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Article

Integration of Earth Observation and Field-Based Monitoring for Morphodynamic Characterisation of Tropical Beach Ecosystems

by
James Murphy
1,*,
Jonathan E. Higham
1,
Andrew J. Plater
1,
Kasey E. Clark
1,2 and
Rachel Collin
2
1
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool L69 7ZX, UK
2
Smithsonian Tropical Research Institute, Luis Clement Avenue, Bldg. 401 Tupper, Ancon 0801, Panama
*
Author to whom correspondence should be addressed.
Environments 2025, 12(6), 205; https://doi.org/10.3390/environments12060205
Submission received: 24 March 2025 / Revised: 3 June 2025 / Accepted: 8 June 2025 / Published: 16 June 2025
(This article belongs to the Special Issue Monitoring and Assessment of Environmental Quality in Coastal Ecosystems, 4th Edition)
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Abstract

:
Coastal erosion poses a significant threat to small tropical island regions, where coastal tourism and infrastructure play vital economic roles. However, the processes affecting tropical beaches, particularly in Central America, remain underexplored due to a lack of data on waves and atmospheric conditions. We propose a novel approach that utilises low-cost smartphone and satellite imagery to characterise beach ecosystems, where typically expensive and technologically intensive monitoring strategies are impractical and background data are scarce. As a test of its performance under real conditions, we apply this approach to four contrasting beaches in the low-lying islands of the Bocas del Toro Archipelago, Panama. We employ Earth Observation data and field-based monitoring to enhance understanding of beach erosion. Optical flow tracking velocimetry (OFTV) is applied to smartphone camera footage to provide a quantitative metric of wave characteristics during the high wave energy season. These data are combined with satellite-derived shoreline change data and additional field data on beach profiles and grain size. The results reveal distinct patterns of accretion and erosion across the study sites determined by wave climate, beach morphology, and grain size. Accreting beaches are generally characterised by longer wave periods, more consistent wave velocities, and finer, positively skewed sediments indicative of swell-dominated conditions and dissipative beach profiles. Conversely, more erosive sites are associated with shorter wave periods, more variable wave velocities, coarser and better-sorted sediments, and a shorter, steeper beach profile. Seasonal erosion during the high-energy wave season (January–April) and subsequent recovery were observed at most sites. This work demonstrates how foundational data for evidence-based coastal management can be generated in remote locations that lack essential baseline data.

1. Introduction

Coastal erosion poses a significant threat to communities worldwide, where cities, vital infrastructure, and tourist economies are often located adjacent to the coast [1,2,3,4]. This presents a particular challenge in tropical and remote areas, where limited funding, expertise, and available data can hinder the sustainable management of marine and coastal assets [5]. Threats to beach ecosystems from coastal erosion will become even more pressing with climate change due to sea-level rise and changes in the magnitude and frequency of storms and hurricanes [6]. Coastal zone management aimed at mitigating these pressures necessitates robust data on contemporary and historical trends, which are often lacking and can be costly and resource-intensive to acquire [7].
Significant portions of the Caribbean coastline in Latin America remain under-researched, and the absence of data on wave and climatic conditions presents challenges for effective coastal management [8,9]. Nevertheless, local reports have highlighted increasing rates of erosion along the sandy shorelines of southern Costa Rica, near the Panamanian border [10,11]. The straight beaches along this coastline, like numerous others on the Caribbean coast, are particularly susceptible to concentrated wave energy [12], which is often linked with erosional behaviour [13]. While qualitative monitoring of tropical beach ecosystems has provided significant insights into local beach conditions, these efforts are generally focused on areas that are particularly vulnerable to extreme weather events, such as tropical cyclones [14].
Sandy shores throughout the Caribbean are highly valued for recreation [15], yet they are increasingly vulnerable to erosion due to their inherent geomorphological variability and growing anthropogenic pressure. Many of these coastlines are closely linked with coral reef systems, which not only enhance their aesthetic appeal for tourism but also provide critical ecosystem services, such as wave energy attenuation and sediment stabilisation, thereby contributing to beach resilience [16]. These are characteristic of perched beaches, which are coastal environments where natural, shallow-submerged features like coral reefs function similarly to submerged breakwaters, dissipating wave energy and offering protection to the adjacent shoreline.
Reef systems can also introduce temporal and spatial variability in coastal processes, driving localised patterns of beach erosion and accretion [17]. In particular, the presence of reef flats influences wave dynamics by increasing the surface roughness, altering wave transformation and energy dissipation across the nearshore zone [18]. The interconnected nature of these reef–beach systems, therefore, adds complexity to understanding local coastal morphology, as reef health directly influences shoreline stability [19,20].
To understand local coastal dynamics, it is essential to characterise wave behaviour accurately. A common approach involves using wave buoys; however, there are a limited number of buoys with long records in the Caribbean [21], and they often require specialised teams for regular calibration, maintenance, and data collection, necessitating robust maintenance plans and the availability of spare parts, which limits their dissemination in resource-limited settings. [22,23]. To address these challenges, researchers have explored a variety of cost-effective strategies for wave monitoring, including open-source and DIY wave loggers and gauges made with commercially available tools [24,25,26]; low-cost wave measurement devices such as GNSS buoys [27,28] and spotter buoys [29,30] and land-based vertical seismometers [31]. While these techniques offer an accurate and cost-effective means of collecting wave dynamics, their implementation is particularly beneficial in the short term, sometimes requiring specialised and dedicated teams for maintenance and calibration.
In addition to hardware-based approaches, numerical wave models have become an important resource for characterising wave conditions, particularly in areas lacking consistent in situ data. Global and regional models, such as WaveWatch III, and operational products from platforms like the Copernicus Marine Environment Monitoring Service (CMEMS), provide publicly available, continuously updated data on a variety of spatial and temporal scales. These model outputs have been widely adopted by the scientific community and have been validated against in situ measurements around the globe [32,33] demonstrating their utility for estimating wave parameters.
Optical video footage has proven effective in estimating nearshore bathymetry and hydrodynamic processes [34,35,36] This effectiveness stems from the capability for feature tracking that video footage offers. While particle image velocimetry (PIV) and particle-/feature-tracking velocimetry (PTV) have been extensively employed to quantify motion from sequences of images or videos [37,38,39,40], considerable opportunities remain for optical flow velocimetry (OF) techniques, which have shown notable advantages over PIV methods [41,42,43]. These data can be collected easily and consistently using dedicated video camera systems or even a mobile phone camera.
This paper addresses this data resource challenge through the development of a novel multi-platform methodology tested in real-world conditions through the case study of Bocas del Toro Archipelago in Panama. The study aims to integrate in situ measurements with freely available online Earth Observation (EO) data to create an accessible, comprehensive toolkit for quantifying and comparing beach morphodynamic characteristics. We present an approach that integrates beach slope, sediment size, and smartphone video footage collected during the high-energy wave season with beach width data derived from satellite imagery [44]. Using an optical flow tracking velocimetry (OFTV) algorithm, we extract wave velocities from video footage, enabling the analysis of wave speeds and periods across four study sites. By linking the wave variability, sediment characteristics, and beach slope, we quantify the processes driving long-term accretion and erosion trends observed in satellite data. Finally, we assess the effectiveness of this multi-platform toolkit in comparing coastal processes across different environments, providing new insight into the factors influencing beach response.

