Toward Sustainable Wetland Management: A Literature Review of Global Wetland Vulnerability Assessment Techniques in the Context of Rising Pressures

Abstract

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies.

1. Introduction

Wetlands are land–water ecotones formed by interactions between terrestrial and aquatic systems [1].

The Ramsar Convention on Wetlands defines wetlands as areas of marsh, fen, peatland, or water—whether natural or artificial, permanent or temporary—with still or flowing water that may be fresh, brackish, or saline, including areas of seawater up to a depth of 6 m at low tide [2]. Another technical definition states that a wetland is “an ecosystem that arises when inundation by water produces soils dominated by anaerobic processes, which, in turn, forces the biota, particularly rooted plants, to adapt to flooding” [3].

In addition, wetlands are considered amongst the most productive and biologically diverse ecosystems worldwide, contributing significantly to a range of vital ecological functions and values [4].

Although they cover only 6% of the Earth’s surface, wetlands support 40% of global biodiversity, including approximately one-third of all threatened and endangered species [5].

They play a key role in hydrological and biogeochemical processes, providing a wide range of ecological, economic, and socio-cultural services to humankind. These valuable ecosystems contribute to climate regulation by sequestering carbon. They are considered as the most effective carbon sinks on the planet [6,7,8]. They are often referred to as the “kidneys of the Earth” [9]. As natural sponges, they help to purify water, reduce flooding and replenish groundwater. Among their many ecological functions, wetlands help to prevent soil erosion, reduce the impact of natural disasters, and conserve biodiversity [10]. They also provide resources for agriculture, fisheries, tourism, and transportation, while enhancing recreational functions [4].

Despite all these important ecosystem services, wetlands are considered among the most fragile systems and are vulnerable to various external stressors such as climate change [11]. About half of the global wetlands in the world have been degraded since 1990 [12]. Wetland loss is estimated to have accelerated at an average annual rate of 0.78% since 1990 because of human activities such as rapid land transformation, pollution, and fragmentation, combined with the accelerating impacts of climate change [13].

There are different definitions of “vulnerability”; there is no single widely accepted definition of the term [14,15]. It is increasingly conceptualized as a condition that comprises the characteristics of exposure, susceptibility, and adaptive capacity [16]. In other words, vulnerability is an inherent attribute of an ecosystem that reflects its sensitivity and stability in relation to various internal and external factors [17], as well as how it responds to their effects [18]. Meanwhile, wetland vulnerability refers to the degree to which wetland ecosystems are susceptible to various pressures or disturbances that may have negative impacts on their ecological integrity, processes, and functions. It includes three major aspects: the ecological environment, the geological environment, and the quality of the water and soil environment [19].

Given that vulnerability assessment originated in the study of natural disasters, it has been associated with disaster management and adaptation to climate change [20].

Most disaster management studies are based on the relationship between vulnerability, risk, and coping capacity [21]:

$$Vulnerability=risk-coping$$

(1)

In addition, it has progressively advanced from theoretical models to the application of various technical approaches.

There are many methods for vulnerability assessment [22,23,24,25]. Scholars have applied various assessment methodologies across multiple scales to evaluate potential vulnerabilities. Indeed, monitoring and assessing wetland vulnerability are crucial for ecological conservation and management strategies [18]. Thus, it is essential to establish a systematic and comprehensive wetland assessment framework to measure the vulnerability of regional wetland ecosystems [18]. Various previous studies have proposed indices and improved frameworks of indicators, producing results that are easy to interpret and analyze.

Most existing wetland vulnerability assessments are largely based on the ecological vulnerability assessment framework. Notable models derived from this framework include the driver–pressure–dtate–impact–response (DPSIR) model, the ecological–geological–water and soil quality model, the natural influence–human interference model, and the character–process–service model [18]. The pressure–state–response (PSR), along with its extended versions, the pressure–support–state–response (PSSR), and the DPSIR are causal frameworks used to describe the interactions between society and the environment and have been applied to the risk and vulnerability assessment of wetlands [26].

