Introduction

Historically, urban settlements have developed in coastal zones around the world and are home to diverse ecological landscapes, having significant biotic and economic value (Ahsanullah et al. 2021; Yan et al. 2022). Coastal cities are important economic centers which serve as gateways to the hinterland and facilitate socio-economic activities, including trade, tourism, transportation and fisheries. However, due to a limited ability for self-remediation (Yan et al. 2021a, b); the coastal zones have fragile ecological environments, under constant pressure from natural and human activities (Cenci et al. 2018; Aslam et al. 2020; Baig et al. 2020), which lead towards increase in coastal vulnerability. Land use in coastal regions has undergone drastic change in recent decades, as a response to construction of ports, increased marine aquaculture, and the expansion of land resources through reclamation of land from sea (Huang et al. 2018; Yan et al. 2022). This change manifests also in the variability of coastal erosion and accretion processes; and hence, the evolution in position of shorelines. Assessment of spatiotemporal shoreline changes is important, since shoreline position is a crucial geomorphological indicator in Integrated Coastal Management (ICM) for coastal resource development, disaster risk assessment and policy formulation (Daoudi and Niang 2021).

Traditionally, field surveying has been used for delineation of shoreline changes (Cendrero 1989); however, comprehensive long-term monitoring of shoreline changes through this conventional method is expensive, time-consuming and resource-intensive (Elnabwy et al. 2020). In this context, remote sensing data constitutes an optimum solution for shoreline change studies, and has been extensively used because of temporal and synoptic data coverage, multispectral sensing and relative cost-effectiveness (Lillesand et al. 2015; Baig et al. 2020; Nazeer et al. 2020). The interface between marine and land domains i.e. the waterline, is the basic indicator of instantaneous shoreline position (Nassar et al. 2019; Patel et al. 2021). Numerous image manipulation techniques have been used in literature to extract this geomorphic indicator from remote sensing images, such as edge detection approaches, band thresholding methods and classification techniques (Toure et al. 2019). Most notable of these methods include satellite-based indices, principal component analysis, density slicing, supervised and semi-automated image classification (Mondal et al. 2020; Nazeer et al. 2020; Abualtayef et al., 2021). Although a majority of techniques have similar inherent theoretical principles, methodological distinctions result in slight variation in results, even for identical shorelines. After the delineation of historical shoreline proxy indicators, these can be analyzed in a Geographic Information Systems (GIS) environment to analyze the change between past and present shoreline locations (Esmail et al. 2019; Velsamy et al. 2020). Based on the change statistics, variations in the length and type of various coastal sections, land use/cover evolution and type conversion, land and sea area quantification, and vulnerability assessment of coastal features against natural and human-induced hazards can be accomplished (Cai et al. 2022; Kumar et al. 2022). One of the widely used open source GIS software for shoreline studies, the Digital Shoreline Analysis System (DSAS), is a robust tool to evaluate the dynamics of coastal environments (Yiğit et al. 2022). The coastal indices in DSAS measure the change in historical shorelines by taking measurements of sections perpendicular to the coast. The subsequent change statistics can further be used to forecast position of future shorelines in naturally evolving coasts. A number of published examples in literature have successfully utilized DSAS to analyze spatio-temporal change in coastal areas (Baig et al. 2020; Nazeer et al. 2020; Abualtayef et al. 2021; Mondal et al. 2020).

Gwadar, the flagship project of China Pakistan Economic Corridor (CPEC), is a deep sea port, located in Pakistan’s province of Baluchistan, bordering the Arabian Sea. The idea of transformation of this small fishing town to an international seaport first emerged in 1992; however, it could only be materialized until after a decade with Chinese assistance. After the completion of first phase of development, the state-owned China Overseas Port Holding Company, has been responsible for the port infrastructure development and maintenance since 2013 (Hassan 2020). Due to its immense anticipated positive outcomes, Gwadar holds an important geostrategic and economic importance for Pakistan (McCartney 2021). However, negligence towards environmental sustainability and coastal vulnerability assessment can be a serious cause of concern for Gwadar coastal area in the future, and implications for climate, natural resources and livelihoods of associated communities are anticipated.

