Impact of Spatial Segmentation on the Assessment of Coastal Vulnerability—Insights and Practical Recommendations

Abstract

Coastal areas are dynamic multidimensional systems challenged by the complex interactions between natural, environmental, and human-induced pressures, as well as the ever-changing climate. A comprehensive evaluation of their spatial and temporal features enables the development of effective practices required to apply integrated coastal zone management (ICZM) policies. ICZM seeks to address the vulnerability of coastal areas in an attempt to mitigate their weaknesses and increase their resilience. Hence, coastal vulnerability assessment is a prerequisite to proceed with optimal adaptation or upgrading actions. Currently, assessments are performed by considering different approaches related to dividing coastal areas into segments to observe the spatial variations of vulnerability. The present research seeks to investigate the impact of the spatial segmentation of coastal areas on the assessment of their vulnerability. To achieve this, a case study of the coastal zone of the Municipality of Thebes, located in the Northeastern Corinthian Gulf, Greece, is examined. Five segmentation approaches are applied in terms of a physical-based vulnerability assessment for two different time horizons, (a) the present and (b) the future, by incorporating the climate change impacts. This study allows for optimizing practices to estimate vulnerability parameters and obtain reliable results for practical applications while reducing time-consuming analyses.

1. Introduction

Coastal complexity is a reality portrayed in the continuous interaction of physical, social, ecological, engineering, and management stressors, intensified by the impacts of the ever-changing climate [1]. Coastal urban development, natural hazards, extreme events, biophysical characteristics, and socioeconomic conditions affect coastal dynamics and form vulnerable coastal systems [2,3]. Gaining deep insights into coastal systems’ vulnerability is crucial to fostering a resiliency culture while mitigating their weaknesses [4]. The vulnerability of a coastal system, expressed as its propensity or predisposition to be negatively affected, threatens its capacity to tackle and adapt to potential or foreseen challenges [5]. Hence, understanding and properly assessing the vulnerability of coastal areas are vital tools for decision-makers to build effective management strategies [6].

Coastal vulnerability assessment has been addressed in several studies over the years, dating back to the 1990s, when Gornitz et al. [7] introduced the Coastal Vulnerability Index (CVI), formulated by a set of physical vulnerability parameters. This study stimulated several researchers who sought to modify, enhance or adjust the index according to their research interest. New practices involved the integration of different physical [8,9,10], environmental [9], or socio-economic dimensions [11,12,13] in an attempt to strengthen coastal vulnerability assessment [14]. This broader contextualization of vulnerability assists in planning integrated coastal zone management (ICZM) strategies that tend to explore and resolve the conflicting interactions between the diverse factors affecting coastal complexity [15,16].

The incorporation of coastal vulnerability assessment approaches into ICZM strategies has been already discussed in previous studies, dating from the 1990s [17] to more recent years [18]. Synergetic governance at the sectoral, administrative, and geographical levels is required to develop interactive, adaptive, and promising ICZM strategies [19], thus affecting ICZM planning and application in terms of the prevailing local, regional, and national governance conditions [20]. To this end, the substantial role of the local authorities in delivering integrated solutions for coastal issues is highly recognized [21]. The local authorities are able to gather essential information about local conditions, engage with local stakeholders, make agreements or resolve disputes, and ensure the effective implementation of foreseen actions. This demanding role requires a comprehensive understanding of the weaknesses of the coastal systems they manage and consequently the spatial variations of their vulnerabilities [6].

Recent trends in assessing the spatial changes in coastal vulnerability involve the segmentation of the coastal areas into sections, i.e., division into smaller units [8,10,22], aiming at observing the variations within the examined zone. However, the impacts of this division on the final vulnerability results and the optimal length of these sections have not yet been explored. Current studies have used different length approaches. For example, Kantamaneni et al. assessed the vulnerability of the waterfront of Southampton in the United Kingdom (examined coastline length—21 km) by dividing the coastline into 500 m-long sections [10], as did Pantusa et al. [9] for the Calabria region of Italy (examined coastline length—20 km). Ružić et al. proceeded in delivering detailed vulnerability results for the coastline of Stara Baška located in the northern Adriatic (examined coastline length 7.7 km) by using 5 m-long sections [22], while Vandarakis et al. divided the coastline of Rhodes, Greece (examined coastline length 253 km) into 50 m-long sections [8]. Given the different approaches applied for investigating the spatial changes in vulnerability, one main challenge that ICZM decision-makers should confront is to achieve desired detail in vulnerability assessment reporting within a specific timeframe without sacrificing resources (i.e., human, financial, and equipment). Hence, within the framework of investigating coastal vulnerability, it is crucial to identify whether dividing coastal areas into shorter sections is enforced or vulnerability analyses can be limited by using longer sections.

Based on the above, the purpose of this research is to examine the impact of different segmentation approaches on the spatial changes in coastal vulnerability. To achieve this, an investigation is undertaken in the case study of the coastal zone of the Municipality of Thebes located in the Northeastern Corinthian Gulf in central Greece, where ICZM strategies are developed to address coastal vulnerability and prioritize mitigation actions. Although, as mentioned above, vulnerability assessment practices also involve the estimation of other types of parameters such as socio-economic and environmental [22,23,24], the present study is focused on a physical-based vulnerability assessment aiming at exploring the variations of a set of parameters considered within a coastal engineering framework. The typical CVI method proposed by [7] is enhanced with additional physical parameters considering both current and future conditions (incorporating the climate change impacts) of the study area to examine the spatial variations for two time periods.

