Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites

Abstract

Nature-based solutions have gained popularity as an approach to tackling hydro-meteorological hazards (HMHs) in both urban and rural settings. Despite this popularity, challenges persist regarding the evidence base for their effectiveness and data scarcity at the feature or site scale. Flood modelling is a common approach to quantifying the effectiveness of NbS; however, the accuracy of these models heavily depends on the accuracy of the DEM, land cover, and hydraulic/hydrological data utilised. Remote and rural settings often face data scarcity due to the challenging nature of data collection, and insufficient funding for monitoring. Additionally, NbS features vary in size and scale, with many being small (<1 m in width), posing challenges for accurate representation in national LiDAR datasets. Technological advancements in remote sensing technologies, such as unmanned aerial vehicles, handheld LiDAR, and GPS-GNSS, offer opportunities to gather high-resolution, high-accuracy data in these challenging locations. This article proposes a methodological framework for collecting elevation data at remote NbS sites that can tackle areas affected by both sparse and dense vegetation cover. This approach proves valuable in both pre-NbS implementation, through facilitating NbS opportunity and environmental risk identification, and post-NbS implementation, through aiding in geo-spatial feature location, improving existing DEM data for flood modelling, and monitoring temporal changes.