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At the moment of writing this page, during the 2nd technical meeting of the Technical Work Group of EMODnet in the Sea Aquarium of Genova in July 2017, the technical workgroup is discussing discussions about improvements on the services are the main topic. Until now the different EMODnet lots have carried out a great job to collect and harmonize all kind kinds of datasetsdata sets. Now it is the time to invest in harmonizing all portal portals so end users can find data a bit better.
At this technical meeting Deltares gave a presentation where a Use Case the use case presented got much attention. The Use Case was "I want to visualise the relation between the sea floor and the occurrence (with depth) of a specific animal (example is for Amphiura filiformis)". Some constraints were formulated, these are:

  • use the metadata delivered by the central portal ( --> product catalogue)
  • only by using OGC services
  • only by using free searches on species names

A stepwise step wise approach was followed to work out the use case is as follows. Steps followed:

  1. go to the catalogue of EMODnet to find the data sets, these are:
    1. bathymetry
    2. biotic observations (without some specific knowledge it is hard to find datasetdata set(s) where abundance information of Amphiura filiformis can be found)
  2. Go to the specific subportals sub portals via the links presented above and find out which services are available, these are:
    1. for bathymetry the following services are made available
      WCS gives you the real data. The example will be worked out for this service. Find out more about WCS at the page WCS primer.
    2. for biotic observations the challenge is to find abundance information on the VLIZ geoserver
      Geoserver can advertise WMS, WFS and WCS information, in case of species distribution in relation to depth the real observations are the way to go. Read more about WFS at the WFS primer page. Checking the complete geoserver and filtering on Eurobis it reveals the best dataset data set to be used is Aggregated points (stations) of EurOBIS occurences.
  3. At this point insight is gained in the available sources and possible datasets data sets that can be used. Close investigation reveals however that the links offered for Bathymetry is are not really usable. Reverse engineering via the downloade download area of interest button on the EMODnet bathymetry portal reveals that the dataset data set with the real data is not advertised. This is shown via the get capabilities request of the WCS services provided.

    Code Block
    titleWCS GetCapabilities

    this gives:

    The developer tools gives information that a different layer is used, the coverage emodnet:mean is not mentioned in the getCapabilities but is used as layer to download.

  4. Now a list of exact datasource data sources is know, these are:
    1. for bathymetry ( coverage emodnet:mean)
    2. for biota ( layername=Eurobis:eurobis_points)
  5. On the primer pages present code snippets for python. These code snippets are adjusted to the particular example presented here.

    Code Block
    titleWCS code snippet
    # import packages
    import os
    from owslib.wcs import WebCoverageService
    from osgeo import gdal
    # define the connection	
    url = ''
    wcs = WebCoverageService(url,version='1.0.0',timeout=320)
    # define variables
    clipfile = r'..\temp.tif'
    requestbbox = (2.097,52.715,4.277,53.935)
    layer = 'emodnet:mean_atlas_land'
    # get the data
    bathyml = 'emodnet:mean'
    sed = wcs[layer] #this is necessary to get essential metadata from the layers advertised
    cx, cy = map(int,sed.grid.highlimits)
    bbox = sed.boundingboxes[0]['bbox']
    lx,ly,hx,hy = map(float,bbox)
    resx, resy = (hx-lx)/cx, (hy-ly)/cy
    width = cx/1000
    height = cy/1000
    gc = wcs.getCoverage(identifier=bathyml,
    fn = clipfile
    f = open(fn,'wb')
    filetiff = gdal.Open(clipfile)
    theImage = filetiff.GetRasterBand(1).ReadAsArray()
  6. for querying the WFS following code is used

    Code Block
    titleWFS querying
    # import the packages
    import pandas
    import sys
    import os
    from owslib.fes import *
    from owslib.etree import etree
    from owslib.wfs import WebFeatureService
    # define the input variables
    requestbbox = (2.097,52.715,4.277,53.935)
    fn = r'..\afile'
    wfs = ''  
    layer = 'Eurobis:eurobis_points'
    afilter = PropertyIsEqualTo(propertyname='Eurobis:ScientificName', literal='Amphiura filiformis')
    # define the link to the service and carry out the request
    wfs11 = WebFeatureService(url=wfs, version='1.1.0',timeout=320)
    response = wfs11.getfeature(typename=layer, bbox=bbx,srsname='urn:x-ogc:def:crs:EPSG:4326',outputFormat='shape-zip')   
    if os.path.isfile(fn):
    out = open(fn, 'wb')
    # define pandas dataframe for the output
    out = open(fn, 'wb')
    df = pandas.read_csv(fn,header=0,sep=',')
  7. Now the result (if there is data in the boundingbox, there is no testing involved, zou thus the result can be empty, this is not tested) is there, in 2 objects (Pandas DataFrame with observation information and a Numpy array with bathymetry). This can be plotted in a 3D Scatter plot using the Matplotlib. See next code.

    Code Block
    titlePlotting the data
    img = theImage (see above)
    bbox = requestbbox (see above)
    def plotraster(img,df,resx,resy,bbox):
        from mpl_toolkits.mplot3d import Axes3D
        import matplotlib.pyplot as plt
        from matplotlib import cm
        from matplotlib.ticker import LinearLocator, FormatStrFormatter
        import numpy as np
        fig = plt.figure()
        ax = fig.gca(projection='3d')
        #plot the vectors
        xv = df['Longitude']
        yv = df['Latitude']
        zv = df['MaximumDepth']
        # Make data.
        X = np.linspace(bbox[0],bbox[2], img.shape[1])
        Y = np.linspace(bbox[1],bbox[3], img.shape[0])
        X, Y = np.meshgrid(X, Y)
        #R = np.sqrt(X**2 + Y**2)
        #Z = np.sin(R)
        Z = img
        # Plot the surface.
        surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
                               linewidth=0, antialiased=False,alpha=0.5)
        # Customize the z axis.
        ax.set_zlim(Z.min().round(), Z.max().round())
        # Add a color bar which maps values to colors.
        fig.colorbar(surf, shrink=0.5, aspect=5)
  8. The result is visualised in the following graph