2. Materials and Methods

2.1. Study Area

The study area was the coastal zone of the Municipality of Thebes located in the Northeastern Corinthian Gulf (Figure 1). The Corinthian Gulf is a long, semi-enclosed, and very deep gulf in central Greece situated between the Ionian Sea (west) and the Canal of Corinth (east). The coastline length of the study area is approximately 62 km. Its coastal zone is affected by physical (e.g., wave forces and geomorphological conditions), environmental (e.g., the presence of critical habitats or areas of Natura 2000), and human-driven stressors (e.g., urbanization, touristic activities, port facilities, etc.), thus requiring an effective ICZM program that will assist in maintaining the brittle balance between these pressures. Therefore, vulnerability assessment of the coastal zone is essential to identify its weaknesses and proceed with suitable ICZM actions.

Along the coastal zone of Thebes, six coastal areas hold significant importance, namely Sarantis Beach, Agios Nikolaos, Aliki, Livadostra, Kalamaki, and Agios Vasilios. These areas were designated as being of special interest based on the following criteria: (a) intensity of problems and challenges that exist in each area according to the perspective of the stakeholders and the representatives of the municipality; (b) geomorphological conditions (e.g., existence of headlands, on either side of the area, that form an independent coastal cell, a significant change in the shoreline orientation, and a significant change in the sea bottom slope); (c) exposure to the incident wave climate; (d) hydrodynamic and sediment transport conditions; (e) hydrological and sedimentological characteristics; and (f) coastal uses (e.g., port area, swimming beach, etc.). The examined areas are depicted in Figure 1. The length of the coastline of each area is given in

Table 1. Coastline length for each coastal area of special interest in the Municipality of Thebes.

No.	Area	Coastline Length (km)	Coordinates (Greek Geodetic Reference System ‘87)
			Xstart	Ystart	Xend	Yend
1	Sarantis Beach	0.95	402326.71	4232300.36	403191.77	4232230.19
2	Agios Nikolaos	0.95	414343.93	4229713.78	414771.27	4228987.44
3	Aliki	1.88	416021.01	4228236.71	416391.63	4226897.95
4	Livadostra Beach	0.68	421858.52	4228822.67	422558.56	4228608.21
5	Kalamaki	0.30	423067.67	4228238.64	423207.57	4227997.23
6	Agios Vasilios	0.43	424768.13	4226240.16	424695.80	4225888.08

along with its starting and ending coordinates (according to the Greek Geodetic Reference System 1987). Given the geomorphological conditions, all six areas can be considered independent coastal cells, while Sarantis Beach (1), Agios Nikolaos (2), Aliki (3), and Agios Vasilios (6) can be classified as pocket beaches. Each area is characterized by specific features regarding the existing infrastructure, the main wave direction to which each area is exposed, and the identified threats. These features are presented in

Table 2. Characteristic features of the six coastal areas of the coastal zone of the Municipality of Thebes.

No.	Area	Infrastructure	Main Wave Direction	Identified Threats
1	Sarantis Beach	Fishing and leisure harbor	S-SW	Beach erosion and unbound pavement partial failure (Figure 2a)
2	Agios Nikolaos	Fishing and leisure harbor	S-SW	Erosion possibly occurring via reflection phenomena due to the construction of a vertical retaining wall of the pedestrian pavement and road (Figure 2b)
3	Aliki	Fishing and leisure harbor	W	Beach erosion and asphalt pavement partial failure. Expansion of food service establishments onshore along a large part of the beach limiting the beach width (Figure 2c)
4	Livadostra Beach	Absent	S-SW-SE	Absence of Stravopotamos River outfall works (Figure 2d)
5	Kalamaki	Absent	S-SW	Partial failure of the retaining wall of the unbound pavement (Figure 2e)
6	Agios Vasilios	Fishing and leisure harbor	SW	Significant scouring and subsequent partial collapse of the vertical waterfront wall (Figure 2f)

, while representative images of the current coastal conditions, captured during a site visit carried out on 19 July 2021 by the research team members [25], are shown in Figure 2.

2.2. Research Methodology

The present section includes the description of a framework for investigating the impact of using different spatial lengths on assessing coastal vulnerability (Figure 3). Although this framework refers to examining the optimal length for pocket beaches, which are the case study of the current research work, it can be adjusted to the needs of other types of beaches by modifying spatial lengths.

2.2.1. Vulnerability Framework

The direct quantification of vulnerability, the establishment of a metric system, and the adoption of universal assessment criteria have been recognized as challenging tasks [26,27]. Indicator-based or index-based assessments have been proposed to examine the vulnerability of the entire coastal system [14,28]. The cornerstone of an index-based vulnerability assessment lies in the studies conducted by Gornitz et al. [7] and Thieler and Hammar-Klose [29]. These studies examined the physical vulnerability of a coastal system, i.e., its exposure and sensitivity to physical processes such as tidal inundation, sea level rise, erosion–sedimentation, etc. [30,31]. Based on these studies, the CVI was estimated by integrating a set of six physical parameters into Equation (1):

$$\mathrm{CVI}=\sqrt{\frac{\mathrm{a}\times \mathrm{b}\times \mathrm{c}\times \mathrm{d}\times \mathrm{e}\times \mathrm{f}}{6}}$$

(1)

where a represents the geomorphology, b represents the coastline erosion-deposition rate, c represents the coastal slope, d represents the relative sea level rise rate, e represents the mean wave height, and f represents the mean tide range.

