensing and GIS in evaluating turbidity and suspended sediment concentrations (SSC) in real-time is based on the notion that suspended silt increases the reflectance of visible (VIS) and Near-Infrared (NIR) bands from the water surface [26]. The availability of high-resolution satellite imageries and advances in digital image classification algorithms have made studying variations in SSC in large estuarine systems easier [27,28]. There have also been scientific studies that used unmanned aerial systems (UASs) to assess sediment concentration, total suspended solids, and turbidity in estuarine systems [29,30,31]. In several studies, correlation analyses between SSC and the spectral reflectance of water bodies have been undertaken using satellite datasets like Landsat 8, Sentinel-2A, and MODIS [14,32,33].
Several authors have previously addressed the erosion-accretion dynamics in the Bhagirathi-Hugli [34,35,36], but all of these studies are focused on a specific area and deal with a single problem. Therefore, understanding the whole scenario of erosion and accretion from the Farraka dam to Sagar Island was essential to implement future policies and identifying the most vulnerable zones. The current study aims to examine the Bhagirathi-Hugli River using remotely sensed data, spatial statistics, and quantitative approaches to estimate riverbank erosion-accretion and the resulting sediment dynamics (sediment concentration, sediment load, sediment discharge) persisting along this river. This study also aims to assess the effect of sedimentation on the spatial extent of the Bhagirathi-Hugli estuary and its adjacent estuarine islands (Sagar, Nayachar, and Balari), as well as how the sediment dynamics have affected the port facilities of Kolkata Dock System (KDS) and Haldia Dock Complex (HDC) in this estuary.

2. Background

Rivers are dynamic equilibrium systems that balance the flow of water and sediment mobility, making them a crucial component of the natural environment [37,38]. In a river system, erosion and accretion tend to bring equilibrium, with accretion almost compensating for erosion in some areas [38,39]. River migration significantly affects riverbank erosion, sediment flow, and modifying hydrodynamic characteristics across the tropical monsoon region [40]. When river courses change due to natural dynamic hydrologic factors, the river reshapes in size, profile, and structure to reinstate its prior stability or equilibrium [41]. However, anthropogenic influences significantly affect the river’s hydrologic characteristics more than natural factors and cause autogenic planform modifications [42]. Anthropogenic operations such as deforestation, gravel extraction, dam and bridge construction, and land-use transitions negatively impact the river dynamics and structure. It may result in altered channel position and geometry and accelerate the rate of erosion [43,44]. Among the river’s three phases of youth, maturity, and old, the completion of the mature phase is more vulnerable to forming meanders, bars, and braids near the onset of the adult phase [45]. The emergence of the meanders and river meandering processes can be observed in the adult stage of the river systems across the floodplain areas [46].
The suspended load of the Bhagirathi-Hugli exhibits an approximate annual run-off of 493 km3 and transports suspended solids of approximately 616·106 tonnes toward this estuarine outlet [47]. In a quantitative context, about 75% of the entire sediment budget within the river comes from upstream sources due to substantial bank erosion, with the other 25% coming from marine sources [5]. This continuous massive sedimentation in the Bhagirathi-Hugli river’s estuary has made it difficult [29] for the large vessels to access the port of Kolkata (administered by Kolkata Port Trust (KoPT), built in 1870 during British rule) along the bank of the Bhagirathi-Hugli, situated approximately 150 km inland. After that, the Farakka barrage was constructed (1971) across the Bhagirathi-Hugli to regulate the periodic discharge of Ganges waters to maintain navigable water depths [48,49]. The Farakka Barrage diverts Gangetic water into a canal nearby Tildanga, Malda district, providing sufficient water to the Bhagirathi-Hugli even during the dry season for drinking water processing facilities, agricultural irrigation, aquaculture, and manufacturing purposes [50]. The Farakka barrage was built to relieve the Hugli River of navigational hazards. However, the issue of sedimentation throughout the estuary has not been resolved; instead, it is getting worse, and its purpose has remained elusive to date [51]. According to Rudra [51], the Ganges has shifted significantly eastward since the completion of Farakka in 1971; a large bend has formed, probably due to the sediment deposited above the barrage.
Moreover, the disappearance of riparian vegetation along the banks of the Bhagirathi-Hugli River due to development and anthropogenic activities has aided the instability of the banks and sediment control of the river channel [52]. Therefore, along the Bhagirathi-Hugli River, meanders, oxbow lakes, and paleochannels have developed, altering the river’s sedimentological composition [53]. This study deals with the erosion and subsequent sediment deposition in the Bhagirathi-Hugli River estuary and provides a contextual idea of sediment dynamics in the estuary.

