| Home | E-Submission | Sitemap | Contact Us |  
Environ Eng Res > Volume 29(1); 2024 > Article
Safaval, Neshaei, Heidarian, Khanmohammadi, Ghanbarpour, Azizi, Behzadi, and Stefanakis: Studying the effect of Anzali port breakwaters on sedimentation in Anzali wetland using remote sensing


The construction of new breakwaters in Anzali port has had a significant impact on the water body of the Anzali international lagoon. The Anzali wetland is under threat from sediment influx from mountainous regions, and the study used satellite image processing to demonstrate how the construction of new breakwaters impedes the natural transfer of sediment from the lagoon to the sea. The methodology employed a hybrid approach combining two methods: normal water index (MNDWI) and supervised classification (SVM) to detect sediment accumulation in the wetland water zone. Following the construction of new breakwaters in 2009, an island formed and expanded exponentially in parts of Sorkhankol Wildlife Sanctuary’s water body. This phenomenon is attributed to decreased water flow caused by increased cross-section current and volume of water, creating a dam-like function against channel flow leading to the sea. Consequently, sediments and suspended loads settle in Sorkhankol’s water zone, leading to an increase in island area from 0.39 hectares to over 26 hectares during the studied period. Result showed Kappa coefficients by SVM algorithm for years 2002, 2010, 2012 and 2017 which were found to be 0.76, 0.62, 0.71 and 0.86 respectively indicating that SVM outperforms MNDWI in effectively monitoring landform changes.

Graphical Abstract

1. Introduction

Wetlands are ecosystems that hold significant social and environmental importance. They are defined as areas that are submerged by surface or groundwater, natural or artificial, permanent or temporary, static or flowing, with a depth of less than six meters at low tide [1]. However, wetlands are globally threatened due to human activities and other factors [2].
Wetlands are among the most valuable ecosystems in the world due to their delivery of ecosystem services (ES), but they are particularly vulnerable to drivers of land-use change [35]. Moreover, different wetland types, due to their location (e.g., coastal or inland), are affected by various factors, and therefore they need to design approaches to address the human impact [6]. Despite the high value of ecosystem services derived from wetlands, they have been systematically drained and filled for agriculture, urban expansion, and other developments [7]. The loss of wetlands is estimated to be between 50% and 65% worldwide[8]. Human activities pose a serious threat to these ecosystems as they cause habitat homogenization globally [9, 10]. Although it is believed that habitat homogenization may have adverse effects on ecosystem functions, more accurate scientific surveys are needed to confirm this claim [11]. Protected areas have been constructed by governments and decision-makers to reduce the destructive impact of human activities on different ecosystems; however, they seem inadequate in providing efficient habitats for various species [12]. A survey has revealed that only 9% of 1451 migratory bird species are provided with protected areas during their annual cycle [13]. The number of migratory bird species has decreased to half in all major flyways during the last three decades due to insufficient protection, habitat loss and deterioration, and destructive meteorological parameters such as climate changes [14].
Anzali wetland is one of the largest and most critical aquatic ecosystems in Iran that is located on the southwest coast of the Caspian Sea. This vast wetland (15000 ha), which is connected to the Caspian Sea through a canal, is among the first wetlands that were listed in the Ramsar Convention (1975) [15].
Any wetland that meets at least one of the criteria mentioned in criteria Ramsar Convention, can be designated as Ramsar Site. Anzali Wetland meets eight out of nine of these criteria, which reveals its significant ecological values [16, 17].
This wetland serves as an ecotone between terrestrial and marine environments [18], and is facing severe environmental challenges [19, 20], to the extent that it is included in the Montreux list [15]. Pirali Zefrehei et al. showed the decrease in transparency of Anzali wetland from 1985 to 2018. This indicates an increase in the concentration of suspended and organic particles in the wetland, which leads to a decrease in water quality in water bodies [21, 22]. Anzali wetland provides spawning and nursery grounds for commercial species of the Caspian Sea [17]. It is interesting that more than 70% of the birds in the wetland are of the type of winter migrants, among which 26% are globally threatened, and a number of these species are facing critical conditions and extinction [23]. Today, with the increase of human activities and interference in nature, several environmental problems have endangered the ecosystem [24]. In addition to numerous ecological threats, the deposition of sediment carried by entering rivers is a major threat to Anzali Wetland [25].
The largest commercial port on the southern coasts of the Caspian Sea was constructed where the wetland connects to the sea in 1895 to 1914. At first, the construction of the Ghazian and Anzali breakwaters began with a length of 750 meters and 520 meters, respectively, completed in 1914 [26].
For effective mapping and monitoring of wetlands, it is essential to use remote sensing technology that can provide a practical approach specifically for wetlands in remote areas. [27]. Monitoring sediment and suspended solids entering wetlands is crucial for managing and protecting aquatic ecosystems, which can be effectively achieved through remote sensing techniques [2830], since the restoring water quality is expensive [3133].
The indices in the studies related to wetlands are considerably varied, and a combination of multiple remote sensing methods can be applied for a wide range of purposes. In this regard, Qureshi et al. (2020) have simultaneously applied the NDVI vegetation index and LST method to evaluate the surface variation in the Gomishan wetlands using satellite image processing [34, 35]. In another study, Szabó et al. used the NVDI vegetation index for the estimation of sedimentation and vegetation in artificial lakes [36]. Chen et al. (2013) prepared a flooding map for the wetlands in Australia using the MNDWI index [37]. Sharifzadeh and Adhikari have studied the application of the SVM learning algorithm for the extraction of water from the TM images of the Landsat satellite located in the northwest Minnesota and east-central of Wisconsin and compared the results with the normal index of MNDWI. They concluded that MNDWI is more accurate than the SVM algorithm concerning the kappa coefficient and overall accuracy [38]. Karim et al. diagnosis of water body change and sedimentation rate in Moulay Bousselham wetland using MNDWI index [39].
Therefore, according to the literature, remote sensing and its different algorithms can make it possible for researchers to use their new approaches and different potentials in monitoring and managing water bodies, including wetlands.
A remarkable number of studies have been carried out using remote sensing and geographical information system (GIS) for qualitative and quantitative assessment of the Anzali wetlands.
As mentioned earlier, the sediments that are brought into the lagoon through the reservoirs leading to the lagoon, reduce the depth of the water and endanger the life of the lagoon's inhabitants. Before the development of the new breakwaters of Anzali port (before 2009), due to the natural hydrological flow of water from the lagoon to the sea, a significant amount of sediments poured into the sea. But at the same time as the new breakwaters were built, sediment deposition became faster as the speed of water flow decreased. In this research, by using remote sensing techniques, we want to show the effect of the new breakwaters of Anzali port on increasing the amount of sedimentation in the water body of the lagoon, which is in the form of an island at the junction of the Nahangroga channel and the central water area of the lagoon between Anzali International (Sorkhankal Wildlife Sanctuary). Therefore, the difference between this research and previous studies is the importance of wetland sediments in the habitat and habitats of this ecosystem.
Investigations carried out using satellite data have shown that the Anzali wetland has been greatly degraded in the last decades, and the trend of changes in various phenomena, including the type of vegetation and the reservoir area, has tended to increase eutrophication. In addition, the wetland area was strongly affected by the fluctuations of the Caspian Sea level and its backward movement [40].
Mirzajani et al. conducted a study on the biological situation of the wharf zone and its surroundings in the Anzali wetland and the Caspian Sea up to the depth of 20 m within a research project focusing on the simultaneous development of Anzali port and new wharfs. The results of their study revealed that salinity, calcium, magnesium, and chlorure concentration were increased by entering the new wharf area while nutrients loads showed a decreasing trend. Comparisons between previous studies have shown a reduction in the variety, and population of the fauna and an increase in nutrient load in the study area of this research. Ports development and ships traffic increase will intensify the probability of pollution and reduction of ecological variety, consequently. A few studies have also focused on sedimentation in the stilling basin of the port [41]. Aghsaei et al. investigated the effects of land use changes in the sedimentation of the Anzali wetland watershed. Their results revealed that the maximum changes occur in transforming the forest into an agricultural area. This, in turn, can increase of the input sediment load to the wetland due to the increase in soil erosion. This fact can endanger the ecological function of the wetland [42]. Knowledge of land use/land cover changes with high accuracy using satellite images is very important for territorial planning [43, 44].
This article aims to demonstrate the impact of constructing new breakwaters in Anzali on sedimentation in the wetland using satellite image processing. The objective is to highlight the role of decision-makers in managing valuable ecosystems that can cause irreparable changes in the environment and result in environmental problems that may only be partially remedied at significant expense. To achieve this, we applied a combination of the MNDWI index and support vector machine (SVM) to satellite images to clarify the effects of developing breakwaters at Anzali port on Anzali Wetland. Our goal is to establish a meaningful relationship between breakwater development and increased sedimentation in the wetland’s water body using remote sensing techniques.

