Comparative Modeling of Coastal Clay Layer Depths Using Spatial Interpolation Techniques Versus Conventional Methods: A Case Study in Chonburi Province, Thailand

1. Introduction

The emergence of Thailand’s coastline traces back to approximately 10,000 years, attributed to shifts in sea levels leading to sedimentary deposition. Over time, this coastline has evolved into significant morphological characteristic signifying coastal alterations [1].

The coastal regions of the Gulf of Thailand host a delicate balance of ecological systems, where land meets sea in a dynamic interplay of geological processes. Among the critical factors shaping these coastal landscapes, sedimentation stands as a fundamental force, influencing shoreline stability, habitat formation, and land-use dynamics. In this context, understanding the sedimentary rates of coastal soils becomes paramount for assessing environmental changes and implementing sustainable coastal management strategies.

In the province of Chonburi, situated along the eastern coast of Thailand as depicted in Figure 1, the intricate relationship between land and sea unfolds with particular significance. Chonburi, renowned for its vibrant coastal communities and thriving economic activities, also grapples with the challenges posed by coastal erosion, sediment deposition, and land subsidence. Against this backdrop, investigating the sedimentary rates of coastal soils emerges as a pertinent endeavor, offering insights into the evolving coastal landscape and its implications for both natural and human systems. Chonburi Province boasts a coastline stretching roughly 157 kilometers, extending toward Rayong Province [2]. Situated on the continental shelf, this coast exhibits a gradual incline. Predominantly characterized by sandy beaches, its formation dates back to a sea-level decline during the Holocene period, around 4000–5000 years ago.

Understanding the depth and distribution of clay layers along coastlines is of paramount importance due to its multifaceted implications for coastal management, infrastructure development, environmental conservation, and climate change adaptation. Studying clay layer depth along coastlines is crucial for several reasons as follows:

• Coastal erosion and stability: Understanding the depth and distribution of clay layers can provide insights into the stability of coastal landforms. Clay layers often play a role in stabilizing coastlines by resisting erosion and providing structural support to coastal features such as cliffs and bluffs. Knowledge of clay layer depth helps in assessing the vulnerability of coastal areas to erosion and landslides [3, 4, 5].

• Groundwater management: Clay layers can act as barriers to groundwater flow, influencing aquifer recharge rates and groundwater quality. Studying clay layer depth helps in identifying areas where groundwater resources may be confined or protected from contamination. This information is crucial for sustainable groundwater management along coastlines, especially in areas with high population density and intensive agricultural activities [6, 7].

• Coastal infrastructure development: Clay layers affect the feasibility and cost-effectiveness of coastal infrastructure projects, such as harbors, ports, and coastal defense structures. Knowledge of clay layer depth allows engineers to design foundations and structures that can withstand coastal erosion, wave action, and other environmental factors. Understanding the spatial distribution of clay layers is essential for selecting suitable locations for infrastructure development and minimizing risks associated with construction [8, 9].

• Environmental management: Clay layers influence the ecological dynamics of coastal ecosystems by regulating sediment transport, nutrient cycling, and habitat availability. Changes in clay layer depth can affect the distribution and abundance of coastal flora and fauna, including wetland habitats and marine biodiversity. Studying clay layer depth helps in assessing the impact of human activities, such as dredging, land reclamation, and coastal development on coastal ecosystems and guiding conservation efforts [10, 11].

• Climate change adaptation: Coastal areas are particularly vulnerable to the impacts of climate change, including sea-level rise, storm surges, and coastal flooding. Understanding the depth and distribution of clay layers is essential for assessing the resilience of coastal landscapes and identifying adaptation strategies to mitigate the risks associated with climate change. Clay layers may play a role in natural coastal defense mechanisms, and their study can inform coastal management strategies aimed at enhancing resilience and reducing vulnerability to climate-related hazards [12].

