ee.FeatureCollection.reduceToImage

Crée une image à partir d'une collection d'entités en appliquant un réducteur sur les propriétés sélectionnées de toutes les entités qui croisent chaque pixel.

Utilisation	Renvoie
FeatureCollection.reduceToImage(properties, reducer)	Image

Argument	Type	Détails
ceci : collection	FeatureCollection	Collection d'entités à croiser avec chaque pixel de sortie.
properties	Liste	Propriétés à sélectionner dans chaque fonctionnalité et à transmettre au réducteur.
reducer	Réducteur	Réducteur permettant de combiner les propriétés de chaque entité croisée dans un résultat final à stocker dans le pixel.

Exemples

Éditeur de code (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');

Configuration de Python

Consultez la page Environnement Python pour en savoir plus sur l'API Python et sur l'utilisation de geemap pour le développement interactif.

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