2. Materials and Methods

2.1. Study Area

Anzali wetland is one of the most extensive wetlands in Iran, which is located on the southern coast of the Caspian Sea in the north of Guilan province. The main connecting canal of the wetland with the sea is located at the end of Sorkhankol wildlife sanctuary with an area of 1156 ha in the east of the Anzali wetland complex, which is located in geographical east longitude 49°27′18″ and 49°24′26″ and geographical latitude 37°26″20′ and 37°23′47″. The watershed of Anzali wetland has ten main tributaries that enter the wetland from different parts (Fig. 1). The main focus of this research is the aquatic area of Sorkhankol wildlife sanctuary.

2.2. Data

The satellite data used in the research were obtained from Landsat 7 and 8 platforms (Enhanced Thematic Mapper Plus and Operational Land Imager sensors) and from the USGS EROS archive [45, 46], which has L1T, attributes for the years 2002, 2010, 2012, and 2017. It is noteworthy to mention, as the beginning of breakwaters’ increase was around 2009, we picked up 2002 as the base year to have a chance of inspecting the wetland in the past years. The focus of the study is investigating the outlet of the Nahangroga canal into the Sorkhankol water body over time, due to its determining role in transferring sediments from Masulerudkhan and Pishrudbar rivers into the wetland and the Caspian Sea. Given the SLC (Scan Line Corrector) issue, although the error of stripes on the images of 2010 and 2012 is evident in the ETM+ sensor, the applied images in the study area are of sufficient quality; therefore, the gap-filling is not necessary.

2.3. Methodology

As mentioned above, to investigate the effects of the construction of the new Anzali breakwaters on the increase in sediment in the Sorkhankol Wildlife Sanctuary, we used the historical remote sensing capability, especially the Landsat satellite image archive. For this purpose, four-year images with different sequences were considered. Then, to correct for atmospheric effects, the ETM + and OLI band data were converted to TOA (above atmospheric) planetary reflectance using reflection coefficients. For instance, the following equation is the method to convert DN values of OLI band data to TOA reflectance without correction for solar angle as follows [4749] (Fig. 2).
where ρλ’ is the TOA planetary reflectance, Mρ is the Band-specific multiplicative rescaling factor from the metadata, Aρ is the Band-specific additive rescaling factor from the metadata, and Qcal is the Quantized and calibrated standard product pixel values (DN).
And then, TOA reflectance with a correction for the sun angle was applied as follows:
where ρλ is the TOA planetary reflectance, θSE is the Local sun elevation angle, and θSZ is the Local solar zenith angle; θSZ = 90° - θSE.
After pre-processing steps, the study area from the original images was cropped. Land use maps are provided using the Support vector machine, a supervised classification technique to recognize and compare the resulting changes from the expansion of breakwaters. Besides, to be more confident and compare the results, the study area was assigned to the water and non-water parts by applying the Modified Normalized Difference Water Index (MNDWI). The following is a brief description of the above methods for extracting the land use maps and the water bodies:

2.3.1. Support vector machines

Support vector machine is a supervised non-parametric statistical learning algorithm. Hence, there is no hypothesis on the underlying data distribution that is appropriate for classifying non-linear data and can assign more classes [5052]. SVMs, especially in remote sensing, often offer higher classification accuracy than traditional methods because of their ability to manage small training datasets successfully [53, 54]. The underlying principle that serves SVMs is the learning process that obeys what is known as structural risk minimization. Following this scheme, SVMs decrease classification errors on hidden data without prior assumptions on the probability distribution of the data [55]. While statistical methods like maximum likelihood estimation usually presume that data distribution is known a priori [50]. Furthermore, literature shows that SVMs are not relatively sensitive to training sample size, and scientists have developed SVMs to successfully work with the limited quantity and quality of training samples [50]. Therefore, this method was used in the present study, due to the above capabilities.