This study aims to investigate the spatial distribution of clay layer depth in Chonburi, Thailand, by employing various spatial interpolation techniques, such as Inverse Distance Weighting (IDW), Natural Neighbor, Spline, and Ordinary Kriging. The performances of the spatial interpolation techniques were evaluated to identify the most suitable model for estimating clay layer depth along the coastline. The visualization of these interpolation surfaces was compared with conventional profiles and cross-sections commonly used for representing clay layer depth.

2. Study area

Chonburi Province, situated in the eastern region of Thailand, holds significant geographical and ecological importance, making it an ideal study area for examining sedimentary rates along the Gulf of Thailand coast. Its location along the eastern seaboard of the Gulf of Thailand, between latitudes 12°30′N and 13°36′N and longitudes 100°40′E to 101°45′E as depicted in Figure 1, exposes it to various environmental factors influencing sediment deposition.

The province’s coastline stretches approximately 157 kilometers, extending toward Rayong Province, and features predominantly sandy beaches with a gentle slope. This coastal morphology, shaped by sea-level fluctuations over millennia, offers a unique opportunity to investigate sedimentary processes and rates in a dynamic coastal environment [2].

Chonburi Province’s proximity to Bangkok, located just 79 kilometers away [13], facilitates accessibility for research purposes while presenting contrasts between urbanized and natural coastal settings. Moreover, its administrative division into 11 districts [14] provides a comprehensive spatial framework for sampling and analysis.

Chonburi, located in Thailand’s eastern region along the Gulf of Thailand, experiences significant coastal dynamics due to its geography, human activities, and natural processes. Here are some key aspects:

• Coastal erosion and deposition: Chonburi’s coastline is subject to erosion and deposition processes. Erosion occurs when waves and currents remove sediment from the shore, often exacerbated by human activities such as coastal development, which can disrupt natural sediment transport. Deposition occurs when sediment settles on the coastline, influenced by factors like wave energy and sediment supply.

• Natural factors: Chonburi’s coastal dynamics are influenced by natural factors, such as tides, storms, and sea-level variations. Tropical storms and monsoons can bring intense waves and rainfall, affecting erosion rates and sediment transport. Sea-level rise due to climate change also poses a long-term challenge to coastal stability.

• Human activities: Rapid urbanization and tourism development in Chonburi have altered the coastal landscape. Construction of ports, resorts, and residential areas often involves coastal modification, which can disrupt natural sediment flows and increase erosion in some areas while promoting deposition in others.

• Ecological impact: Coastal dynamics in Chonburi affect marine and coastal ecosystems. Changes in sedimentation patterns can impact habitats for marine life, while erosion can threaten mangrove forests and other coastal vegetation that provide crucial ecosystem services, such as shoreline protection and habitat for biodiversity.

• Management and mitigation: To manage coastal dynamics, Chonburi employs various strategies, such as beach nourishment (adding sediment to eroded beaches), seawalls, and mangrove restoration. These efforts aim to stabilize shorelines, protect infrastructure, and preserve natural habitats.

• Socioeconomic implications: Coastal dynamics also have socioeconomic implications for Chonburi. Tourism, fishing, and port activities are vital to the local economy but can be vulnerable to coastal hazards. Sustainable coastal management practices are increasingly important to balance development with environmental and societal needs.

Overall, understanding and managing coastal dynamics in Chonburi involves a multidisciplinary approach that considers both natural processes and human activities to ensure the long-term sustainability of the region’s coastal resources.

Chonburi, located in Thailand’s eastern region along the Gulf of Thailand, boasts diverse and significant ecological features, despite its heavily urbanized and industrialized landscape. The following are some notable ecological aspects of Chonburi:

• Mangrove forests: Chonburi is home to several mangrove forests, particularly in areas like Bang Phra and Khao Sam Muk. Mangroves provide crucial habitats for various marine species, protect coastlines from erosion, and serve as nurseries for fish and other aquatic organisms.

• Marine biodiversity: The coastal waters off Chonburi support a rich diversity of marine life, including coral reefs, seagrass beds, and numerous fish species. These ecosystems contribute to local fisheries and tourism activities.

