ee.FeatureCollection.reduceToImage
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Creates an image from a feature collection by applying a reducer over the selected properties of all the features that intersect each pixel.
Usage | Returns | FeatureCollection.reduceToImage(properties, reducer) | Image |
Argument | Type | Details | this: collection | FeatureCollection | Feature collection to intersect with each output pixel. |
properties | List | Properties to select from each feature and pass into the reducer. |
reducer | Reducer | A Reducer to combine the properties of each intersecting feature into a final result to store in the pixel. |
Examples
Code Editor (JavaScript)
// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
.filter('country_lg == "Belgium"');
// Create an image from features; pixel values are determined from reduction of
// property values of the features intersecting each pixel.
var image = fc.reduceToImage({
properties: ['gwh_estimt'],
reducer: ee.Reducer.sum()
});
// The goal is to sum the electricity generated in 2015 for the power plants
// intersecting 10 km cells and view the result as a map layer.
// ee.FeatureCollection.reduceToImage does not allow the image projection to be
// set because it is waiting on downstream functions that include "crs",
// "scale", and "crsTransform" parameters to define it (e.g., Export.image.*).
// Here, we'll force the projection with ee.Image.reproject so the result can be
// viewed in the map. Note that using small scales with reproject while viewing
// large regions breaks the features that make Earth Engine fast and may result
// in poor performance and/or errors.
image = image.reproject('EPSG:3035', null, 10000);
// Display the image on the map.
Map.setCenter(4.3376, 50.947, 8);
Map.setLocked(true);
Map.addLayer(
image.updateMask(image.gt(0)),
{min: 0, max: 2000, palette: ['yellow', 'orange', 'red']},
'Total estimated annual electricity generation, 2015');
Map.addLayer(fc, null, 'Belgian power plants');
Python setup
See the
Python Environment page for information on the Python API and using
geemap
for interactive development.
import ee
import geemap.core as geemap
Colab (Python)
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
'country_lg == "Belgium"'
)
# Create an image from features pixel values are determined from reduction of
# property values of the features intersecting each pixel.
image = fc.reduceToImage(properties=['gwh_estimt'], reducer=ee.Reducer.sum())
# The goal is to sum the electricity generated in 2015 for the power plants
# intersecting 10 km cells and view the result as a map layer.
# ee.FeatureCollection.reduceToImage does not allow the image projection to be
# set because it is waiting on downstream functions that include "crs",
# "scale", and "crsTransform" parameters to define it (e.g., Export.image.*).
# Here, we'll force the projection with ee.Image.reproject so the result can be
# viewed in the map. Note that using small scales with reproject while viewing
# large regions breaks the features that make Earth Engine fast and may result
# in poor performance and/or errors.
image = image.reproject('EPSG:3035', None, 10000)
# Display the image on the map.
m = geemap.Map()
m.set_center(4.3376, 50.947, 8)
m.add_layer(
image.updateMask(image.gt(0)),
{'min': 0, 'max': 2000, 'palette': ['yellow', 'orange', 'red']},
'Total estimated annual electricity generation, 2015',
)
m.add_layer(fc, None, 'Belgian power plants')
m
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Last updated 2023-10-06 UTC.
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