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

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.