Monitoring coastal land-water topography – Very high resolution satellite data analytics reveal data in space and time

Autor: Hartmann, K., Reithmeier, M., Knauer, K., Adhiwijna, D., Kleih, C., Heege, T., Filippone, M.
Rok vydání: 2023
Zdroj: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
DOI: 10.57757/iugg23-3204
Popis: Coastal morphology is key to modern understanding of threats and nature risks to coastal communities. A quarter of the world’s beaches are eroding due to natural or man-made activities. Dynamics of coastal morphology occurs in space and time (4D), making it difficult to measure using standard survey approaches. We introduce a technique that uses multispectral satellite data and satellite lidar to provide frequent spatial observations of coastal zones. Thanks to international cooperation through the Horizon 2020 program, the analytical processes are based on a highly automatic inversion of the radiative transfer equation (RTE), which enables to model shallow water depth without the access to local survey data. The workflow is installed on AWS cloud environment with directly linked to the satellite data archive. Leveraging these Copernicus satellite data, the solution can generate high frequent and high-resolution shallow water bathymetric data continuously and for the past decade. In combination with land topographic models derived from stereo satellite imagery and verification with bathy-topo data from a space-born green lidar system (IceSat-2 Atlas), this allows for verification and control of vertical accuracies.This solution allows for a rapid and scalable approach to generate shallow water bathymetric and topographic surfaces from small sites to a national level. It serves as a crucial dataset to manage coastal resilience strategies and reduce disaster risk. This will be demonstrated in pilot sites from the southern Baltic to the Caribbean where we can quantify and visualise the changes of the coastal morphology in 4D.
The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)
Databáze: OpenAIRE