River plume identification through a deep-learning model: an innovative approach

Abstract

ABSTRACT River plumes are complex physical phenomena that occur at the interface between riverine and marine systems. The lack of direct measurements of sediment concentration complicates the study of these geomorphological features, since the boundaries of river plumes are often gradual and unclear. Therefore, the identification and digitalization of river plumes is not simple and the methods applied in different study areas are not always objective and replicable. The aim of this work is to provide a valid approach based on a deep-learning model that uses Convolution Neural Network (CNN) layers for the digitalization of river plumes. We describe the methodology applied to implement the input dataset used for training the model, the errors obtained, and an application for a study area of about 300 km located in the Mediterranean. The model uses Sentinel-2 Level-1C images. The application of the model to a specific study area allowed us to understand the possibility of investigating these geomorphological features to obtain results in agreement with previous works. As a matter of fact, by using the red band as a proxy of sediment concentration, we were able to investigate the average behaviours of sediment dispersion along the coast and to extract innovative data related to specific events for the study of morphological characteristics such as dimension and direction.