To overcome the challenges of monitoring wetland changes, mainly limitations in spatial and temporal resolution and data availability, it is important to integrate advanced technologies such as remote sensing, geographic information systems (GISs), and artificial intelligence (AI). GIS and remote sensing tools have gained considerable attention in wetland vulnerability research over recent decades, producing scientifically validated results [27]. Methods such as the Analytical Hierarchical Process (AHP) [28], the Frequency Ratio (FR) model, Artificial Neural Network (ANN), and the logistic regression model are the most common methods used in combination with geospatial data for wetland vulnerability mapping. Among these techniques, the FR model has demonstrated strong performance as a hazard assessment method, with a high level of accuracy. Some studies have employed the geodetector model [29], random forest model [30], geographically weighted regression (GWR) model [17], and other methods to explain the impacts of driving factors.

It is important to analyze the various approaches used worldwide to assess wetland vulnerability, in order to inform and strengthen regional assessment frameworks. Several review studies on wetland vulnerability have been conducted. However, these reviews are limited in scope, and there remains a critical need for more comprehensive review studies. For instance, Dube et al., (2023) presented a comprehensive literature review exploring the effects of artisanal mining on wetlands in semi-arid regions of sub-Saharan Africa [10]. The authors highlighted that among the frequentist interpretation methods used in most wetland assessment studies in semi-arid regions of sub-Saharan Africa, Bayesian Belief Networks (BBNs), geospatial and Earth observation analysis, and expert elicitation are particularly prominent. The review work by Bhowmik (2022) highlights the importance of different types of wetlands, the threats they face from both anthropogenic and natural causes, and focus areas for management strategy development [31]. Lamsal et al. (2017) examined investigations conducted in different geographic regions in order to identify existing knowledge gaps and possible implications from such vulnerability in the context of Nepal, along with available adaptation programs and national-level policy support [32].

Although several reviews on wetland vulnerability exist, none comprehensively synthesize the diverse techniques applied across different contexts, regions, and wetland types. Such a synthesis is essential to provide an overview of methodological approaches, identify research gaps, and guide future studies. Hence, the novelty of this study lies in presenting different approaches used to evaluate wetland vulnerability in different contexts. Thus, the aim of this study is to analyze different contributions using the technologies used to monitor wetland changes and to extract valuable knowledge from these experiences. To achieve our main objective, we addressed the following questions:

I.

What methods are used to evaluate wetland vulnerability in various contexts, and what are their main attributes?

II.

How are wetland vulnerability indices formulated, and what environmental, ecological, and socio-economic parameters are typically considered in their development?

III.

What are the principal insights derived from the analysis of existing literature, and which knowledge gaps remain to be addressed?

2. Materials and Methods

To provide a comprehensive overview of how wetland vulnerability is assessed across different geographical areas and types of wetlands, peer-reviewed papers were systematically selected and analyzed based on defined inclusion and exclusion criteria (

Table 1. Selected papers.

Citation	Approach Used	Year	Region	Wetland Type
[33]	GIS	2020	North America	Inland wetland
[34]	GIS + Remote sensing	2018	North America	Coastal wetland
[35]	PSA	2012	Africa	Inland wetland
[36]	GIS	2020	Asia	Inland wetland
[37]	Statistical techniques	2023	South America	Different types
[38]	GIS (MNDWI)	2023	Asia	Inland wetland
[39]	Hydrological and Climate models	2019	Mediterranean Basin	Seasonally flooded wetlands
[40]	DPSIR	2017	Asia	Inland wetland
[41]	FDAHP method	2021	Asia	Urban wetland
[42]	General Framework	2011	Asia	Mountain wetland
[43]	GIS + Remote sensing	2024	Asia	Inland wetland
[44]	AI	2023	Not specified	Different types
[45]	ML +Bivariate models	2020	Asia	Inland wetland
[1]	Remote sensing +AI	2021	Asia	River basin
[46]	WRI index	2022	Asia	Coastal wetland
[47]	WDI index	2022	Asia	Inland wetland
[48]	PSR approach	2014	South America	Coastal wetland
[49]	PTA	2020	Mediterranean Basin	Coastal wetland
[50]	Micro climatic drivers	2016	North America	Coastal wetland
[51]	RSET-MH method	2013	Not specified	Coastal wetland
[52]	Fuzzy MCDM method	2019	Asia	Urban wetland
[53]	SPRC model	2015	Asia	Coastal wetland
[54]	Statistical techniques	2025	Asia	Floodplain wetland
[18]	MGWR model	2024	Asia	Plateau wetland
[55]	GIS	2018	Australia	Monsoonal wetland
[56]	Socio-economicapproach	2019	Asia	Coastal wetland
[57]	Remote Sensing and statistical technique	2023	South America	Coastal wetland
[58]	Statistical methods	2019	Asia	Floodplain wetland
[59]	A comprehensive evaluation system	2020	Asia	Coastal wetland
[28]	Remote sensing +GIS+ econometric models	2022	Asia	Floodplain wetland

and

Table 2. Inclusion/exclusion criteria.

