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‎examples/README.md‎

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# Examples
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This folder contains process graphs that show how the openEO processes work in practice.
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* [NDVI-UC1: Deriving maximum NDVI measurements over pixel time series](ndvi-uc1/README.md)
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* [ZONAL-UC3: Compute time series of zonal (regional) statistics over user-specified polygons](zonal-uc3/README.md)
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* [EVI: Deriving minimum EVI measurements over pixel time series](evi/README.md)
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Please feel encouraged to add your owns via Pull Requests!
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‎examples/evi/README.md‎

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# EVI (Enhanced Vegetation Index)
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This example derives minimum EVI measurements over pixel time series of Sentinel 2 imagery. The EVI is defined as follows: `(2.5 * (nir - red)) / ((nir + 6.0 * red - 7.5 * blue) + 1.0)`
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The process graph could be visualized as follows:
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![Graph visualization](visual.png)
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This process graph is meant to be used as batch job or can be lazy evaluated. It returns a GeoTiff file with the computed results.
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This process graph assumes the dataset is called `Sentinel-2`. The temporal extent covered is January 2018 and bands `B02` (blue), `B04` (red) and `B08` (nir) are used for the computation. Please note that the order of the bands in `get_collection` is important as they are requested by their order (index) in the callback.
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## Process Graph
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```json
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{
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"dc": {
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"process_id": "load_collection",
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"process_description": "Loading the data; The order of the specified bands is important for the following reduce operation.",
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"arguments": {
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"id": "Sentinel-2",
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"spatial_extent": {
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"west": 16.1,
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"east": 16.6,
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"north": 48.6,
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"south": 47.2
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},
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"temporal_extent": ["2018-01-01", "2018-02-01"],
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"bands": ["B08", "B04", "B02"]
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}
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},
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"evi": {
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"process_id": "reduce",
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"process_description": "Compute the EVI. Formula: 2.5 * (NIR - RED) / (1 + NIR + 6*RED + -7.5*BLUE)",
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"arguments": {
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"data": {"from_node": "dc"},
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"dimension": "spectral",
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"reducer": {
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"callback": {
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"nir": {
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"process_id": "array_element",
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"arguments": {
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"data": {"from_argument": "data"},
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"index": 0
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}
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},
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"red": {
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"process_id": "array_element",
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"arguments": {
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"data": {"from_argument": "data"},
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"index": 1
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}
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},
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"blue": {
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"process_id": "array_element",
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"arguments": {
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"data": {"from_argument": "data"},
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"index": 2
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}
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},
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"sub": {
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"process_id": "subtract",
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"arguments": {
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"data": [{"from_node": "nir"}, {"from_node": "red"}]
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}
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},
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"p1": {
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"process_id": "product",
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"arguments": {
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"data": [6, {"from_node": "red"}]
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}
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},
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"p2": {
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"process_id": "product",
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"arguments": {
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"data": [-7.5, {"from_node": "blue"}]
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}
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},
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"sum": {
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"process_id": "sum",
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"arguments": {
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"data": [1, {"from_node": "nir"}, {"from_node": "p1"}, {"from_node": "p2"}]
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}
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},
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"div": {
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"process_id": "divide",
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"arguments": {
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"data": [{"from_node": "sub"}, {"from_node": "sum"}]
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}
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},
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"p3": {
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"process_id": "product",
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"arguments": {
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"data": [2.5, {"from_node": "div"}]
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},
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"result": true
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}
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}
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}
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}
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},
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"mintime": {
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"process_id": "reduce",
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"process_description": "Compute a minimum time composite by reducing the temporal dimension",
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"arguments": {
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"data": {"from_node": "evi"},
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"dimension": "temporal",
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"reducer": {
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"callback": {
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"min": {
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"process_id": "min",
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"arguments": {
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"data": {"from_argument": "data"}
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},
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"result": true
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}
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}
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}
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}
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},
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"save": {
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"process_id": "save_result",
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"arguments": {
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"data": {"from_node": "mintime"},
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"format": "GTiff"
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},
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"result": true
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}
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}
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```
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## Result
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TBD

‎examples/evi/visual.png‎

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‎examples/ndvi-uc1/README.md‎

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# NDVI (POC Use Case 1)
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This example derives maximum NDVI measurements over pixel time series of Sentinel 2 imagery.
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This process graph is meant to be used as secondary web service. If you want to run it as a batch job, you'd need to store the results to a file with the `save_result` process.
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This process graph assumes the dataset is called `Sentinel-2`. It receives the spatial extent for the data to compute from the secondary web service as variables in `spatial_extent_west`, `spatial_extent_east` etc. The temporal extent covered is January 2018.
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Process Graph #1 computes the NDVI based on the common band names in the metadata. If these entries are not available, you'd need to switch the process to `normalized_difference` in combination with `filter_bands` to manually specify the red and nir bands. This is shown in Process Graph #2.
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## Process Graphs
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### #1
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```json
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{
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"loadco1": {
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"process_id": "load_collection",
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"arguments": {
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"id": "Sentinel-2",
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"spatial_extent": {
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"west": {
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"variable_id": "spatial_extent_west"
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},
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"east": {
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"variable_id": "spatial_extent_east"
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},
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"north": {
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"variable_id": "spatial_extent_north"
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},
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"south": {
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"variable_id": "spatial_extent_south"
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}
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},
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"temporal_extent": [
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"2018-01-01",
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"2018-02-01"
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]
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}
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},
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"ndvi1": {
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"process_id": "ndvi",
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"arguments": {
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"data": {
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"from_node": "loadco1"
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}
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}
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},
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"reduce1": {
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"process_id": "reduce",
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"arguments": {
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"data": {
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"from_node": "ndvi1"
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},
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"dimension": "temporal",
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"reducer": {
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"callback": {
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"max1": {
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"process_id": "max",
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"arguments": {
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"data": {
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"from_argument": "data"
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}
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},
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"result": true
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}
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}
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}
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},
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"result": true
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}
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}
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```
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### #2
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```json
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{
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"load_collection": {
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"arguments": {
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"id": "Sentinel-2",
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"spatial_extent": {
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"west": {
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"variable_id": "spatial_extent_west"
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},
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"east": {
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"variable_id": "spatial_extent_east"
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},
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"north": {
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"variable_id": "spatial_extent_north"
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},
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"south": {
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"variable_id": "spatial_extent_south"
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}
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},
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"temporal_extent": [
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"2018-01-01",
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"2018-02-01"
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]
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},
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"process_id": "load_collection"
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},
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"b1": {
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"arguments": {
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"data": {
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"from_node": "load_collection"
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},
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"bands": [
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"B4"
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]
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},
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"process_id": "filter_bands"
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},
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"b2": {
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"arguments": {
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"data": {
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"from_node": "load_collection"
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},
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"bands": [
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"B8"
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]
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},
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"process_id": "filter_bands"
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},
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"normalized_difference": {
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"arguments": {
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"band1": {
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"from_node": "b1"
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},
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"band2": {
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"from_node": "b2"
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}
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},
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"process_id": "normalized_difference"
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},
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"reduce": {
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"arguments": {
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"data": {
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"from_node": "normalized_difference"
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},
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"dimension": "temporal",
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"reducer": {
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"callback": {
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"max": {
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"arguments": {
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"data": {
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"from_argument": "data"
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}
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},
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"process_id": "max",
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"result": true
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}
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}
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}
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},
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"process_id": "reduce"
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}
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}
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```
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## Result
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Executing this process graph on Google Earth Engine gives the following exemplary results for a user-defined area:
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![Resulting Image](gee-result.png)

‎examples/ndvi-uc1/gee-result.png‎

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