While a significant number of studies have addressed shoreline movement for various coastal zones at global level (Cenci et al. 2018; Esmail et al. 2019; Elnabwy et al. 2020; Patel et al. 2021; Santos et al. 2021; Yan et al. 2022), no such study exists in the context of shoreline evolution along Gwadar coastal area, to the best of our knowledge. As a result of coastal development and port extension, anthropogenic factor has been the major reason behind the spatial and temporal patterns of shoreline change. The rapid land reclamation and urban development in Gwadar coastal zone has led towards the encroachment of land on the marine environment, which could possibly result in alteration of hydrodynamics of the area as a response to synergic effects of climatic change and anthropogenic factors. Moreover, as per the research work of Aslam et al. (2020), Gwadar city lies in zone of very high tsunami vulnerability. Thus, any changes affecting this coastal area have a significant impact on the environment, morphology and socio-economics of the region. Given the scenario, change analysis of historical shoreline positions from satellite data will prove to be an important tool for gaining better insight into morphological dynamics of this coastal area. Additionally, knowledge of corresponding landuse in different times in the past is pertinent to understand the evolution of this coastal area as an emerging socio-economic hub. Since rapid urbanization often results in increased coastal erosion, marine water pollution and ecological degradation (Daoudi and Niang 2021; Yan et al. 2022), it is also worthwhile to analyze the coastal vulnerability.

The main aim of this study is to analyze the shoreline and built-up area changes in Gwadar coastal zone from 1987 to 2021, and to assess the relative vulnerability of various geomorphic sections of the coastline using multi-temporal remote sensing data and GIS techniques. The statistical methods used in this study include change evaluation indices in DSAS, and Erase tool in ArcGIS to compute the long-term and short-term erosion and accretion rates along the shoreline respectively. The landuse change has been analyzed using spectral classification techniques, as it is useful for examining the coastal area evolution, with emphasis on the main anthropogenic driving factor. As studies on shoreline evolution in Pakistan are limited to only eastern parts of the national coastline (Nazeer et al. 2020; Ahsanullah et al. 2021), this study is significant to aid the relevant authorities in planning sustainable development of Gwadar coastal zone; addressing issues such as disaster aversion, risk adaptation and mitigation.

Research methodology

Study area

Gwadar city is situated in district Gwadar, the ninth largest district in Balochistan, bordering the Arabian Sea. The city is world renowned for the newly-built Gwadar Port; a warm-water, deep-sea port. It lies on a natural hammerhead peninsula, formed by curved semi-circle bays on either side, named as the Gwadar West and East Bays. The climate of Gwadar is hot and humid, receiving an annual average rainfall of about 100 mm (Zahid et al. 2021). This maritime zone is one of the most productive in the world, owing to its coastal orientation and south-west monsoon wind circulation pattern. Rocky cliffs and headlands are prominent features of the coastline along Gwadar. Typically, natural morphological processes continually modify and reshape the coastal borders due to erosion and deposition (IUCN Pakistan 2012). On the anthropogenic front, Gwadar is undergoing huge developmental changes due to CPEC activities, which aim at making Gwadar a major trading port in the Arabian Sea to realize the Maritime Silk Route initiative of the Chinese government. With such a scale of development, the ecology, geomorphology and human settlements of Gwadar coastal area would be sensitive to various new challenges. Primarily, the harbor expansion and increasing demand for civic amenities has been leading to land use changes with significant impacts on surrounding ecosystems and coastline evolution processes. Moreover, the area is prone to effects of sea level rise, marine extreme weather events, tsunamis and storm surge (Aslam et al. 2020). The map of study area is illustrated in Fig. 1.

Fig. 1
figure 1

Study area: (A) Pakistan (B) Gwadar coastal zone (C) Gwadar Eastern and Western zones

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Data

A total of 35 remote sensing images (Table 1) were accessed from Google Earth Engine (GEE) cloud computing platform, captured by Landsat series of satellites from 1987 to 2021. Amongst open source satellite datasets, the Landsat data archive, providing optical data with moderate resolution of 30 m, is the longest continuous planetary observation and monitoring record. Due to its temporal longevity, it is an extremely valuable resource to study long-term shoreline and coastal evolution trends (Pardo-Pascual et al. 2012; Cenci et al. 2018; Santos et al. 2021). Cloud free images of the same months in multi-decadal satellite data provided the opportunity to extract shorelines with minimal inherent measurement and positional uncertainty. The choice of images pertained to non-flooding months (December to April), keeping in view the stability of marine conditions, and to avoid the months in which tropical storms mainly prevail (Nazeer et al. 2020). The ETM + images containing Scan Line Corrector (SLC) error were used after applying correction using the”Kernel-based SLC-off gap filling algorithm” available in GEE.