3. Materials and Methods

3.1. Study Area

The River Bhagirathi-Hugli, also spelt Hooghly or Bhagirathi-Hooghly, (historically known as the Ganga), is an important river in the Indian state of West Bengal (Figure 1). It splits from River Ganges via Farakka Barrage at Murshidabad District, West Bengal, and flows 460 km across West Bengal before draining towards the Bay of Bengal [54]. It formed the border between Nadia and Hooghly at Kalna, continued south through the Hooghly district, and entered Kolkata through Chunchura, Konnagar Halisahar, and Kamarahati. There are four primary tributaries of the Bhagirathi-Hugli River: Rupnarayan, Ajay, Haldi, Damodar, and Rasulpur.
The Bhagirathi-Hugli River has three channel sections: upper, lower, and estuary. All three sections have been considered for the current study (Figure 1) mainly to meet the abovementioned objectives. The upper section, which runs 96.56 km from Nadia district towards Hooghly district, does have an unsteady course, and traverses consistently through a vast stretch of land but progressively alters its path. Although for the sake of the current study, we have considered the upper section of the Bhagirathi-Hugli River extending from Farakka Barrage to Hooghly district (Figure 1). In contrast, the lower system remains static [48]. It flows between Hooghly district and Kantabaria (Haora district) over 112.65 km up to the estuary’s head. From Chinsurah to Kantabaria, the lower channel receives tremendous tidal pressures [48]. Then there is the estuarine section, which comprises a funnel-shaped macro-tidal estuary (tidal range > 4 m) that stretches 250 km upstream to Nabadwip and is 50 km wide near Sagar Island [12].
The Bhagirathi-Hugli River is a part of the world’s largest fluvio-deltaic and shallow marine-sedimentary drainage basin called the GBM delta [55]. The Bhagirathi-Hugli River typically flows eastward along its left bank because the Bhagirathi-Hooghly basin is a part of the northwest Stable Shelf. This shelf has an easterly dipping, isolated from the Precambrian Indian Shield to the west by the Basin Margin Fault Zone’ and has experienced displacements and catalases [56]. This easterly dipping induces an imbalance in the proportion of sand (34.08%), and silt (52.4%) on the left bank, which induces riverbank failures across the year, most significantly during the monsoon season [57]. The sand and sediment on the Bhagirathi-Hugli River’s left bank are too high [1], which mainly causes instability during floods throughout the year since the Bhagirathi-Hooghly basin is among the world’s thickest sedimentary basins, with approximately 21 km broad early Cretaceous-Holocene sedimentary sequence [58]. Hence, in the last 32 years, Bhagirathi-Hugli induced land degradation to about 245 km2 area along its banks [1], mainly due to an imbalance between the shear stresses put on riverbank materials by the gravitational force on the downslope. Then the Bhagirathi-Hugli River’s north-south flow is an active geomorphic region characterized by severe meandering with all of its historical channel remnants, including cut-off meanders, meander scars, ox-bow lakes, and deserted channels [59] (depicted in appendices). Furthermore, the flow of the Bhagirathi-Hugli River is impacted by mid-channel bars or Chars such as the Mangaldwip Char [60]—a newly formed island inhabited by a population. As a result, the river’s curved banks and tidal influence produces tremendous hydraulic activity beyond its shear strength [57].
Due to a significant influx of fresh water, sediments, and nutrients from upstream, the estuarine system is marked by linear shoals or Chars, which partially emerge in the offshore zone, lined perpendicular to the shoreline [61]. This original course of erosion and accretion has posed a perennial problem for KoPT, and HDC (Figure 1), as their navigational routes are situated in this estuarine system. As a result, this particular river has been selected for the current study with a specific set of objectives, considering the phenomenon of rapid channel shifting, and riverbank erosion-accretion, which have led to alteration of the riverine morphology and channel’s navigability [11].

3.2. Satellite Data

Landsat TM and OLI multi-temporal satellite imagery of various resolutions were used in this investigation. The satellite imageries were acquired between 1990 and 2020. The details of Landsat datasets are depicted in Table 1. These satellite datasets were used to demarcate riverbank lines and detect the riverbank erosion (Table 1) and the level of SSC. However, in the case of estimating the spatiotemporal changes in the areal extent of estuarine islands, the Landsat imageries were acquired between 1990–2020 on a 5-year interval (1990, 1995, 2000, 2005, 2010, 2015, and 2020). Then the Sentinel-2A imagery has been used in this study for comparison and increasing accuracy while generating SSC. Additionally, in the current study, two DEM datasets, namely SRTM (Version 3, 2015), and ASTER (Version 3, 2013) of 30-m spatial resolution, have been applied to estimate the erosional volume across the Bhagirathi-Hugli River.

3.3. In-Situ Data

The secondary dataset of SSC utilised in this study has been obtained from Pitchaikani et al. [62]. Pitchaikani et al. [62] observed SSC (from 21°37′8.15″ N to 21°52′50.93″ N) at Hugli estuary using standard techniques during high tides and transitions between flooding and ebbing tides in 2015 (between 1 February and 23 March between 11:30 a.m.–1:00 p.m.) (Table 2). The remote sensing reflectance-based SSC has been calculated in those coordinates where Pitchaikani et al. [62] collected in-situ SSC samples. Thus, validation was achieved using secondary datasets, and the detailed process is mentioned in the following sections.

3.4. Methods

This section’s major focus is the detailed analysis of the methods required to study scenarios of riverbank line shifting over time, erosion-accretion, and sediment concentration in various sections of the Bhagirathi-Hugli River. The methodology adapted in the current study has been outlined below (Figure 2).

3.4.1. Remote Sensing Data Processing

The Landsat dataset has been chosen for this investigation since they are freely available for a long back, and its relatively satisfactory spatial resolution (30 m) is appropriate for mapping large rivers [63]. For the current study, these datasets were processed using the following steps illustrated in Figure 2.
  • Atmospheric Correction
At first, the Landsat images were acquired through visual inspection to ensure minimal cloud cover. Then, the top-of-atmosphere (TOA) radiance (λ), recorded by the satellite data as is then transformed into TOA reflectance [64] ((Equation (1)), depicted in appendices along with following other equations)
Then another method of atmospheric correction called fast line-of-sight atmospheric analysis of hypercubes (FLAASH) is implemented in the satellite images to compute the surface reflectance ( ρ λ ) utilizing moderate resolution atmospheric transmission radioactive transfer code (MODTRAN) [65,66]. The mathematical assumptions used in the FLAASH are based on the physical understanding of the Radiative Transfer algorithm (Equation (A2)) depicted in appendices (Appendix B).
  • Cross-band Adjustment
This procedure’s penultimate step is co-registering the NIR bands of Landsat TM and OLI (B-5) having a 30-m-resolution with the Sentinel-2A NIR band (B-8) having a 10-m resolution [67] to refine the data for retrieving improved outputs. However, the image pairings of Landsat-8 and Sentinel 2A covering the study area do not align due to different tiling systems and geolocation frameworks, which include residual errors among Landsat Path/Rows [67]. As a result, they were aligned using a feature-based and area-based least squares matching approach before registration.
  • Adjacency Correction
The distribution of aerosol heights influences the adjacency effect, which can alter the accuracy of the image following atmospheric correction [68]. Cross-radiation off bright river banks, especially in urban areas, converse the optical signal collected by the sensor [69]. This method of adjacency effect correction is selected for the current study because it can compute scene-average visibility (haze and aerosol quantity), and uses cutting-edge methods for dealing with challenging atmospheric conditions, including clouds [70]. The FLAASH mentioned above atmospheric correction method incorporates adjacency effect correction [70], which is used in this study. As a result, the pixel-to-pixel background contrast has been lowered within the kernel window to lessen the adjacency effect.
  • De-glinting
Sun glint is among the various interference issues which require particular emphasis for correction [71] when evaluating water parameters through the remote-sensing technique. The de-glint procedure is employed in this study to remove the disturbing impact brought on by sunlight reflecting on the tides. In this investigation, de-glinting was performed using the approach of dark object subtraction, commonly used for sun, and sky glint rectifications, since no in situ measurements are required [3,72,73]. While clean water absorbs heavily in the NIR and shortwave infrared bands (SWIR), there is no water-leaving radiance in the TOA radiance at SWIR. For de-glinting, this work has employed the sun glint index (SGI) (Equation (3)) proposed by Zhang et al. [74], which is based on the Green Band and SWIR. A threshold has been established based on SGI, meaning pixels with an SGI < 0.5 were considered devoid of sun glint interference.
  • Land-Cloud-White-cap-Masking
After the de-glinting process, masking was carried out to separate out clouds, lands, and whitecaps. For the present study, cloud masking is necessary because, despite being selected with <30% cloud cover, the obtained Landsat data can often get distorted by even a minuscule cloud presence and shadow [73]. The whitecaps observed in satellite data, which have high reflectance related to wind speed and geometry [4], have been masked out from the satellite data since they cannot be distinguished from sediment deposits and sand shoals due to the similarity between them [75]. Then the adjacent land areas have been masked mainly due to the study’s focus on the Bhagirathi-Hugli River’s water surface.

