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Welcome to the eo-bathymetry wiki!
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https://tidesandcurrents.noaa.gov/map/index.shtml?region=North%20Carolina
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EMODnet Bathymetry page: http://www.emodnet-bathymetry.eu/
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JetSKI Sand Engine bathymetry: http://opendap.tudelft.nl/thredds/data2/zandmotor/morphology/JETSKI/catalog.html http://opendap.tudelft.nl/thredds/data2/zandmotor/morphology/JETSKI/gridded/catalog.html http://opendap.tudelft.nl/thredds/data2/zandmotor/morphology/LIDAR/catalog.html
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Latest bathymetric survey data (Deltares intarnal): P:\1204421-kpp-benokust\GIS_data\bathymetrie\OVERIGE\data_op_orde_2019
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FAST project results: http://fast.openearth.eu/, The Science behind the MI-SAFE tool
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BASE Platform satellite + crowd sensed bathymetry (http://base-platform.com/data)
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Vaklodingen: http://opendap.deltares.nl/thredds/catalog/opendap/rijkswaterstaat/vaklodingen/catalog.html
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Kusthoogte: http://opendap.deltares.nl/thredds/catalog/opendap/rijkswaterstaat/kusthoogte/catalog.html
For EMODnet:
- Coastline as a vector for LAT, MLW, MSL, MHW, HAT(?)
- Intertidal bathymetry
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GD: export scene boundary (envelope polygon) + time stamp into GeoJSON (2017)
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GH: extract model results for these time stamps and export back into GEE as (cell_geom, time, value) FC. Alternative is to extract water levels from http://volkov.oce.orst.edu/tides/tpxo8_atlas.html
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GD: for every tile, detect a set of very accurate isolines based on cloud-free images
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GD: use CDF-clip (-25%) combined with Otsu thresholding, maybe multi-class
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GD, GH: assign water level values for isolines by overlapping them with model results - WL
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GD, GH: interpolate/extrapolate water depth isolines into a surface - WL, use Krigging
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GD, GH: perform linear regression between WL ~ P, where P - water occurrence
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GD, GH: transform water occurrence into bathymetry using regression coefficients from the previous step
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GD, GH: estimate LAT, MLW, MSL, MHW, HAT(?) from bathymetry image
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for LAT, HAT - extrapolate bathymetry (Krigging)
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extra - use optical-based depth estimates for extrapolation, only if visible bands correlate with bathymetry.
Supplementary:
- extract time monitoring stations times series (in-situ)
these could be found on the web via EU dataportal, Dutch RWS or opendata: wetwetwet.nl:
- given a polygon (maybe satellite image bounds or a processing box), find intersecting model cells and extract water levels for these cells at given times (extracted from image start_time)
(time, model_domain_id, cell_id, value)
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convert all model grids to shapefiles, to import to EE
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select boxes to perform processing
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implement prototype algorithm to generate (partial) water masks from satellite date
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horizontal bias removal
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See also FAST project results:
- Computation takes several hours for a single tile, ~20-30%
- Too complex algorithm, too many components
- Multi-class Otsu
- Histogram smoothing (KDE)
- http://matplotlib.org/cmocean/
- http://soliton.vm.bytemark.co.uk/pub/cpt-city/
- https://github.com/pyugrid/pyugrid/blob/develop/notebook_examples/Delft3D%20examples.ipynb
Effect of water on light tranmission:
- http://oceanography-leahmoore.blogspot.nl/2010/10/light-attenuation-for-various-colors-of.html
- http://www.seafriends.org.nz/phgraph/water.htm
- http://www.waterencyclopedia.com/La-Mi/Light-Transmission-in-the-Ocean.html
- Tides and tidal currents
- export multiple isolines (polygons), at least for some tests
- northern parts - check multiple sensors for coastline only (S1, MODIS, etc., look only on summer)
- add search algorithm for lowest water level, using 30 years of data
- generate confidence images (on coarser scale)
- where to stop inland?
- compare with legal baselines
- method to compare fuzzy coastlines