Table 1 List of satellite images used
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Methods

The main methodology for this study is presented in Fig. 2, and is composed of four major steps, as elucidated in the following sub-sections.

Fig. 2
figure 2

Flowchart of methodology

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Shoreline extraction

To detect shoreline changes, a proxy indicator of the instantaneous interface between land and water, representing the shoreline position, needs to be determined. For the present study, the extraction of instantaneous waterline (IWL) indicator from satellite images under stable oceanic conditions in non-flooding months was performed through Normalized Difference Water Index (NDWI) in GEE. This index has been reported to perform with high efficiency for shoreline delineation in a number of studies (Nazeer et al. 2020; Santos et al. 2021). The formula for calculation of NDWI is given as:

$$NDWI= \frac{Green-NIR}{Green+NIR}$$
(1)

Subsequently, a land and water mask was developed by thresholding method. The binary raster images obtained after thresholding were converted to linear vectors, representing the shoreline, by vectorization in GIS environment.

Built-up area extraction

To analyze the landuse changes in Gwadar coastal region during the study period i.e. from 1987 to 2021, five landcover/landuse maps were prepared using Landsat imagery. A Random Forests (RF) classifier, trained with training samples obtained from high resolution imagery and multispectral indices; Normalized difference Vegetation Index (NDVI) and Normalized difference Built-up Index (NDBI), was used to perform the supervised classification in GEE. Since the objective of this research work pertained to analysis of the effect of anthropogenic driving factor on Gwadar coastal environment, three main landcover/landuse classes namely Built-up, Bare soil and Water were included in the classification scheme. The kappa coefficient of resultant maps was greater than 0.8, depicting the sufficiency of accuracy of classification for this research work.

Long-term change analysis

For assessment of long term shoreline changes, Digital Shoreline Analysis System (DSAS) version 4.4 has been used in this study. Since a transect-based indicator has been proposed in literature for coastlines with simple morphologies (Patel et al. 2021), DSAS is an ideal GIS environment to conduct shoreline change analysis for Gwadar coastal area. Developed by the USGS Coastal and Marine Geology Program, DSAS is a toolbox for statistical measurement of historical shoreline changes relative to the baseline, using transect based approach (Das et al. 2021). The baseline is constructed at an analyst-specified distance from the source shoreline, parallel to the reference shoreline orientation at the specific offset distance. Statistical metrics or indices are then computed based on measured differences between historic shoreline positions along transects to quantify the degree of change (Dereli and Tercan 2020; Selvan et al. 2020).

Firstly, multi-decadal shorelines were merged as a single feature to create a unified feature class consisting of shoreline features, stored in an exclusive geodatabase. These multiple shoreline features were augmented with five distinct attribute fields in the attribute table; ObjectID, shape, shape length, date and associated uncertainty values. A baseline created at a fixed offset to the 1987 shoreline position was subsequently used to cast transects at mutual distance of 200 m, for computation of change statistics. Among the various change assessment indices provided within the DSAS toolbox, the ones used to determine changes in the Gwadar shoreline during the study period are Net Shoreline Movement (NSM), End Point rate (EPR) and Linear Regression Rate (LRR). NSM, measured in linear units of distance, is a measure of net change in historical shoreline position from the earliest to the latest shoreline (Mishra et al. 2021). On the other hand, EPR and LRR represent the rate of change in the shoreline position. EPR is obtained through dividing NSM by the time lapse between the earliest and latest measurements at each transect (Yan et al. 2021a, b). Whereas, the LRR method involves fitting a least squares regression line to transect intersection locations, whereby the associated slope is used to calculate the rate of shoreline change (Baig et al. 2020; Dereli and Tercan 2020).