Criteria	Excluded	Included
Type of publication	Non-peer-reviewed publications (editorials, book chapters, meetings, conference posters and abstracts, etc.)	Peer-reviewed publications, which are highly relevant in applied conservation efforts.
Language	Non-English articles	English articles to ensure consistency and accessibility to widely recognized research.
Date of publication	Articles published prior to 2011	Articles published prior to April 2025.
Methods used	Studies limited to general or foundational methods without the use of advanced techniques	Studies that utilize advanced, specialized, or technical methodologies, such as AI, GIS, remote sensing, statistical tools, etc.

). As illustrated in Figure 1, the method was completed in three main steps.

This review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Supplementary Materials).

The included peer-reviewed papers were identified through a keyword search conducted in the Scopus database.

Keywords used in the search included [“wetland vulnerability∗” OR “wetland ∗” OR “wetland ecosystem∗”] OR [“assessment∗” OR “monitoring ∗”] OR [“advanced techniques ∗” OR “Artificial Intelligence ∗” OR “Machine Leaning ∗”] OR [“Remote Sensing ∗” OR “GIS ∗”].

The selected documents were categorized based on the publication date, type of wetland, and the methodologies employed to assess vulnerability. Notably, the use of artificial intelligence (AI) was specifically highlighted, in addition to the numerous studies employing geographic information systems (GISs).

A key aspect of this review is its capacity to offer a comprehensive overview of case studies from diverse regions worldwide. These case studies are systematically categorized by region (Table 1). The importance of this classification consists of highlighting the interest in using advanced techniques for assessing vulnerability all over the world.

Among the reviewed documents, thirteen were published between 2011 and 2020, which was the warmest decade on record according to a new report from the World Meteorological Organization (WMO). In fact, according to NASA scientists, the global average surface temperature in 2011 was the ninth warmest since 1880.

Meanwhile, the rest of the selected documents were published after 2020, specifically to present case studies that address the aftermath of the COVID-19 pandemic—a pivotal event that has fundamentally altered perceptions of health and the environment, fostering greater awareness of the importance of nature for health [60].

This review includes case studies of coastal, seasonally flooded, inland, urban, remote, and tropical wetlands, adopting diverse methodologies to assess wetland vulnerability.

Ranging from general methods and frameworks to highly specific studies employing the latest advanced techniques, this review highlights the importance of assessing wetland vulnerability using diverse approaches across different contexts. This enables the identification of existing gaps in the literature and provides direction for future research.

3. Results

Despite the recognized importance of assessing wetland vulnerability, significant limitations persist in monitoring associated changes. Numerous studies have attempted to address these challenges; for instance, a general approach was adopted in assessing the vulnerability of wetland ecosystem services in three remote wetlands in Nepal’s Himalayan region [42].

It presents a simplified approach to vulnerability assessment that seeks to address challenges related to limited data and resources [42].

Although similar studies and frameworks exist, it remains fundamental to integrate advanced techniques, such as remote sensing and artificial intelligence, to enhance the accuracy, consistency, and scalability of wetland monitoring efforts.

3.1. Different Approaches Used to Assess Wetland Vulnerability

3.1.1. Integrating Multivariate Statistical Techniques in Wetland Vulnerability

Robust statistical techniques are essential for analyzing the multifaceted factors that contribute to wetland vulnerability.

For instance, PCA is an ordination-based statistic data exploration tool that converts a number of potentially correlated variables into a set of uncorrelated variables. In addition, being a non-parametric analysis, it enables the identification of patterns within complex multivariable data [61].

Abson et al. (2012) explore the application of principal component analysis (PCA) techniques to develop information-rich spatially explicit aggregate indices of socio-ecological vulnerability [35]. Pairwise correlation tests were used to reduce the initial set of metrics to a smaller subset of non-highly correlated metrics [35].