2.3.2. MNDWI index

MNDWI method, as a robust index for extracting water features, has been commonly used [5658]. By developing spectral water indices, water bodies are removed with remote sensing images, which is done by calculating the normalized difference between the two image bands. Then, a suitable threshold is applied to classify the results into two categories: 1) water and 2) non-water features [59].
Due to higher reflectance in band 2 (Green) than in band 5 (SWIR), the values representing the water features have positive values, whereas non-water features have negative values [56]. To divide MNDWI results into two classes, a threshold value (e.g., simply a matter for zero) can be set for MNDWI.

3. Results and Discussion

3.1. Results

Masulehrudkhan is one of the tributaries of the watershed of Anzali wetland, as long as 308 km that flows from the mountain heights upstream of Masuleh historical town along with Pishrudbar River with a length of 307 km towards Anzali wetland and the Caspian Sea. The sizes of this area in Masuleh are one of the poorest areas regarding vegetation suffering from severe soil erosion in this part of the mountain during recent decades. The degree of erosion in this area is significantly high, and the sediments are carried toward the Anzali wetland and the Caspian Sea by the Masulehrudkhan River. The adverse effects of the erosion of the upstream areas affect the Anzali wetland (Fig. 3) [25, 60, 61].
This river is connected to another river called Pishrudbar after entering the Anzali wetland area and before inflowing the water body of Sorkhankol Wildlife Sanctuary. Then, it flows into the Caspian Sea through Nahangroga Canal.
Table 1. shows the amount of sediments which is being carried to the wetland by each tributary river. These data are adapted from the JICA report on prioritizing the river basin during the second phase of the project of Anzali wetland ecological management [17]. It indicates that Masulehrudkhan River, with annual sedimentation of 86000 tons, and Pishrudbar River, with 8200 tons of sedimentation, carry 168,900 tons of sedimentation per year into Sorkhankol wildlife sanctuary.
Images of 2002 were classified into six classes. In the zoomed image, the ending part of Nahang roga Canal, where it joins Sorkhankol wildlife sanctuary, can be seen. The island’s area in 2002 (before constructing the breakwaters) was 0.39 ha (Fig. 4-a1).
The map produced by applying the MNDWI index (Fig. 4-a2) also shows this geographical feature adjacent to the outlet of the canal to Sorkhankol water body in 2002, and its area was estimated to be 0.73 ha.
The images of 2010 were taken one year after the construction of the new marine structures and the formation of stilling basin of the port with a more extensive area. These images were also categorized into six classes. The appearance of a stain as wide as 1.5 ha in the studied area is noteworthy, which can be seen in Fig. 4-b1.
The map produced by applying the MNDWI index in 2010 also shows an increase in the area of this feature, estimating its size as 2.06 ha (Fig. 4-b2).
Subsequently, the images of 2012 (three years after constructing the new breakwaters) were classified into 7 classes. The considerable increase in the island’s area is noticeable in this map during these three years after developing the port, from 0.39 ha in 2010 to 15.28 ha. Besides, the distribution of suspended load in the southern part which involves half of the Sorkhankol water body is recognized (Fig. 4-c1).
As it can be seen in the map by applying the MNDWI index in 2012 (Fig. 4-c2), the increase in the area of this phenomenon is notable comparing the one from 3 years ago. The area in which this index was applied was 11.88 ha.
The image of 2017 was the last image processing in this research (Fig. 4-d1). In this image, in addition to a considerable increase in the island area of 15.25 ha in 2012 to 27.37 ha in 2017, the distribution of suspended load is also noticeable in half of the size of Sorkhankol’s water body area.
The area of the island in 2017 in the map by applying the MNDWI index is calculated to be 25.93 ha, which is by the area obtained by the SVM method (Fig. 4-d2).
As seen in the generated maps, the phenomenon is an island with coordinates of 37°26′16″ N, and 49°27′20″ E that is formed between 2010, and 2017 and its area increased each year. It was essential to address the significant association of the start formation and expansion of the island with the construction of the new breakwaters in Anzali port. Fig. 5-a, produced by the SVM method, shows the process of increase in the area of the island caused by sedimentation. Fig. 5-b, shows the process of increase in the island’s area caused by sedimentation by applying the MNDWI index.
To make a precise comparison between the two methods applied in this study, the kappa coefficient and overall accuracy are calculated for the maps provided by SVM and MNDWI methods. The presented results in Table 2 reveal that SVM showed a good sufficiency for studying the impacts of breakwaters developments on sedimentation increase compared to the MNDWI index, which is a specific method.