Short-term change analysis

Although change indices from DSAS, such as NSM, EPR and linear regression are the most commonly employed techniques for describing shoreline rates of change, these lack consideration of short-term variability in shoreline morphodynamics during the intervening periods, which may hinder or expedite trends of erosion or accretion (Baig et al. 2020; Daoudi and Niang 2021). Specifically, coastlines where complex anthropogenic factors of shoreline evolution dominate cannot be adequately analyzed using these statistical indices because of the assumption of linear trend of evolution, thus misrepresenting change. Therefore, short-term change analysis is required to overcome this discrepancy to highlight morphodynamic trends between different periods. To achieve this objective, erase tool in ArcGIS 10.5 was used to analyze the erosion and accretion processes during shorter time intervals. Five equal-width temporal windows; 1987–1994, 1994–2001, 2001–2007, 2007–2014 and 2014–2021, were used to conduct the short-term change analysis, allowing us to reconstruct the main stages of development of Gwadar shoreline.

Results and discussion

Built-up area evolution

Anthropogenic activities have important implications for the natural coastlines resulting due to population settlement, commercial hub developments and change of natural landcover types in the coastal zones. To analyze the evolution of Gwadar coastal zone specifically due to anthropogenic driving factor, we analyzed the rate of increase in built-up area at various temporal stages. As per the classification results, the area occupied by built-up category in study area, in different years, is given in Fig. 3.

Fig. 3
figure 3

Built-up area statistics

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In 1987, the built-up area occupied a small fraction of total land area i.e., a mere 6.11%. However, after the initiation of CPEC activities and first phase of development of Gwadar port area, it more than doubled to occupy 13.51% of land area, reaching a spatial extent of 7.29 km2 in 2007. Afterwards, it increased by about 3% to cover an area of 9.40 km2 in 2014. Lately, in the second phase of port development from 2014 to 2021, the built-up area increased by 3.5% to cover an extent of 11.4 km2, about 20% of land area. Therefore, we find that within the study period, human settlements, commercial areas, port infrastructure, road communication network and other associated human cultural landscapes in Gwadar coastal zone increased by a magnitude of 8.05 km2.

Long-term change analysis

The long-term morphodynamic evolution of Gwadar shoreline was studied for the period 1987–2021, encompassing a total of 34 years. The change analysis of historic shoreline positions was accomplished using DSAS change indices NSM, EPR and LRR. The results of index values at each transect distinguished major areas of accretion and erosion along the shoreline. Positive values of indices depict accretion of sediments along the coast, while negative values are associated with erosional phenomena. The Western zone exhibited accretion as the dominant littoral process along the shoreline position, along with few sites experiencing erosion. Whereas, the Eastern zone depicted both accretion and erosion, with majority of depositional features associated with Gwadar port expansion. Figures 4 and 5 depict the results of NSM and LRR, representing net shoreline movement and rate of shoreline movement across the coastline respectively.

Fig. 4
figure 4

Net shoreline movement

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Fig. 5
figure 5

Rate of shoreline movement

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Gwadar Western zone comprised of 177 transects, with Transect IDs 1–177. According to the NSM values for the Western zone, the highest shoreline movement of 635.2 m in this zone was observed in Gwadar Western bay near the northern margins of hammerhead peninsula, where major land accretion took place. A jetty structure near this margin was also constructed by the end of 2019, which protected the shoreline against erosion from fluvial and tidal processes by acting as a barrier. Therefore, high NSM values of transects 112 to 118 can be visualized in Fig. 4, which is the spatial location of this structural form. The maximum EPR and LRR rates for the corresponding locations have been calculated to be 18.68 m/yr and 17.78 m/yr respectively. The lowest NSM recorded in this zone showed that the shoreline recessed by an amount of -30.83 m with a rate of movement of -0.9 m/yr as calculated from EPR, and -0.4 m/yr as calculated from LRR. DSAS change indices of Gwadar Western zone indicate that overall this region showed stability from erosive action and did not alter drastically, apart from certain limited portions exhibiting high accretion and sediment trapping.