3.4.2. Remote Sensing Reflectance

The satellite datasets have been employed in this study to assess Remote sensing reflectance ( R r s ) .   R r s refers to the proportion of radiance leaving the upwelling water to incident irradiance on the down-welling water [76], and any variation in R r s   is influenced by the level of suspended sediment concentration at the water’s surface [77]. As a result, R r s is estimated (Equation (A4)—Appendix B) as an indicator in the current study to generate SSC variation across the Hugli estuary. Consequently, the following equation has been used to determine R r s which corresponds to the water’s surface reflectance in the orientation of the sensor’s viewing angle.

3.4.3. Regression Analysis

In this process, the R r s values were collected from the sample points of secondary in-situ data utilised for model training and used for regression analysis. Linear regression was performed between in-situ SSC measurements and R r s values calculated from Landsat data. The Landsat data were collected for the exact dates as the secondary in-situ collection (1 February and 23 March 2014). Regression analysis was conducted between in-situ data and the estimated R r s values. The expression (Equation (A5)) has been used for the regression analysis is depicted in appendices (Appendix B). Then based on the expression derived from linear regression (Equation (A5)), the SSC distribution of 1990 and 2020 has been estimated. Then the regression analysis was evaluated (Appendix A) based on the coefficient of determination (R2) (Appendix C). We allocated 70% of the in-situ training data for the regression analysis and 30% for model validation.

3.4.4. SSC Estimation

SSC is a metric used to quantify the amount of suspended sediment transported in water bodies; therefore, for understanding the dynamics of riparian, estuarine, and coastal dynamics, evaluation of the nature of suspended sediment (their generation and transportation) is crucial [78]. Then, based on the equation derived from regression analysis on secondary in-situ sediment data R r s , the SSC from the satellite datasets has been retrieved. The derived Equation was based on the 2014 in-situ and satellite data; however, due to the time frame selected for this study, the same equation was used to derive the SSC of the study area between 1990 and 2020.

3.4.5. Bank Line Detection

This method of extracting the riverbank lines to assess riverbank erosion has been used to analyse previous bank-line positions using satellite imageries and following water indices.
  • Normalized difference water index (NDWI)
NDWI [79] was calculated using ERDAS Imagine version 14.0 to distinguish between land and river. The value of NDWI was calculated by applying Equation (A6), depicted in appendices (Appendix B).
  • Modified normalized difference water index (MNDWI)
Xu [22] developed the model MNDWI for retrieving water bodies from the surface. The Equation (A8) used for calculating MNDWI is depicted in appendices (Appendix B).
  • Automated water extraction index (AWEI)
Feyisa et al. [80] utilized AWEI as a parameter to detect water bodies. Since water bodies possess a lower reflectance, notably in Landsat’s SWNIR and SWIR bands, the coefficients as well as combinations of the appropriate bands were developed to retrieve water bodies. The mathematical formulation (Equation (A9)) of AWEI is depicted in appendices (Appendix B).