3.2. Discussion

Anzali port, as the most prominent trading port on the south coast of the Caspian Sea, has one of the unique locations among the coastlines. Unlike many small and big ports worldwide, this essential and strategic port is built on the joining point of Anzali international wetland with the Caspian Sea connecting via a canal. There are a few numbers of ports with similar properties, among which Velserbroek in the Netherlands can be mentioned as it relates Markermee joining Markermeer Lake to the North Sea via a canal, and other lagoons such as Vistula Lagoon in Poland, Szczecin Lagoon shared by Germany and Poland, and Santa Gilla lagoon in Italy. Fig. 6 shows the similarities between Anzali wetland and these four other lagoons in Europe.
The location and development of Anzali port at the joining point of two aquatic ecosystems with different characteristics have caused numerous problems and difficulties to maintain the port complex. Specifically, the continuous discharge of sediments stocked in the stilling basin of Anzali port leads to adverse environmental impacts on the wetland body from both physical and ecological aspects.
Processing satellite images revealed a considerable settling of sediments that entered Sorkhankol wildlife sanctuary in the Anzali wetland. The location of the island studied in this research is around the joining point of Nahangroga Canal to the Sorkhankol wildlife sanctuary’s water body in coordinates of 37°26′16″ N and 49°27′20″ E.
Surveying the development of the island caused by the sediments on the sides of Nahangroga Canal and at the joining point with Sorkhankol water body area from the maps by SVM and MNDWI index to satellite images during 2002 and 2017, showed a meaningful correlation between the coincidence of construction and development of Anzali port’s breakwaters and the formation of the island in 2010, a year after building the new breakwaters. Mass conservation is the fundamental law in fluid mechanics that can be written in the form of Eq. (4) in case of stability of the discharge of the flow.
Q, A, and V are the flow rate of water per time, a cross-section of the flow, and the flow velocity, respectively. According to law of continuity (mass conservation law), when the cross-section in a flow increase, its speed will decrease consequently. We know that this rule applies to constant flows, but if we suppose that the speed of flow changes in different seasons and conditions, the environmental conditions are constant. This rule can still be applied [6265].
As mentioned before, Anzali port is built on the estuary of the flows of Anzali wetland to the Caspian Sea. In the past, it had faced the challenge of high waves with short marine flows into the port, which was due to the longitudinal situation and direction of the breakwaters. In recent years and considering because the existing wharfs were not sufficient, especially at the port’s rush hours, the project of developing the breakwaters was followed, which made it possible to build more wharfs. Moreover, by expanding the breakwaters, the problem of entering high waves was solved, and the navigation capacity of the port also effectively increased. However, these developments caused challenges regarding intensifying sedimentation. The Convention on Biological Diversity (CBD) must be considered carefully against environmental impact assessment (EIA) to assess any possible negative impact on biodiversity Slootweg and Kolhoff [66]. Despite some well-known limitations, Ecological Impact Assessment (EcIA) plays a vital role in decisions making on projects having probable impacts on biodiversity and ecosystems [67]. Mohammadinejad and Hakimzadeh studied the alternations of waves and the flow pattern caused by constructing new breakwaters using MIKE 21. Their survey showed a decrease in flow velocity and an increase in sedimentation in the pond of the port. After providing a 3D model of wave and flow (FM) and after applying numeral hydrodynamic modeling of the region for different scenarios of wind flow and velocity, they managed to derive the boundary conditions for the local model from the regional one. The numerical results of their simulation showed that new breakwaters caused entrapment of a part of the sediments that used to naturally enter the sea before building them. In other words, the developed part of the port acts as a sedimentary trap [68].
Fathi et al. through studying the new arrangement of the Anzali port breakwaters, concluded that the unique geometry of the breakwaters had caused an increase in the wetland’s water level, which results in a reduction is the water flow velocity from the wetland to the sea. Therefore, water exchange between the wetland and the sea in significantly reduced and the sedimentation rate is subsequently increased in the wetland [69].
Considering the difference between the models used in the mentioned study, and the present research, the consistency of the results confirms that development the breakwaters of Anzalli port had a determining role in accelerating sedimentation in the water body of Anzali wetland in Sorkhankol wildlife sanctuary.
On the other hand, according to the report of a project that was carried out to evaluate the port developing, the elongation of western and eastern breakwaters had no remarkable effects on Anzali wetland. Also, the bioenvironmental benefits of the wetland will increase by applying the development a project because of the increase in discharge of sedimentation and pollution [70]. However, the results of the present paper claim the opposite about developing project in Anzali port. A reduction in water flow velocity and a decrease in the rate of sediment transportation to the Caspian Sea, consequently, are the most important reasons that can be mentioned to support this claim. Newly-constructed breakwaters with a length of 1345 m in the eastern wing and 1690 m in the western wing have altered a depth of 5.5 to 6 m and an area of about 80 ha of the sea to a stilling basin.
As can be seen in Fig. 6-f, sedimentation is carried from the wetland to the sea, but the narrowness of the mouth and an increase in the cross-section of the canal as well as the high amount of water in stilling basin of Anzali port due to its geometric shape, have caused a significant decrease in water flow velocity from the wetland to the sea.
Based on the maps obtained from the website of Guilan Ports and Maritime Administration, it is evident that alternative plans, such as parallel or semi-parallel structures with the coast, would not mitigate flow velocity and sedimentation carry as effectively as the current convergent breakwaters geometry [71].
As a result, these plans were not accepted by Guilan Ports and Maritime Administration, and the current project was implemented.
In addition to the principles outlined in the law of continuity, the presence of a water volume exceeding 4 million m3 and breakwaters with narrow openings serve as a barrier that impedes the flow from the wetland to the sea, increasing the likelihood of sedimentation. This decrease in flow velocity leads to an increase in sedimentation within the wetland and Sorkhankol’s lagoon, which acts as a significant sedimentary trap. Consequently, land areas in the form of islands are formed and developed within the water (Fig. 4-c1 and 4-d1). The majority of sediments in this area consist of suspended loads and delicate bed loads. However, satellite images between 2002 and 2017 reveal that due to new breakwaters, bed sediments settled at the end of Nahang roga Canal, resulting in an alluvial island with a surface area of 25–27 ha. The SVM method and MNDWI index were used to measure the island’s surface area, with results showing that both methods produced similar measurements.
Allahviranloo has also tried to locate the best positions for the breakwaters of Anzali port using multiple-criteria decision-making (MCDM). The results of this study have proven the current option that is applied in the port [72]. However, it is essential to remember that an identical score is designated to the environmental impacts of all of the suggested designs for the breakwater. The main criteria of Allahviranloo’s research are economic, technical, and managerial ones studied in this research. Ignoring the environmental factors and the consequences of construction, especially in dynamic environments such as sea and wetlands, which have been under different environmental pressures, is equal to neglecting sustainable development goals and is only a response to short-term and mid-term necessities. There is no doubt ignoring the ecological consequences of development projects results in enormous costs and adverse direct and indirect effects on the ecosystems. As it was mentioned, the results of the present study revealed that the geometry of the breakwater had an immediate destructive impact on the Anzali wetland.
Kouhzad et al. have proposed solutions based on the ecosystem for sustainable development of the ports for preventing sedimentation in the Anzali port area using MIKE 21 software. They suggested two solutions; 1) keeping the sediments on the move and 2) keeping the sediments outside the area. In keeping the sediments on move to increase flow velocity in the port pond using a jet, the amount of the sediments has noticeably decreased. In the option of keeping the sediments outside by separating the river path by a detour canal, the sedimentation has stopped in the port pond. The possibility of splitting the river from the port pond has been introduced as a sustainable and effective method for the port and the ecosystem-based solution. During the evaluation, the ultimate solution is compared with the conventional design. Using this method in Anzali port has resulted in a more sustainable design. Finally, applying this solution is considered an effective method for obtaining a more sustainable design in the construction phase of the port development in this research. The results of their study have evaluated the low velocity of the sediments flow from the wetland to the port pond as the leading cause of the sedimentation in the port [73]. Therefore, it can be concluded that a reduction in the velocity within the water body of the wetland is higher in the Sorkhankol area due to a higher cross-sectional area that is indicative of a higher chance of sedimentation according to the results for the present study regarding the reason of an increase in sedimentation which is reducing flow velocity.