Statistical analysis plays a pivotal role in developing vulnerability indexes for accurate assessment [37]. In the case of a study conducted in Costa Rica, cluster analysis served as a confirmatory method for the construction of the vulnerability index (VI) using statistical techniques. Vulnerability drivers were also confirmed using regression analysis. In order to classify wetlands, an ANOVA was fit to compare VI values with Cluster Condition and Cluster Factor groupings [37].

Floodplain wetlands are critical for ecosystem health and human well-being. Yet, they are particularly vulnerable due to their inherent reliance on hydrological dynamics [62,63]. Hence, a study conducted in the Poyang Lake Wetland, China, aimed to quantify vegetation vulnerability in a floodplain wetland, using wetland vegetation mapping. In this case, Pearson rank correlation analysis was used to assess the cumulative response of the vegetation to hydrological variability. In addition, partial correlation analysis was employed, controlling for critical temporal hydrological variability, to evaluate the seasonal vegetation growth carryover (VGC). Vegetation vulnerability was modelled under various extreme hydrological scenarios [54]. Similarly, a study on the Atreyee River Basin in India and Bangladesh explored the physical vulnerability of wetlands using logistic regression (LR) and fuzzy logic (FL) approaches for both pre- and post-dam periods [58]. FL was used to explore the best possible alternative interwoven relation among the parameters employed.

A third study on the Qinghai–Tibet Plateau (QTP), China, investigated spatiotemporal variations in wetland vulnerability between 1990 and 2020 using the ecosystem pattern–process–function framework. The key driving factors were identified by partial least squares structural equation modelling (PLS-SEM) and multiscale geographically weighted regression (MGWR) models [18].

3.1.2. Geospatial-Based Approaches for Assessing Wetland Vulnerability

Compared to point- or transect-based methods, geospatial datasets and numerical model solutions provide valuable resources for conducting well-founded assessments of wetland systems. Geospatial datasets derived from remote sensing and aerial imagery have already been widely used to inventory and classify wetlands [33,64,65]. Hence, many studies use remote sensing-based approaches in order to assess wetland vulnerability. An effort has been made to map flash flood vulnerability and conduct needs assessments in the Haor regions of Bangladesh based on vulnerability classifications derived from satellite remote sensing, geographic information systems (GISs), and econometric models [28].

In addition, a geospatially resolved wetland vulnerability approach was developed in order to assess wetland vulnerability to chronic and episodic physical drivers in New Jersey.

The study proposes a novel approach to dividing a marsh complex into hydrologically delineated marsh units and summarizes the vulnerability index computed at each unit [33]. In this study, principal component analysis (PCA) and cluster analyses were applied to explore the interrelationships among data layers and delineate regions based on shared characteristics.

Similarly, a study conducted by Akumu et al. (2018) in Middle Tennessee, United States, utilized satellite remote sensing combined with GIS to classify and predict inland wetlands, analyze distribution patterns, and assess vulnerability to natural and human-induced stressors in the study area [34]. Performed in the ArcGIS environment, the study permitted the analysis of spatial distribution patterns of delineated inland wetland types using the nearest neighbor analysis technique. The prediction of vulnerability in this case was based on variables including roads, land cover/land use, and climate data [34].

Then, the vulnerability of coastal wetlands in Chile was assessed using a multiscale approach that incorporated drivers related to climate change and land cover change [57]. Multiple drivers of vulnerability were assessed by analyzing multiple remote sensing data on land cover change, wildfires, climatic variables, functional traits of vegetation, water surface area, and the importance for biodiversity. A multifactorial vulnerability index was constructed based on the variables analyzed, which provided a map of coastal wetland vulnerability. The main drivers associated with the vulnerability of each coastal wetland were explored by performing a principal component analysis with Agglomerative Hierarchical Clustering [57].

Additionally, Boyden et al. (2018) investigated the spatial vulnerability of monsoonal wetland habitats to para grass invasion in Kakadu National Park, northern Australia, using GIS with an existing Landsat vegetation map [55]. The study produced maps that are crucial for implementing effective weed control strategies across Australia’s monsoonal wetland habitats.