Equation (1) is often modified to include additional parameters and/or replace existing ones (e.g., incorporating the mean vertical coastal land movement rate in [8], the beach width in [9], and the land use in [22]). In addition to Equation (1), alternative approaches for assessing coastal vulnerability involve aggregating the scored vulnerability parameters or calculating the geometric mean of these parameters [10,32,33]. Further details about applied equations to deliver a CVI are given in [34]. In the present research, Equation (1) was adjusted to the vulnerability assessment requirements of the considered study area, i.e., the coastal zone of the Municipality of Thebes. Fourteen parameters were considered to estimate the CVI and analyze the spatial variations of vulnerability along the six coastal areas (

Table 3. Physical vulnerability parameters for investigating the spatial changes along the coastal zone of the Municipality of Thebes.

No.	Parameter	Units	Description
P1	Seabed sediment thickness	m	The degree of erodibility of the soil formations considering the geological substratum of the coastal areas
P2	Beach sediment	-	The different types of sediment depending on the resistance of the coastal landforms to erosion, i.e., erodibility
P3	Distance from major faults	m	The proximity to major faults in case of an earthquake incident that may have adverse impacts
P4	Shoreline evolution	m/yr	The hazard of erosion in a coastal area
P5	Land slope	%	The coastal land morphology
P6	Marine slope	%	The coastal marine morphology
P7	Sea level rise due to climate change	m	The coastal inundation due to the impacts of climate change and sea level increase
P8	Mean range of the astronomical tide	cm	The sea level trend due to astronomical phenomena
P9	Storm surge	cm	The local sea level rise due to extreme weather events
P10	Significant wave height	m	The significant wave height representing the mean wave conditions in the study area
P11	Extreme significant wave height	m	The wave height generated from extreme wind speeds potentially leading to coastal flooding and intense erosion phenomena
P12	Cross-shore profile erosion	m	The risk of shoreline retreat due to the combined occurrence of extreme waves and sea level rise in the case of extreme weather events
P13	Beach width	m	The free width between the coastline and the physical (e.g., vegetation, presence of rocks, etc.) or man-made obstacles (e.g., coastal road, vertical waterfront, housing, etc.)
P14	Distance from vegetation	m	The proximity to vegetation increasing the risk of altering the natural landscape

). Further to incorporating the additional physical parameters represented by the codes P12, P13, and P14, all six parameters included in Equation (1) were taken into account with the following modifications:

• The geomorphology (a) was subdivided into three distinct parameters: seabed sediment thickness, beach sediment, and distance from major faults, coded as P1, P2, and P3, respectively.
• The coastal slope (c) was subdivided into two parameters: land slope and marine slope, coded as P5 and P6, respectively.
• The mean wave height (e) was modified to incorporate the mean significant wave height, represented by the parameter P10. This parameter provides an indication of the mean annual wave conditions within the study area. Additionally, the extreme wave height was accounted for and coded as P11. Both parameters fall under the category of wave characteristics.
• The sea level variations (f) were subdivided into two parameters: mean range of astronomical tide (P8) and storm surge (P9).

Therefore, taking into account the aforementioned modifications, the calculation of the CVI within the framework of the current research was based on the modified version of Equation (1) as shown in Equation (2):

$$\mathrm{CVI}=\sqrt{\frac{\mathrm{P}1\times \mathrm{P}2\times \mathrm{P}3\times \mathrm{P}4\times \mathrm{P}5\times \mathrm{P}6\times \mathrm{P}9\times \mathrm{P}10\times \mathrm{P}11\times \mathrm{P}12\times \mathrm{P}13\times \mathrm{P}14}{12}}$$

(2)

Parameters that were deemed to be constant for the study area, namely P8 (mean range of the astronomical tide) and P7 (sea level rise due to climate change), were excluded from the calculation of the CVI.

It is noted that other studies may interpret the impact of the parameters on the vulnerability in a different manner. For example, Ref. [9] described the influence of vegetation in terms of its width, i.e., the width of grasses, plants, trees, etc. Hence, the vulnerability increases when the vegetation width decreases, since the sediment transport and the wave impact act are adversely affected. On the contrary, within the context of this research, vegetation was expressed in terms of its distance from the examined areas and consequently, the greater the distance, the less vulnerable the area. Considering these variations in the interpretation of the parameters, it is important to explicitly justify their interrelationship with vulnerability to avoid misjudgment and erroneous assessment.

The spatial changes in vulnerability of the six coastal areas of the Municipality of Thebes were examined in the framework of two different time periods to assess (a) the current vulnerability linked to the present conditions and (b) the future vulnerability by incorporating the climate change impacts (such as mean sea level rise). Considering that Equation (2) primarily addresses the vulnerability of a coastal system, in relation to future climate change threats (i.e., parameter d: mean sea level rise rate), parameter P7 was specifically considered for future vulnerability assessments.

To assess the future vulnerability of the six coastal areas, the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) [35] was considered herein. Given that an ICZM program is usually carried out over a time horizon of approximately 50 years (i.e., around the year 2070), the Representative Concentration Pathway (RCP) 4.5 was applied to incorporate the effect of climate change and project the physical vulnerability parameters. RCP 4.5 presented almost identical values for the average sea level rise compared to the worst-case scenario of RCP 8.5 for the study area, while more unfavorable forecasts were predicted for the coasts of the Municipality of Thebes for other meteorological variables (e.g., wind speed) [36].

