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

• Published in: PeerJ (San Francisco, CA) CA), 2022-05, Vol.10, p.e13413, Article e13413
• Main Authors: Checon, Helio Herminio, Shah Esmaeili, Yasmina, Corte, Guilherme N, Malinconico, Nicole, Turra, Alexander
• Format: Article
• Language: English
• Subjects: Accuracy; Beaches; Biodiversity; Brazil; Brazilian coast; Calibration; Classification; Classification schemes; Coastal ecosystems; Coastal management; Coasts; Conditional tree inference; Conservation Biology; Ecology; Ecosystems; Environmental modelling; Environmental Monitoring - methods; Eretmochelys imbricata; Marine Biology; Methods; Morphology; Natural Resource Management; Prediction models; Remote sensing; Spatial and Geographic Information Science; Tourism
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13413

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 environmental characterization and beach type classification. These sites were used to derive the predictive model of beach morphodynamics from the remotely assessed metrics, using conditional inference trees. An additional 77 beach sites, with a previously known morphodynamic type, were also remotely evaluated to test the model accuracy. Intertidal width and exposure degree were the only variables selected in the model to classify beach type, with an accuracy higher than 90% through different metrics of model validation. The only limitation was the inability in separating beach types in the reflective end of the morphodynamic continuum. Our results corroborated the usefulness of this method, highlighting the importance of a locally developed model, which substantially increased the accuracy. Although the use of more sophisticated remote sensing approaches should be preferred to assess coastal dynamics or detailed morphodynamic features (e.g., nearshore bars), the method used here provides an accessible and accurate approach to classify beach into major states at large spatial scales. As beach type can be used as a surrogate for biodiversity, environmental sensitivity and touristic preferences, t