3.1.3. The Importance of Artificial Intelligence in Assessing Wetland Vulnerability

Remote sensing can be utilized, along with other techniques, such as machine learning (ML), to assess wetland vulnerability. This is exemplified in a study conducted at a Ramsar in Sindh Province, Pakistan [43]. In this case, advanced machine learning methods, including artificial neural networks (ANNs), random forests (RF), and SMILE CART were used to model the nonlinear relationships between the various pressures affecting wetland ecosystems. The random forest algorithm, which is an ensemble learning method operating through the construction of decision trees during training, demonstrates strong effectiveness and robustness in modeling wetland vulnerability, achieving a high accuracy of 89.5% [43]. Based on the vulnerability of the ecological environment, Yang et al. (2021) proposed a new methodology of urban wetland planning and management, as they are important ecosystems that provide multi-dimensional ecological and social services [41,52,66].

Therefore, the fuzzy multi-criteria decision-making approach was applied in this case by incorporating different factors influencing wetland conversion and applying a knowledge-based approach to assess urban-induced wetland vulnerability.

Within the same context, wetland habitat vulnerability (WHV) was assessed in two case studies, combining AI and other advanced techniques [1,45]

As the wetland habitat vulnerability is a complex interactive system governed by a range of non-linearly associated factors, the two studies highlight the importance of modelling this type of vulnerability.

In addition, given the scarcity of WHV studies and the near absence of multi-model approaches, the first case addressed its estimation by combining bivariate models with ML algorithms [45], while the second integrated remote sensing data with swarm intelligence and artificial intelligence techniques [1].

In fact, the first study questioned whether ML models can produce better results than statistical models by using ten conditioning parameters, including wetland hydrological and land use/land cover dynamics. According to the study, the results of the machine learning-based model were more precise, with better predictability and success rate than those of the bivariate models [45].

The second study analyzes the damming effects on wetland in the Punarbhaba River Basin across the Barind plain of India and Bangladesh using a hybrid model that combines swarm intelligence and artificial intelligence to predict wetland habitat vulnerability [1].

Thus, metaheuristic algorithms such as particle swarm optimization (PSO) were used, leading to the creation of optimized ensemble ML algorithms based on satellite image-derived hydrological, surface composition, and water quality parameters that represent key conditioning factors.

The study indicates that the use of swarm and artificial intelligence-based modelling produced highly reliable and consistent results and recommends prioritizing the hydrological variables in the design of habitat vulnerability models [1].

Beyond modeling WHV, artificial intelligence (AI) plays a pivotal role in preserving the environment and developing sustainable systems. AI and ML are powerful tools that can tackle fundamental challenges in water environments, offering innovative solutions for monitoring, managing, and restoring wetlands [67].

Generally, the importance of developing hybrid AI models is undeniable: techniques such as random forest (RF) and convolutional neural networks (CNNs), along with decision trees (DTs), k-nearest neighbors (KNNs), and support vector classifiers (SVCs), play a crucial role in extracting important features, recognizing significant mechanisms across various problems, and detecting hidden patterns within observed data [44,68].

3.1.4. The Use of Driver–Pressure–State–Impact–Response (DPSIR) Model for Wetland Vulnerability Assessment

Vázquez-González et al. (2014) used an integrated coastal index developed by Seingier et al. (2011b) to assess the vulnerability coastal wetlands in the Alvarado Lagoon System (Mexico) through a pressure–state–response (PSR) approach [48].

The coastal vulnerability index (CVI) evaluates the vulnerability of each municipality in the study area and includes indicators of land use and vegetation cover. It evaluates the anthropogenic pressures arising from economic activities and the ecological condition of the area. The CVI is the sum of the pressure sub-index (PSI) and the state sub-index (SSI) (Equation (2)).

$${CVI}_{ij}=\sum \left({PSI}_{ij},{SSI}_{ij}\right)$$

(2)

The PSI integrates the different types of land use, while the SSI incorporates indicators that correspond to marsh vegetation. The structure of the proposed index and its associated indicators allows the results of this study to serve as a reliable baseline for vulnerability assessment.

Malekmohammadi and Jahanishakib (2017), proposed a Multi-Criteria Decision Making (MCDM) method to assess the vulnerability of wetland ecosystem services in the Choghakhor International Wetland landscape in south-western Iran [40]. The driver–pressure–state–impact–response (DPSIR) framework was employed in this case to analyze the interactions between human activities and the environment within the studied wetland system. Thus, a vulnerability index for ecosystem services was calculated as follows:

$$\begin{array}{l}Vulnerabilityvalue\left(V\right)\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=Importanceofthreat\left(I\right)\times Severityonecosystemservices\left(S\right)\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\times Probabilityofoccurence\left(P\right)\end{array}$$

(3)

The MCDM tool applied to this research was the Analytical Hierarchy Process (AHP).