2.2.2. Spatial Segmentation Approaches

As mentioned in the Introduction, this research attempted to investigate the importance of dividing coastal areas into further sub-sections within the context of examining the vulnerability changes on the spatial scale. The effectiveness of planning management actions for coastal areas depends on the optimization of the amount of performed analyses, the time consumed for data collection, the required detailed outcome, and the robustness of the results. Reducing the length of the segments allows for limiting the required number of analyses, thus avoiding tedious and time-consuming pre- and post-processing procedures. On the other hand, decreasing the segmentation length can potentially provide a more detailed vulnerability assessment, thus facilitating a more thorough examination of the vulnerable parts of the areas under investigation. To explore whether an optimal segmentation length can be determined, five spatial segmentation approaches were applied to estimate the vulnerability parameters of the study area (i.e., the six coastal areas of the coastal zone of the Municipality of Thebes):

• Dividing each coastal area into 25 m segments
• Dividing each coastal area into 50 m segments
• Dividing each coastal area into 100 m segments
• Dividing each coastal area into 200 m segments
• Dividing each coastal area into sub-areas based on significant and noticeable variations observed along the coastal area (e.g., presence of human activities and morphological features, a process typically undertaken in coastal engineering studies, leading to division into independent littoral cells) (Figure 3).

2.2.3. Estimation of Vulnerability Parameters

Within the framework of applying an index-based assessment of coastal vulnerability, the estimation of the parameters is achieved by: (a) collecting data from open-data sources, (b) conducting in situ inspections and measurements, and (c) performing numerical simulations and state-of-the-art practices. The present investigation involves all three approaches to achieve a detailed and reliable calculation of the vulnerability parameters’ values, as shown in Table 4.

The estimated values for the vulnerability parameters and the CVI were classified into 5 classes with the percentile approach of 20% of the total number of the values included in each one of the classes. Hence, scoring values from 1 to 5, with 1 denoting the lowest vulnerability and 5 denoting the highest vulnerability, were assigned to each estimated parameter for each segmentation approach. The classification of the parameters was useful for mapping vulnerability information along the examined areas depending on the segmentation approach applied each time. In particular, Geographic Information System (GIS) tools were used to map both the acquired raw and classified vulnerability data, accomplish further advanced analyses, and visualize the vulnerability output [37].

Table 4. Estimation of the physical vulnerability parameters of the coastal areas of the Municipality of Thebes.

Parameter	Method	Current Vulnerability	Future Vulnerability	Change between the Coastal Areas
P1: Seabed sediment thickness	Digitization/rasterization of the results of [38] with GIS tools	●	●
P2: Beach sediment	In situ inspections conducted by the research team members, including visual inspection and evaluation, as well as collection of sample materials for further assessment	●	●
P3: Distance from major faults	Open-data sources (https://zenodo.org/record/4897894 (accessed on 28 April 2023)) and GIS tools	●	●
P4: Shoreline evolution	Use of DSAS methodology [39] by analyzing aerial imagery acquired by both the Hellenic Military Geographical Service (HMGS) for three different years, 1945, 1969, and 1992, and the UAV flights conducted in the year 2021.	●	●
P5: Land slope	Generation of DEMs by the orthophotos provided by the Hellenic Cadastre.	●	●
P6: Marine slope	Digitization of the bathymetrical data acquired by the database “Corine Land Cover (CLC)-Copernicus Land Monitoring Service” (https://land.copernicus.eu/ (accessed on 28 April 2023)).	●	●
P7: Sea level rise due to climate change	Use of the metocean database Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) by using the product: “Water level change time series for the European coast from 1977 to 2100 derived from climate projections” (https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-timeseries (accessed on 24 April 2023)).	-	●	-
P8: Mean range of the astronomical tide	Use of data from the sea level recorder (tide gauge) at the Port of Posidonia acquired by the Hellenic Navy Hydrographic Service (HNHS)	-	-	-
P9: Storm surge	Calculation based on the formulation of [40]	●	●
P10: Significant wave height	Extraction at a nearshore depth of 10 m utilizing wind measurements from HNMS (http://www.emy.gr/emy/en/index_html? (accessed on 6 April 2023)) and performing numerical modeling of wave propagation	●	●
P11: Extreme significant wave height	Extraction at a nearshore depth of 10 m utilizing wind data with a 50-year return period [41] and performing numerical modeling of wave propagation.	●	●
P12: Cross-shore profile erosion	Based on the formulation of [42]	●	●
P13: Beach width	Photointerpretation of the orthophotos	●	●
P14: Distance from vegetation	Photointerpretation of the orthophotos	●	●

The symbols “●” and “”denote the integration of the parameter into the current and/or future vulnerability assessment and whether the values are changing between the coastal areas, respectively. On the contrary, the symbol “-“ denotes that the specific parameter was not considered in the current and/or future vulnerability assessment, and its value was the same for all coastal areas.

Table 4. Estimation of the physical vulnerability parameters of the coastal areas of the Municipality of Thebes.