2. Materials and Methods

2.1. Data and Study Sites

The beaches of the Bocas del Toro Archipelago, located on the Caribbean coast of Panama, are particularly vulnerable to coastal erosion, with some areas of the coastal road being notably at risk due to their proximity to the shoreline. Bocas del Toro province is one of the poorest in Panama, where significant economic activity stems from ocean-based tourism and banana plantations on the nearby mainland’s coastal plain [45]. In Bocas del Toro province, about 95% of all economic activity is linked to the tourism sector [46], which continues to grow, especially with the rise of accessible digital infrastructure, such as travel applications, for international tourists [47] Thus, understanding the coastal dynamics within the archipelago is vital. Of immediate concern is the effect of coastal erosion on essential and culturally important infrastructure, such as the airport, reservoir, hospital, and roads—much of which lies directly adjacent to exposed beaches.
Four sites were selected for a real-world test of the multi-platform approach, from February to April 2024, known as the dry season, which typically experiences high wave energy. The sites were chosen based on a combination of local knowledge and accessibility (Figure 1).
We characterised the beaches using shoreline change analysis conducted with CoastSat [44]. The selected sites provide a representative cross-section of beaches in the Bocas del Toro Archipelago, as they exhibit differences in wave climate, geology, and sediment type.
Primary data collection involved beach profile measurements (slope and beach width), sediment sampling for grain-size analysis, and video footage to document wave dynamics. At each beach, multiple transects were established to capture the spatial variability. Transects were spaced as evenly as possible along the shoreline, subject to accessibility constraints. The number of transects per beach was as follows: Playa Bluff—4; Playa Istmito—3; Playa Carenero—3; and Playa Wizard—3.
Little to no secondary data are available regarding the morphologies of the selected sites and many beaches in the surrounding archipelago, and the hydrodynamics of the archipelago were not comprehensively monitored at the time of this study. This data gap presents both a challenge and an opportunity; while it highlights the need for an accessible monitoring toolkit with easy-to-deploy methods and data collection techniques, it means that baseline information on historical coastal evolution is largely absent. Consequently, long-term monitoring and management strategies will need to be developed in the absence of historical context, limiting the ability to detect longer-term hydrological trends.
In the absence of local historical wave data, regional models become essential. The nearest available NOAA wave buoy is the Western Caribbean wave buoy [48], located approximately 800 km from the archipelago. Consequently, the wave climate can be validated using a regional wave model. The WaveWatch III regional wave model was used in this case, as covered in Section 3.2.2.
Of the four study sites, Playa Wizard (also known as Playa Primera, see Figure 1a) measures approximately 1.2 km in length and is located on the western end of Isla Bastimentos, facing north–east towards the Caribbean Sea. Like the Bluff, this site is characterised by higher-energy waves and strong riptides, particularly during the dry season (January to April). Playa Bluff (Figure 1b) is situated in northern Isla Colon; at 4 km, it ranks as one of the largest beaches in the archipelago. The beach faces north–east towards the Caribbean Sea and is immediately backed by a rainforest that covers much of northern Isla Colón, separated by Bluff Beach Road—a road that follows the coastline from the central narrow strip of Isla Colón. This beach is a popular destination for tourism and surfing, hosting the Bocas Invitacional surfing competition at this location. Playa Istmito (Figure 1c) is positioned in the La Cabana neighbourhood in central Isla Colon. This beach is just over 1.3 km long and lies on a narrow strip of land connecting the island’s northern part to Bocas Town in the south. Although facing northeast, it is in a bay sheltered by northern Isla Colon to the north and Isla Carenero to the east. Isla Carenero (Figure 1d) is an island east of Bocas Town on Isla Colon and is one of the smallest of the main islands within the Bocas del Toro Archipelago. It serves as a hub for recreational activities, boasting several restaurants and bars along the island’s eastern beaches. This side of Isla Carenero experiences low wave energy due to its position between Isla Bastimentos to the east and Isla Solarte to the southeast. The beaches here are narrow, spanning only a few metres.

2.2. Primary Data Collection

2.2.1. Optical Flow Tracking Velocimetry (OFTV)

Optical flow tracking velocimetry (OFTV) [49] is especially suitable for monitoring dynamic wave fields because of its capability to handle the varying lighting conditions between the whitewater of breaking waves and the surrounding water. In contrast to traditional methods, such as particle image velocimetry (PIV) [37], which depends on seeded particles for tracking, OFTV tracks natural-intensity features within the video sequence directly, providing a non-intrusive and efficient approach to wave analysis [50].
This method was considered optimal for the study due to the challenges of installing permanent wave monitoring equipment in remote tropical regions. The region’s climate and environmental conditions are highly damaging to stationary equipment, and the logistical difficulties of setting up and maintaining large instruments across scattered islands and beaches further complicate traditional monitoring approaches. The OFTV method provides a cost-effective alternative, requiring only an optical camera or smartphone to capture footage, making it particularly advantageous for use in these remote coastal settings.
Video footage of the wave environment was collected with an iPhone 12 Mini at a resolution of 1920 × 1080 and a video quality of 1080p. The phone was secured atop a tripod at a height of 5′1″. Each recording featured at least ten full wave periods to provide a robust dataset for analysis (see Appendix A Table A1 for a full breakdown of the survey times by site). For each beach transect, two videos were recorded, as follows: one near the waterline and another farther inland to provide a broader view of the incoming waves (Figure 2a). At Carenero Beach, where waves break very close to the shore, only a single field of view was recorded due to spatial constraints. Footage was taken weekly or as often as feasible. At times, certain transects could not be collected due to unexpected events, such as recreational surfing events, dangerous conditions, and algae growth that obstructed the installation of rectification markers.
All videos incorporated rectification markers within the frame to ensure accurate wave velocity calculations. These markers provided a known reference scale for the OFTV algorithm, enabling real-world distances to be derived from pixel measurements (Figure 2b). The pixel-to-distance conversion was calibrated by integrating four points of a known distance apart, ensuring precise and comparable wave speed calculations across all study sites.
Wave crest tracking was conducted using the Lucas–Kanade optical flow algorithm [51], which estimates motion by solving for the pixel displacement between consecutive video frames. A binary mask was applied to each frame, focusing on isolating regions of significant wave activity and ensuring that only regions of actual wave crests were retained for further analysis. Subsequently, a nearest-neighbour distance criterion was employed to track them across frames, ensuring continuous and reliable measurement of the wave motion dynamics (Figure 2c). The tracked horizontal positions of the wave crests were recorded as time-series data, capturing their displacement over time (i.e., their velocity, Figure 2d). These values served as reference points for the wave spectral analysis. To compare the variability in wave energy across the four beaches, velocity data were aggregated into representative distributions for the entire study period. These were then transformed into cumulative density functions (CDFs), supporting quantitative comparisons across sites through a simple metric linked to energy consistency (Figure 2e). These CDFs were log-scaled to linearise the naturally curved CDFs, enabling a slope calculation (Figure 2f). The slope of the linearised CDF, calculated as the gradient (dy/dx), offered a quantitative measure of the wave velocity variability; a lower gradient reflects more consistent, uniform wave velocities, while a steeper slope indicates a higher variability. For each beach, the mean slope across all transects was established, along with the standard deviation, to account for the variability in velocity distributions.
A wave period analysis was also conducted using Welch’s power spectrum method [52]. This technique decomposed wave motion into its constituent frequencies, allowing for the identification of the dominant wave frequency. The peak of the power spectral density function was identified as the dominant wave frequency, which was then converted into the wave period by taking the inverse of this frequency. This process enabled in-depth examination of the wave dynamics, allowing for a refined understanding of how the wave energy is distributed across the different study sites. To assess the accuracy and applicability of the OFTV-derived wave periods, we conducted a comparative analysis against predictions from the NOAA/NCEP WaveWatch III (WWIII) model—a widely used, third-generation wave model [53]. For this comparison, we selected the WWIII grid point at 9°30′00.0″ N 82°00′00.0″ W, located approximately 27 km northeast of Isla Bastimentos. This location was chosen as it is the closest model point to Playa Wizard, one of the study sites facing north–east towards the Caribbean Sea.