• Natural reserves: Chonburi includes natural reserves and protected areas like Khao Kheow-Khao Chomphu Wildlife Sanctuary and Khao Sam Muk-Muang Pattaya. These reserves help conserve biodiversity, including endangered species such as gibbons and various reptiles.

• Environmental challenges: Despite its ecological richness, Chonburi faces environmental challenges, such as habitat loss due to urbanization and industrialization, pollution from urban runoff and industrial discharge, and coastal erosion. Efforts are ongoing to balance development with conservation through sustainable practices and conservation initiatives.

• Community conservation efforts: Local communities and organizations in Chonburi actively participate in conservation efforts, including mangrove restoration projects, beach cleanups, and education programs aimed at raising awareness about environmental issues and biodiversity conservation.

In summary, while Chonburi’s rapid development has posed challenges to its ecological health, efforts to protect and conserve its diverse ecosystems are crucial for sustaining the region’s natural heritage and supporting both local livelihoods and tourism.

Coastal erosion has long been a persistent issue in Chonburi Province, attributed to both natural phenomena and human activities. The erosion rate has been reported at 1–5 meters per year. Naturally occurring erosion is primarily driven by seasonal waves during the southwest monsoon from May to August. Anthropogenically induced erosion results from an unbalanced sediment budget and the construction of man-made coastal structures, such as piers, seawalls, revetments, and reclamations. The interaction between coastal erosion and clay layers is critical in shaping the stability and resilience of coastal environments. Clay layers, due to their composition and properties, play a significant role in coastal sediment dynamics. They often act as barriers that reduce the infiltration and drainage of water, which can help stabilize shorelines by minimizing the loss of underlying sediment and mitigating the erosive impact of wave energy. This natural protection is especially valuable in coastal areas where erosion poses a threat to infrastructure, habitats, and communities. However, despite their protective qualities, clay layers can themselves be vulnerable to erosion under certain conditions. When the overlying sediment or vegetation cover is disturbed or removed—whether due to natural processes like storm surges or human activities such as coastal development or dredging—the exposed clay becomes susceptible to erosion by wave action. This can lead to the destabilization of the coastline and exacerbate erosion rates in affected areas.

Therefore, Chonburi was selected as the study area due to its strategic location in the Gulf of Thailand and its diverse coastal environments, ranging from sandy beaches to mangrove forests. By focusing on Chonburi Province as a case study, researchers can delve into the sedimentary dynamics of coastal soil in the Gulf of Thailand, shedding light on crucial processes shaping the region’s coastal landscapes and informing sustainable coastal management strategies.

3. Methodology

3.1 Data collection

The data were collected on January 18, 2023 along the Chonburi coastline involved field surveys, during which sediment samples were gathered from multiple locations utilizing standard sampling techniques. Trenches measuring 0.30 × 1.00 × 0.60 m (W × L × H) were dug along the coastline. The depth of clay layer was determined from the ground surface of the coast using measurement tape. The coordinates of trenches were meticulously recorded with a handheld global navigation satellite system (GNSS) receiver (Trimble Geo-XT), ensuring submeter accuracy [15]. These boreholes were segmented into 12 sections, organized along 3 lines running parallel to the coastline (Lines A, B, and C), as depicted in Figure 2.

The study utilized a spatial interpolation technique to create a model of the clay layer beneath the sand layer along the beach within the designated area. These interpolations were then compared with the conventional method of soil profile presentation, which involved the use of cross-sections and profile plots.

The data collection strategy focused on 36 specific points along the coastline for several reasons. First, limiting the points helps mitigate the influence of tidal changes, which can significantly alter coastal conditions and measurements. By selecting specific points, researchers can better control for these variations and obtain more consistent data. Second, choosing non-urban and less tourist-impacted areas for data collection reduces potential interference from human activities during the data collection.