Parameter	Method	Current Vulnerability	Future Vulnerability	Change between the Coastal Areas
P1: Seabed sediment thickness	Digitization/rasterization of the results of [38] with GIS tools	●	●
P2: Beach sediment	In situ inspections conducted by the research team members, including visual inspection and evaluation, as well as collection of sample materials for further assessment	●	●
P3: Distance from major faults	Open-data sources (https://zenodo.org/record/4897894 (accessed on 28 April 2023)) and GIS tools	●	●
P4: Shoreline evolution	Use of DSAS methodology [39] by analyzing aerial imagery acquired by both the Hellenic Military Geographical Service (HMGS) for three different years, 1945, 1969, and 1992, and the UAV flights conducted in the year 2021.	●	●
P5: Land slope	Generation of DEMs by the orthophotos provided by the Hellenic Cadastre.	●	●
P6: Marine slope	Digitization of the bathymetrical data acquired by the database “Corine Land Cover (CLC)-Copernicus Land Monitoring Service” (https://land.copernicus.eu/ (accessed on 28 April 2023)).	●	●
P7: Sea level rise due to climate change	Use of the metocean database Copernicus Climate Data Store (https://cds.climate.copernicus.eu/) by using the product: “Water level change time series for the European coast from 1977 to 2100 derived from climate projections” (https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-timeseries (accessed on 24 April 2023)).	-	●	-
P8: Mean range of the astronomical tide	Use of data from the sea level recorder (tide gauge) at the Port of Posidonia acquired by the Hellenic Navy Hydrographic Service (HNHS)	-	-	-
P9: Storm surge	Calculation based on the formulation of [40]	●	●
P10: Significant wave height	Extraction at a nearshore depth of 10 m utilizing wind measurements from HNMS (http://www.emy.gr/emy/en/index_html? (accessed on 6 April 2023)) and performing numerical modeling of wave propagation	●	●
P11: Extreme significant wave height	Extraction at a nearshore depth of 10 m utilizing wind data with a 50-year return period [41] and performing numerical modeling of wave propagation.	●	●
P12: Cross-shore profile erosion	Based on the formulation of [42]	●	●
P13: Beach width	Photointerpretation of the orthophotos	●	●
P14: Distance from vegetation	Photointerpretation of the orthophotos	●	●

The symbols “●” and “”denote the integration of the parameter into the current and/or future vulnerability assessment and whether the values are changing between the coastal areas, respectively. On the contrary, the symbol “-“ denotes that the specific parameter was not considered in the current and/or future vulnerability assessment, and its value was the same for all coastal areas.

To increase the robustness of vulnerability results, a comprehensive mapping of the coastal areas was delivered by analyzing unmanned aerial vehicle (UAV) imagery collected during the in situ inspections conducted by the research team members in July 2021. The Digital Elevation Models (DEMs) and the orthophotos of the coastal areas generated by applying the photogrammetry method [25] enabled the three-dimensional reconstruction of objects and terrains in a form that assists in precisely measuring lengths, areas, distances, elevations, etc. [43,44]. The produced orthophotos are depicted in Figure 4 and were used as the baseline for the vulnerability analyses.

Specifically for calculating P10 and P11, a 3rd-generation spectral wave propagation model [45] was incorporated to estimate wave characteristics in the nearshore, later used in the CVI calculations. Although numerical modeling has already been applied in recent research efforts to support the calculation of CVI [8,46], the issue of whether the wave characteristics are obtained in deep or shallow water is not clearly defined. For the calculation of the significant wave height (P10), due to the absence of reliable metocean data (e.g., from https://marine.copernicus.eu/ (accessed on 1 August 2023)), the significant wave height offshore for each of the six coastal areas was estimated based on wind measurements from the station of Velos, maintained by the Hellenic National Meteorological Service, through the empirical method of [47]. Thereafter, the estimated offshore wave characteristics were propagated in the nearshore using the spectral wave model TOMAWAC [45] to obtain the significant wave height values along the contour line with a water depth of 10 m. Extracting wave characteristics at a depth of 10 m is of particular importance for coastal engineering applications [48], especially for pocket beaches like those belonging to the Municipality of Thebes, since the effect of shoaling and refraction can greatly alter the wave field from offshore to nearshore. Moreover, the estimation of the extreme significant wave height (P11) was based on a similar procedure. The offshore wave characteristics at each coastal area were obtained by performing wind-wave generation simulations with TOMAWAC, produced with 50-year return period wind data acquired by [41]. Thereafter, for each of the six coastal areas, a distinct wave propagation simulation was once again carried out to ultimately extract the extreme significant wave heights along the 10 m-depth contour line.

3. Results and Discussion

To ultimately investigate whether the different segmentation approaches of the coastal areas had a noteworthy effect on the calculation of the CVI and the corresponding vulnerability parameters, the results are presented as follows: (i) in Section 3.1, a brief comparative evaluation of each vulnerability parameter for the six coastal areas is carried out (only for the 25 m segregation) to provide initial insights on how to prioritize adaptation measures based on their relative vulnerability; (ii) in Section 3.2, an investigation is performed to assess whether inconsistencies arise between the segmentation approaches and how the latter can potentially affect decision-making; and (iii) in Section 3.3, the identification of critical vulnerability parameters and the examination of their variations for each segmentation approach are undertaken to provide practical recommendations on the optimal segment length for coastal engineering applications.

3.1. Overview of Obtained Vulnerability Parameters

The scoring values of the vulnerability parameters for all six coastal areas and all five segmentation approaches were processed with the QGIS software to map both current and future vulnerability information. Moreover, for each coastal area, the relative pie charts that indicate the percentage of the vulnerability scoring along the coastline were generated. The generated maps and the pie charts for all vulnerability parameters and coastal areas can be found at [49].

A presentation of the vulnerability assessment outcome for each parameter is displayed based on the 25 m segmentation approach (

Table 5. Vulnerability assessment of the six coastal areas of the coastal zone of the Municipality of Thebes for each parameter based on the 25 m segmentation approach.

