Coastal erosion is critical in many locations along the northern Yucatan Peninsula. The area is characterized by a micro-tidal regime and low-energy wave conditions, with a high-incidence angle with respect to the shoreline. Port and harbor infrastructure for fisheries, commercial, and tourist activities has promoted the growth of coastal communities settled on barrier islands. However, the human settlements have degraded the coastal ecosystems and interrupted the littoral transport. Due to coastal development in the region, the land use of the remaining pristine coastal areas is expected to change in forthcoming years. Thus, understanding coastal changes occurring along the northern Yucatan Peninsula is fundamental for improving coastal planning. We employed open access remote sensing data sets and reanalysis information to investigate shoreline changes at different spatial and temporal scales. Shoreline position was obtained along a 150-km stretch of coast from satellite imagery using CoastSat. Firstly, reanalysis and satellite-derived information were validated with in situ measurements in the vicinity of coastal structures. A satisfactory agreement was found for characterizing the forcing conditions (waves and sea level) and shoreline evolution at different temporal scales. A dominant direction of alongshore sediment transport (50,000–80,000 m³/year) make the shoreline highly sensitive to any nearshore disturbance. We found that coastal erosion occurred in 50% of the analyzed transects, whereas beach accretion occurred in only 30%, suggesting net beach losses. Erosive trends are strongly correlated with the presence of coastal structures. The 6-km long Progreso pier induced significant beach erosion along O(10) km, while sheltered harbors induced downdrift erosion along O(1) km. Detached breakwaters and groins have an overall negative impact on downdrift areas (O(100) m). On the other hand, significant erosion was also observed in pristine areas located downdrift of a coastal lagoon due to the sediment impoundment associated with the growth of a sand spit. Moreover, shoreline sand waves drive 40-m shoreline oscillations and propagate (alongshore) at a rate of 300 m/year. The generation of sand waves seems to be related to both natural and anthropogenic perturbations, in combination with the high-incidence wave angle. Their propagation plays a key role in the shoreline dynamics of this region.

Video monitoring has become an indispensable tool to understand beach processes. However, the measurement accuracy derived from the images has been taken for granted despite its dependence on the calibration process and camera movements. An easy to implement self-fed image stabilization algorithm is proposed to solve the camera movements. Georeferenced images were generated from the stabilized images using only one calibration. To assess the performance of the stabilization algorithm, a second set of georeferenced images was created from unstabilized images following the accepted practice of using several calibrations. Shorelines were extracted from the images and corrected with the measured water level and the computed run-up to the 0 m contour. Image-derived corrected shorelines were validated with one hundred beach profile surveys measured during a period of four years along a 1.1 km beach stretch. The simultaneous high-frequency field data available of images and beach surveys are uncommon and allow assessing seasonal changes and long-term trends accuracy. Errors in shoreline position do not increase in time suggesting that the proposed stabilization algorithm does not propagate errors, despite the ever-evolving vegetation in the images. The image stabilization reduces the error in shoreline position by 40 percent, having a larger impact with increasing distance from the camera. Furthermore, the algorithm improves the accuracy on long-term trends by one degree of magnitude (0.01 m/year vs. 0.25 m/year).