Second, choosing non-urban and less tourist-impacted areas for data collection reduces potential interference from human activities. Urban areas and popular tourist spots often have complex dynamics influenced by human infrastructure, activities, and interventions (such as seawalls or beach grooming) that can obscure natural coastal processes.

According to Figure 3, it is evident that urban development is spread along the Chonburi coastline. Data collection within these urban areas could be adversely affected by human activities. Therefore, by conducting data collection in less developed and less frequented coastal areas, researchers aim to capture more pristine and representative environmental conditions. This approach enhances the reliability and accuracy of the data gathered, supporting more robust scientific analysis and conclusions about coastal processes and changes.

4. Results and discussions

4.1 Clay layer cross-sections

The data from all 36 boreholes collected in the field were presented as cross-sections (sections 1 to 12). The depth of the clay layer beneath the soil layer along the beach at each section is illustrated in Figure 4.

Figure 4 illustrates the cross-sectional profiles of the clay layer depth perpendicular to the coastline in Chonburi Province, Thailand, across 12 different sections. Each section is measured at various distances from a starting point from point A (left trench) to point C (right trench) at each section in Figure 2.

The figure shows variations in clay layer depths, which typically indicate where clay soil begins or where a significant layer of clay is present near the coast. The depths range from −10 to −30 cm, with an average depth of around −20 cm. The variations of clay layer depth at each cross-sectional profile are presented in Table 2.

Cross-section number	Clay layer depth explanation
1	The depth begins at around −30 cm and fluctuates, reaching −10 cm at 5 meters before decreasing to around −25 cm at 10 meters and ending at approximately −15 cm at 20 meters.
2	The depth remains relatively constant, starting at about −25 cm at 0 meters and maintaining the same depth with slight fluctuations up to 20 meters.
3	A consistent decrease in depth from around −20 cm at 0 meters to about −40 cm at 20 meters.
4	Fairly constant depth with minor fluctuations, starting at approximately −25 cm at 0 meters and ending at a similar depth at 20 meters.
5	The depth begins at −25 cm, increases slightly to −20 cm at 5 meters, and then remains constant, ending at approximately −20 cm at 20 meters.
6	Starts at about −25 cm at 0 meters, increases slightly at 5 meters, decreases back to around −25 cm at 10 meters, and remains relatively constant until 20 meters
7	Begins at about −25 cm at 0 meters and maintains a relatively constant depth with slight fluctuations, ending at around −25 cm at 20 meters.
8	The depth starts at about −10 cm at 0 meters and consistently decreases to about −45 cm at 20 meters.
9	Starts at about −25 cm at 0 meters, shows a slight increase, and remains relatively constant, ending at around −25 cm at 20 meters.
10	Starting at about −10 cm at 0 meters and ending at about −35 cm at 20 meters.
11	Exhibits similar patterns of decreasing depth, with Section 10. Starts at about −25 cm at 0 meters, shows a slight increase, then decreases again, ending at approximately −25 cm at 20 meters.
12	Starts at about −20 cm at 0 meters and remains relatively constant with slight fluctuations, ending at around −20 cm at 20 meters

Table 2.

The depth variation of the clay layer at each cross-section.

***Remark: The starting point at 0 meters is designated as point A.

To analyze the fluctuations in clay layer depth, one can visualize the data through cross-sectional representations akin to those used in road construction. Furthermore, the cross-sectional profiles can be tabulated, as demonstrated in Table 2. However, interpreting the clay depth information from either Figure 4 or Table 2 can prove challenging. Consequently, users relying on these visualization methods may encounter difficulty in interpretation, necessitating a considerable degree of imagination. These challenges represent inherent drawbacks of conventional cross-sectional data presentation techniques.

The cross-section in Figure 4 illustrates clay layer depth that is perpendicular to profile view, offering insights into the thickness and continuity of clay layers across different locations. By examining clay depths in cross-sections, variations in clay depth can be visualized.

