Home

Welcome to the eo-bathymetry wiki!

Links

• http://uhslc.soest.hawaii.edu/thredds/

• http://waterinfo.rws.nl/

• https://tidesandcurrents.noaa.gov/map/index.shtml?region=North%20Carolina

• EMODnet Bathymetry page: http://www.emodnet-bathymetry.eu/

• 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

• FAST project results: http://fast.openearth.eu/, The Science behind the MI-SAFE tool

• FAST MI SAFE webinar (delivered on 20th July 2017)

• Geostience Australia Paper on intertidal bathymetry

• BASE Platform satellite + crowd sensed bathymetry (http://base-platform.com/data)

• Vaklodingen: http://opendap.deltares.nl/thredds/catalog/opendap/rijkswaterstaat/vaklodingen/catalog.html

• Kusthoogte: http://opendap.deltares.nl/thredds/catalog/opendap/rijkswaterstaat/kusthoogte/catalog.html

Deliverables

For EMODnet:

• Coastline as a vector for LAT, MLW, MSL, MHW, HAT(?)
• Intertidal bathymetry

Roadmap

• GD: export scene boundary (envelope polygon) + time stamp into GeoJSON (2017)

• 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

• GD: for every tile, detect a set of very accurate isolines based on cloud-free images

• GD: use CDF-clip (-25%) combined with Otsu thresholding, maybe multi-class

• GD, GH: assign water level values for isolines by overlapping them with model results - WL

• GD, GH: interpolate/extrapolate water depth isolines into a surface - WL, use Krigging

• GD, GH: perform linear regression between WL ~ P, where P - water occurrence

• GD, GH: transform water occurrence into bathymetry using regression coefficients from the previous step

• GD, GH: estimate LAT, MLW, MSL, MHW, HAT(?) from bathymetry image

• for LAT, HAT - extrapolate bathymetry (Krigging)

• 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)

• convert all model grids to shapefiles, to import to EE

• select boxes to perform processing

• implement prototype algorithm to generate (partial) water masks from satellite date

• horizontal bias removal

• See also FAST project results:

Risks

• Computation takes several hours for a single tile, ~20-30%
• Too complex algorithm, too many components

Compute statistics for TOA values

• Multi-class Otsu
• Histogram smoothing (KDE)

Visualisation

• 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

Bathymetry from optical images

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

Notes from the meeting (28-11-2017)

• 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