4. Conclusions

Sedimentation poses a significant threat to the optimal performance of ports. The behavioral functions of marine structures, such as breakwaters, are complex and influenced by numerous input parameters under varying environmental conditions. The costs associated with these structures have increased, making their design more sensitive. The rate of sedimentation in ports and canals is dependent on several factors, including the geometry and location of the port and channel, sediment properties, wave characteristics, and dominant flow patterns in the area [74]. Comprehensive investigations are necessary for marine structure construction projects in estuaries to avoid sedimentation deposit problems and destructive environmental effects. For instance, Idku Lagoon in Abu Quir Bay located in the Nile estuary experienced decreased water depth due to sedimentation resulting from a lack of feasibility studies and design problems with the length and width of the system [75].
The precise estimation of environmental consequences is crucial when developing essential substructures such as ports, particularly in areas like Anzali port where the sea and wetland ecosystem are directly connected. The Caspian Sea, being the largest lake globally, has specific flow patterns due to its vast area. Consequently, Anzali international wetland, which is connected to the sea through a canal ending in Anzali port, is directly impacted by the sea. Given the significance of Anzali International Wetland and the numerous environmental challenges that threaten its ecosystem health, it is imperative to consider factors that exacerbate these threats. Research on Anzali wetlands reveals that one of its significant problems is the influx of a large number of sediments carried by rivers originating from mountains and ending in the wetlands [76]. This study analyzed maps acquired from processing satellite images and found a meaningful relationship between constructing breakwaters at Anzali port simultaneously and expanding an island at Nahangroga Canal's side. The new breakwater has significantly reduced sediment flow velocity from the wetland to the sea by increasing cross-sectional area through forming a new stilling basin covering an area of 66.6 ha.
This study examines the impact of Anzali's breakwater on the depth of the wetland's water body. The construction of the breakwater has created an approximate barrier of 3 to 4 million m3 of water, which slows down the velocity of the water stream entering the sea and increases sediment and suspended load settling time. The lack of a comprehensive environmental impact assessment for civil projects can cause severe damage to the environment, as demonstrated by this research. Anzali wetland is internationally significant as one of the first recorded wetlands in Ramsar's convention, making this article particularly important. Building a port at the meeting point of two critical marine ecosystems with different biological and functional structures requires comprehensive studies that were not conducted during establishment or development. Any manipulation or development must meet environmental impact assessment criteria. Efforts have been made in Iran to protect and restore this wetland including the Anzali Wetland Ecological Management Project, JICA and Iran's Environmental Organization and which provides valuable data for researchers and protection committees to use for better management and restoration.


Conflict of interest

The authors declare that they have no conflict of interest.

Author Contributions

(P.A.S.) Conceptualization, methodology, investigation, visualization, writing original draft; (S.A.N.) Writing review and editing, supervision; methodology in topics related to fluid velocity changes (water flow) and sedimentation, writing review and editing; (P.H.) Methodology in Remote Sensing, writing review and editing; (M.K.) Methodology in Wetland Science, writing review and editing; (F.G.) Methodology in topics related to fluid velocity changes (water flow), writing review and editing; (Z.A.) Methodology in Remote Sensing and GIS, writing review and editing. (S.B.) Methodology in Remote Sensing and GIS, writing review and editing. (A.S.) Methodology in Wetland Science, writing review and editing.


1. Wu H, Zhang J, Ngo HH, Guo W, Hu Z, Liang S, et al. A review on the sustainability of constructed wetlands for wastewater treatment: design and operation. Bioresour. Technol. 2015;175:594–601. https://doi.org/10.1016/j.biortech.2014.10.068
crossref pmid

2. De Groot RS, Fisher B, Christie M, Aronson J, Braat L, Haines-Young R, et al. Integrating the ecological and economic dimensions in biodiversity and ecosystem service valuation. In the Economics of Ecosystems and Biodiversity (TEEB). Ecological and Economic Foundations. 2010;

3. Costanza R, d’Arge R, De Groot R, Farber S, Grasso M, Hannon B, et al. The value of the world's ecosystem services and natural capital. Nature. 1997;387(6630)253–60. https://doi.org/10.1038/387253a0

4. Maltby E, Acreman MC. Ecosystem services of wetlands: pathfinder for a new paradigm. Hydrol. Sci. J. 2011;56:1341–59. https://doi.org/10.1080/02626667.2011.631014

5. Ricaurte LF, María Helena O, Juliana C, Diana L, Johanna A. Max Finlayson, et al. Future impacts of drivers of change on wetland ecosystem services in Colombia. Global Environ. Change. 2017;44:158–69. https://doi.org/10.1016/j.gloenvcha.2017.04.001

6. Zedler , Joy B, Kercher S. Wetland resources: status, trends, ecosystem services, and restorability. Annu. Rev. Environ. Resour. 2005;30:39–74. https://doi.org/10.1146/annurev.energy.30.050504.144248

7. Mitsch WJ, Gosselink JG. The value of wetlands: importance of scale and landscape setting. Ecol. Econ. 2000;35(1)25–33. https://doi.org/10.1016/S0921-8009(00)00165-8