4.2 Clay layer profiles

Building on the insights from the preceding section, it becomes apparent that relying solely on cross-sections is insufficient for adequately representing the variability in clay layer depth. Therefore, it is imperative to complement these cross-sections with additional plots depicting the clay layer depth as profiles. In this context, the term “profile” aligns with its meaning in road construction, illustrating elevations or depths along the longitudinal axis of a road project [38]. The profile view in this study presents a horizontal perspective of the soil profile (along the coastline), showcasing the horizontal extent and continuity of clay layers. This view allows for the assessment of how clay layers vary across the landscape and their relationship with environmental factors.

Thus, in this study, the profile represents the clay layer depth along the coastline, specifically along points A, B, and C, as delineated in Figure 2. The profiles of clay layer depth along the coastlines (Lines A, B, and C) are illustrated in Figure 5.

Figure 5 depicts variations in the depth of the clay layer along different distances, showing both increases and decreases in depth across the measured profiles. The variations in depth could indicate differences in sediment deposition or erosion processes along the coastline.

4.2.1 Profile A

The clay depth along the studied beach area varies significantly, ranging from approximately −10 to −40 cm. Initially, at the starting point of the measurement line (0 meters), the clay depth is around −20 cm. As the measurement progresses, there is a slight decrease in depth, reaching around −10 cm between 200 and 400 meters. However, beyond 400 meters, the depth begins to increase steadily, peaking at about −30 cm at 600 meters. From 600 to 1000 meters, the depth fluctuates slightly between −20 and − 40 cm, indicating a relatively consistent pattern of sedimentation along this stretch of the coastline. This variation in clay depth provides valuable insights into the sedimentary processes and environmental dynamics of the coastal area under study.

4.2.2 Profile B

The clay depth fluctuates between approximately −20 and − 40 cm, suggesting varying sedimentation patterns along the coastline. At the beginning of the measurement line (0 meters), the clay depth starts relatively deep, around −30 cm, indicating significant sediment accumulation. As the measurement progresses, there’s a slight increase in depth to around −20 cm at 200 meters, followed by a decrease to approximately −35 cm at 400 meters, possibly reflecting changes in sediment deposition and erosion dynamics. Subsequently, there’s a steady increase in depth to about −20 cm at 600 meters, suggesting renewed sedimentation processes. However, this trend is interrupted by a decrease to approximately −40 cm at 800 meters before showing a slight fluctuation, eventually stabilizing around −30 cm at 1000 meters. This complex pattern of clay depth variations underscores the dynamic nature of coastal sedimentation processes and highlights the importance of detailed spatial analysis in understanding such phenomena.

4.2.3 Profile C

The clay depth along Profile C exhibits a considerable range, spanning from approximately −10 to −45 cm, indicating diverse sedimentation patterns along the coastline. Initially, at the starting point of the measurement line (0 meters), the clay depth is relatively shallow, around −10 cm, suggesting minimal sediment accumulation. However, as the measurement progresses, there’s a steady decrease in depth, reaching approximately −40 cm by 200 meters, implying increased sediment deposition. Subsequently, there’s a slight increase in depth to about −30 cm at 400 meters, possibly indicating localized sedimentary dynamics. This is followed by another decrease and subsequent increase, resulting in the depth fluctuating between −20 and − 40 cm until 1000 meters, suggesting a complex interplay of sedimentation and erosion processes along the coastline. This variability underscores the dynamic nature of coastal environments and emphasizes the importance of detailed spatial analysis for comprehensive understanding and management of coastal ecosystems.

Profiles A, B, and C along the studied beach area reveal significant variations in clay depth, ranging from approximately −10 to −45 cm, indicating diverse sedimentation patterns. Profile A begins with a depth of around −20 cm at 0 meters, decreasing slightly to −10 cm between 200 and 400 meters before steadily increasing to −30 cm at 600 meters. The depth fluctuates between −20 and − 40 cm from 600 to 1000 meters, suggesting consistent sedimentation. Profile B starts deeper at −30 cm, fluctuating between −20 and − 40 cm, with notable increases and decreases along the measurement line. Profile C spans from −10 to −45 cm, with depths decreasing steadily from −10 cm at 0 meters to approximately −40 cm at 200 meters, followed by fluctuations between −20 and − 40 cm until 1000 meters. These variations underscore the dynamic nature of coastal sedimentation processes, highlighting the need for detailed spatial analysis for comprehensive understanding of the clay sedimentation at the study area.