Gwadar Eastern zone comprised of 171 transects, represented by Transect IDs 178–348. This zone is significant in terms of shoreline change, as highest shoreline movement and land accretion was observed at the site of Gwadar port area in this zone. The high NSM values of transects 225 to 250 in Fig. 4 depict this site of Gwadar port development, accompanied by high LRR rates in Fig. 5. Highest value of shoreline advancement was observed in this zone i.e., 827.9 m towards sea. The corresponding rates of movement of 24.35 m/yr and 42.62 m/yr were calculated from EPR and LRR respectively. A large number of transects with negative NSM value in the extreme Eastern margins of the shoreline inside this zone determines that the erosive action was also significant, with highest value of shoreline recession recorded as -65.67 m. The associated rates of shoreline recession at this location, as computed from EPR and LRR, are -1.93 m/yr and -2.52 m/yr respectively. It is also pertinent to note that Gwadar Eastern zone has experienced much intense human activity and landuse variations than the Western zone, and therefore, it has been more prone to erosion. The DSAS change indices for this zone depict significant areas of both shoreline retreat and advance through a combination of human and natural factors.

Overall, 151 (43%) of the total number of 348 transects along the Gwadar shoreline showed erosion across the study period, whereas, 197 (57%) transects experienced accretion trends. The comparatively high magnitude of accretion and shoreline change along the coastline were mainly due to the anthropogenic developments, in contrast to the small magnitude of shoreline movement due to natural littoral processes. Moreover, discrepancy between the EPR and LRR rates indicates that there were periodic trend changes, and non-uniform acceleration of rate of shoreline change between different temporal phases within the study period. The net shift between shoreline positions of 1987 and 2021 in Eastern and Western zones can be visualized in Fig. 6.

Fig. 6
figure 6

Shoreline position shifts between 1987 and 2021

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Short-term change analysis

Short-term analysis of coastal zone highlights intermittent trends of accretion and erosion that might explain the short-term variability in shoreline movement within the study period. In the case of a coastal area experiencing intense anthropogenic development activities, such as Gwadar, this analysis has allowed us to follow the various stages of infrastructure development on shore. The assessment of evolutionary shoreline movement in different temporal phases was done based on the statistics derived from erase tool operation.

In the first phase of analysis i.e., from 1987–1994, the shoreline morphology was dominantly modified by natural littoral processes with both erosion and accretion occurring in parallel, and the coastal area experienced net expansion of about 0.20 km2. However, in the second phase of evolution, coastal erosion far exceeded accretion and the shoreline experienced net recession, losing about 0.67km2 of coastal land area. The third phase, from 2001–2007, coincides with the initiation of first phase of Gwadar port development. As a result of port construction and vigorous land reclamation in this period, a net land gain of 2.96 km2 took place. The fourth phase of shoreline evolution, from 2007–2014, saw an increase in erosion process with about 0.23 km2 of loss of coastal land area. This observed increase in rate of coastal erosion may possibly be due to the construction of port and harbor in the previous phase (2001–2007), which could have interrupted the natural course of sediment transportation alongshore. Finally, from 2014–2021, the second phase of Gwadar port development and upgradation was initiated, which led to a net accretion of 0.86 km2 of land area in Gwadar coastal zone. These intermittent inverse trends of erosion and accretion can be visualized in Fig. 7.

Fig. 7
figure 7

Erosion and accretion trends

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The results of erosion and accretion depict that, overall, the shoreline advanced towards the sea from 1987 to 2021 with a net addition of 3.07 km2 of land area to Gwadar coastal zone. The majority of accretion occurred due to anthropogenic activities in Gwadar Eastern bay, where development of port infrastructure took place. In addition the appearance of jetties in both Eastern and Western zones of the study area also modified the cartography of natural shoreline and contributed towards accreted area. Apart from these man-made structures, the rest of coastal zone experienced natural erosion and accretion processes of minor magnitude throughout the study period. The coastal modification along Gwadar Eastern bay, representing the focal point of highest shoreline change and intense human activity, has been illustrated in Fig. 8.

Fig. 8
figure 8

Shoreline evolution due to port development in Gwadar Eastern

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Coastal vulnerability assessment

It is important to evaluate the relative vulnerability of various sections of the coastline in order to identify the potential zones prone to natural hazards and human interventions. The analysis of coastal vulnerability is also critical for the present times, considering the imminent sea-level rise, coastal erosion, flooding and storm surges. Based on the scheme proposed by Mahapatra et al. (2015), the entire coast was divided into five vulnerability classes, utilizing LRR change index from DSAS as the basis for classification. Table 2 describes type of the vulnerability classes used and the corresponding criterion, along with the number of transects belonging to each class.