8. Lotze HK, Lenihan HS, Bourque BJ, Bradbury RH, Cooke RG, Kay MC, et al. Depletion, degradation, and recovery potential of estuaries and coastal seas. Science. 2006;312:1806–9. https://doi.org/10.1126/science.1128035
crossref pmid

9. Hewitt J, Thrush S, Lohrer A, Townsend M. A latent threat to biodiversity: consequences of small-scale heterogeneity loss. Biodiversity Conserv. 2010;19:1315–23. https://doi.org/10.1007/s10531-009-9763-7

10. McKinney ML, Lockwood JL. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 1999;14:450–3. https://doi.org/10.1016/S0169-5347(99)01679-1
crossref pmid

11. Alsterberg C, Roger F, Sundbäck K, Juhanson J, Hulth S, Hallin S, et al. Habitat diversity and ecosystem multifunctionality—The importance of direct and indirect effects. Sci. Adv. 2017;3:e1601475 https://doi.org/10.1126/sciadv.1601475
crossref pmid pmc

12. Mora C, Sale PF. Ongoing global biodiversity loss and the need to move beyond protected areas: a review of the technical and practical shortcomings of protected areas on land and sea. Mar. Ecol. Prog. Ser. 2011;434:251–66. https://doi.org/10.3354/meps09214

13. Runge CA, Watson JE, Butchart SH, Hanson JO, Possingham HP, Fuller RA. Protected areas and global conservation of migratory birds. Science. 2015;350(6265)1255–8. https://doi.org/10.1126/science.aac9180
crossref pmid

14. Kirby JS, Stattersfield AJ, Butchart SH, Evans MI, Grimmett RF, Jones VR, et al. Key conservation issues for migratory land-and waterbird species on the world's major flyways. Bird Conserv. Int. 2008;18(S1)S49–S73. https://doi.org/10.1017/S0959270908000439

15. Ramsar C. List of Wetlands of International Importance Included in The Montreux Record. Retrieved from www.Ramsar.org 2020. [Available from: https://www.ramsar.org/document/list-of-wetlands-of-international-importance-included-in-the-montreux-record

16. Criteria TRS. The nine criteria for identifying Wetlands of International Importance. 2014. https://www.ramsar.org/sites/default/files/documents/library/ramsarsites_criteria_eng.pdf

17. JICA. Mid-Term Plan for Conservation of The Anzali Wetland For 2020–2030 Department of Environment Gilan Provincial Government Islamic Republic of Iran. Japan International Cooperation Agency; 2019.

18. Kimbal KD, Kimbal SF. The limnology of the Anzali Mordab, Iran: A study of eutrophication problems. Human Environment Division, Department of the Environment; Iran: 1974.

19. Mirzajani AR, Kiabi B, Jamalzadeh F, Fallahi M, Kamali A, Abdollahpour H, et al. Limnological survay of Anzali wetland data during 1990–2003 by use of GIS system. Iranian Fisheries Research Organization; 2009. Report No: 88-111

20. Panahandeh AM. Investigation of Land Sequence Trend of Anzali Wetland Using Landscape Ecology Approach. J. Wetl. Ecobiol. 2021;13(48)5–22.

21. Truong DD, Tri Doan Quang, Nguyen CD. The impact of waves and tidal currents on the sediment transport at the sea port. Civ. Eng. J. 2021;7(10)1634–49. https://doi.org/10.28991/cej-2021-03091749

22. Zefrehei ARP, Hedayati A, Fallah M. Mapping Changes of Water Quality Parameters Pattern in Anzali International Wetland Using Remote Sensing. J. Water Environ. Technol. 2021;19(3)130–8. https://doi.org/10.2965/jwet.20

23. Ashoori A, Varasteh Moradi H, Hosseini Tayefeh F. The Species Diversity of the Birds of Anzali International Wetland. Biol. Abstr., B. 2021;10:39–54. https://doi.org/10.30473/EAB.2021.56016.1808

24. Mousazadeh R, Ghaffarzadeh H, Nouri J, Gharagozlou A, Farahpour M. Land use change detection and impact assessment in Anzali international coastal wetland using multi-temporal satellite images. Environ. Monit. Assess. 2015;187(12)1–11. https://doi.org/10.1007/s10661-015-4900-0
crossref pmid

25. Khalili Vavdareh S, Shahnazari A, Sarraf A. Investigating Anzali Wetland Sediment Estimation Using the MPSIAC Model. Front. Earth Sci. 2022;10:154 https://doi.org/10.3389/feart.2022.736125

26. Port and Maritime Administration of Guilan P. History. https://anzaliport.pmo.ir/en/general/history2020

27. Martin J. Mapping wetland areas on forested landsacpes using Radarasat-2 and Landsat-5 TM data. 2012;

28. Gholizadeh MH, Assefa M, Melesse , Lakshmi R. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors. 2016;16:1298 https://doi.org/10.3390/s16081298
crossref pmid pmc

29. Klemas V. Remote sensing of wetlands: case studies comparing practical techniques. J. Coast. Res. 2011;27:418–27. https://doi.org/10.2112/JCOASTRES-D-10-00174.1

30. Ritchie JC, Paul VZ, Everitt James H. Remote sensing techniques to assess water quality. Photogramm. Eng. Remote Sens. 2003;69:695–704. https://doi.org/10.14358/PERS.69.6.695

31. Panagopoulos A, Vasiliki G. Brine management (saline water & wastewater effluents): Sustainable utilization and resource recovery strategy through Minimal and Zero Liquid Discharge (MLD & ZLD) desalination systems. Chem. Eng. Process.: Process Intensif. 2022;176:108944 https://doi.org/10.1016/j.cep.2022.108944

32. Panagopoulos A, Vasiliki G. Techno-economic assessment and feasibility study of a zero liquid discharge (ZLD) desalination hybrid system in the Eastern Mediterranean. Chem. Eng. Process.: Process Intensif. 2022;178:109029 https://doi.org/10.1016/j.cep.2022.109029

33. Panagopoulos A, Vasiliki G. Comparative techno-economic and environmental analysis of minimal liquid discharge (MLD) and zero liquid discharge (ZLD) desalination systems for seawater brine treatment and valorization. Sustain. Energy Technol. Assess. 2022;53:102477 https://doi.org/10.1016/j.seta.2022.102477

