Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics.

Classification of beaches into morphodynamic states is a common approach in sandy beach studies, due to the influence of natural variables in ecological patterns and processes. The use of remote sensing for identifying beach type and monitoring changes has been commonly applied through multiple methods, which often involve expensive equipment and software processing of images. A previous study on the South African Coast developed a method to classify beaches using conditional tree inferences, based on beach morphological features estimated from public available satellite images, without the need for remote sensing processing, which allowed for a large-scale characterization. However, since the validation of this method has not been tested in other regions, its potential uses as a trans-scalar tool or dependence from local calibrations has not been evaluated. Here, we tested the validity of this method using a 200-km stretch of the Brazilian coast, encompassing a wide gradient of morphodynamic conditions. We also compared this locally derived model with the results that would be generated using the cut-off values established in the previous study. To this end, 87 beach sites were remotely assessed using an accessible software (i.e., Google Earth) and sampled for an

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©2022 Checon et al.

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Guilherme N. Corte is an Academic Editor for PeerJ. The other authors declare that they have no competing interests.