3.4.6. River Bank Erosion-Accretion Assessment

The water mentioned above indices (NDWI, MNDWI, and AWEI) were calculated to estimate riverbank erosion along the Bhagirathi-Hugli River between 1990 and 2020 using the spectral bands mentioned in Table 3. Finally, riverbank erosion and accretion estimates have been made from the courses of the Bhagirathi-Hugli River collected over different years (1990, 1995, 2000, 2010, 2015, and 2020).
Following Tha et al. [81], this work categorised the pixels as erosion when they changed from being a non-water pixel in one year to a water pixel in a different year. In contrast, pixels were labelled as accretion when they changed from a water pixel in one year to a non-water pixel in the subsequent year. The computed results showed the difference between water and land over a spatiotemporal scale, as shown in Figure 3. The riverbank erosion and accretion rate have also been evaluated in this study by detecting the spatiotemporal change in the river.
  • Erosional Vulnerability Zonation
This method does not need substantial fieldwork and has the potential to generate erosional vulnerability zonation based on the indices mentioned above (NDWI, MNDWI, and AWEI). This method involves the computation of an erosional vulnerability mapping that includes zones vulnerable (very high, high, moderate, low, very low) to riverbank erosion.
  • Erosion Volume Estimation
Digital elevation models (DEMs) created from continuous topographic surveys may be used to create DEMs of Difference (DoD), which evaluate height fluctuations, and calculate volumetric changes over time. Applying DEM differencing could quantify and represent sedimentation, or erosion in both the channel reach, thereby allowing sediment mobility studies to be conducted [82]. Therefore, in the current study, two DEM datasets, namely SRTM (Version 3, 2015), and ASTER (Version 3, 2013), both having a 30-m spatial resolution, have been applied. Then as per the erosion volume, the classification of erosion zones (very high, high, moderate, low, very low) along the Bhagirathi-Hugli River has been conducted, outlined in the result section below.
  • Depositional Area Assessment
One of the most often utilised classification techniques for satellite imageries is the grey level co-occurrence matrix (GLCM) [83], which was used for the estimation of the area formed due to the deposition of sediment that took place along the riverine islands (Sagar, Nayachar, and Balari) of Bhagirathi-Hugli River’s estuary. Firstly, all pixel values in the input bands were converted to an 8-bit integer format. Given every input integer band, the number of occurrences of various grey-level combinations (pixel brightness values) was computed in the GLCM format [84]. The GLCM texture features that were used in this study have been summarised in appendices (Appendix B). To create textural characteristics. One of the several advantages of GLCM is accommodating all the texture components [85]. GLCM can be computed utilizing a singular input layer and a user-specified window size (i.e., 7 × 7), and delivered to one or even more output layers relying upon the measures (i.e., variance, entropy, homogeneity, etc.).
According to the empirical data, a window dimension of 7 × 7 gives improved results for generating the GLCM textures and the following texture properties: contrast, mean, variance, entropy, homogeneity, angular second moment, and correlation [85]. To estimate the island area, Landsat 8 OLI bands 3 and 4 were used, while Landsat 5 TM band 4 was utilised to produce textural features. For 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we observed sedimentation patterns across the Hugli estuary’s Sagar, Nayachar, and Balari islands, several Chars.
GLCM has been utilised for the current study to create cluster textural indices. Machine Learning classifiers such as Support Vector Machine (SVM) were used to accomplish the areal assessment (Figure 2). The complete feature set was normalised before using in the study due to adaptation of the SVM classification in the subsequent stages, which needed the data to be normalised. The test feature subsets were then selected to use the trained partition points from the baseline dataset. The SVM classifier has been trained using this sampled training data. The SVM technique includes several user-defined parameters, such as kernel type and a cost factor. Although SVM appears more complicated due to the necessity for kernel selection, change, and other input parameters, it is a perfect classifier [86]. The classification between landmass and newly formed islands is based primarily on sediment texture. The variety of these classification procedures depends on datasets of higher quality and appropriate feature combinations, which directly influence the dependability of the classification outcomes [87].

4. Results and Discussion

4.1. Erosion Analysis

According to the current analysis, the composite bar diagrams in Figure 3, demonstrate the temporal extent of erosion in the Bhagirathi-Hugli River.
As per Figure 3, from 1990 to 2000, approximately an area of 53.31 km2 was eroded; then, from 2000 to 2010, a vast area of 85.81 km2 was eroded; and approximately 36.93 km2 area got eroded during 2010–2020. Thus, extreme erosion poses a significant threat to the communities on both sides of the Bhagirathi-Hugli riverbank. Since the 1980s, the Bhagirathi-Hugli River’s changing location has been accelerated by continual changes in velocity and eroding force [40]. Right bank shifting across Bhagirathi-Hugli was prominent from 1954 to 1980 near Basatpur, Jhaudanga, Naliapur, and Parmedia, while the left bank shifted more, namely at Mertala, Uttar Srirampur, Gopipur, and Sankarpur [88]. High meander belt width, escalating sinuosity, and increasing meander migration are directly associated with this river system’s scenario of severe riverbank erosion [57].
According to the current study, the riverbank erosion is very high, mainly across the upstream section of Bhagirathi-Hugli. As per Islam et al. [89], upstream erosion induces subsequent deposition on the riverbed, which causes the reduction of the bed’s slope to culminate in bar formation (mid-channel bars and side-attached bars) in the lower channel section of the Bhagirathi-Hugli River. As river dynamics are inextricably linked to bank erosion and the formation of bars resulting from sediment accumulation [48], hence, in this river system of Bhagirathi-Hugli, erosion is intricately linked with accretion. Other than that, lateral shifting and bank erosion occur throughout the Bhagirathi-Hugli River, accompanied by significant morphological changes [48]. Furthermore, due to the intense erosion in the Bhagirathi-Hugli river system, millions of tons of sediment are transported and deposited on the riverbed of its estuarine section. This sediment build-up can lead to morphological changes in the estuary, causing poor water quality, channel obstructions, and reduced in navigable river depth.
As mentioned above, several riverbank erosion across the Bhagirathi-Hugli have threatened the riverine ecosystem and led to extreme damage to properties and settlements. Therefore, it is essential to map the geographical extent of the riverbank erosion to assess the severity of the problem [90]. Figure 4 depicts the geospatial outputs illustrating the decadal erosion that occurred over the riverbanks of Bhagirathi-Hugli between 1990 and 2020, further supported by Figure 5. Figure 4 shows the spatiotemporal vulnerability zones along the Bhagirathi-Hugli River across three decades (1990–2000, 2000–2010, and 2010–2020). As per Figure 4, the erosional vulnerability zonation comprises five zones: very high, high, moderate, low, and very low, as depicted by the multicolour legend.
Previous data indicate that >1.50–4.50 × 106 m3 of land was degraded yearly in the tail and middle reaches, whereas the head experienced 2.50·106 m3 of bank erosion in 1999, 2000, 2001, 2003, and 2004 [91]. As per Figure 4, during 1990–2000, the study area’s northern section comprises zones less vulnerable to erosion. In the upper-middle section, there are several zones with moderate erosional vulnerability and a single zone with high erosional exposure. At the same time, the middle and lower middle stretches have more zones with high to very high erosional vulnerability. However, the lower section had a shallow erosional exposure during 1990–2000. Then in the next decade (2000–2010), the erosional vulnerability has further aggravated, as the zones of upper-middle and middle stretch with moderate erosional vulnerability converted to high and very high erosional exposure. However, in the last decade (2010–2020), the erosional vulnerability has comparatively lowered in the upper-middle stretch (Figure 4), although it has remained higher compared to other sections of Bhagirathi-Hugli, and this same scenario has been confirmed in visual estimation in Figure 3. Thus, it can be inferred from the estimation of the current study (Figure 4) scenario of erosional vulnerability in the northern and southern sections of the Bhagirathi-Hugli has not changed much for all periods. In contrast, in the upper-middle and middle stretch, erosion is gradually intensifying from time to time. Furthermore, the erosion intensity can be profoundly ascertained by examining the estimates of erosion volume across the Hugli. The estimated erosion volume (Figure 5) in the current study confirms the above-illustrated erosional vulnerability, taking place over the three decades across Bhagirathi-Hugli. Because of the dynamic erosion rate persisting along different sections of the Bhagirathi-Hugli, the eroded volume has been approximated in this study into five zones: very high (>22.0 M m3), high (21.9–10.8 M m3), moderate (10.8–5.3 M m3), low (5.3–1.8 M m3), and very low (<1.8 M m3).
Based on Figure 5, the estimated erosion volume statistics indicate that during 1990–2000, the eroded volume was low to very low across the upper (northern) stretch of the Bhagirathi-Hugli. Along the upper-middle section of the Bhagirathi-Hugli River are zones of high to very high erosion volume. Then in the next decade (2000–2010), similar to the erosional vulnerability estimation (Figure 4), erosion volume (Figure 4) intensified mainly in the upper-middle stretch of the Bhagirathi-Hugli River. Then during 2010–2020, when the erosional vulnerability has comparatively lowered; therefore, the erosion volume has also lowered compared to the previous decade (2000–2010). It has also been observed that there has been immense meandering across this particular section; thus, intense meandering is inducing a heavy erosion volume at this river’s bank across the Murshidabad district. However, during the three decades, the volume of erosion across the lower (southern) stretch of the Bhagirathi-Hugli River has been low to very low. This indicates that the eroded volume of sediment from the upper-middle section has been flowing into the lower stretch, due to which this zone has not been vulnerable to erosion.
According to this geospatial analysis (Figure 5), the middle section of the Bhagirathi-Hugli, commonly called the Nabadwip-Katwa section, is subject to high to moderate erosion volume, which has induced severe flooding (in 1978 and 2000). In 2011, the population here was 1,048,699, with 5% living along the river [88]. According to Das et al. [88], they suffer from erosion and the negative consequences of rehabilitation, land degradation, crop devastation, and other factors every year. Then the estuarine stretch of Hugli has become a zone of lesser vulnerability to erosion because of the change in water flux due to Farakka Barrage’s construction [92]. This barrage was commissioned to safeguard the KoPT against siltation and increase freshwater flow into the Hugli estuary [91].