34. Mafi M, Azizi Z, karimi P, Alemi Safaval P. Investigating the trend of water level changes in Allahabad wetland by using temporal images. Iran. J. Appl. Ecol. 2021;8(2)321–329. 10.22059/ije.2021.311887.1397

35. Qureshi S, Alavipanah SK, Konyushkova M, Mijani N, Fathololomi S, Firozjaei MK, et al. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sens. 2020;12(18)2989 https://doi.org/10.3390/rs12182989

36. Szabó L, Deák B, Bíró T, Dyke GJ, Szabó S. NDVI as a Proxy for Estimating Sedimentation and Vegetation Spread in Artificial Lakes—Monitoring of Spatial and Temporal Changes by Using Satellite Images Overarching Three Decades. Remote Sens. 2020;12(9)1468 https://doi.org/10.3390/rs12091468

37. Chen Y, Huang C, Ticehurst C, Merrin L, Thew P. An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping. Wetlands. 2013;33:823–35. https://doi.org/10.1007/s13157-013-0439-4

38. Sharifzadeh S, Adhikari S. A support vector machine-based water detection analysis in a heterogeneous landscape using Landsat TM imagery. 2020;

39. Karim M, Maanan M, Maanan M, Rhinane H, Rueff H, Baidder aL. Assessment of water body change and sedimentation rate in Moulay Bousselham wetland, Morocco, using geospatial technologies. Int. J. Sediment Res. 2019;34:65–72. https://doi.org/10.1016/j.ijsrc.2018.08.007

40. Zebardast L, Jafari H. Assessing the trend of changes in Anzali wetland using remote sensing and proposing managerial solutions. J. Environ. Sci. 2011;37(57)57–64.

41. Mirzajani A, Sabkara J, Makaremi M, Sayadrahim M, Bagheri S, Ghane A, et al. A biological study in the new harbor zone of the Bandar e Anzali cost. Iranian Fisheries Science Research Institute; 2017. Contract No.: 18830646

42. Aghsaei H, Mobarghaee Dinan N, Moridi A, Asadolahi Z, Delavar M, Fohrer N, et al. Effects of dynamic land use/land cover change on water resources and sediment yield in the Anzali wetland catchment, Gilan, Iran. Sci. Total Environ. 2020;712 https://doi.org/10.1016/j.scitotenv.2019.136449
crossref pmid

43. Dibs H, Alaa Hussein A, Nadhir A, Salwan A. Fusion Landsat-8 thermal TIRS and OLI datasets for superior monitoring and change detection using remote sensing. Emerging Science Journal. 2023;7:428–44. 10.28991/ESJ-2023-07-02-09
crossref pdf

44. Jalilzadeh A, Behzadi S. Machine learning method for predicting the depth of shallow lakes using multi-band remote sensing images. J. Comput. Civ. 2019;3:54–64. https://doi.org/10.22115/SCCE.2019.196533.1119

45. Karimi P, Kherkhah Zarkesh M, Alemi Safaval P, Azizi Z, Yousefi H. Investigation of the effect of flood on the restoration of the water body of selected wetlands in the Molab Watershed by remote sensing, case study: Gori-Balmak Wetland and Poldokhtar triple wetlands. Watershed Eng. Manag. 2022;14(3)362–375. https://doi.org/10.22092/ijwmse.2021.352253.1844

46. USGS. earthexplorer.usgs.gov. 2018;

47. Ihlen V, Zanter K. Landsat 8 (L8) Data Users Handbook: Department of the Interior, U.S. Geological Survey, Version 5.0. EROS, Sioux Falls; South Dakota: Available online 2019 (a)

48. Ihlen V, Zanter K. Landsat 7 (L7) Data Users Handbook: Department of the Interior, U.S. Geological Survey. Version 2.0. EROS; Sioux Falls, South Dakota: Available online 2019 (b)

49. Teixeira P, Cibele X, Jing L. Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using in Situ Measurements. Remote Sens. 2020;12(16)2597 https://doi.org/10.3390/rs12162597

50. Bhavsar H, Panchal MH. A review on support vector machine for data classification. Int. j adv. res. comput. sci. softw. 2012;1:185–9.

51. Mountrakis G, Jungho I, Ogole C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011;66(3)https://doi.org/10.1016/j.isprsjprs.2010.11.001

52. Norouzi E, Behzadi S. Evaluating machine learning methods and satellite images to estimate combined climatic indices. J. Num. Meth. Civ. Eng. 2019;4(1)30–8. https://doi.org/10.52547/NMCE.4.1.30

53. Karimi P, Alemi Safaval P, Azizi Z, Kheirkhah Zarkesh MM, Kavusi Kalashami H. Flood Risk Zoning using Geographical Information System Case Study: Khorramabad Flood in April 2019. Acta Hydrotech. 2023;35(63)89–100. 10.15292/acta.hydro.2022.07

54. Mantero P, Gabriele M, Serpico SB. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans. Geosci. Remote Sens Geosci. Remote Sens. 2005;43:559–70. https://doi.org/10.1109/TGRS.2004.842022

55. Hamoudzadeh A, Behzadi S. Predicting user’s next location using machine learning algorithms. Spat. Inf. Res. 2021;29:379–87. https://doi.org/10.1007/s41324-020-00358-2

56. Ji L, Zhang L, Wylie B. Analysis of dynamic thresholds for the normalized difference water index. Photogramm Photogramm. Eng. Remote Sens. 2009;75:1307–17.

57. Lu S, Wu B, Yan N, Wang H. Water body mapping method with HJ-1A/B satellite imagery. Int J Appl. Earth Obs. Geoinf. 2011;13(3)428–34. https://doi.org/10.1016/j.jag.2010.09.006

58. Xu , Hanqiu . Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006;27(14)3025–33. https://doi.org/10.1080/01431160600589179

59. Sarp G, Ozcelik M. Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. J Taibah Univ. Sci. 2017;11(3)381–91. https://doi.org/10.1016/j.jtusci.2016.04.005

60. JICA. The Study on Integrated Management for Ecosystem Conservation of the Anzali Wetland in the Islamic republic of iran. Department of Environment Gilan Provincial Government Islamic Republic of Iran. Japan International Cooperation Agency; 2005.