Through five key steps, the used methodology uses threats as drivers and then the drivers as the base to determine management strategies and conservation priorities.

Given the ecological importance of the Choghakhor International Wetland, which has been recognized as a Ramsar site since 2010 [2], other studies have been conducted to assess its vulnerability [36].

3.2. Different Indices Developed to Assess Wetland Vulnerability

Giving the importance of assessing the vulnerability of wetlands, many indices have been developed to assess wetland vulnerability, including different parameters.

The WRASTIC index is a composite environmental index that was used to assess the potential impact of land use activities affecting the Choghakhor International Wetland [36]. The environmental vulnerability assessment conducted for the period from 1985 to 2018 using landscape metrics revealed an increase in human activity-related land uses. In this study, the WRASTIC index was employed as follows:

$${WRASTIC}_{index}=W_R{W}_W+R_R{R}_W+A_R{A}_W+S_R{S}_W+T_R{T}_W+I_R{I}_W+C_R{C}_W$$

(4)

where the index W represents the weight assigned to each factor, and the R index is related to each of the model parameters. Concerning the watershed settings, W represents waste water discharge, R refers to recreational impact, A is agricultural impact, S is the size of the watershed, T indicates transportation routes, I denotes industrial impact, and C stands for vegetation cover.

As previously mentioned, remote sensing techniques play a pivotal role in assessing wetland vulnerability. They enable the computation of spectral water indices, including the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the Modified Normalized Difference Water Index (MNDWI), using the multispectral bands of the satellite images used. NDWI can be expressed as follows:

$$NDWI={\displaystyle \frac{Green-NIR}{Green+NIR}}$$

(5)

The NDWI values range from −1 to 1. The positive value signifies water pixels and the negative value denotes non-water pixels [20].

The Modified Normalized Difference Water Index (MNDWI) has been employed in northwest Bangladesh, a floodplain region [38]. MNDWI can be expressed as follows:

$$MNDWI={\displaystyle \frac{Green-MIR}{Green+MIR}}$$

(6)

Based on a geospatial approach, the wetland vulnerability index (WVI) is developed for coastal wetlands in New Jersey. It is defined as the arithmetic mean of the ranked values (Equation (7)):

$$WVI={\displaystyle \frac{I_1+I_2+I_3+\dots +I_N}{N}}$$

(7)

where $I_i$ is the ranked indicator for data layer I, and N is the total number of data layers.

In this case, the geospatial data used are based on aerial imagery; remote sensing; regulatory information; and hydrodynamic modeling, including elevation, tidal range, unvegetated-to-vegetated marsh ratio (UVVR), shoreline erosion, potential exposure to contaminants, residence time, marsh condition change, change in salinity, sediment concentration, and salinity exposure.

Another wetland vulnerability index (VI) was calculated for Costa Rican wetlands based on location, characteristics of the wetland, land use in the vicinity, and threats [37]. The vulnerability index, whose values range between 0 and 1, is composed of two dimensions: a condition index (CI) and a hazard index (HI).

Wetland damage analysis, ecological risk assessment, and vulnerability assessment are crucial in wetland research. They have different origins but similar concepts [47].

Thus, constructing a wetland index that characterizes the destruction and risk of wetlands such as the wetland risk index (WRI) and the wetland damage index (WDI) is necessary [46,47].

WRI assessment is used to identify the extent of wetland disturbance caused by external stresses. It is based on an external hazard sub-index (EHI) and an internal vulnerability sub-index (IVI) [46].

EHI includes natural disasters and human activity. Internal vulnerability is calculated as a function of wetland area, structure, and function. WRI is calculated as follows (Equation (8)):

$$WRI=EHI\times IVI$$

(8)

The study results indicate that the WRI is significantly impacted by the expansion of built-up land, cropland occupation, road disturbance, and changes in water quality.

While the WDI can be used to assess the extent of damage to different wetland types, it is based on variations in wetland area, human pressure, and a remote sensing-based ecological index [47].