2.2.2. Sediment Sampling and Beach Profile

Sediment was collected from the low- and high-tide points at each transect location at each beach. For a quantitative assessment of the beach grain size, fifty sediment grains for each site were examined using a Leica MS5 microscope, and the grain size was converted into millimetres (mm). The grain sizes in mm were converted into the Krumbein phi scale (see [54]) for a statistical analysis of the sediment size featuring sorting, skewness, and kurtosis (for the equations, see [55]).
To obtain beach profile data, the beach width was measured from the vegetation line, which was typically rainforest, to the point of low tide along the waterline, for every transect on every beach. The beach slope was measured at every two metres (every one metre for Carenero, as the beach width was significantly smaller) in degrees using a TruPulse 200 laser rangefinder. The average profile across the transects was then calculated to provide a representative slope for each beach.

2.3. Secondary Data Collection

CoastSat and Tidal Correction

A time-series analysis of the satellite images provided a longer-term context for the primary data collection to determine underlying trends in coastal erosion and accretion. This analysis was conducted using CoastSat [44], an open-source toolkit that extracts shoreline positions from satellite imagery over a specified time period. CoastSat uses a pre-trained machine learning algorithm to identify the water/land boundary and corrects this by employing data from nearby tide gauges. The limitations of using optical satellite data for shoreline monitoring are often exacerbated in areas with significant cloud cover, such as tropical regions [56]. Although the algorithm used by CoastSat incorporates a cloud filter, the prevalence of extensive cloud cover in the Bocas del Toro region rendered several images unusable for shoreline detection, resulting in a reduced temporal resolution of the available imagery.
To fill these data gaps as much as possible, various optical satellites with overlapping datasets—Landsat-7 (30 m resolution) and Sentinel-2 (10 m resolution)—were employed in this case. A total of 114 and 338 images were analysed for Landsat-7 and Sentinel-2, respectively. For a detailed list of the satellite observation dates, platforms, and their respective spatial resolutions, refer to Appendix A Table A2. The differing resolutions of these satellite platforms can influence shoreline precision; although CoastSat incorporates adjustments to normalise these differences across datasets, the Sentinel-2 imagery generally allowed for a more accurate shoreline delineation, particularly where the beach width was smaller. Visual verification was conducted image by image to ensure consistency across datasets and correct for any anomalies.
Limited satellite imagery, mainly due to cloud cover, hindered the coverage of the region prior to the launch of Sentinel-2A in 2016. As a result, the CoastSat analysis conducted here begins in 2016, providing a higher temporal resolution. Shorelines were extracted from satellite imagery between 1 January 2016 and 13 December 2023 to align with available tide gauge data.
Tide gauge data [57] used for correction were taken from a standard mechanical float-type tide gauge in Limon, Costa Rica (10.00 N, 83.02 W). Tidal data were collected hourly and available consistently from October 2009 until December 2023, with some data missing between late 2021 and early 2022. Following the tidal correction process, outliers in the datapoints are removed through an Otsu thresholding filter. In addition, further outliers were removed using a z-score method [58], which removed data points that sat outside a given deviation from the mean. Following this filtering process, the number of datapoints that remained for analysis for Playa Bluff, Playa Wizard, Playa Carenero, and Playa Istmito were 72, 146, 114, and 120, respectively.
The annual rate of change for each beach is calculated by first extracting shoreline positions using CoastSat and then deriving cross-shore distances from a fixed baseline to each shoreline. These distances were averaged annually, and the differences between consecutive years were used to compute rates of change, along with their standard deviations. To account for the natural high seasonal variability of shorelines [59], cross-shore distances were analysed not only over the entire study period but also monthly. This approach facilitated an examination of both long-term trends and short-term fluctuations arising from the shoreline extraction.

3. Results

3.1. CoastSat Shoreline Analysis

Throughout the entire study period, as shown in Figure 3, Playa Bluff and Playa Istmito exhibited a general increase in cross-shore distance, particularly in recent years. However, the data for Playa Bluff demonstrated considerable variability, especially in 2023, indicating that while long-term accretion occurs, significant short-term fluctuations are also evident.
In contrast, no discernible predominant pattern could be detected at Carenero and Playa Wizard over the long term, indicating that these areas exhibit more complex seasonal and interannual processes. For Playa Wizard, Playa Bluff, and Playa Istmito, there is a significant reduction in cross-shore distances during the high wave season (January to April), followed by a general recovery over the subsequent months. Across all sites, the greatest variability in shoreline patterns was observed outside the high wave season, suggesting that while consistent high-energy waves cause immediate and noticeable shoreline changes, other factors—such as tidal currents, fluctuations in sediment supply, and anthropogenic activity, such as coastal developments or boat traffic—likely contribute to variability throughout the rest of the year.
Carenero, however, deviates from the general seasonal pattern displayed by the other study sites. At this location, the cross-shore distance appears to significantly decrease between June and July, followed by subsequent recovery outside this period. This could be attributed to the unique characteristics of Carenero’s protected coastline or other seasonal phenomena that differ from those affecting the more exposed study sites.
Wizard Beach experienced a significant reduction in cross-shore distance in March, which appears to be unique among the study sites. Notably, personal observations indicated that the geomorphology of Playa Wizard was actively changing throughout the study period, distinctly more so than the other sites.
A notable limitation is the potential for error in the shoreline positions extracted for the beaches of Carenero. Given their small size, even a sub-pixel accuracy error (~10 m) could affect the measurements. This potential error must be considered when interpreting the results, as it can likely account for the additional variability within the shoreline position and, subsequently, the cross-sectional distance. More broadly, the use of both Landsat (30 m resolution) and Sentinel-2 (10 m resolution) imagery likely introduced spatial inconsistencies in the shoreline detection. Although CoastSat applies sub-pixel edge detection, the difference in the native resolution between the sensors affects the accuracy of the change detection, particularly in sites with narrow or rapidly changing shorelines. Nonetheless, combining the two sensors extends the coverage of the region, and all imagery was manually screened to ensure quality and comparability across time. This trade-off is addressed further in Section 4.1

3.2. Integrated Monitoring of Wave Dynamics and Beach Morphology

3.2.1. OFTV-Derived Wave Characteristics

Figure 4 presents the OFTV-derived velocities (velocity magnitude) over time and the power spectral density for a single video, providing an example of wave energy distribution and flow dynamics at a specific stage in space and time. The site selected for this example was Carenero, chosen for simplicity, as the active wave zone was closer to the shore than at other sites. The velocity magnitude reveals a sinusoidal pattern, where peaks in wave speed indicate incoming waves. This pattern tends to be relatively repetitive over time, suggesting a more stable flow of incoming waves. For this transect, the calculated velocities remained below 1 m/s, representing a relatively low-energy wave climate. Observing the power spectra, the predominant wave energy occurs at a frequency of approximately 0.25 Hz, indicating a wave period of about 4 s. This spectral profile was a benchmark for exploring how the wave energy and flow patterns differed across the study sites. The characteristic frequency distributions and flow intensities were captured by averaging the data for each beach.

3.2.2. Data Verification Against Wave Model Output

Figure 5 compares the peak wave periods from the WWIII model and those derived from the OFTV algorithm based on the footage collected at Playa Wizard. Generally, the two data sources align, suggesting that OFTV is reliable for capturing the dominant wave periods under various conditions. Some differences are noted between the wave model output and the OFTV-derived wave periods. For instance, on 8 March 2024, the OFTV-derived wave period showed an increase from previous measurements, whereas the WWIII wave period showed a decrease from this point. Deviations between the wave periods calculated by these two methods may be explained by phenomena such as the following: (1) the WWIII estimates are provided hourly, while the OFTV-derived periods are calculated instantaneously in situ; and (2) the offshore nature of the singular WWIII grid point likely does not capture local environmental factors (shoaling, refraction, reflection, and interference) influencing wave conditions. OFTV, being directly observational, will likely better capture these local variances, whereas the model provides a larger scale, offshore perspective.