4.3 Spatial interpolation surface

Spatial interpolation techniques, including Inverse Distance Weighting (IDW), Spline, Natural Neighbor, and Kriging [39], are invaluable tools in estimating the depth of the clay layer across a geographical area. In the context of clay layer depth estimation, spatial interpolation serves as a crucial method for generating continuous surfaces from discrete measurement points, providing insights into the spatial distribution and variability of clay deposits. By applying these interpolation methods, we can extrapolate clay depth values to unmeasured locations, facilitating a comprehensive understanding of sedimentary processes, geological characteristics, and environmental dynamics along the coastline. This comparative analysis of interpolation techniques allows us to identify the most suitable approach for accurately representing the clay layer depth, thereby aiding in informed decision-making for coastal management. The data presented in Figures 3 and 4 were used as input in the interpolations.

Figure 6(a) illustrates that the clay layer depth exhibits spatial variability across the mapped area. The depth of the clay layer is represented by different color gradients, ranging from dark brown (indicating the deepest clay layers) to light yellow (indicating shallower clay layers). The deepest accumulation observed near row 6, ranging from −40 to −37 cm, suggesting localized clay deposition or geological features. Conversely, shallower depths are found near the northern and southern edges, ranging from −13 to −10 cm. The transition from deeper to shallower layers occurs gradually, with intermediate depths dominating the midregions, reflecting the gradual change in clay depth. IDW interpolation emphasizes this gradual transition, presenting a smooth and continuous surface that captures the spatial distribution of clay depth based on sampled points, with row 6 displaying the deepest value indicative of a distinct geological feature. Other points, such as 1, 2, and 12, exhibit shallower depths, highlighting geological variability or differences in depositional environments. It is evident that IDW generates bull’s-eye patterns around the measured points [40].

Figure 6(b) depicts the results of a Natural Neighbor interpolation of clay layer depth along the coastline, with depth measurements taken at 12 points marked on the map. The deepest clay layers, ranging from −40 to −37 cm, are concentrated around row 6 as in Figure 6(a), as indicated by the dark brown color. This suggests that the clay layer in this region is significantly thicker compared to other areas. Adjacent to this zone, slightly shallower depths are observed, represented by a medium brown shade extending toward points 5 and 7. This gradation implies a gradual thinning of the clay layer as one moves away from the central region of the study area. Moving further from the central zone at row 6, the clay depth continues to decrease, indicated by the lighter brown and yellow shades. For instance, near rows 2 and 7, the depth ranges from −16 to −13 cm and − 28 to −25 cm, respectively. This pattern continues with further shallowing, with depths of −22 to −19 cm near rows 9 and 10, and even shallower depths (−16 to −13 cm and − 13 to −10 cm) near rows 11 and 12 at the southern end of the section. The interpolation suggests a clear spatial trend in the clay layer depth, with the thickest accumulation around the central part of the study area at row 6, and gradually decreasing toward both the northern and southern ends. The method’s use of Natural Neighbor interpolation ensures a smooth and continuous representation of clay layer depths, making it a reliable tool for such spatial analyses. The significance of Natural Neighbor spatial interpolation lies in the fact that the boundary of the interpolation surface does not extend beyond the data points (the feature boundary is preserved). Consequently, the interpolated surface in Figure 6(b) does not exhibit a rectangular shape like the other interpolation surfaces. Furthermore, the interpolated Natural Neighbor values consistently fall within the range defined by the highest and lowest values of the data points, ensuring that they never exceed the maximum or dip below the minimum measured values [24].