Table 2 Coastline Vulnerability classes, criterion and transect details
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The vulnerability assessment results revealed that about 15.22% of Gwadar shoreline is highly vulnerable, 58.9% of shoreline is moderately vulnerable, while remaining 25.86% is relatively stable and least vulnerable to natural hazards. The sites exhibiting highest erosion rates (-4 m/year < LRR < -1 m/year) in the study area have been identified only in the Eastern zone, along the eastern ends of coastline. Whereas, areas of moderate vulnerability are mostly found along the western stretches of the shoreline inside the Western zone, and at a few sites in the Eastern zone. The head of tombolo peninsula has been found to be relatively stable to change, with net accretion ranging from 0.1 m/year to 1 m/year. Both the Eastern and Western bays show low vulnerability to erosion and have exhibited moderate accretion rates ranging from 1 m/year to 4 m/year. Similarly, the port and harbor construction sites have been evaluated as least vulnerable areas, associated with maximum accretion rates of greater than 4 m/year. There is no region along Gwadar coast which has been classified as very highly vulnerable.

The effect of sea-level variations, hydrodynamics or coastal landuse patterns on shoreline vulnerability is largely determined by the marginal landforms. Along the Arabian Sea, the main geomorphic structures of Gwadar coastal zone primarily consist of rocky cliffs, headlands, terraces, and elevated beaches. The bedrock exposed along the shoreline in the headlands, terraces and rocky cliffs is an aggregation of sandstone and mudstone (Ali et al. 2017), which is significantly resistant to fluvial erosion (Kumar et al. 2022). Specifically, the geological composition of Gwadar headland is of thick shell and coquina beds, with depositions of 15 to 20 m thick massive sandstone and mudstone units (Ali et al. 2017), with an elevation of about 150 m. Similar to this tombolo peninsula, the rest of tall rocky outcroppings in the Arabian Sea are less prone to erosion and therefore exhibit low vulnerability to imminent climatic and hydrodynamic factors. Gwadar port, bays in the Eastern and Western zones and the jetty structures are also identified as areas with no erosion and maximum sediment entrainment, resulting in high accretion rates. On the other hand, various sections along the 12 km long narrow isthmus and beaches have been found to undergo moderate erosion and hence are moderately vulnerable, due to sandy composition. Intense anthropogenic activity and resultant structures have been identified as the most influential factors in changing morphology of the Gwadar coastal area throughout the study period. Dynamism of anthropogenic forms within short spans of time can introduce climatic and hydrodynamic variations, enhancing the coastal vulnerability that requires further scrutiny in our study area.

Conclusion

This study presented a multi-decadal geospatial analysis of Landsat images, from 1987–2021, to determine shoreline evolution, built-up area changes and coastal vulnerability assessment for Gwadar coastal zone. Significant morphodynamic changes were observed during the study period, depicting the impact of rapid economic development and urbanization of Gwadar. As a result of initiative to turn the coastal city into a major deep sea port, human-induced morphological changes were dominant, such as port, harbor, jetties and land reclamation for industrial and other economic developments. Besides, natural changes also influenced the morphology of shoreline through erosive action, albeit with low to moderate intensity. An analysis of anthropogenic influence in this coastal zone showed that the built-up area increased from 6.11% to 19.97% of total land area during the entire study period. The development of Gwadar port in Eastern zone of the study area was the major change in this coastal zone, where a net 827.9 m of shoreline advancement towards Arabian Sea was observed. Apart from land accretion due to land reclamation and harbor development in this zone, erosional phenomenon of minor and moderate magnitude was experienced in Western and Eastern zones respectively. The amount of shoreline recession in both zones was far smaller than the shoreline advancement i.e., 65.67 m and 30.83 m in Eastern and Western zones respectively. From short term trend analysis of littoral accretion and erosion, it was revealed that these processes dominated intermittently during different phases of Gwadar coast evolution. Greatest erosive action was observed during the period 1994–2001, when 8.7 km2 of land area eroded away. However, highest accretion of land area amounting to 3.13 km2 was observed during the period 2001–2007. As per the results of coastal vulnerability assessment, 15.22% of the shoreline is highly vulnerable to erosion and other natural disasters, while rest of the shoreline is relatively stable and less vulnerable. Therefore, this research work highlights the importance of geospatial analytics to gain detailed insights into the kinematics and geomorphology of coastal areas, which is essential to map the evolution of these invaluable resources for their optimal utilization and sustainable management.