3.2.3. Summary of Beach Characteristics

Observing Table 1, the beaches are arranged from top to bottom according to the overall accretive/erosive pattern of the change derived from the CoastSat beach width analysis, considering both the calculated rate of change (average ± SD) and the time-series of the change. The satellite data for the four studied beaches reveal a gradient from accretionary to erosional behaviours. In the table, a positive rate of change denotes accretion, while a negative rate indicates erosion. Istmito showed the most significant positive shoreline accretion, sustaining a consistent trend throughout the study period, followed by Wizard and Bluff, both displaying moderate accretion. Carenero exhibited a nearly neutral shoreline change over time, with a slight retreat in the final year of the dataset.
Sedimentological analysis further supports the shoreline changes derived from satellite observations, revealing systematic differences in the skewness and, to some extent, the mean and sorting across the four beaches. The transition from Istmito to Carenero is a reliable sedimentological indicator of a stable, accretive beach compared to a less stable, recently eroding one. Table 1 also indicates a general systematic decrease in sorting and an increase in grain size from top to bottom. Thus, the more accretionary trend is characterised by a combination of decreasing sorting, positive skew, and finer grain size. Following the classification of [56] kurtosis offers further insight into Bluff and Carenero. These locations exhibit platykurtic distributions alongside well-sorted, negatively skewed, and coarser grain sizes. Such a distribution suggests active sediment transport and redistribution under high-energy conditions, typical of dynamic, erosive coastal environments. Notably, Wizard and Bluff display similar trends in their satellite analysis and grain size statistics, with slight differences in kurtosis and skewness.
The wave velocity analysis shows consistent patterns with sediment characteristics and shoreline trends, highlighting the hydrodynamic forces shaping these environments. A general shift from shoreline progradation to recent retreat corresponds with a systematic decrease in the wave velocity gradient, suggesting that higher rates of beach accretion are linked to more consistent wave velocities. Although wave period data only partially align with this pattern, longer wave periods are observed at the accreting sites (Istmito and Wizard). In contrast, shorter periods are noted at Bluff and Carenero, likely due to localised wave modification near the shore. This inconsistency in the alignment of the wave period may arise from limitations in the temporal sampling of the OFTV data, which might not fully capture the variability of wave conditions. These findings imply that shorter-period waves and more variable wave velocities are less conducive to beach accretion.
Further insights arise from the beach slope data, which reflect each beach’s capacity to dissipate wave energy and reinforce observed trends in sediment and shoreline data. Istmito and Wizard have shallower beach slopes, likely promoting wave energy attenuation and supporting accretion. In contrast, Bluff’s slightly steeper profile aligns with its transition to more moderate erosion, while Carenero—with the steepest slope and narrowest beach width—experiences minimal wave attenuation, resulting in stronger erosional forces and sediment loss, as indicated by satellite observations and the negative skew in particle size distribution.

4. Discussion

4.1. Efficacy of the Multi-Platform Toolkit for Beach Characterisation

Integrating optical flow tracking velocimetry (OFTV) with the satellite-derived shoreline data, sedimentological measurements, and morphological characteristics provides a powerful multi-faceted approach to beach characterisation. This combination enables a cheap, low-cost depth of analysis that bridges observational data with explanatory insights, revealing potential causal mechanisms within coastal dynamics.
Using OFTV as a direct measure of the wave velocity, coupled with satellite-based shoreline trends from CoastSat, clarifies seasonal and long-term shoreline dynamics. The satellite data alone determine clear accretion and erosion patterns in time and space. Adding the local wave velocity analysis allows us to interpret these trends regarding the hydrodynamic forces driving them. For example, as the results indicate, accretion at sites such as Istmito and Wizard is associated with more consistent wave velocities, while the variable velocities—observed at Carenero and Bluff—correlate with erosional trends. This finding suggests that beaches with more stable hydrodynamic regimes are better able to retain sediments and promote accretion, while those subjected to variable velocities experience higher erosional pressures. However, the extent to which these relationships indicate causality rather than correlation requires further scrutiny. For instance, external factors such as nearshore geomorphology, human intervention, or episodic weather events may also play significant roles and are not fully captured within the current dataset. Future research might include human activity mapping or 3D modelling of shoreline structures using drones to better account for these influences.
OFTV-derived wave characteristics also enhance our understanding of sedimentological attributes such as grain size, skewness, and sorting. Coarser, negatively skewed sediments at Bluff and Carenero correspond with variable wave velocities, suggesting that dynamic wave forces contribute to the ongoing sediment redistribution observed at these sites. Therefore, OFTV offers an indirect yet meaningful perspective on sediment transport mechanisms, which are otherwise difficult or costly to observe directly, particularly in remote tropical locations.
Beach slope measurements add another layer to this characterisation, linking sediment retention with the beaches’ capacity to dissipate wave energy. The relatively shallow slopes at Istmito and Wizard appear to dissipate incoming wave energy, reinforcing accretionary trends. At the same time, the steeper profiles of Bluff and Carenero are less effective at attenuating wave forces, enhancing erosion potential. By correlating OFTV wave data with these morphological profiles, we can identify mechanisms that help explain why certain beaches are more susceptible to erosion or conducive to accretion.
Fundamentally, this integrated multi-platform toolkit also assists in overcoming the limitations inherent to each data type. For instance, the seasonal and interannual shoreline fluctuations derived from CoastSat are susceptible to short-term anomalies, particularly on smaller beaches such as Carenero, where the pixel resolution can introduce variability. However, when combined with the OFTV-derived wave patterns, these fluctuations can be better contextualised, distinguishing between anomalous short-term events and sustained morphodynamic responses. The use of OFTV has been effectively employed to track features and their velocities from high-speed video [49,60,61,62]. Unlike satellite observations, which capture longer-term trends, OFTV enables the direct observation of fine-scale fluctuations in wave energy that significantly influence coastal sediment transport and erosion processes. Furthermore, this framework provides a comparative baseline against predictive wave models, such as WWIII, offering a localised validation tool that accounts for nearshore environmental variability often overlooked by offshore model data.
One methodological consideration in interpreting the satellite-derived shoreline positions is the combined use of Landsat-7 and Sentinel-2 imagery within the CoastSat framework. While CoastSat applies sub-pixel edge detection techniques to improve shoreline delineation, the differing native resolutions between these sensors can introduce differences in positional accuracy that can affect change detection, particularly in narrow or highly dynamic beach environments [44]. This is particularly relevant for sites like Carenero, where the scale of the beach width approaches the resolution threshold, and small shifts in shoreline position may fall within the margin of spatial uncertainty [63]. Nonetheless, combining these datasets was necessary to ensure a greater spatial and temporal coverage of the Bocas del Toro region, especially since this area experiences significant cloud cover, a common limitation of shoreline delineation in tropical regions [56]. To minimise inconsistencies related to resolution, all images were manually reviewed prior to shoreline extraction.
In this context, Sentinel-2 imagery offered more detailed shoreline delineation in recent years, while Landsat-7 offered valuable long-term continuity. Previous studies have quantified the impact of the resolution on the shoreline extraction accuracy. [64] found that the mean positional error for Landsat-derived shorelines was reduced from around 12.9 ± 15.3 m to 3.75 ± 7.0 m, using sub-pixel and georeferencing corrections, as utilised in CoastSat. Sentinel-2, with its finer native resolution, typically produces lower error margins. While variability under 10–15 m should be interpreted cautiously, the observed seasonal and interannual patterns remained consistent across the sensors and were further corroborated by OFTV-derived wave dynamics.
In sum, combining OFTV, sediment, morphological, and satellite-based shoreline data transitions beach characterisation from simple description to explanation. This new integrated approach captures both direct and indirect interactions between hydrodynamic forces and sediment transport, setting a new standard for multi-dimensional coastal analysis. Such comprehensive insights have broad implications for coastal resilience planning, where understanding the nuanced drivers of erosion and accretion can inform targeted interventions for beach preservation and adaptation.