4.2. Sediment Concentration Analysis

As per the estimations mentioned above, and previous studies, various sources of erosion contribute towards sediment load into the Bhagirathi-Hugli River and the estuary. It has been worked out by KoPT that the annual sediment load transported below Diamond Harbour is 23.68·106 t, and about 13.20·106 t between Nabadweep, and Diamond Harbour, whereas nearly 26.93·10 6 tonnes get deposited or remains in circulation between Diamond Harbour, and Sagar each year [93]. The diagrams in Figure 6 illustrate the sediment load that takes place between various sections of the Bhagirathi-Hugli, where it can be observed that the sediment load gets higher in the lower section of the river, supporting our current study’s findings.
The statistics mentioned above have an immediate influence on soil loss owing to the erosion of river banks and floods, resulting in the loss of human lives and property on both sides of the Bhagirathi-Hooghly basin each year [89]. Due to this increase in sediment load, there has been an ever-increasing amount of suspended sediment concentrations (SSC), mainly across the Hugli estuary [51,92,93].

4.2.1. SSC Concentration across Hugli Estuary

According to the estimation of the current study, the SSC varies noticeably across the Hugli Estuary. Figure 7a,b depicts the distribution of SSC across this estuary in 1990, and 2020, based on the estimated R r s from integrated satellite data and secondary in-situ data. Besidesthat, the seasonality factor has also been considered in the SSC analysis since the concentration of SSC and variation is highly influenced by the monsoon [94]. Therefore, the pre-monsoonal and post-monsoonal scenarios of the SSC distribution during 1990, and 2020, of the Hugli estuary have been illustrated in Figure 7a,b.
As per Figure 7a, during the pre-monsoon of 1990, the concentration of SSC distribution is high across the Southwestern section of the Hugli estuary and the Northern section (near the estuary’s mouth) around the estuarine islands Nayachar, and Ghoramara. Whereas, during the post-monsoon (1990), the concentrated SSC declined across the same regions (mentioned above), mainly due to the increase in river water discharge, which increased the flow of sediments leading to a decline in the concentration of suspended sediments. Thus, during pre-monsoon, there has been a higher concentration of SSC due to a decrease in river water discharge through the Hugli estuary during this season. When the discharge decreases in pre-monsoon, sediment mobility within the estuary reduces; thus, sand bars and Chars forms across the estuary [93], wherein higher concentrations of SSC occur. Hence, it can be deduced that a cycle develops of higher concentration of SSC during pre-monsoon followed by a decline in the concentration of SSC during the post-monsoon. Yet, Figure 7 shows that by 2020, this condition has worsened (Figure 7b). Compared to the pre-monsoon of 1990, the distribution of SSC has become highly concentrated across the estuary, particularly in the northern and southwestern regions, which has seriously impacted the navigability of Eden and Auckland channels. Therefore, sandbars and Chars formed after these channels, which are 6.1, and 6.3 metres at zero tides, respectively, have risen to the riverbed, rendering the navigation channel shallow. The canal’s depth is less than necessary for loaded container ships to transit from the Bay of Bengal to the port [94].
It can be inferred from the assertions mentioned above regarding sedimentation/siltation across the estuary that it is evident that two main riverine and estuarine dock facilities (KDS and HDC) of KoPT have been affected by erosion upstream (Figure 4, Figure 5 and Figure 6). As a result of the vast sediment load carried by the Bhagirathi-Hugli River, intense siltation occurs, necessitating constant maintenance through dredging [95]. Siltation issues have long plagued the Eden-Upper Auckland-Jellingham canal, which is currently the route to HDC. This channel has been hampered by siltation due to several bars, shoals, and crossings, including Upper Jellingham Shoal, Haldia Anchorage Area, and Upper Eden Bar [96]. The navigational depths in the area of Jellingham Shoal are constantly under threat owing to its dynamic equilibrium. There have been a few spots along the navigational channel where the depth of the Jellingham Shoal has decreased, making it difficult to transit of vessels [97]. Even during the post-monsoon of 2020, the SSC level has been moderate across the Northern and Southwestern section, and the SSC level is high mainly along the margins of the estuarine islands (Figure 7b). A higher amount of SSC, especially mud, accumulates in the riverine system, where islands and bars are located [98]. As a result, islands and bars receive suspended particle deposits, decreasing the waterways’ depth.