Parameters	Vulnerability Score	Percentage along the Coastal Area (%)
		Sarantis Beach	Agios Nikolaos	Aliki	Livadostra Beach	Kalamaki	Agios Vasilios
P1: Seabed sediment thickness	1	100	0	0	0	0	0
	2	0	0	0	0	0	100
	3	0	0	0	0	0	0
	4	0	100	100	100	100	0
	5	0	0	0	0	0	0
P2: Beach sediment	1	13	0	11	0	0	18
	2	0	0	0	0	0	6
	3	63	100	36	100	100	76
	4	24	0	0	0	0	0
	5	0	0	53	0	0	0
P3: Distance from major faults	1	100	5	0	0	0	0
	2	0	69	24	0	0	0
	3	0	21	37	0	0	0
	4	0	5	39	0	0	76
	5	0	0	0	100	100	24
P4: Shoreline evolution	1	63	21	36	48	34	18
	2	8	42	12	0	8	0
	3	8	32	11	11	25	0
	4	10	5	22	8	33	12
	5	11	0	19	33	0	70
P5: Land slope	1	21	3	25	15	33	18
	2	21	3	12	11	59	29
	3	26	0	23	18	0	23
	4	21	18	23	19	0	18
	5	11	76	17	37	8	12
P6: Marine slope	1	82	0	0	22	25	6
	2	18	8	8	26	17	94
	3	0	24	28	45	58	0
	4	0	16	35	7	0	0
	5	0	52	29	0	0	0
P9: Storm surge	1	100	0	0	0	0	0
	2	0	0	17	0	100	100
	3	0	0	54	100	0	0
	4	0	89	0	0	0	0
	5	0	11	29	0	0	0
P10: Significant wave height	1	32	0	27	0	0	53
	2	39	0	31	0	0	47
	3	29	0	16	74	8	0
	4	0	74	5	26	92	0
	5	0	26	21	0	0	0
P11: Extr. signif. wave height	1	3	0	54	0	0	29
	2	8	34	17	0	67	59
	3	10	66	4	11	25	12
	4	8	0	25	59	8	0
	5	71	0	0	30	0	0
P12: Cross-shore prof. erosion	1	42	0	54	0	0	0
	2	50	0	17	0	0	6
	3	8	0	4	11	17	82
	4	0	8	25	59	83	12
	5	0	92	0	30	0	0
P13: Beach width	1	8	29	15	59	59	29
	2	16	47	29	22	33	47
	3	39	19	16	11	8	12
	4	26	5	23	4	0	6
	5	11	0	17	4	0	6
P14: Distance from vegetation	1	0	13	40	8	0	0
	2	8	37	21	59	0	24
	3	13	16	11	15	58	35
	4	34	24	19	7	42	29
	5	45	10	9	11	0	12
Coastal Vulnerability Index	1	82	0	7	0	0	18
	2	18	0	23	22	33	76
	3	0	24	29	44	50	6
	4	0	26	29	15	17	0
	5	0	50	12	19	0	0

) for a detailed comparative evaluation. Observing the corresponding CVI values, Agios Nikolaos was identified as the most vulnerable coastal area, with Sarantis Beach being the least vulnerable. The parameters that seem to shift the vulnerability for Agios Nikolaos to higher values (e.g., P5, P6) are closely connected to the mild values of coastal slope which lead to higher vulnerability with respect to the parameters of sea level rise (P9), wave height (P10) and cross-shore profile erosion (P11). On the other hand, Sarantis Beach consistently scored as one of the least vulnerable areas, since it is fairly protected from the prevailing wind directions blowing in the study area and is characterized by significantly steeper slopes.

3.2. Comparative Analysis of the CVI Values for the Different Segmentation Approaches

Once the assignment of the scoring values to the vulnerability parameters was complete, the calculation of the CVI was performed by implementing Equation (2). An intercomparison of the CVI values was carried out for each segmentation approach to conclude whether the spatial length of each segment significantly affects the estimated values. By comparing the CVI values for each segmentation approach between the six coastal areas, Agios Nikolaos was identified as the most vulnerable, while Sarantis Beach was the least vulnerable area (Figure 5 and Figure 6). The same conclusion can be drawn for the scenario of incorporating the climate change impacts.

Notable differences were observed between the estimated CVI values depending on each segmentation approach. These differences can be attributed to a variety of factors, such as the spatial resolution of the numerical models (if they were used to support the vulnerability estimation) and the actual spatial variations of each parameter.

To highlight these differences, the CVI classification values for Agios Nikolaos for the current and future vulnerability assessments are presented in Figure 5a,b, respectively. As far as the current vulnerability assessment is concerned, dividing the coastline into 25 m and 50 m segments provided similar CVI scores for the specific area (Figure 5a). The differences became more noticeable when larger spatial steps (100 m and 200-m) were used to divide the coastline. The division of the coastline into 100 m segments led to 58% of the coastline length being classified as very high vulnerability, while the corresponding percentage for the 200 m segments was 37%. If the 25 m and 50 m segments are considered as the baseline, with the percentages for very high vulnerability being 47% and 53%, respectively, it can be stated that no clear overestimation or underestimation pattern was associated with the increasing spatial segmentation step. For the areas examined, dividing the coastline into sub-areas, even for a straight coastline with parallel depth contours such as the beach of Agios Nikolaos (Sub-area 2-2 in Figure 4), led to a significant overestimation of the vulnerability of the beach, compared to the finer segmentation approaches. Moreover, for the scenario of incorporating the climate change impacts into the future vulnerability assessment, an average transition of about 10% of the total coastline length to very high from high vulnerability scores was observed compared to the scores for the current vulnerability assessment. However, the same conclusions as the present assessment can be drawn with regard to the effect of the segmentation on the future CVI.

The corresponding CVI scores for Sarantis Beach for the current and future vulnerability assessments are shown in Figure 6a,b. Contrary to Agios Nikolaos, the effect of the segmentation for Sarantis Beach (i.e., the least vulnerable area) was not as prevalent, with all segmentation approaches leading to similar scores and percentages for the coastal vulnerability. Therefore, for smaller CVI classification scores, segmentation did not affect the results. To understand the effect of segmentation, an attempt was made to identify important parameters that are affected by spatial segmentation from a coastal engineering perspective, aiming at providing initial guidelines and recommendations on the optimal segmentation length selection.

