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EMODnet is intended to advertise all data in the European Marine Domain as Open Data. It can however be quite a challenge to find and use the data as intended. You find more on the page on EMODnet and Services.

EMODnet and Services

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 improvements on the services. Until now the different EMODnet lots have carried out a great job to collect and harmonize all kind of datasets. Now it is the time to invest in harmonizing all portal so end users can find data a bit better. 
At this technical meeting Deltares gave a presentation where a Use Case 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:

  • only by using OGC services
  • only by using free searches on species names

A stepwise approach followed to work out the use case is as follows:

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  1. bathymetry
  2. biotic observations (without some specific knowledge it is hard to find dataset(s) where abundance information of Amphiura filiformis can be found)

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Find

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more

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Code Block
languagexml
titleWCS GetCapabilities
http://ows.emodnet-bathymetry.eu/wcs?service=WCS&request=GetCapabilities&version=1.0.0

this gives Image Removed
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.
Image Removed

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  1. for bathymetry (http://ows.emodnet-bathymetry.eu/wcs? coverage emodnet:mean)
  2. for biota (http://geo.vliz.be/geoserver/Eurobis/ows? layername=Eurobis:eurobis_points)

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here.

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languagepy
titleWCS code snippet

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for querying the WFS following code is used

Code Block
languagepy
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 = 'http://geo.vliz.be/geoserver/Eurobis/ows?'  
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):
    os.unlink(fn)
out = open(fn, 'wb')
out.write(response.read())
out.close()
    
# define pandas dataframe for the output
out = open(fn, 'wb')
out.write(response.read())
out.close()
df = pandas.read_csv(fn,header=0,sep=',')
os.unlink(fn)

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Now the result (if there is data in the boundingbox, there is no testing involved, zou the result can be empty, this is not tested) is there in 2 objects (Pandas DataFrame and a Numpy array). This can be plotted in a 3D Scatter plot using the Matplotlib. See next code.

Code Block
languagepy
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']
    ax.scatter(xv,yv,zv,label=df['ObservedIndividualCount'])
    
    # 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())
    ax.zaxis.set_major_locator(LinearLocator(10))
    ax.zaxis.set_major_formatter(FormatStrFormatter('%.f'))
    
    # Add a color bar which maps values to colors.
    fig.colorbar(surf, shrink=0.5, aspect=5)
    plt.savefig(r'd:\temp\emodnet\test.png',dpi=600)
    
    plt.show()

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The result is visualised in the following graph
Image Removed