4.2.2. Sedimentation across Estuarine Islands

According to the above discussion, sediment accumulation occurs in vast quantities in the estuary due to high SSC. Therefore, the estuarine islands receive massive amounts of sediment, resulting in their area’s gradual expansion and forming Chars adjacent to them. Thus, to assess the effect of sediment on the areal extent of the estuarine islands, the Landsat imageries (1990–2020) at five years interval has been utilised, and the estimations are illustrated in Figure 8 and Figure 9. Figure 8 shows that three estuarine islands of the study area, Sagar, Balari, Nayachar, and several Chars, have been expanding in their areal extent due to sedimentation.
As per Figure 8, it can be observed in the case of Balari, which was not evident visually during 1990; but it can be observed that gradually with time, Balari has expanded due to sedimentation, and by 2020, it can be observed that due to expansion, Balari is on the verge of merging with Nayachar. Figure 9 shows the graphical estimates of the change in the areal extent of the estuarine island between 1990 and 2020.
During 1990, the total area of the estuarine islands was 288.46 km2 (Figure 9), which has increased to 341.59 km2, which implies that there has been an increase of 53.13 km2 between 1990–2020. Thus, it can be learned from Figure 9, that there has been an increase in the areal extent of the islands of the Hugli estuary due to a vast amount of sedimentation. However though there is a consistent increase, there are evidence of submergence (Lohachara, and Supribhanga) [99], and erosion (Ghoramara), which is also facing constant shrinkage due to sea-level rise (SLR) [100] and local hydrodynamics. A dual scenario of heavy sedimentation (mainly from upstream erosion), submergence, shrinkage, and within the Hugli estuary has led to a complex interplay between fluvial and marine dynamics influencing the Bhagirathi-Hugli River estuary. The erstwhile small islands located in the Hugli estuary, such as Lohachara and Supribhanga, have completely disappeared, and Ghoramara is on the brink of destruction [101]. Hence, their disappearance, destruction or erosion could be attributed to a mixture of several factors like high waves generated by tropical cyclones, and storm surges [102], the tidal flow of the Hugli estuary [99], high sand content in the soil, which rendered them prone to erosion [92]. This scenario of submergence might also have occurred due to complex sediment dynamics across the estuary. Anthropogenic activities like the construction of guide walls which altered the hydrodynamics of the river resulting in wave convergence towards Ghoramara Island were also a factor behind rapid erosion [103]. Though these incidents occurred, still, there is a steady increase in the landmass within the estuary, contributed by the eroded landmass from upstream.

5. Conclusions

The Hugli estuary’s ability to hold back upstream water is declining due to massive inflows of sediment-filled water from upstream erosion, which is clogging the Eden and Auckland canals and impairing the navigability of the port facilities of HDC. Nevertheless, HDC has been threatened with closure since silt deposits at the entrance of the Bhagirathi-Hugli River have left its normal operations nearly impassable, which increases worries that India’s second-largest container port would be closed down shortly. Therefore, regular dredging is required for sustainable navigation throughout these facilities, but dredged materials often include pollutants and negatively affect coastal habitats and water quality.
Hence, the problem of a heavy influx of sediments is gradually worsening due to the Hugli estuary’s funnel-like shape, which clogs its canals resulting in geomorphological changes across the estuary. The vast flux of massive sediments into this estuary, which is deposited along the port facilities, and estuarine islands and chars (Nayachara and Balari etc.). These islands are expanding in size and the adjacent coastal waters have become highly turbid, which is negatively affecting the surrounding aquatic biodiversity. However, despite massive sedimentation into this estuary due largely to riverine erosion, there has also been a constant rise in sea level in this estuary. The latter is reflected in the shrinking of the estuarine island Ghoramara and the past submerging of two other west-situated islands, Lohachara and Supribhanga. Along with the SLR impact, local hydrodynamics also plays a significant role in the erosion of Ghomara Island. Considering the impact of sedimentation and SLR on the Hugli estuary, it is reasonable to conclude that the estuary is gradually decaying due to the interplay between sedimentation and SLR.
The current study demonstrates that the primary sources of SSC in the Hugli estuary include the discharge of sediment-laden water, erosion, and tidal and wave action. Massive load of sediments coming from the upstream erosion is one of the significant issues to be addressed urgently to sustain the port and the shipping channels. The study’s findings will be valuable for enhanced administration and long-term monitoring of estuarine and coastal islands.

Author Contributions

Conceptualization, A.M. and M.H.; methodology, N.P., A.M. and R.A.; software, R.A. and N.P.; formal analysis, R.A. and N.P.; investigation, A.M. and R.A.; data, R.A. and N.P.; writing—original draft preparation, A.M., R.A. and J.P.H.; writing—review and editing, R.A., J.P.H., T.G. and M.K.S.; visualization, A.M. and R.A.; supervision, A.M., M.H., I.P. and T.G.; project administration, A.M. and I.P.; funding acquisition, I.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by UKRI GCRF Living Deltas Hub, grant number NE/S008926/1.

Data Availability Statement

Publicly available data sets were analysed in this study. These data can be found here: Earth Explorer—USGS (https://earthexplorer.usgs.gov/), accessed on 20 January 2023.

Acknowledgments

This paper is an outcome of the research carried out as part of the UKRI-GCRF project “Living Deltas Hub” (2019–2024).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Geomorphic Features of Hugli

