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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) | ||
| * [ZONAL-UC3: Compute time series of zonal (regional) statistics over user-specified polygons](zonal-uc3/README.md) | ||
| * [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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| # 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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| 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 | ||
| { | ||
| "dc": { | ||
| "process_id": "load_collection", | ||
| "process_description": "Loading the data; The order of the specified bands is important for the following reduce operation.", | ||
| "arguments": { | ||
| "id": "Sentinel-2", | ||
| "spatial_extent": { | ||
| "west": 16.1, | ||
| "east": 16.6, | ||
| "north": 48.6, | ||
| "south": 47.2 | ||
| }, | ||
| "temporal_extent": ["2018-01-01", "2018-02-01"], | ||
| "bands": ["B08", "B04", "B02"] | ||
| } | ||
| }, | ||
| "evi": { | ||
| "process_id": "reduce", | ||
| "process_description": "Compute the EVI. Formula: 2.5 * (NIR - RED) / (1 + NIR + 6*RED + -7.5*BLUE)", | ||
| "arguments": { | ||
| "data": {"from_node": "dc"}, | ||
| "dimension": "spectral", | ||
| "reducer": { | ||
| "callback": { | ||
| "nir": { | ||
| "process_id": "array_element", | ||
| "arguments": { | ||
| "data": {"from_argument": "data"}, | ||
| "index": 0 | ||
| } | ||
| }, | ||
| "red": { | ||
| "process_id": "array_element", | ||
| "arguments": { | ||
| "data": {"from_argument": "data"}, | ||
| "index": 1 | ||
| } | ||
| }, | ||
| "blue": { | ||
| "process_id": "array_element", | ||
| "arguments": { | ||
| "data": {"from_argument": "data"}, | ||
| "index": 2 | ||
| } | ||
| }, | ||
| "sub": { | ||
| "process_id": "subtract", | ||
| "arguments": { | ||
| "data": [{"from_node": "nir"}, {"from_node": "red"}] | ||
| } | ||
| }, | ||
| "p1": { | ||
| "process_id": "product", | ||
| "arguments": { | ||
| "data": [6, {"from_node": "red"}] | ||
| } | ||
| }, | ||
| "p2": { | ||
| "process_id": "product", | ||
| "arguments": { | ||
| "data": [-7.5, {"from_node": "blue"}] | ||
| } | ||
| }, | ||
| "sum": { | ||
| "process_id": "sum", | ||
| "arguments": { | ||
| "data": [1, {"from_node": "nir"}, {"from_node": "p1"}, {"from_node": "p2"}] | ||
| } | ||
| }, | ||
| "div": { | ||
| "process_id": "divide", | ||
| "arguments": { | ||
| "data": [{"from_node": "sub"}, {"from_node": "sum"}] | ||
| } | ||
| }, | ||
| "p3": { | ||
| "process_id": "product", | ||
| "arguments": { | ||
| "data": [2.5, {"from_node": "div"}] | ||
| }, | ||
| "result": true | ||
| } | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "mintime": { | ||
| "process_id": "reduce", | ||
| "process_description": "Compute a minimum time composite by reducing the temporal dimension", | ||
| "arguments": { | ||
| "data": {"from_node": "evi"}, | ||
| "dimension": "temporal", | ||
| "reducer": { | ||
| "callback": { | ||
| "min": { | ||
| "process_id": "min", | ||
| "arguments": { | ||
| "data": {"from_argument": "data"} | ||
| }, | ||
| "result": true | ||
| } | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "save": { | ||
| "process_id": "save_result", | ||
| "arguments": { | ||
| "data": {"from_node": "mintime"}, | ||
| "format": "GTiff" | ||
| }, | ||
| "result": true | ||
| } | ||
| } | ||
| ``` | ||
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| ## Result | ||
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| TBD |
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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 | ||
| { | ||
| "loadco1": { | ||
| "process_id": "load_collection", | ||
| "arguments": { | ||
| "id": "Sentinel-2", | ||
| "spatial_extent": { | ||
| "west": { | ||
| "variable_id": "spatial_extent_west" | ||
| }, | ||
| "east": { | ||
| "variable_id": "spatial_extent_east" | ||
| }, | ||
| "north": { | ||
| "variable_id": "spatial_extent_north" | ||
| }, | ||
| "south": { | ||
| "variable_id": "spatial_extent_south" | ||
| } | ||
| }, | ||
| "temporal_extent": [ | ||
| "2018-01-01", | ||
| "2018-02-01" | ||
| ] | ||
| } | ||
| }, | ||
| "ndvi1": { | ||
| "process_id": "ndvi", | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "loadco1" | ||
| } | ||
| } | ||
| }, | ||
| "reduce1": { | ||
| "process_id": "reduce", | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "ndvi1" | ||
| }, | ||
| "dimension": "temporal", | ||
| "reducer": { | ||
| "callback": { | ||
| "max1": { | ||
| "process_id": "max", | ||
| "arguments": { | ||
| "data": { | ||
| "from_argument": "data" | ||
| } | ||
| }, | ||
| "result": true | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "result": true | ||
| } | ||
| } | ||
| ``` | ||
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| ### #2 | ||
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| ```json | ||
| { | ||
| "load_collection": { | ||
| "arguments": { | ||
| "id": "Sentinel-2", | ||
| "spatial_extent": { | ||
| "west": { | ||
| "variable_id": "spatial_extent_west" | ||
| }, | ||
| "east": { | ||
| "variable_id": "spatial_extent_east" | ||
| }, | ||
| "north": { | ||
| "variable_id": "spatial_extent_north" | ||
| }, | ||
| "south": { | ||
| "variable_id": "spatial_extent_south" | ||
| } | ||
| }, | ||
| "temporal_extent": [ | ||
| "2018-01-01", | ||
| "2018-02-01" | ||
| ] | ||
| }, | ||
| "process_id": "load_collection" | ||
| }, | ||
| "b1": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "load_collection" | ||
| }, | ||
| "bands": [ | ||
| "B4" | ||
| ] | ||
| }, | ||
| "process_id": "filter_bands" | ||
| }, | ||
| "b2": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "load_collection" | ||
| }, | ||
| "bands": [ | ||
| "B8" | ||
| ] | ||
| }, | ||
| "process_id": "filter_bands" | ||
| }, | ||
| "normalized_difference": { | ||
| "arguments": { | ||
| "band1": { | ||
| "from_node": "b1" | ||
| }, | ||
| "band2": { | ||
| "from_node": "b2" | ||
| } | ||
| }, | ||
| "process_id": "normalized_difference" | ||
| }, | ||
| "reduce": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_node": "normalized_difference" | ||
| }, | ||
| "dimension": "temporal", | ||
| "reducer": { | ||
| "callback": { | ||
| "max": { | ||
| "arguments": { | ||
| "data": { | ||
| "from_argument": "data" | ||
| } | ||
| }, | ||
| "process_id": "max", | ||
| "result": true | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "process_id": "reduce" | ||
| } | ||
| } | ||
| ``` | ||
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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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|  |
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