61. Noori H, Siadatmousavi SM, Mojaradi B. Assessment of sediment yield using RS and GIS at two sub-basins of Dez Watershed, Iran. Int. Soil Water Conserv. Res. 2016;4(3)199–206. https://doi.org/10.1016/j.iswcr.2016.06.001

62. Baron JS, Poff NL, Angermeier PL, Dahm CN, Gleick PH, Hairston NG, et al. Meeting ecological and societal needs for freshwater. Ecol. Appl. 2002;12:1247–60. https://doi.org/10.1890/1051-0761(2002)012[1247:MEASNF]2.0.CO;2

63. Britannica, Redrawn by authors. Continuity principle. 2021. [Available from: https://www.britannica.com/science/continuity-principle

64. D'Alpaos A, Mudd SM, Carniello L. Dynamic response of marshes to perturbations in suspended sediment concentrations and rates of relative sea level rise. J. Geophys. Res. Earth Surf. 2011. 116F4 https://doi.org/10.1029/2011JF002093

65. NASA. How a Delta Forms. Where River Meets Lake. Retrieved 06-09-2021 2014 [Available from: https://www.nasa.gov/jpl/msl/pia19071

66. Slootweg R, Kolhoff A. A generic approach to integrate biodiversity considerations in screening and scoping for EIA. Environ. Impact Assess. Rev. 2003;23(6)657–81. https://doi.org/10.1016/S0195-9255(03)00114-8

67. Mandelik Y, Dayan Tamar, Feitelson E. Planning for biodiversity: the role of ecological impact assessment. Conservation biology. 2005;19:1254–61. https://doi.org/10.1111/j.1523-1739.2005.00079.x

68. Mohammadnejad Marian H, Hakimzadeh H. Numerical Study of Wave and Current Induced Sedimentation in Anzali Port with Respect to the Developed Breakwaters. 2016;

69. Fathi Ozenbolagh S, Niksokhan MH, Karbasi A. Investigation of water level changes in Bandar and Anzali wetlands under the influence of new arrangement of breakwaters in Bandar Anzali by MIKE-21 model. In : Fourth International Conference on Environmental Planning and Management; Tehran, Iran. 2018;

70. CEPMZ . Hydrodynamic modeling and determination of sedimentation rate of Bandar Anzali marine port and study of its ecological impacts to Anzali wetland. 2013;

71. Islamic Republic of Iran Ports and Maritime Organization. Value engineering in the development plan of Anzali port breakwater section. 2018;

72. Allahviranloo M. Use of Multi-criteria Methods in Choosing the Best Scenario for Designing Anzali Port Breakwater. J. Marit. Transp. Ind. 2016;1(3)4–12.

73. Kouhzad MJ. Providing an ecosystem-based design method for sustainable port development to address sedimentation problems; Case study of Anzali port. Shahrood University of Technology; 2018.

74. Khakpour AM. Investigation of the effect of geometry and morphology of ports on sediment optimization (Case study of Nowshahr port). Shahrood University of Technology; 2016.

75. Frihy OE. The necessity of environmental impact assessment (EIA) in implementing coastal projects: lessons learned from the Egyptian Mediterranean Coast. Ocean Coast. Manage. 2001;44:489–516. 10.1016/S0964-5691(01)00062-X

76. Gharibreza M, Zaman M, Arabkhedri M, Sobh-Zahedi S. The off-site implications of deforestation on sedimentation rates and pollution in Abkenar open water (Anzali Lagoon, Caspian Sea) using radionuclide techniques and sediment quality indices. Int. J. Sediment Res. 2021; https://doi.org/10.1016/j.ijsrc.2021.08.006

Fig. 1
a: Anzali wetland location in Iran. b: Anzali wetland watershed and rivers, Source: JICA Expert Team-1028. C: Sorkahnkol Water body (Case study) in Anzali Wetland Complex and location of the Nahang roga Canal
Fig. 2
The flowchart of the method
Fig. 3
a: Location of the Masulerudkhan River. b: Vegetation cover of the Masuleh mountain area
Fig. 4
Map of developing the island in Sorkhankol wildlife sanctuary, land-use map using SVM (a1: 2002, b1: 2010, c1: 2012, d1: 2017) and the map of the water body area using the MNDWI(a2: 2002, b2: 2010, c2: 2012, d2: 2017)
Fig. 5
a: Changes in the area of the island in Sorkhankol lagoon by SVM (ha). b: changes in the area of the island in Sorkhankol lagoon applying MNDWI index (ha).
Fig. 6
Similarities between a: Anzali wetland and b: Velserbroek in the Netherlands, c: Vistula Lagoon in Poland, d: Szczecin Lagoon in Germany and Poland, and e: Santa Gilla lagoon in Italy, f: Sediment flow from the port canal to the sea
Table 1
Amount of sedimentation carried by each river ending in Anzali wetland [17]
Amount of sediments (tons/year) River
78000 Pirbazar
127000 Pasikhan
82900 Pishroodbar
86000 Masoulehroodkhan
123000 Bahambar
1345000 Morghak and Khalkaie
347000 Palangvar
No Data Khomamrood
No Data Chafrood
Table 2
Kappa coefficient and overall accuracy for the maps of each year by applying SVM
Year Overall Accuracy Kappa
2002 79.38 0.71
2010 71.55 0.62
2012 81.48 0.76
2017 89.07 0.86
Year Overall Accuracy Kappa
2002 80.41 0.73
2010 72.41 0.63
2012 79.62 0.74
2017 87.39 0.85
PDF Links  PDF Links
PubReader  PubReader
Full text via DOI  Full text via DOI
Download Citation  Download Citation
Editorial Office
464 Cheongpa-ro, #726, Jung-gu, Seoul 04510, Republic of Korea
TEL : +82-2-383-9697   FAX : +82-2-383-9654   E-mail : eer@kosenv.or.kr

Copyright© Korean Society of Environmental Engineers.        Developed in M2PI
About |  Browse Articles |  Current Issue |  For Authors and Reviewers