On dry land

This workflow uses the VAT to evaluate the distribution of Calopteryx splendens in dependence of the land use classification from the Ökosystematlas and a temporal aggregation of the average air temperature.

The purpose of this notebook is to demonstrate the capabilities of Geo Engine. Therefore, some useful techniques will be shown:

  • Using the GBIF data catalogue
  • Point in polygon selection
  • Raster vector join of occurrence data with land use classification
  • Plotting a class histogram
  • Nested workflows

When building your own nested workflow, it is recommended to build it in several steps as shown in this notebook.

Documentation about the operators and how to use them in Python can be found here: https://docs.geoengine.io/operators/intro.html

Preparation

#Import packages
import geoengine as ge
import geoengine_openapi_client
from datetime import datetime
from geoengine.types import RasterBandDescriptor
import altair as alt
import asyncio
import nest_asyncio
import warnings

warnings.filterwarnings('ignore')
alt.renderers.enable('default')

RendererRegistry.enable('default')

#Initialize Geo Engine in VAT
ge.initialize("https://vat.gfbio.org/api")

#Get the GBIF DataProvider id (Useful for translating the DataProvider name to its id)
root_collection = ge.layer_collection()
gbif_prov_id = ''
for elem in root_collection.items:
    if elem.name == 'GBIF':
        gbif_prov_id = str(elem.provider_id)
        
gbif_prov_id

'1c01dbb9-e3ab-f9a2-06f5-228ba4b6bf7a'

Load boundaries of Germany for later GBIF occurrence extraction (optional)

This chapter is not needed and only shows that country boundaries are available

#Create workflow to request germany boundary
workflow_germany = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "OgrSource",
        "params": {
            "data": "germany",
        }
    }
})

workflow_germany

2429a993-385f-546f-b4f7-97b3ba4a5adb

#Set time
start_time = datetime.strptime(
    '2010-01-01T12:00:00.000Z', "%Y-%m-%dT%H:%M:%S.%f%z")
end_time = datetime.strptime(
    '2011-01-01T12:00:00.000Z', "%Y-%m-%dT%H:%M:%S.%f%z")

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_germany.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot()

<Axes: >

output_9_1.png

Load Ökosystematlas (detailed) for later raster vector join with occurrence data (optional)

This chapter is not needed and only shows that raster data is also available.

#Create workflow to request the oekosystematlas raster data
workflow_oekosystematlas = ge.register_workflow({ 
    "type": "Raster",
    "operator": {
        "type": "GdalSource",
        "params": {
            "data": "oekosystematlas_detail"
        }
    }
})

workflow_oekosystematlas

f447601c-0ba1-57c3-9127-b0622f982231

#Request the data from Geo Engine into a xarray dataarray
data = workflow_oekosystematlas.get_xarray(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot(vmax=75)

<matplotlib.collections.QuadMesh at 0x714e5ee6fc70>

output_13_1.png

Load Average temperature for later raster vector join with event data (optional)

This chapter is not needed and only shows that raster data is also available.

#Create workflow to request the average temperature raster data
workflow_t_avg = ge.register_workflow({ 
    "type": "Raster",
    "operator": {
        "type": "GdalSource",
        "params": {
            "data": "daily_mean_air_temperature"
        }
    }
})

workflow_t_avg

b29f3579-2e15-5280-96b6-c1aef29517a6

#Preparing of the boundaries for the workflow raster stream
bbox = ge.QueryRectangle(
    ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
    ge.TimeInterval(start_time, end_time),
    resolution=ge.SpatialResolution(0.1, 0.1),
    srs="EPSG:4326"
)

#Request the data from Geo Engine into a xarray dataarray
data = workflow_t_avg.get_xarray(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, start_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot(vmin=-3, vmax=3)

<matplotlib.collections.QuadMesh at 0x714e5ed69ea0>

output_18_1.png

Processing Calopteryx splendens

None of the following steps are necessary in theory, as the entire workflow will be projected in the nested request in the end. However, the steps are intended to show the capabilities of Geo Engine and how to logically build nested workflows.