The WDI is composed of the rate of wetland area decline, the human pressure index, and the remote sensing-based ecological index (RSEI). The WDI is adaptable to diverse data types and research scales, enabling the calculation of indicators across different study areas and grid sizes.

Yang et al. (2021) used the FDAHP method, combining the analytic hierarchy process (AHP) and the fuzzy Delphi method (FDM), to determine and evaluate the weights of the main control factors [41].

The vulnerability assessment was performed using the Spatial Analysis module in ArcGIS 10.3, where the integrated assessment value ($S_i$) was derived by summing the weighted values of all relevant control factors, as defined in the following equation:

$$S_i={\sum }_{k=1}^9{W}_k{C}_{ik}$$

(9)

where $W_k$ is the weight of the k-th evaluation factor, $C_{ik}$ is the evaluation value of the k-th evaluation factor of grid i, and $S_i$ is the comprehensive evaluation value of grid i.

3.3. The Importance of Assessing Coastal Wetland Vulnerability

Coastal wetlands offer a wide range of ecosystem services; however, they are especially vulnerable to sea-level rise due to their positioning in low-lying areas. A total of 41.9% of coastal wetlands are affected by climate change, while 52.8% are affected by land use change [57]. It is projected that by the end of the century, 20–78% of global coastal wetland extent could be submerged [69]. Shoreline erosion, eutrophication, sediment supply, and other external forces, such as anthropogenic modification, episodic events such as coastal storms, climate change, and sea level rise, affect the stability of those ecosystems [33].

Thus, given that assessing coastal wetlands vulnerability is of utmost importance, numerous modelling approaches have been used for regional wetland monitoring assessments [49,50,51,53,56].

Leberger et al. (2020) used a multivariate partial triadic analysis (PTA) to quantify climate and land cover change for 236 wetland sites located in the Mediterranean basin, providing a simple and reliable overview of the wetland state in the region [49].

While Webb et al. (2013) presented a global standard for monitoring coastal wetland vulnerability to accelerated sea-level rise (SLR) using the Rod Surface Elevation Table-Marker Horizon (RSET-MH) method—an easily replicable approach for measuring local surface elevation change [51]—Osland et al. (2016) emphasized the importance of incorporating macroclimatic drivers alongside SLR in coastal wetland vulnerability assessments [50].

Cui et al. (2015) analyzed the potential impacts of sea level rise (SLR) on the coastal wetlands in the Yangtze Estuary, China, using the SPRC (source–pathway–receptor–consequence) model [53]. The rate of SLR, subsidence rate, habitat elevation, mean daily inundation duration of habitat, and sedimentation rate were selected as the key indicators. The vulnerability index in this case was calculated by adopting a spatial assessment method based on a GIS platform.

Incorporating socio-economic factors in the assessment of coastal wetlands is crucial. A study by Al-Mahfadi and Dakki (2019) highlights the main socio-economic causes, both direct and indirect, of coastal wetland degradation in Yemen [56]. The results show that livestock, agricultural production, and waste generation are the main socio-economic causes, in addition to tourism and port activities near the wetland area [56]. Moreover, a comprehensive evaluation system and model of coastal wetland ecological vulnerability was constructed and applied to reveal spatial heterogeneity of the ecological vulnerability of the Yellow River Delta Wetland (YRDW), China. A coastal wetland ecological vulnerability (CWEV) evaluation system, which reflects the land–sea dual features, natural and anthropogenic attributes, and spatial heterogeneity of the wetland ecosystem, was established to evaluate the ecological vulnerability of the wetland under the dual influence of land and sea [59].

4. Discussion

A total of 66% of the wetlands studied were localized in Asia (Figure 2), which proves that wetlands are a key feature of ecosystems across this rich continent. Nevertheless, those valuable ecosystems are very vulnerable, and wetlands in Central Asia and South America are the most vulnerable due to the combined effects of land use change [5]. In fact, by 2009, over 33% of the world’s wetlands had been lost, with Asian countries experiencing the greatest areal loss [16]. This explains the growing trend in Asia toward utilizing advanced computational methods for wetland vulnerability assessments.

Thus, composite environmental indexes were developed in different Asian wetlands using remote sensing, along with AI techniques [38,40,41,43,45,46,52].