4.2. Interpreting Patterns and Underlying Coastal Dynamics

The integrated dataset provides compelling insight into the processes shaping the accretive and, to some extent, erosive behaviour of the different beaches. By examining the results across the sediment, wave, and shoreline data, distinct patterns emerge that suggest underlying morphodynamic mechanisms at play. The more notable accretion observed as Istmito is associated with more consistent wave velocities, as indicated by the steeper wave velocity gradient derived from the OFTV data—phenomena also observed by [65] in various beaches across the globe. Additionally, wave period analysis provides further evidence supporting these dynamics. Beaches with higher accretionary rates, such as Istmito, generally display longer wave periods, which are typically associated with dominant swell waves generated offshore [66,67]. These longer wave periods promote sediment deposition by allowing finer sediments to settle, resulting in positively skewed grain size distributions and poorer sorting. However, Istmito’s location within a bay suggests that its naturally sheltered conditions may influence its wave climate relative to more open beaches, potentially amplifying its accretive characteristics [68,69].
In contrast, less accretive sites characterised by a shallower wave velocity gradient, suggest greater variability in wave forcing. This process results in the deposition of coarser, better-sorted beach sediments with a more symmetrical grain size distribution. Indeed, finer sediments may be transported offshore, leading to negative skewness. Further, a more erosive shoreline trend is associated with variability in wave velocity coupled with a reduced wave period, which is indicative of locally generated waves. This pattern aligns with the OFTV-derived shorter wave periods observed at Carenero, where increased wave frequency likely contributes to intensified sediment transport and erosion, taking finer sediment offshore while leaving behind coarser, well-sorted beach deposits [70,71].
The sediment analysis results complement the wave velocity and wave period measurements findings and provide insight into local morphodynamic interactions. For example, the sediment profile at Istmito displays finer, well-sorted grains with a positive skew, indicative of low-energy conditions conducive to accretion and a dissipative beach profile [72]. Conversely, the sediment statistics at Carenero are characteristic of more erosional conditions, with the steeper, shorter profile being more reflective [72].
The seasonal trends in wave climate also play an important role. At Playa Wizard and Playa Bluff, seasonal wave variations lead to cyclic patterns of erosion and recovery. During the high-energy wave season (January to April), wave forces lead to erosion, followed by sediment deposition and shoreline recovery in the subsequent months when wave energy reduces. However, Carenero deviates from this pattern, significantly reducing cross-shore distance during June and July when other beaches recover. While the specific conditions pertaining to this anomaly are not explored here, seasonal differences in sediment transport are likely the result of localised phenomena such as offshore reef formations, seagrasses, or trade winds [73,74]. Future research should seek to integrate these observations with bathymetric and reef topography data, where available, to better understand their influence on the nearshore wave climate.

5. Conclusions

This study highlights the utility of combining satellite-based shoreline data, sedimentological and morphological analyses, and wave velocity variability and wave period derived from OFTV into a multi-platform toolkit for characterising coastal morphodynamics across four diverse beach ecosystems during the high-energy wave season. Long-term trends and short-term fluctuations in shoreline positions reveal complex interactions between hydrodynamics, sedimentology, and beach morphology.
Several key insights emerged by combining these easy-to-use methods and easy-to-collect datasets into a toolkit. Beaches that exhibit accretionary behaviour, such as Istmito, have more consistent wave velocities; longer wave periods; finer, poorly sorted, positively skewed sediments; and shallow, wider beach profiles. These characteristics indicate swell-dominated conditions operating across a dissipative beach profile. In contrast, Isla Carenero shows an erosional trend, characterised by variable wave velocities and shorter wave periods, coarser, negatively skewed, and well-sorted sediments. These properties suggest more locally influenced wave hydrodynamics at work over a more reflective beach profile.
Field testing of this new multi-platform toolkit in real-world conditions demonstrates significant advantages in capturing multiscale dynamics, bridging the gap between fine-scale local conditions and broader, long-term shoreline changes. Although there are limitations, such as sub-pixel errors in the satellite-derived shoreline trends on smaller beaches, integrating these datasets fosters a deeper understanding of coastal morphodynamics, particularly for remote tropical beaches. The development of this practical, low-cost toolkit for a comprehensive assessment of local beach dynamics offers a valuable and affordable foundation for targeted, evidence-based management interventions in dynamic coastal systems. Its adaptable, scalable design also makes it particularly well suited for replication in other developing nations facing similar logistical and environmental challenges.

Author Contributions

Conceptualisation, J.M., A.J.P., R.C., J.E.H. and K.E.C.; methodology, J.M., J.E.H. and A.J.P.; software, J.M. and J.E.H.; validation, J.M. and J.E.H.; formal analysis, J.M., J.E.H. and A.J.P.; investigation, J.M., J.E.H. and A.J.P.; resources, R.C.; data curation, J.M. and J.E.H.; writing—original draft preparation, J.M.; writing—review and editing, J.M., J.E.H., A.J.P., R.C. and K.E.C.; visualisation, J.M. and J.E.H.; supervision, J.E.H., A.J.P. and K.E.C.; project administration, J.M., K.E.C. and R.C.; funding acquisition, J.E.H., A.J.P., R.C. and K.E.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Adrienne Arsht Community-Based Resilience Solutions Initiative at the Smithsonian Institution. 2024.