3.3. Dependency of Vulnerability Parameters on the Spatial Segmentation Length

To define how each physical vulnerability parameter included in Table 3 is affected by the selected segment length, a cause–effect approach must be undertaken to estimate how the vulnerability parameters interact with each other. The main impacts associated with the increased coastal vulnerability are coastline erosion (expressed by parameters P4 and P12) and coastal flooding. The main driving factors for both of these impacts are predominantly the waves (i.e., parameters P10 and P11) and sea level rise (i.e., parameters P7, P8, and P9). In turn, waves are subject to various transformation processes when propagating in shallower waters (e.g., shoaling, refraction, and depth-induced breaking), and consequently, they are affected by parameter P6. The eroding capacity of the waves, apart from the wave characteristics [50], is associated with the capability of each sea-state to set sediments (parameters P1 and P2) into motion [51]. Additionally, the imminent threat of coastal flooding and inundation is correlated with the land slope (parameter P5), with milder slopes increasing the coastal vulnerability significantly. Hence, the identification of vulnerable areas and the subsequent action prioritization and adaptation, from a coastal engineering perspective, focus mostly on counteracting the main causes of erosion and coastal flooding, addressed by designing either “hard” coastal protection structures or nature-based solutions [52]. Considering the above, for the study area under investigation, the parameters that should be examined more closely in the context of identifying coastal vulnerability are the significant and extreme significant wave height (i.e., parameters, P10 and P11, respectively) and the land and marine slope (i.e., parameters P5 and P6, respectively).

The importance of the effect of the segmentation of the coastline and the calculation of the wave characteristics in the nearshore is illustrated in Figure 7 for Sarantis Beach and Aliki. Utilizing nearshore wave characteristics (affected by refraction and shoaling) is considered to provide a better identification of vulnerable coastal areas, instead of proceeding with offshore sea-state wave characteristics [8]. This is especially important for pocket beaches like those examined herein, which are bounded by capes and rocky formations, where refraction significantly alters the wave field.

Even for a coastal area with comparatively low vulnerability, significant vulnerability score variations were obtained for the extreme wave heights when using the different segmentation approaches, especially near morphological features (e.g., the sand spit below the salt marsh is sub-area 3-2) where the effect of wave refraction is dominant and influences the obtained wave heights (Figure 7b). For Sarantis Beach (Figure 7a), variations were observed mostly in the western part of the coastal area, near the fishing shelter, where once again the effect of refraction alters the wave field. Observing the vulnerability scores between the segmentation lengths, it is concluded that the 25 m and 50 m segments are similar. Therefore, for pocket beaches, it is advised to not utilize segments larger than 50 m when estimating coastal vulnerability, with respect to the significant wave height parameters. The numerical model utilized to assess the coastal vulnerability should also adhere to this rule, while the spatial mesh resolution should not be larger than 50 m. However, in the presence of coastal protection works parallel to the shoreline (e.g., detached breakwaters), the point where wave characteristics are extracted should be situated in the shadow area of the structure (and not at the 10 m depth contour). For this case, it is advised to utilize a segmentation of at least ${\mathrm{L}}_{\mathrm{s}}/4$, where ${\mathrm{L}}_{\mathrm{s}}$ is the structure length. When dividing the coastline into sub-areas, the vulnerability was significantly overestimated (sub-area 2.2 was scored with the maximum vulnerability), and the variations of the significant wave height computed by the numerical modeling simulations were not captured.

The effect of the segmentation for the parameter of land slope for Livadostra and Agios Vasilios in terms of the current vulnerability assessment is shown in Figure 8. Similar conclusions to the significant wave height parameters were drawn for the coastal slope parameters. Despite both areas being characterized by relatively straight coastlines, the presence of notable morphological features (e.g., dunes or the stream’s mouth at Livadostra Beach) led to variations in the vulnerability scores for the land slope parameter. The 25 m and 50 m segments had similar scores, with significant differences observed for the spatial steps of 100 m and especially 200 m. The utilization of a singular sub-area for the calculation of coastal vulnerability resulted in an erroneous assessment, since it failed to capture the variations of the land slope parameter.

It should be noted that other important parameters for the assessment of coastal vulnerability, i.e., the geomorphological parameters that vary at a spatial scale significantly larger than the segmentation length of the coastline (e.g., parameter P3: distance from major faults), were not affected by the segmentation. This is illustrated in Figure 9 for the current vulnerability scores of Sarantis Beach and Kalamaki.

Indicatively, to support the above analysis of the dependency of the vulnerability parameters on the spatial segmentation length, a summary of the statistics for the parameters of distance from major faults (P3) and extreme significant wave height (P11) for the different segmentation approaches for the coastal area of Sarantis Beach is included in

Table 6. Parameters’ statistics for the different segmentation approaches for the coastal area of Sarantis Beach.

Parameters	Units	Statistics	25-m	50-m	100-m	200-m	Sub-Areas
P3: Distance from major faults	m	μx	7125.87	7118.93	7119.78	7119.75	7120.71
		σχ	148.35	151.26	151.37	146.44	85.82
		CV	2.08	2.12	2.13	2.06	1.21
		SR	−44.38	−43.55	−43.62	−45.46	−80.46
P11: Ext. signif. wave height	m	μx	1.98	1.96	1.93	2.02	2.05
		σχ	0.48	0.49	0.49	0.45	0.28
		CV	24.19	25.13	25.41	22.16	13.75
		SR	−0.65	−0.60	−0.97	−1.67	−5.05

. Although almost no variations for the mean (${\mathsf{\mu }}_{\mathrm{x}}$) and standard deviation (${\mathsf{\sigma }}_{\mathrm{x}})$ values were noticed, the estimated coefficients of variation (CV) indicated that lower values (i.e., for the parameter P3) denote no vulnerability variations along Sarantis Beach, thus showing that finer segmentation is not required. On the contrary, the extreme significant wave height varied along the specific area, and consequently, detailed vulnerability analysis is necessary. The standardized range (SR) values for parameter P11 also indicate that 50 m segments seem to be the optimal selection for the coastal areas examined in the framework of this research.