Figure 6(c) illustrates the results of a Spline interpolation of clay layer depth along the coastline. The Spline interpolation technique creates a smooth surface that passes through the measured points. The figure shows a pronounced variation in clay layer depths, with the deepest zones concentrated in the upper left and lower right corners of the interpolated surface, while the shallowest zone is observed at the north of the study area. Additionally, the clay layer depth is constant at every measured point (rows 1 to 12). This result reveals that the Spline interpolation technique is not appropriate for this type of geological data. Spline interpolation tends to create overly smooth surfaces that may not accurately reflect the actual variations and discontinuities present in the subsurface clay layers. This smoothing effect can lead to unrealistic representations of depth, as it forces the interpolated surface to pass through all measured points, potentially exaggerating or underestimating the actual clay thickness in some areas. The Spline interpolation appears to generate clay layer depth surface that does not align with the expected geological formations based on field observations. For example, the abrupt changes in depth at the upper left and lower right corners of the interpolated surface might not be as pronounced in reality, and the smooth transitions between different depth zones may overlook significant geological features. Therefore, while Spline interpolation can be useful for certain types of spatial data, it is not suitable for accurately representing the complex and variable nature of clay layer depths in this context [41].

Figure 6(d) presents the results of Ordinary Kriging interpolation for clay layer depth along a coastline, with depth measurements taken at 36 points. Ordinary Kriging is a geostatistical method that provides an optimal interpolation by considering both the spatial autocorrelation of the data and the inherent variability in the measurements. The Kriging interpolation reveals a clear spatial trend where the clay layer depth increases as one moves further away from the coastline. This trend is evident from the color gradation on the map. Near the coastline, along Profile A, the clay depth is relatively shallow, indicated by the light yellow and light brown colors, representing depths between −19 and − 16 cm. As we move inland, the clay layer depth gradually increases. Profiles B and C show moderate depths, with the color transitioning to medium brown shades, representing depths from −22 to −19 cm and − 25 to −22 cm. The deepest regions are found further inland, where the dark brown and reddish-brown colors indicate depths ranging from −34 to −31 meters and − 40 to −37 meters. This pattern clearly aligns with the geological expectation that the clay layer depth increases as the distance from the beach increases.

In summary, the interpolation surfaces depicted in Figure 6 effectively represent the variations in clay layer depth within the study area. These surfaces provide clear interpretations of the clay layer depth information generated by different spatial interpolation techniques (Figure 6(a)–(d)). However, these surfaces show different characteristic of the surface within the same study area. Therefore, further investigation into the accuracy of each technique is necessary to determine the most suitable interpolation method for representing clay layer depth in this study.

5. Conclusion

Spatial interpolation techniques offer several advantages over conventional techniques such as profiles and cross-sections for representing clay layer depth along coastlines. First, spatial interpolation provides a continuous surface representation of clay layer depth, allowing for a more comprehensive understanding of spatial variability compared to discrete point measurements obtained from profiles or cross-sections. This continuous representation enables better visualization and analysis of clay layer distribution patterns, facilitating informed decision-making in coastal management and planning. Additionally, spatial interpolation techniques can extrapolate clay layer depth values to unmeasured locations, providing a more complete picture of the coastal environment. Such continuous models enhance visualization, facilitate analysis, and support informed decision-making in coastal management and planning. Ordinary Kriging, in particular, proved successful in modeling the variation in clay layer depth, highlighting the trend of increasing depth with distance from the shoreline. This method accurately captures the spatial structure of the clay layer, reflecting subsurface conditions realistically and reliably. Unlike other methods, Kriging considers the spatial correlation between data points, providing a nuanced and accurate representation of subsurface conditions. Overall, spatial interpolation techniques, especially Ordinary Kriging, present an efficient and effective approach to studying and managing coastal ecosystems, ensuring a better understanding of the dynamic coastal sedimentation processes.

