Mapping overlooked wetlands: automated detection of wet meadows, ponds, and artificial wetlands in urban and peri-urban environments

Abstract

Mapping wetlands is essential for conservation, but their dynamic nature makes them difficult to detect, especially drier-end types. These challenges are amplified in urban and peri-urban areas, where wetlands are small, fragmented, and frequently under-represented in inventories. Furthermore, many mapping efforts lack the methodological transparency requiredfor replicability and adaptation. This study addresses these gaps by presenting a novel, replicable, and cost-effective methodological framework for mapping complex wetlands. The approachis structured as a hierarchical workflow that leverages open data and computational resources. It integrates multi-temporal optical, SAR, and LiDAR-derived features within Google Earth Engineto generate a time-series of annual Land Use/Land Cover (LULC) classifications. This is followed by a post-processing workflow that produces a final filtered Wetlands map, a LULC map (mode), and a Frequency map, allowing for a nuanced understanding of wetland dynamics. We developed and evaluated this framework in the Metropolitan area of Buenos Aires, Argentina, a landscape where such wetlands are often overlooked. The framework successfully delineated multiple wetlandclasses (Ponds, Wet meadows, Artificial wetlands and Rivers) from non-wetland classes. The final Wetlands map was evaluated using 372 independent points with an overall accuracy of0.91 (95% CI: [0.87, 0.94]), estimated from the population error matrix to provide unbiased accuracy and area estimates. The primary contribution is the transparent methodological framework itself. All scripts are openly shared to facilitate full replication and adaptation, providing environmental managers a robust tool for inventorying and monitoring challenging wetland systems.