EO Products / Monitoring of shore changes, shore erosion and sediment deposits
The shipping industry faces significant challenges related to monitoring onshore changes, shore erosion, and sediments in coastal zones. These areas are critical for maritime activities, tourism, fisheries, and ecological balance.
Source: https://www.copernicus.eu/en/news/news/observer-what-role-copernicus-programme-coastal-monitoring
Product Description
The shipping industry faces significant challenges related to monitoring onshore changes, shore erosion, and sediments in coastal zones. These areas are critical for maritime activities, tourism, fisheries, and ecological balance. Sediment dynamics affect shipping channels, harbor entrances, and nearshore areas. Coastal processes, wind waves, and subsiding coastlines impact sediment movement. An holistic approach combining satellite observations, long-term data, advanced processing techniques, and numerical models is crucial for effective monitoring and sustainable coastal zone management in the shipping industry.
Product Specifications
BUSINESS PROCESS | SO |
DESCRIPTION | Costal land cover and land use identifying alteration of land use. |
Bathymetry to identify underwater topography, coastal erosion, and sedimentation. | |
Shoreline shift and subsidence. | |
EO INFORMATION OF INTEREST | Land cover and usage change, bathymetry, subsidence. |
MAIN PROCESS STEPS | Land usage changes: Supervised classification using algorithms (e.g., maximum likelihood) identifies land cover classes |
Bathimetry can be retrieved using several approaches and satellite data (see xxx (Coastal National Elevation Database Application Project, 2019; Pe’rei et al., 2013; Shaban et al., 2021; Xie et al., 2023)), e.g. 1. Combining Sentinel-2 and ICESat-2 Datasets with Machine Learning, involving · Adaptive Ellipse DBSCAN (AE-DBSCAN) algorithm, which extracts a priori bathymetric data from ICESat-2 observations, adapting to terrain complexity. · These a priori bathymetric data are then used in Sentinel-2 images to build a model between remote sensing reflectance (Rrs) and water depth. · Three machine learning (ML) methods are employed to estimate bathymetry, with root mean square error (RMSE) of less than 1.5 meters 2. Passive Optical Satellite Remote Sensing, utilizing satellite imagery acquired from platforms such as Landsat 8, DigitalGlobe WorldView, Sentinel 2, 3. By analyzing the relationship between reflected radiation and water depth in shallow water, bathymetry elevation values can be estimated. 3. Blue-Green Band Ratio Algorithm, where dry land and most clouds, speckle noise are removed using the Near-Infrared and blue-green band. And a blue-green band ratio algorithm is applied to derive bathymetry. | |
| Subsidence can be estimated using a differential SAR interferometry (DInSAR). The technique detects ground deformation by comparing SAR images acquired at different times. Differential interferograms are created by comparing pairs of SAR images. These interferograms reveal ground motion with centimeter-level accuracy. By analyzing the phase differences in the interferograms, land subsidence rates are calculated. |
INPUT DATA SOURCE | Landsat,Sentinel-2, MODIS, Sentinel 1, etc.. |
SPATIAL RESOLUTION AND COVERAGE | From 15/30 to 500 m |
ACCURACY / CONSTRAINS | The accuracy and constrains varies based on the specific satellite sensor, the variable being monitored and the areas of interest |
LIMITATIONS | Cloud cover and solar illumination for variables retrieved using passive sensors. Risks of misuse without remote sensing education |
TEMPORAL RESOLUTION | Almost daily |
UPDATE FREQUENCY | The frequency of updates varies based on the specific satellite sensor, the variable being monitored, and the desired level of temporal resolution. |
DELIVERY / OUTPUT FORMAT | NetCDF-3, NetCDF-4 |
ACCESSIBILITY | Copernicus Marine Service and Artic Hub |
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