The images below, obtained from Google Earth, indicate changes in the fluvial geomorphic features in the upper middle portion of the Bhagirathi-Hugli River, as the characteristics displayed below are located in Purbasthali village (Purba Bardhaman district) along the Hugli. According to the images below, the geomorphic characteristics have changed dramatically between 1990 and 2020 due to intensive erosion-accretion and transportation. Ox-bow Lake, Meander scrolls, depressions, sloughs, and natural levees are the principal characteristics that could be observed.
As per Figure A1a, Ox-bow Lake is the primary cut-off part of a meander bend, created by strong meandering. The river then developed meander scrolls, depressions, and elevations on the convex face of bends as it migrated laterally towards the concave bank. Natural levees along river banks have also formed in this area. They arise when elevated berms or narrow shelves above the floodplain surface close to the channel, generally holding coarser materials deposited when flood flows over the top of the channel banks. Then there are sloughs, pools of stagnant water generated in meander-scroll depressions and along valley walls when flood flows go directly down the valley, eroding nearby valley walls. These eroding valley walls have contributed to the upstream erosion, which has brought a huge amount of sediments into the estuary, leading to the formation of linear shoals, sandbars, as well expansion of estuarine islands (as shown in Figure A1b), damaging the navigability of the KoPT and HDC.
Water 15 01285 g0a1a 550Water 15 01285 g0a1b 550
Figure A1. (a) A section in the upper-middle stretch of the Hugli River (along Purbastahli village) depicting drastic evolution of fluvial geomorphic features between 1990 and 2020. (Source: Google Earth). (b) Formation of bars and expansion of estuarine islands (Nayachars) in the Eastern section in the Hugli estuary between 1990 and 2020. (Source: Google Earth).
Figure A1. (a) A section in the upper-middle stretch of the Hugli River (along Purbastahli village) depicting drastic evolution of fluvial geomorphic features between 1990 and 2020. (Source: Google Earth). (b) Formation of bars and expansion of estuarine islands (Nayachars) in the Eastern section in the Hugli estuary between 1990 and 2020. (Source: Google Earth).
Water 15 01285 g0a1aWater 15 01285 g0a1b

Appendix B

Applied Equations

  • TOA reflectance equation
TOA reflectance equation (Equation (A1)) is applied for atmospheric corrections.
ρ T O A λ = π L T O A λ E 0 λ   c o s θ
where, ρ T O A is TOA, L T O A ( λ ) is detected radiance by sensors, E 0 λ is solar spectral irradiance at TOA, c o s θ is cosine of solar zenith angle   θ .
  • FLAASH Algorithm
FLAASH Algorithm (Equation (A2)) is also applied for the purpose atmospheric corrections.
L * = A ρ / 1 − ρ e   S + B ρ e   / 1 − S + L * a
where, L * is spectral Radiance at a given pixel, ρ is surface reflectance at a given pixel, ρ e     is surface reflectance for nearby pixels that takes into consideration adjacency effects, S is atmospheric spherical albedo, A , B are coefficients relying on atmospheric and geometric factors, L * is atmospheric backscattered radiances.
  • Sun Glint Index (SGI)
SGI (Equation (A3)) has been applied in this study for the purpose of de-glinting using the approach of dark object subtraction, which is commonly utilised for sun, and sky glint rectifications.
S G I = S W I R + G R E E N S W I R − G R E E N
  • Remote Sensing Reflectance ( R r s )
R r s has been computed (Equation (A4)) as an indicator in this study to assess the variation in SSC across the Hugli estuary.
  R r s ( λ i ) = 1 π ∫ λ s λ e R r s λ F i λ d λ / ∫ λ s λ e F i λ   d λ
where, R r s ( λ i ) is equivalent R r s radiance at a wavelength of Landsat band i (NIR band), F i λ is spectral response function, λ s is initial wavelengths of band i ,     λ e is end wavelengths of band.
  • Regression Equation
Regression analysis has conducted among the secondary in-situ data, and the estimated R r s values, to assess accuracy of the same.
Y = A B X
where, Y is water quantity parameter (SSC), X is measurement from remote sensing data ( R r s ), A ,   B are empirically derived factors.
  • Water Indices
The water indices (NDWI, MNDWI, and AWEI), have been applied to extract the riverbank lines by distinguishing between land and river to assess riverbank erosion. The values of NDWI, MNDWI, and AWEI range from −1.0 to +1.0.
N D W I     = N I R − S W I R N I R + S W I R
M N D W I = G r e e n − S W I R G r e e n + S W I R
  A W E I = B l u e + 2.5 · G r e e n − 1.5 ·   ( N I R + S W I R 1 ) − 0.25 x S W I R 2
  • GLCM Texture Features
The GLCM texture features (Equations (A9)–(A15)), that were used in this study to estimate of the area formed due to the deposition of sediment that took place along the riverine islands (Sagar, Nayachar, and Balari) has been summarised below:
1.
Contrast: It is used to calculate the degree of variance in the image (Equation (A9))
C o n t r a s t = ∑ i , j = 0 N − 1 P i , j i − j 2
where i and j are the grey levels. When the grey levels are equal, the cell becomes diagonal l and weightage is 0, which denotes pixels identical to their adjacent pixels. Whereas, when the grey levels ( i ,   j ) differ by 1, there remains a slight resemblance, and the weightage is 1.
2.
Mean: It is used to calculate the image’s cumulatively distributed grey level value (Equation (A10)).
μ i = ∑ i , j = 0 N − 1 i P i , j         u j = ∑ i , j = 0 N − 1 i P i , j
where the mean is computed using the left-hand algorithm with μ i as the reference digit, and the right-hand algorithm is used to calculate the mean using the adjacent pixel   u j .
3.
Variance: It is used to calculate the degree to which the grey-level distribution is spread out (Equation (A11)).
v 2 i = ∑ i , j = 0 N − 1 P i , j x + a n         v 2 i = ∑ k = 0 n P i , j i − μ i n
Given that the GLCM is symmetrical the grey levels produces the same result in this case.
4.
Homogeneity: It is used to assess the homogeneity (smoothness) of grey-level distributions (Equation (A12)).
H o m o g e n e i t y = ∑ i , j = 0 N − 1 P i , j / 1 + i − j 2
5.
Entropy: It is used to assess the degree of disruption among pixels within an image (Equation (A13)).
              E n t r o p y = ∑ i , j = 0 N − 1 [ − 1 n   P i , j ]  
6.
Correlation: It is used to calculate the linear relationship between grey levels in adjacent pixels (Equation (A14)).
            C o r r e l a t i o n = ∑ i , j = 0 N − 1 i − μ i i − μ i       ( δ i 2 ) δ i 2
7.
Angular Second Moment: It is used to evaluate the image’s grey level distribution’s homogeneity or energy (Equation (A15)).
A S M = ∑ I , J = 0 N − 1 P i , j 2