The appropriateness of using Ordinary Kriging for this interpolation is underscored by its ability to capture the spatial structure of the clay layer depth accurately. Unlike other interpolation methods, Kriging accounts for the spatial correlation between data points, resulting in a more realistic and reliable representation of the subsurface conditions. This technique effectively reflects the gradual deepening of the clay layer as one moves away from the coast, providing insights that are consistent with field observations. This method is particularly suitable for this type of geological data as it provides a nuanced and accurate representation of the subsurface conditions, making it a valuable tool for geospatial analysis in environmental and engineering studies. Overall, spatial interpolation techniques offer a more efficient and effective approach to representing clay layer depth along coastlines, enhancing our ability to study and manage coastal ecosystems.

In coastal engineering and management, understanding the presence and characteristics of clay layers is crucial. Engineers and planners must consider these factors when designing and implementing coastal protection measures, such as seawalls, revetments, and beach nourishment projects. By accounting for the role of clay layers in coastal dynamics, it becomes possible to develop strategies that enhance coastal resilience while balancing the needs of natural ecosystems and human development. Effective management of clay layers and their interaction with coastal erosion is essential for sustainable coastal development and the preservation of coastal habitats and communities.

Therefore, the selection of the appropriate spatial interpolation technique plays a crucial role for accurately representing clay layer depths. Accurate representation of clay layer depths can guide decisions in coastal environmental management, providing stakeholders with valuable information for sustainable land-use planning and resource management strategies. Understanding clay layer depths from these perspectives enhances our ability to predict soil behavior and effectively address challenges related to soil quality, erosion control, and infrastructure development.

6. Limitations

The limitations of the clay layer depth visualization utilizing spatial interpolation techniques in this study are as follows:

• Sampling density and coverage: The study is limited by the number of observation points [36], which might not fully capture the variability in clay layer depth across the entire coastline of Chonburi Province. More extensive sampling could provide a more detailed and accurate spatial representation.

• Interpolation technique limitations: While Ordinary Kriging showed better accuracy compared to other methods, it assumes stationarity and isotropy, which might not always hold true for coastal environments. These assumptions can lead to inaccuracies in areas with non-stationary or anisotropic conditions.

• Environmental and temporal variability: The study only captures a snapshot in time, and coastal environments are dynamic. Seasonal variations, climatic changes, and human activities can influence sediment deposition and clay layer depth, which were not accounted for in this study.

• Equipment and measurement errors: The accuracy of GNSS receivers and the manual measurement of trench depths can introduce errors. Small inaccuracies in data collection can propagate through the interpolation process, affecting the final models.

• Limited comparison with other regions: The findings are specific to Chonburi Province, and the applicability of these results to other coastal regions remains untested. Different coastal environments may exhibit different sedimentation patterns and require tailored interpolation methods.

7. Future works

Based on the limitations highlighted in the previous section, the following recommendations are proposed for future work to enhance the reliability of this study:

• Increasing sampling density: Future studies should aim to increase the number of observation points to improve the resolution and accuracy of spatial models. High-density sampling could provide better insights into local variations in clay layer depth.

• Longitudinal studies: Conducting longitudinal studies that monitor changes in clay layer depth over time would help in understanding the temporal dynamics of sedimentation processes and the impact of environmental changes and human activities.

• Incorporating additional variables: Future research could incorporate other relevant variables, such as soil type, vegetation cover, and hydrodynamic factors, to improve the accuracy and robustness of the spatial interpolation models.

• Advanced interpolation techniques: Exploring advanced geostatistical methods and machine learning algorithms that can handle non-stationary and anisotropic conditions might provide more accurate and flexible modeling options for coastal sedimentation studies.

• Comparative studies: Conducting comparative studies in different coastal regions would help in validating the findings and methodologies. This could also provide insights into the generalizability and adaptability of the interpolation techniques used.

• Integration with coastal management practices: Future work should focus on integrating these spatial interpolation models with practical coastal management strategies. This could involve developing user-friendly tools for policymakers and practitioners to make informed decisions based on the spatial distribution of clay layers.