Load Calopteryx splendens (Optional)

#Create workflow to request Calopteryx splendens occurences
workflow_calopteryx_splendens = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "OgrSource",
        "params": {
            "data": f"_:{gbif_prov_id}:`species/Calopteryx splendens`",
        }
    }
})

workflow_calopteryx_splendens.get_result_descriptor()

Data type:         MultiPoint
Spatial Reference: EPSG:4326
Columns:
  gbifid:
    Column Type: int
    Measurement: unitless
  scientificname:
    Column Type: text
    Measurement: unitless
  basisofrecord:
    Column Type: text
    Measurement: unitless

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_calopteryx_splendens.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot()

<Axes: >

output_23_1.png

Point in Polygon Calopteryx splendens

#Create workflow to request Calopteryx splendens occurrences filtered by German border
workflow_calopteryx_splendens_cut = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "PointInPolygonFilter",
        "params": {},
        "sources": {
            "points": { #Calopteryx splendens ###############################
                "type": "OgrSource",
                "params": {
                    "data": f"_:{gbif_prov_id}:`species/Calopteryx splendens`",
                    "attributeProjection": []
                } 
            }, #####################################################
            "polygons": { #Germany #################################
                "type": "OgrSource",
                "params": {
                    "data": "germany"
                }
            } ######################################################
        } 
    }
})

workflow_calopteryx_splendens_cut

6cf9ef88-8bd3-5904-bc74-f866165b18c3

#Request the data from Geo Engine into a geopandas dataframe
data_calopteryx_splendens = workflow_calopteryx_splendens_cut.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data_calopteryx_splendens.plot()

<Axes: >

output_26_1.png

Nested Point in Polygon and Raster Vector Join Calopteryx splendens

#Create a workflow to request Calopteryx splendens occurrences filtered by the German border and linked to the Ökosystematlas data.
workflow_calopteryx_splendens_cut_join = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "RasterVectorJoin",
        "params": {
            "names": {
                "type": "names",
                "values": ["Ökosystematlas", "Avg_Temperature"]
            }, 
            "temporalAggregation": "none",
            "featureAggregation": "first",
        },
        "sources": {
            "vector": { #Calopteryx splendens cut ######################################
                "type": "PointInPolygonFilter", 
                "params": {},
                "sources": {
                    "points": {
                        "type": "OgrSource",
                        "params": {
                            "data": f"_:{gbif_prov_id}:`species/Calopteryx splendens`",
                            "attributeProjection": []
                        }
                    },
                    "polygons": {
                        "type": "OgrSource",
                        "params": {
                            "data": "germany"
                        }
                    }
                }
            }, ##############################################################
            "rasters": [{ #Ökosystematlas ###################################
                "type": "GdalSource",
                "params": {
                    "data": "oekosystematlas"
                }
            }, ##############################################################
            { #Average temperature
                "type": "GdalSource",
                "params": {
                    "data": "daily_mean_air_temperature"
                }
            }] ##############################################################
        },
    }
})

workflow_calopteryx_splendens_cut_join

bab94dec-6025-56f6-bef8-39c14937939b

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_calopteryx_splendens_cut_join.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    ),
    resolve_classifications=True
)

#Show the geopandas dataframe
data
geometry Avg_Temperature basisofrecord gbifid scientificname Ökosystematlas start end
0 POINT (6.79395 51.93967) 13.400000 HUMAN_OBSERVATION 700582646 Calopteryx splendens Harris, 1780 No data 2010-04-07 00:00:00+00:00 2010-04-07 00:00:00+00:00
1 POINT (6.79395 51.93967) 13.400000 HUMAN_OBSERVATION 700582645 Calopteryx splendens Harris, 1780 No data 2010-04-07 00:00:00+00:00 2010-04-07 00:00:00+00:00
2 POINT (6.75459 52.09421) 8.500000 HUMAN_OBSERVATION 700578315 Calopteryx splendens Harris, 1780 Ackerland 2010-04-12 00:00:00+00:00 2010-04-12 00:00:00+00:00
3 POINT (6.75459 52.09421) 8.500000 HUMAN_OBSERVATION 700578316 Calopteryx splendens Harris, 1780 Ackerland 2010-04-12 00:00:00+00:00 2010-04-12 00:00:00+00:00
4 POINT (7.57250 51.63501) 19.700001 HUMAN_OBSERVATION 920849630 Calopteryx splendens Harris, 1780 Grünland 2010-04-29 00:00:00+00:00 2010-04-29 00:00:00+00:00
... ... ... ... ... ... ... ... ...
535 POINT (7.62722 47.98439) 0.000000 HUMAN_OBSERVATION 3845932111 Calopteryx splendens Harris, 1780 Ackerland 2011-01-01 00:00:00+00:00 2011-01-01 00:00:00+00:00
536 POINT (7.79354 48.33831) NaN HUMAN_OBSERVATION 3844974542 Calopteryx splendens Harris, 1780 Ackerland 2011-01-01 00:00:00+00:00 2011-01-01 00:00:00+00:00
537 POINT (7.80175 48.42811) 0.200000 HUMAN_OBSERVATION 3845548749 Calopteryx splendens Harris, 1780 Laubwälder 2011-01-01 00:00:00+00:00 2011-01-01 00:00:00+00:00
538 POINT (8.10648 48.77593) -0.400000 HUMAN_OBSERVATION 3845803099 Calopteryx splendens Harris, 1780 Grünland 2011-01-01 00:00:00+00:00 2011-01-01 00:00:00+00:00
539 POINT (7.81823 48.60769) NaN HUMAN_OBSERVATION 3845383562 Calopteryx splendens Harris, 1780 No data 2011-01-01 00:00:00+00:00 2011-01-01 00:00:00+00:00