Appendix C

Regression Evaluation

The correlation analysis using linear regression techniques has been executed in the current study to evaluate the expression used to derive using Landsat data based on the secondary in-situ data. The correlation analysis depicted a positive correlation (R2= 0.7102) (Figure A2) between the estimated from the reflectance of Landsat NIR Band and secondary in-situ SSC data.
Water 15 01285 g0a2 550
Figure A2. Regression analysis of the secondary in-situ SSC data and estimated R r s from Landsat data.
Figure A2. Regression analysis of the secondary in-situ SSC data and estimated R r s from Landsat data.
Water 15 01285 g0a2

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Figure 1. Map showing the study area of River Bhagirathi-Hugli with selected sections for the current study.
Figure 1. Map showing the study area of River Bhagirathi-Hugli with selected sections for the current study.
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Figure 2. A flow chart depicting the methodology of the sediment erosion-accretion, and SSC distribution modelling. Explanations: SVM—support vector machine, GLCM—grey level co-occurrence matrix, NDWI—normalized difference water index, MNDWI—modified normalized difference water index, AWEI—automated water extraction index, FLAASH—fast line-of-sight atmospheric analysis of hypercubes, TOA—top of atmosphere radiance, IB—sample taken In Bag, OOB—sample taken Out of Bag, NIR—near-infrared reflectance.
Figure 2. A flow chart depicting the methodology of the sediment erosion-accretion, and SSC distribution modelling. Explanations: SVM—support vector machine, GLCM—grey level co-occurrence matrix, NDWI—normalized difference water index, MNDWI—modified normalized difference water index, AWEI—automated water extraction index, FLAASH—fast line-of-sight atmospheric analysis of hypercubes, TOA—top of atmosphere radiance, IB—sample taken In Bag, OOB—sample taken Out of Bag, NIR—near-infrared reflectance.
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Figure 3. Decadal erosion occurred across the Bhagirathi-Hugli River.
Figure 3. Decadal erosion occurred across the Bhagirathi-Hugli River.
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Figure 4. Erosion vulnerability zones along the Bhagirathi-Hugli River during selected periods.
Figure 4. Erosion vulnerability zones along the Bhagirathi-Hugli River during selected periods.
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Figure 5. Eroded volume zones along the Bhagirathi-Hugli River during selected periods.
Figure 5. Eroded volume zones along the Bhagirathi-Hugli River during selected periods.
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Figure 6. Sediment load taking place at various channel sections calculated by Rudra [92]. The localisation of the selected sections shows in Figure 1.
Figure 6. Sediment load taking place at various channel sections calculated by Rudra [92]. The localisation of the selected sections shows in Figure 1.
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Figure 7. Pre-monsoonal and post-monsoonal distribution of SSC on Bhagirathi-Hugli River estuary during 1990 (a) and 2020 (b).
Figure 7. Pre-monsoonal and post-monsoonal distribution of SSC on Bhagirathi-Hugli River estuary during 1990 (a) and 2020 (b).
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Figure 8. Sedimentation scenarios across the Hugli estuarine islands.
Figure 8. Sedimentation scenarios across the Hugli estuarine islands.
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Figure 9. Change in the areal extent of the estuarine islands between 1990 and 2020.
Figure 9. Change in the areal extent of the estuarine islands between 1990 and 2020.
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Table
Table 1. Details of the satellite imaginary datasets used for the current study.
Table 1. Details of the satellite imaginary datasets used for the current study.
Satellite/SensorSpatial Resolution (m)Time FrameSource
Landsat 5-TM30 m1990–2020http://earthexplorer.usgs.gov (accessed on 10 February 2023)
Landsat 8-OLI
Sentinel 2A-MSI10 m2014https://scihub.copernicus.eu/dhus (accessed on 15 March 2023)
SRTM DEM30 m2013https://www2.jpl.nasa.gov/srtm/ (accessed on 10 February 2023)
ASTER DEM2015https://www.earthdata.nasa.gov/news/new-aster-gdem (accessed on 27 February 2023)
Table
Table 2. Observed SSC by Pitchaikani et al. [62] for the Bhagirathi-Hugli River estuary (Stations 1–24) spanning 21°37′8.15″ N to 21°52′50.93″ N; 87°33′25.03″ E to 88°9′36.42″ E.
Table 2. Observed SSC by Pitchaikani et al. [62] for the Bhagirathi-Hugli River estuary (Stations 1–24) spanning 21°37′8.15″ N to 21°52′50.93″ N; 87°33′25.03″ E to 88°9′36.42″ E.
Date of CollectionStation NameSSC (mg/L)
1 February 2014150
258
336
442
560
635
768
27 March 2014885
954
1046
1163
1238
1348
1445
15172
16186
17255
18110
19252
20240
21236
Table
Table 3. List of spectral bands acquired from Landsat TM and OLI data regarding River course detection.
Table 3. List of spectral bands acquired from Landsat TM and OLI data regarding River course detection.
Sl. No.Landsat 5 TM Landsat 8 OLI
Band NoBand NameBand NoBand Name
1B1BlueB2Blue
2B2GreenB3Green
3B3RedB4Red
4B4NIRB5NIR
5B6SWIR 1B6SWIR 1
6--B7SWIR 2
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Mukhopadhyay, A.; Acharyya, R.; Habel, M.; Pal, I.; Pramanick, N.; Hati, J.P.; Sanyal, M.K.; Ghosh, T. Upstream River Erosion vis-a-vis Sediments Variability in Hugli Estuary, India: A Geospatial Approach. Water 2023, 15, 1285. https://doi.org/10.3390/w15071285

AMA Style

Mukhopadhyay A, Acharyya R, Habel M, Pal I, Pramanick N, Hati JP, Sanyal MK, Ghosh T. Upstream River Erosion vis-a-vis Sediments Variability in Hugli Estuary, India: A Geospatial Approach. Water. 2023; 15(7):1285. https://doi.org/10.3390/w15071285

Chicago/Turabian Style

Mukhopadhyay, Anirban, Rituparna Acharyya, Michał Habel, Indrajit Pal, Niloy Pramanick, Jyoti Prakash Hati, Manas Kumar Sanyal, and Tuhin Ghosh. 2023. "Upstream River Erosion vis-a-vis Sediments Variability in Hugli Estuary, India: A Geospatial Approach" Water 15, no. 7: 1285. https://doi.org/10.3390/w15071285

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