Data Availability Statement

Dataset available upon request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. The date, time, and duration of each video recording. Subtables represent (a) Playa Bluff, (b) Playa Istmito, (c) Playa Wizard, and (d) Isla Carenero.
Table A1. The date, time, and duration of each video recording. Subtables represent (a) Playa Bluff, (b) Playa Istmito, (c) Playa Wizard, and (d) Isla Carenero.
(a)
Playa Bluff
DateTimeDuration (s)
29 February 202410:04126
29 February 202410:06124
29 February 202410:17103
29 February 202410:19109
29 February 202409:55100
29 February 202409:59127
29 February 202410:32121
29 February 202410:36126
7 March 202409:50119
7 March 202409:59122
7 March 202410:25110
7 March 202410:29145
7 March 202411:04104
7 March 202411:09136
13 March 202410:04100
13 March 202410:0771
13 March 202410:2792
13 March 202410:2993
13 March 202410:4892
13 March 202410:5098
13 March 202411:0591
13 March 202411:0792
22 March 202408:3892
22 March 202408:4093
22 March 202409:1792
22 March 202409:1992
22 March 202409:5493
22 March 202409:5792
22 March 202410:1792
22 March 202410:1993
28 March 202408:1397
28 March 202408:1794
28 March 202408:3693
28 March 202408:3899
28 March 202408:5995
28 March 202409:01101
28 March 202409:1595
(b)
Playa Istmito
DateTimeDuration (s)
28 March 202409:2193
1 March 202411:15106
1 March 202411:2477
1 March 202411:27121
11 March 202416:1593
11 March 202416:1991
11 March 202416:2193
11 March 202416:2993
11 March 202416:3196
11 March 202416:4392
11 March 202416:4593
18 March 202416:0191
18 March 202416:0691
18 March 202416:2791
18 March 202416:3094
18 March 202416:5294
18 March 202416:5491
29 March 202416:56105
29 March 202416:5994
29 March 202417:1691
(c)
Playa Wizard
DateTimeDuration (s)
29 March 202417:1892
28 February 202413:49100
28 February 202413:56122
28 February 202414:0991
28 February 202414:14105
28 February 202414:2382
28 February 202414:27103
8 March 202409:3093
8 March 202409:31102
8 March 202409:42101
8 March 202409:4494
8 March 202409:53120
8 March 202409:59102
19 March 202409:5792
19 March 202409:5999
19 March 202410:0993
19 March 202410:1192
19 March 202410:2692
19 March 202410:2892
25 March 202409:2692
25 March 202409:2890
25 March 202409:5991
25 March 202410:0192
25 March 202410:3493
25 March 202410:36121
2 April 202412:44106
2 April 202412:4886
2 April 202413:1491
2 April 202413:1692
2 April 202413:2991
(d)
Isla Carenero
DateTimeDuration (s)
2 April 202413:3191
30 January 202409:0922
27 February 202415:4688
27 February 202415:5753
27 February 202416:0531
12 March 202412:3274
12 March 202412:4194
12 March 202412:5374
21 March 202410:3092
21 March 202410:5394
21 March 202411:0692
27 March 202408:4792
27 March 202409:0093
27 March 202409:0592
Table A2. Every satellite observation used for the CoastSat analysis. S2 = Sentinel-2; L7 = Landsat-7.
Table A2. Every satellite observation used for the CoastSat analysis. S2 = Sentinel-2; L7 = Landsat-7.
SiteDateCloud CoverSatelliteResolution (m)
BLUFF2016-07-19 15:51:12+00:000.11052476L730
BLUFF2016-09-21 15:50:58+00:000L730
BLUFF2016-11-04 15:55:23+00:000S210
BLUFF2016-11-08 15:51:14+00:000L730
BLUFF2017-04-13 15:57:55+00:000S210
BLUFF2017-05-19 15:50:44+00:000L730
BLUFF2017-05-23 15:58:50+00:000S210
BLUFF2017-09-08 15:51:14+00:000L730
BLUFF2017-11-11 15:50:50+00:000L730
BLUFF2017-11-14 15:58:56+00:000.05404932S210
BLUFF2018-04-08 15:55:57+00:000S210
BLUFF2018-05-06 15:48:59+00:000.15043509L730
BLUFF2018-05-18 15:57:58+00:000.17213233S210
BLUFF2018-05-23 15:56:34+00:000S210
BLUFF2018-06-12 15:55:23+00:000S210
BLUFF2018-08-16 15:55:24+00:000S210
BLUFF2018-09-10 15:55:16+00:000.00083375S210
BLUFF2018-09-11 15:46:37+00:000.01755119L730
BLUFF2018-09-25 15:55:50+00:000.0130723S210
BLUFF2018-09-27 15:46:15+00:000L730
BLUFF2018-09-30 15:57:15+00:000S210
BLUFF2018-10-15 15:55:22+00:000.0012043S210
BLUFF2019-02-12 16:00:42+00:000S210
BLUFF2019-02-17 16:00:45+00:000.19530221S210
BLUFF2019-03-09 16:00:43+00:000.02988101S210
BLUFF2019-04-03 16:00:46+00:000S210
BLUFF2019-04-13 16:00:48+00:000.30771369S210
BLUFF2019-05-23 16:00:49+00:000S210
BLUFF2019-06-02 16:00:48+00:000.00605237S210
BLUFF2019-07-12 16:00:51+00:000.00241889S210
BLUFF2019-07-17 16:00:54+00:000S210
BLUFF2019-08-31 16:00:46+00:000S210
BLUFF2020-04-17 16:00:46+00:000.00205863S210
BLUFF2020-07-14 15:18:32+00:000.01264976L730
BLUFF2020-07-16 16:00:50+00:000S210
BLUFF2020-08-20 16:00:49+00:000.25062788S210
BLUFF2020-08-31 15:15:06+00:000L730
BLUFF2020-09-19 16:00:47+00:000S210
BLUFF2020-10-04 16:00:51+00:000.03706563S210
BLUFF2020-10-09 16:00:49+00:000S210
BLUFF2021-02-06 16:00:45+00:000S210
BLUFF2021-03-03 16:00:45+00:000S210
BLUFF2021-05-17 16:00:47+00:000.00169837S210
BLUFF2021-06-01 16:00:46+00:000.48851285S210
BLUFF2021-07-16 16:00:47+00:000.00313941S210
BLUFF2021-09-03 14:46:42+00:000L730
BLUFF2021-09-19 14:45:41+00:000L730
BLUFF2021-10-05 14:43:52+00:000L730
BLUFF2022-04-07 16:00:46+00:000.15902915S210
BLUFF2022-05-22 16:00:46+00:000.18941453S210
BLUFF2022-06-06 16:00:53+00:000S210
BLUFF2022-07-06 16:00:58+00:000.10995142S210
BLUFF2022-07-16 14:16:43+00:000.25490196L730
BLUFF2022-07-16 16:00:58+00:000S210
BLUFF2022-08-30 16:00:46+00:000.44499341S210
BLUFF2022-09-09 16:00:47+00:000.0313941S210
BLUFF2022-10-04 16:00:52+00:000.03369977S210
BLUFF2022-10-14 16:00:50+00:000.1197402S210
BLUFF2022-10-24 16:00:48+00:000S210
BLUFF2023-03-18 16:00:49+00:000.00428195S210
BLUFF2023-04-12 16:00:46+00:000S210
BLUFF2023-04-22 16:00:44+00:000.0002985S210
BLUFF2023-04-27 16:00:47+00:000S210
BLUFF2023-05-12 16:00:45+00:000.01034461S210
BLUFF2023-05-17 16:00:50+00:000.00062788S210
BLUFF2023-05-27 16:00:50+00:000S210
BLUFF2023-06-11 16:00:49+00:000S210
BLUFF2023-06-12 13:37:30+00:000L730
BLUFF2023-07-06 16:00:51+00:000S210
BLUFF2023-07-09 13:37:32+00:000L730
BLUFF2023-08-25 16:00:53+00:000S210
BLUFF2023-08-30 16:00:51+00:000.08399209S210
CARENERO2016-01-09 15:49:47+00:000L730
CARENERO2016-04-14 15:50:29+00:000.05726634L730
CARENERO2016-07-19 15:51:12+00:000L730
CARENERO2016-09-21 15:51:22+00:000L730
CARENERO2016-11-04 15:55:23+00:000S210
CARENERO2016-11-08 15:50:50+00:000L730
CARENERO2017-01-11 15:50:25+00:000.01381065L730
CARENERO2017-04-13 15:57:55+00:000S210
CARENERO2017-05-23 15:58:50+00:000S210
CARENERO2017-07-17 15:55:25+00:000S210
CARENERO2017-08-31 15:59:09+00:000.01699219S210
CARENERO2017-11-11 15:51:14+00:000L730
CARENERO2017-12-24 15:55:13+00:000S210
