Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models

Abstract

As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this study we assess changes to the São Paulo State (SE Brazil) coastline over the last 36 years. The study innovatively employs big data remote sensing techniques and multivariate statistical models to evaluate and generate erosion/accretion rates (1985–2021) relative to beach orientation and slope. Shoreline change rates have been obtained for sandy beaches at 485 one-kilometer-spaced transects. Our findings capture the complexity and heterogeneity of the analyzed coastline, at a regional and local scale. No association was found between shoreline changes and beach face orientation. Nonetheless, a dependency relationship was found between dissipative beaches with moderate to high accretion. Beaches facing south, with relative stability, were prone to sediment accumulation. Locations with slow accretion, like sandy spits and tombolo-protected beaches, were associated with dissipative beaches with moderate to high accretion. The southeast-oriented beaches are more prone to erosion due to storm waves from the south. Results provide a broad, fast, and relatively low-cost methodology that can be used in any sandy beach context, bringing essential information for coastal management and decision-making related to the use and occupation of the coastal zones.

Global patterns in sandy beach erosion: unraveling the roles of anthropogenic, climatic and morphodynamic factors

“…Sandy beaches, however, exhibit diverse forms and settings, with variations in orientation and exposure to wave action, resulting in site-specific and temporally variable responses to sea level rise. Therefore, there is no single, uniform response of beaches to rising sea levels, and regional-scale lenses of observation can overlook such fine-scale nuances (Castelle et al, 2018;Orlando et al, 2019;Cooper et al, 2020;Ferreira et al, 2023). Further, because of the high spatial variability in crustal subsidence rates (Menard, 1983;Di Natale et al, 2008), wave climates and tidal regimes, it is the set of local conditions, rather than a single global mean sea-level trend, that determines each locality's vulnerability (Cooper et al, 2020).…”