540 rows × 8 columns

Nested Full Workflow Calopteryx splendens Plot Ökosystematlas

#Create a workflow to plot Calopteryx splendens occurrences filtered by the German border and merged with the ecosystematlas data as a class histogram.
workflow_calopteryx_splendens_full_öko = ge.register_workflow({
    "type": "Plot",
    "operator": {
       "type": "ClassHistogram",
       "params": {
          "columnName": "Ökosystematlas"
       },
        "sources": {
            "source": { #Calopteryx splendens cut join #####################################
                "type": "RasterVectorJoin",
                "params": {
                        "names": {
                            "type": "names",
                            "values": ["Ökosystematlas", "Avg_Temperature"]
                        }, 
                        "temporalAggregation": "none",
                        "featureAggregation": "mean",
                },
                "sources": {
                    "vector": {
                        "type": "PointInPolygonFilter",
                        "params": {},
                        "sources": {
                            "points": {
                                "type": "OgrSource",
                                "params": {
                                    "data": f"_:{gbif_prov_id}:`species/Calopteryx splendens`",
                                    "attributeProjection": []
                                }
                            },
                            "polygons": {
                                "type": "OgrSource",
                                "params": {
                                    "data": "germany"
                                }
                            }
                        }
                    },
                    "rasters": [{
                        "type": "GdalSource",
                        "params": {
                            "data": "oekosystematlas"
                        }
                    },
                    {
                        "type": "GdalSource",
                        "params": {
                            "data": "daily_mean_air_temperature"
                        }
                    }]
                }
            } ######################################################################
       }
    }
})
    
workflow_calopteryx_splendens_full_öko

7eec35db-1e35-5569-8354-32c7b31fddb4

#Request the plot from Geo Engine
plot_calopteryx_splendens = workflow_calopteryx_splendens_full_öko.plot_chart(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Show the plot
alt.Chart.from_dict(plot_calopteryx_splendens.spec)

Nested Full Workflow Calopteryx splendens Plot Average Temperature

#Create a workflow to request Calopteryx splendens occurrences filtered by the German border and linked to the Ökosystematlas data.
workflow_calopteryx_splendens_full_avg_temp = ge.register_workflow({
    "type": "Vector",
    "operator": {
        "type": "RasterVectorJoin",
        "params": {
                "names": {
                    "type": "names",
                    "values": ["Ökosystematlas", "Avg_Temperature"]
                }, 
                "temporalAggregation": "none",
                "featureAggregation": "mean",
        },
        "sources": {
            "vector": {
                "type": "PointInPolygonFilter", 
                "params": {},
                "sources": {
                    "points": {
                        "type": "OgrSource",
                        "params": {
                            "data": f"_:{gbif_prov_id}:`species/Calopteryx splendens`",
                            "attributeProjection": []
                        }
                    },
                    "polygons": {
                        "type": "OgrSource",
                        "params": {
                            "data": "germany"
                        }
                    }
                }
            },
            "rasters": [{
                "type": "GdalSource",
                "params": {
                    "data": "oekosystematlas"
                }
            },
            {
                "type": "GdalSource",
                "params": {
                    "data": "daily_mean_air_temperature"
                }
            }]
        },
    }
})

workflow_calopteryx_splendens_full_avg_temp

6ab4ea15-948a-5105-87cd-d9b89548f9bd

#Request the data from Geo Engine into a geopandas dataframe
data = workflow_calopteryx_splendens_full_avg_temp.get_dataframe(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, end_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Show the geopandas dataframe
data.plot(column='Avg_Temperature', legend=True, legend_kwds={'label': 'Average Temperature'})

<Axes: >

output_35_1.png

Further experiments

In this chapter, some other useful links between Geo Engine and Python are shown.

#Overlay plot with context
import geopandas as gpd
import matplotlib.pyplot as plt

#Request the data from Geo Engine into a xarray dataarray
data = workflow_t_avg.get_xarray(
    ge.QueryRectangle(
        ge.BoundingBox2D(5.852490, 47.271121, 15.022059, 55.065334),
        ge.TimeInterval(start_time, start_time),
        resolution=ge.SpatialResolution(0.1, 0.1),
        srs="EPSG:4326"
    )
)

#Plot the data
data.plot(vmin=-3, vmax=3)
data_calopteryx_splendens.plot(ax=plt.gca(), color='red', markersize=3)
plt.show()

output_38_0.png