Studies conducted in Asia can help guide similar research in other parts of the world, taking into account the specific characteristics of each wetland. For instance, in Africa, relatively little attention has been given to assessing wetland vulnerability compared to other ecosystems or regions, partly because wetlands cover only about 1% of the continent’s total surface area [70]. In addition, the usability of spatially explicit data for wetland studies based on remote sensing remains limited in Africa [71]. Therefore, it is important to analyze the various approaches used globally to assess wetland vulnerability, in order to strengthen and adapt regional assessment frameworks within Africa.

Most of the studies were conducted based on environmental geography perspectives, focusing exclusively on ecological factors such as land use, vegetation cover, and associated wetland hydrological dynamics. However, it is also important to include socio-economic factors in this type of research in order to develop more representative composite vulnerability indexes. Thus, the use of the driver–pressure–state–impact–response (DPSIR) model for wetland vulnerability assessment is very important as a foundation for determining management strategies and conservation priorities [40,48].

Developing machine learning-based models is promising, as they tend to produce more precise results, with better predictability and higher success rates than bivariate models. Similarly, the results of studies based on geospatial approaches demonstrate the effectiveness of the methodology employed in identifying and detecting changes in the wetland environment, as well as in quantifying their spatial patterns.

However, several current GIS- and AI-based models often fail to capture the pathways of wetland vulnerability or generate latent variables to simplify the models’ complexity. Beyond assessing factor importance and interactions, identifying causal pathways is essential for effective ecosystem restoration and management. In addition, uncertainty remains a major challenge when interpreting landscape types from remote sensing data [59].

The majority of existing studies on wetland vulnerability have focused on coastal wetlands [33,48,50,51,53,56,57,59], as these are among the most productive ecosystems globally. However, they are also considered some of the most vulnerable ecosystems worldwide [57]. Given their significance, increasingly sophisticated techniques have been adopted to assess coastal wetland vulnerability. Nevertheless, it is equally important to extend such assessments to other types of wetlands, particularly mountain wetlands, which remain understudied [72]. Likewise, insufficient investigations have been dedicated to plateau wetlands [51,57]. Existing research has emphasized the study of large, representative wetlands, often overlooking numerous smaller wetlands [39].

Generally, the framework of wetland vulnerability assessment and the underlying mechanisms have not been well studied [18]. Thus, wetland vulnerability assessments are required to support disadvantaged communities better and build resilience against various hazards. In developing countries, limited data, expertise, and resources restrict the use of advanced technologies for wetland monitoring, highlighting the need for international cooperation. Machine learning can help map and predict wetland vulnerabilities driven by diverse factors, playing a vital role in ecosystem management and conservation planning [43,73].

5. Conclusions

Wetlands are considered the most productive ecosystems. They provide numerous ecosystem services and play a crucial role in maintaining environmental balance and resilience worldwide. Yet, they are threatened by a combination of climate change effects and different anthropogenic activities. Thus, assessing wetland vulnerability is of utmost importance.

Diverse frameworks have been developed for assessing wetland vulnerability in different contexts, generally focusing on two key dimensions, including the value of wetlands and the dynamics of change. Integrating multivariate statistical techniques and geospatial-based approaches in wetland vulnerability analysis plays a pivotal role in developing vulnerability indexes for accurate assessment. The use of the DPSIR model is also emphasized as an effective methodology for developing wetland vulnerability assessments.

This review highlights, particularly, the importance of using artificial intelligence in assessing wetland vulnerability. In fact, advanced machine learning methods, including artificial neural networks (ANNs), random forests (RF), and SMILE CART are used to model the nonlinear relationships among various pressures affecting wetland ecosystems. According to this study, the results of the machine learning-based model are more precise, with better predictive performance and a higher success rate than bivariate models.

This study underscores the critical role of coastal wetlands, highlighting them as the most vulnerable type of wetlands. Monitoring coastal wetland vulnerability to accelerated sea-level rise is necessary. Generally, wetland vulnerability assessment and the underlying mechanisms have not been well studied. It is important to understand that identifying causal pathways is essential for effective ecosystem restoration and management.

Future research and policy efforts should prioritize the inclusion of smaller wetlands in vulnerability assessments to ensure comprehensive ecosystem protection and equitable support. When applied at regional scales, this study can inform policymakers in the development of a standardized national strategy, incorporating additional relevant parameters.