CARENERO2017-12-29 16:00:33+00:000S210
CARENERO2018-02-02 15:58:36+00:000S210
CARENERO2018-02-12 15:57:10+00:000S210
CARENERO2018-03-03 15:49:34+00:000L730
CARENERO2018-03-04 15:56:09+00:000S210
CARENERO2018-03-29 15:55:24+00:000S210
CARENERO2018-04-03 16:00:35+00:000.01523438S210
CARENERO2018-04-08 15:55:57+00:000S210
CARENERO2018-04-28 15:55:27+00:000S210
CARENERO2018-05-06 15:48:59+00:000L730
CARENERO2018-06-12 15:55:23+00:000S210
CARENERO2018-08-16 15:55:24+00:000S210
CARENERO2018-09-10 15:55:16+00:000S210
CARENERO2018-09-27 15:46:15+00:000L730
CARENERO2018-10-10 15:56:19+00:000S210
CARENERO2018-11-04 15:55:21+00:000S210
CARENERO2018-11-24 16:00:38+00:000S210
CARENERO2018-12-29 16:00:42+00:000S210
CARENERO2019-01-13 16:00:41+00:000S210
CARENERO2019-01-18 16:00:45+00:000S210
CARENERO2019-02-12 16:00:42+00:000S210
CARENERO2019-02-18 15:41:57+00:000.20694018L730
CARENERO2019-03-09 16:00:43+00:000S210
CARENERO2019-04-03 16:00:46+00:000S210
CARENERO2019-04-08 16:00:49+00:000.0078125S210
CARENERO2019-05-18 16:00:54+00:000S210
CARENERO2019-05-23 16:00:49+00:000S210
CARENERO2019-06-07 16:00:53+00:000S210
CARENERO2019-07-17 16:00:54+00:000S210
CARENERO2019-08-31 16:00:46+00:000S210
CARENERO2019-09-20 16:00:45+00:000S210
CARENERO2019-09-25 16:00:45+00:000S210
CARENERO2019-09-30 15:33:26+00:000L730
CARENERO2019-11-01 15:32:28+00:000.10612691L730
CARENERO2019-11-04 16:00:46+00:000S210
CARENERO2019-11-24 16:00:42+00:000S210
CARENERO2020-02-07 16:00:37+00:000S210
CARENERO2020-02-21 15:26:31+00:000L730
CARENERO2020-02-22 16:00:42+00:000S210
CARENERO2020-03-08 15:26:03+00:000L730
CARENERO2020-03-08 16:00:41+00:000S210
CARENERO2020-03-13 16:00:43+00:000S210
CARENERO2020-03-18 16:00:42+00:000S210
CARENERO2020-04-07 16:00:43+00:000S210
CARENERO2020-05-12 16:00:44+00:000.0656901S210
CARENERO2020-07-14 15:18:08+00:000L730
CARENERO2020-07-16 16:00:50+00:000.02604167S210
CARENERO2020-08-31 15:15:30+00:000L730
CARENERO2020-09-19 16:00:47+00:000S210
CARENERO2020-10-02 15:13:24+00:000L730
CARENERO2020-10-04 16:00:51+00:000S210
CARENERO2020-10-09 16:00:49+00:000S210
CARENERO2021-01-07 16:00:45+00:000S210
CARENERO2021-01-17 16:00:45+00:000S210
CARENERO2021-01-27 16:00:45+00:000S210
CARENERO2021-02-06 16:00:45+00:000S210
CARENERO2021-03-03 16:00:45+00:000S210
CARENERO2021-03-18 16:00:45+00:000S210
CARENERO2021-03-28 16:00:44+00:000S210
CARENERO2021-05-17 16:00:47+00:000.27552083S210
CARENERO2021-09-19 14:45:41+00:000L730
CARENERO2021-09-29 16:00:50+00:000S210
CARENERO2021-10-21 14:42:51+00:000L730
CARENERO2021-12-28 16:00:47+00:000S210
CARENERO2022-01-25 14:33:14+00:000L730
CARENERO2022-02-01 16:00:40+00:000S210
CARENERO2022-02-06 16:00:45+00:000S210
CARENERO2022-02-16 16:00:46+00:000.03990885S210
CARENERO2022-02-21 16:00:40+00:000S210
CARENERO2022-03-08 16:00:49+00:000S210
CARENERO2022-03-14 14:28:28+00:000L730
CARENERO2022-04-07 16:00:46+00:000S210
CARENERO2022-05-22 16:00:46+00:000S210
CARENERO2022-05-26 14:19:29+00:000L730
CARENERO2022-06-06 16:00:53+00:000S210
CARENERO2022-08-30 16:00:46+00:000.00214844S210
CARENERO2022-10-29 16:00:44+00:000S210
CARENERO2022-12-08 16:00:42+00:000S210
CARENERO2022-12-28 16:00:45+00:000S210
CARENERO2023-01-07 16:00:43+00:000S210
CARENERO2023-02-01 16:00:44+00:000S210
CARENERO2023-02-11 16:00:44+00:000S210
CARENERO2023-03-18 16:00:49+00:000.43307292S210
CARENERO2023-04-12 16:00:46+00:000S210
CARENERO2023-04-17 16:00:47+00:000S210
CARENERO2023-04-22 16:00:44+00:000S210
CARENERO2023-04-27 16:00:47+00:000S210
CARENERO2023-04-29 13:43:00+00:000L730
CARENERO2023-05-04 13:46:58+00:000L730
CARENERO2023-05-12 16:00:45+00:000S210
CARENERO2023-05-17 16:00:50+00:000S210
CARENERO2023-05-27 16:00:50+00:000S210
CARENERO2023-06-11 16:00:49+00:000S210
CARENERO2023-07-06 16:00:51+00:000.02415365S210
CARENERO2023-07-09 13:37:32+00:000L730
CARENERO2023-07-21 16:00:50+00:000S210
CARENERO2023-08-25 16:00:53+00:000S210
CARENERO2023-10-09 16:00:45+00:000S210
CARENERO2023-10-14 16:00:48+00:000.1750651S210
CARENERO2023-11-13 16:00:46+00:000S210
CARENERO2023-12-03 16:00:42+00:000S210
ISTMITO2016-01-09 15:50:11+00:000.03811944L730
ISTMITO2016-07-19 15:50:48+00:000L730
ISTMITO2016-11-04 15:55:23+00:000S210
ISTMITO2016-11-08 15:50:50+00:000L730
ISTMITO2016-12-24 15:59:57+00:000S210
ISTMITO2017-01-11 15:50:49+00:000L730
ISTMITO2017-01-23 15:55:18+00:000S210
ISTMITO2017-04-13 15:57:55+00:000.05828841S210
ISTMITO2017-05-19 15:51:07+00:000L730
ISTMITO2017-05-23 15:58:50+00:000S210
ISTMITO2017-07-17 15:55:25+00:000S210
ISTMITO2017-08-31 15:59:09+00:000S210
ISTMITO2017-11-11 15:50:50+00:000L730
ISTMITO2017-12-24 15:55:13+00:000.00269542S210
ISTMITO2017-12-29 15:50:49+00:000L730
ISTMITO2017-12-29 16:00:33+00:000S210
ISTMITO2018-02-02 15:58:36+00:000S210
ISTMITO2018-02-12 15:57:10+00:000S210
ISTMITO2018-03-03 15:49:34+00:000L730
ISTMITO2018-03-04 15:56:09+00:000S210
ISTMITO2018-03-29 15:55:24+00:000.00269542S210
ISTMITO2018-04-08 15:55:57+00:000S210
ISTMITO2018-04-28 15:55:27+00:000S210
ISTMITO2018-05-18 15:57:58+00:000S210
ISTMITO2018-05-22 15:48:20+00:000.25753769L730
ISTMITO2018-05-23 15:56:34+00:000S210
ISTMITO2018-06-12 15:55:23+00:000S210
ISTMITO2018-08-16 15:55:24+00:000S210
ISTMITO2018-09-10 15:55:16+00:000S210
ISTMITO2018-09-11 15:46:14+00:000L730
ISTMITO2018-09-25 15:55:50+00:000S210
ISTMITO2018-09-27 15:45:51+00:000.281652L730
ISTMITO2018-10-10 15:56:19+00:000S210
ISTMITO2018-10-15 15:55:22+00:000.00516622S210
ISTMITO2018-11-04 15:55:21+00:000.06738544S210
ISTMITO2018-11-09 16:00:43+00:000.02021563S210
ISTMITO2018-11-24 16:00:38+00:000S210
ISTMITO2018-12-19 16:00:40+00:000S210
ISTMITO2018-12-24 16:00:38+00:000S210
ISTMITO2019-01-13 16:00:41+00:000S210
ISTMITO2019-01-18 16:00:45+00:000S210
ISTMITO2019-02-12 16:00:42+00:000S210
ISTMITO2019-03-09 16:00:43+00:000S210
ISTMITO2019-03-24 16:00:44+00:000S210
ISTMITO2019-04-08 16:00:49+00:000S210
ISTMITO2019-04-13 16:00:48+00:000S210
ISTMITO2019-05-18 16:00:54+00:000S210
ISTMITO2019-05-23 16:00:49+00:000S210
ISTMITO2019-06-02 16:00:48+00:000S210
ISTMITO2019-07-12 15:36:48+00:000.25826972L730
ISTMITO2019-07-17 16:00:54+00:000S210
ISTMITO2019-08-31 16:00:46+00:000S210
ISTMITO2019-09-20 16:00:45+00:000S210
ISTMITO2019-09-25 16:00:45+00:000S210
ISTMITO2019-09-30 15:33:50+00:000.16372796L730
ISTMITO2019-11-01 15:32:05+00:000L730
ISTMITO2019-11-04 16:00:46+00:000S210
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Environments 12 00205 g001