To sum up, it is advised to utilize at least 50 m segments to divide the coastline and assess the vulnerability scores of the significant wave height and coastal slope parameters. These parameters are crucial when designing coastal engineering works and adaptation measures, since most actions focus on reducing the nearshore wave heights to combat coastal erosion and enhance the resilience of coastal communities. For parameters that do not vary in such spatial scales (i.e., order of 50 m; e.g., distance from major faults), utilizing larger segments (e.g., 1 km) or even coastal subareas could lead to an acceptable vulnerability assessment.

The herein proposed spatial segmentation of 25 m or 50 m for the assessment of coastal vulnerability holds significant implications for the implementation of integrated coastal zone management (ICZM) strategies. By adopting a finer segmentation approach, the CVI, which serves as a vital tool in ICZM [28,53,54], can provide more detailed and accurate results. The CVI, based on the proposed spatial resolution, allows for a more comprehensive evaluation of vulnerability parameters, such as significant wave height and coastal slope, enabling a better understanding of coastal areas’ susceptibility to natural pressures. This finer level of analysis can aid in the identification of specific vulnerable hotspots along the coastline and prioritize mitigation actions effectively. With the improved precision offered by the proposed segmentation approach, ICZM strategies can be tailored to address the spatial variations of vulnerability observed in coastal areas. The identification of specific coastal segments that exhibit higher vulnerability levels can guide the allocation of resources and efforts towards targeted adaptation and upgrading actions. By focusing on vulnerable hotspots identified through the CVI, ICZM can enhance its efficacy in mitigating weaknesses and increasing the resilience of coastal areas.

Moreover, the adoption of a segmentation approach using segments of at least 50 m aligns with some fundamental principles of ICZM (e.g., holistic approach; participation and collaboration; integrated planning and management; ecosystem-based management; protection and sustainability [55,56]). Firstly, it facilitates a holistic understanding of the complexities and interdependencies within coastal systems, which is crucial for developing integrated management plans. Secondly, the fine-grained analysis allows for better stakeholder engagement and participation, as it provides tangible evidence of vulnerability and the need for mitigation measures. Thirdly, the use of the CVI at a 50 m spatial resolution can aid in the continuous monitoring of coastal vulnerability over time, enabling adaptive management strategies to be implemented as conditions change. Furthermore, the finer resolution provided by the CVI enables a more detailed understanding of the coastal ecosystem’s health and dynamics at different segments.

In conclusion, the research findings regarding the proposed 25 m or 50 m spatial segmentation can significantly enhance the decision-making process, improve the prioritization of mitigation actions, and ultimately contribute to the sustainable management of coastal zones.

4. Conclusions

The present research examines whether the segmentation of coastal areas can potentially affect the estimation of coastal vulnerability and consequently impact decision-making and resource allocation when applying ICZM strategies. The investigation was undertaken by estimating the comparative vulnerability of six coastal areas of the Municipality of Thebes, i.e., Sarantis Beach, Agios Nikolaos, Aliki, Livadostra Beach, Kalamaki, and Agios Vasilios, located in the Corinth Gulf, Greece. The CVI calculation was performed in two different time windows to assess both the current and the future vulnerability, with the latter incorporating the climate change projections.

By investigating the impact of dividing the coastline into (a) 25 m, (b) 50 m, (c) 100 m, and (d) 200 m segments and (e) coastal sub-areas on the vulnerability assessment of the coastal areas, it was concluded that both the 25 m and 50 m segmentation approaches presented a similar behavior, while for the approaches using larger segments, significant variations were observed.

Within the context of considering the inter-dependencies of the physical vulnerability parameters, the significant wave height parameter and the coastal slope were further examined to evaluate if they are significantly affected by the segmentation process. From the comparative analysis, it was verified that the 50 m segmentation approach is the optimal approach to reliably assess the vulnerability of the two aforementioned parameters. The approach using coastal sub-areas to assess these parameters is not recommended, since they exhibit significant variations even in small distances, which were not captured when examining a coastal area in sub-areas with lengths of hundreds of meters. On the other hand, for the geomorphological parameters that exhibit variation in much larger spatial scales compared to the length of the coastal segments, the segmentation in smaller coastal sections did not influence the vulnerability scores. Additionally, the performed analysis showcased the importance of utilizing numerical modeling to estimate nearshore wave characteristics and not in deep water, aiming at obtaining a more comprehensive and concise calculation of the CVI.

The findings of this research have strong implications for selecting the optimal segments to assess coastal vulnerability for pocket beaches, which are prevalent in many countries, especially in the Mediterranean Sea, and obtain reliable results for practical applications while reducing the computational overhead of dividing the coastline into very small (order of 5–10 m) segments. Therefore, decision-makers will be able to optimize resource allocation and prioritize mitigation measures. From a coastal engineering perspective, the accurate and detailed estimation of wave characteristics in the nearshore, as well as the coastal slope, is of paramount importance to identify vulnerable coastal segments and design the appropriate adaptation measures. This investigation can also be applied in terms of other aspects of vulnerability by integrating the technical, environmental, or socioeconomic perspective into coastal vulnerability assessment.