Detection and pre-localization of coastal wetlands in Brittany, France using topographical indices from altimetric and remote sensing data

The role of wetlands in coastal area processes must be recognized and strengthened. Unless the hydrological and related functions of coastal wetlands are maintained, the success of sustainable coastal development is uncertain. In this study, an initial methodology section is dedicated to the calculation and normalization of several topographic indices to assess soil moisture susceptibility in coastal wetland areas. By providing detailed information on the elevation and topographic structure of coastal wetlands, this study uses various topographic indices, including the Topographic Wetness Index (TWI), the Topographic Position Index (TPI), the Multi-Resolution Valley Bottom Flatness (MRVBF). Additionally, the study considers valley depth, which can contribute to a better understanding of hydrological dynamics, water level variations, and water flow zones during the process of mapping and monitoring changes in these environments. The second research aim of this work is evaluation of the pre-localization of potential coastal wetland areas and their evolution over time in relation to impervious surface changes in Brittany. The analysis reveals that between 1990 and 2020, the area of potentially impervious wetlands increased by 18.3% from 145.3 km 2 to 171.95 km 2 . By combining these pre-localization results with Corine Land Cover (CLC) data and OCS GE, the study highlights the influence of urbanized and impermeable area on coastal wetlands dynamics between 1990 and 2020. The third aim in of this article focuses on assessing the quality of the “binary classification” of wetlands and non-wetlands. A inventory focus on coastal wetlands (carried out by stakeholders between 2011 and 2019) is used as reference data to check whether the proposed methodology is effective and, if so, to determine the score at which it gives satisfactory results. Model performance metrics show a high recall of 0.948 for non-wetland areas, though with moderate precision (0.798), suggesting occasional misclassification of wetland areas as non-wetlands. For wetland areas, the approach achieved high precision (0.936) but a lower recall (0.759), indicating challenges in detecting all existing wetland areas. The overall accuracy of 0.854 and a Kappa coefficient of 0.708 point to a solid performance of the binary classification methodology.