Figure 1. (above) The study sites within the Bocas del Toro Archipelago; (inset) the greater Bocas del Toro province, with the coordinates configured in EPSG:4326; (below) in situ photographs of the study sites, featuring (a) Playa Wizard, (b) Playa Bluff, (c) Playa Istmito, and (d) Isla Carenero. The images were taken by the author during field visits from February to March 2024.
Figure 1. (above) The study sites within the Bocas del Toro Archipelago; (inset) the greater Bocas del Toro province, with the coordinates configured in EPSG:4326; (below) in situ photographs of the study sites, featuring (a) Playa Wizard, (b) Playa Bluff, (c) Playa Istmito, and (d) Isla Carenero. The images were taken by the author during field visits from February to March 2024.
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Figure 2. Overview of the OFTV processing pipeline detailing: (a) raw video footage from two fields of view with rectification markers; (b) rectification zone detailing a known distance for further processing; (c) feature tracking using OFTV; (d) deriving wave crest velocities into a time-series; (e) conversion of wave velocities into a CDF; and (f) log scale transformation and calculation of the mean and standard deviation of the linearised CDF.
Figure 2. Overview of the OFTV processing pipeline detailing: (a) raw video footage from two fields of view with rectification markers; (b) rectification zone detailing a known distance for further processing; (c) feature tracking using OFTV; (d) deriving wave crest velocities into a time-series; (e) conversion of wave velocities into a CDF; and (f) log scale transformation and calculation of the mean and standard deviation of the linearised CDF.
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Figure 3. The temporal dynamics in cross-shore distances for each site calculated by the CoastSat algorithm. Temporal dynamics are calculated over the total study period (top) and monthly averages (bottom). The lines represent the mean values for each given time interval, with the shaded area representing the 95th confidence interval for this central tendency. Note: No imagery was available for Playa Bluff for the month of January.
Figure 3. The temporal dynamics in cross-shore distances for each site calculated by the CoastSat algorithm. Temporal dynamics are calculated over the total study period (top) and monthly averages (bottom). The lines represent the mean values for each given time interval, with the shaded area representing the 95th confidence interval for this central tendency. Note: No imagery was available for Playa Bluff for the month of January.
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Figure 4. The OFTV-derived wave characteristics for a given transect at Isla Carenero, displaying the average wave velocity over the video (left) and the power spectra density estimate calculated by use of the Welch equation (right).
Figure 4. The OFTV-derived wave characteristics for a given transect at Isla Carenero, displaying the average wave velocity over the video (left) and the power spectra density estimate calculated by use of the Welch equation (right).
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Figure 5. Peak-wave-period outputs obtained from the WaveWatch III model over the study period. Crosses indicate the OFTV-derived wave period from the in situ footage obtained from Playa Wizard.
Figure 5. Peak-wave-period outputs obtained from the WaveWatch III model over the study period. Crosses indicate the OFTV-derived wave period from the in situ footage obtained from Playa Wizard.
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Table 1. A summary of beach characteristics for each study site reflects the changes in the shoreline data observed from the satellite imagery, ranging from more accretive conditions (Istmito) to more recent sediment loss (Carenero), as illustrated in Figure 3. Profile charts in the first column show cross-shore topography (X-axis: cross-shore distance in meters; Y-axis: height from the back-beach in meters). Characteristics are categorised into slope, wave features via OFTV, sediment analysis, and satellite observations (from 2016 to 2024).
Table 1. A summary of beach characteristics for each study site reflects the changes in the shoreline data observed from the satellite imagery, ranging from more accretive conditions (Istmito) to more recent sediment loss (Carenero), as illustrated in Figure 3. Profile charts in the first column show cross-shore topography (X-axis: cross-shore distance in meters; Y-axis: height from the back-beach in meters). Characteristics are categorised into slope, wave features via OFTV, sediment analysis, and satellite observations (from 2016 to 2024).
Site NameOFTV Wave CharacteristicsSediment CharacteristicsSatellite
Wave Velocity GradientWave Period (s)Mean (mm)Median (mm)Sorting (Φ)Skewness (Φ)Kurtosis (Φ)Avg RoC (m/y)
Environments 12 00205 i001Istmito6.6 ± 3.56.670.160.151.0580.2740.8595.2 ± 5.8
Environments 12 00205 i002Wizard4.8 ± 2.210.000.400.380.4330.0470.9410.2 ± 7.8
Environments 12 00205 i003Bluff4.5 ± 2.44.760.410.400.476−0.2820.657 0.2 ± 8.3
Environments 12 00205 i004Carenero3.6 ± 2.44.000.530.410.304−0.6770.4020.8 ± 4.5
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MDPI and ACS Style

Murphy, J.; Higham, J.E.; Plater, A.J.; Clark, K.E.; Collin, R. Integration of Earth Observation and Field-Based Monitoring for Morphodynamic Characterisation of Tropical Beach Ecosystems. Environments 2025, 12, 205. https://doi.org/10.3390/environments12060205

AMA Style

Murphy J, Higham JE, Plater AJ, Clark KE, Collin R. Integration of Earth Observation and Field-Based Monitoring for Morphodynamic Characterisation of Tropical Beach Ecosystems. Environments. 2025; 12(6):205. https://doi.org/10.3390/environments12060205

Chicago/Turabian Style

Murphy, James, Jonathan E. Higham, Andrew J. Plater, Kasey E. Clark, and Rachel Collin. 2025. "Integration of Earth Observation and Field-Based Monitoring for Morphodynamic Characterisation of Tropical Beach Ecosystems" Environments 12, no. 6: 205. https://doi.org/10.3390/environments12060205

APA Style

Murphy, J., Higham, J. E., Plater, A. J., Clark, K. E., & Collin, R. (2025). Integration of Earth Observation and Field-Based Monitoring for Morphodynamic Characterisation of Tropical Beach Ecosystems. Environments, 12(6), 205. https://doi.org/10.3390/environments12060205

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