ee.Image.reduceRegions
Restez organisé à l'aide des collections
Enregistrez et classez les contenus selon vos préférences.
Appliquez un réducteur à la zone de chaque élément de la collection donnée.
Le réducteur doit avoir le même nombre d'entrées que de bandes dans l'image d'entrée.
Renvoie les éléments géographiques d'entrée, chacun complété par les sorties du réducteur correspondant.
| Utilisation | Renvoie |
|---|
Image.reduceRegions(collection, reducer, scale, crs, crsTransform, tileScale, maxPixelsPerRegion) | FeatureCollection |
| Argument | Type | Détails |
|---|
ceci: image | Image | Image à réduire. |
collection | FeatureCollection | Les fonctionnalités à réduire |
reducer | Réducteur | Réducteur à appliquer. |
scale | Flottant, valeur par défaut : "null" | Échelle nominale en mètres de la projection à utiliser. |
crs | Projection, valeur par défaut : "null" | Projection à utiliser. Si aucune valeur n'est spécifiée, la projection de la première bande de l'image est utilisée. Si spécifié en plus de l'échelle, redimensionné à l'échelle spécifiée. |
crsTransform | Liste, valeur par défaut : "null" | Liste des valeurs de transformation de système de coordonnées. Il s'agit d'un ordre par ligne de la matrice de transformation 3 x 2. Cette option est incompatible avec "échelle" et remplace toute transformation déjà définie sur la projection. |
tileScale | Float, valeur par défaut: 1 | Facteur de scaling utilisé pour réduire la taille des cartes d'agrégation. L'utilisation d'un tileScale plus élevé (par exemple, 2 ou 4) peuvent activer des calculs qui manquent de mémoire avec la valeur par défaut. |
maxPixelsPerRegion | Long, valeur par défaut: null | Nombre maximal de pixels à réduire par région. |
Exemples
Éditeur de code (JavaScript)
// A Landsat 8 SR image with SWIR1, NIR, and green bands.
var img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')
.select(['SR_B6', 'SR_B5', 'SR_B3']);
// Santa Cruz Mountains ecoregions feature collection.
var regionCol = ee.FeatureCollection('EPA/Ecoregions/2013/L4')
.filter('us_l4name == "Santa Cruz Mountains" || ' +
'us_l4name == "San Mateo Coastal Hills" || ' +
'us_l4name == "Leeward Hills"');
// Display layers on the map.
Map.setCenter(-122.08, 37.22, 9);
Map.addLayer(img, {min: 10000, max: 20000}, 'Landsat image');
Map.addLayer(regionCol, {color: 'white'}, 'Santa Cruz Mountains ecoregions');
// Calculate median band values within Santa Cruz Mountains ecoregions. It is
// good practice to explicitly define "scale" (or "crsTransform") and "crs"
// parameters of the analysis to avoid unexpected results from undesired
// defaults when e.g. reducing a composite image.
var stats = img.reduceRegions({
collection: regionCol,
reducer: ee.Reducer.median(),
scale: 30, // meters
crs: 'EPSG:3310', // California Albers projection
});
// The input feature collection is returned with new properties appended.
// The new properties are the outcome of the region reduction per image band,
// for each feature in the collection. Region reduction property names
// are the same as the input image band names.
print('Median band values, Santa Cruz Mountains ecoregions', stats);
// You can combine reducers to calculate e.g. mean and standard deviation
// simultaneously. The resulting property names are the concatenation of the
// band names and statistic names, separated by an underscore.
var reducer = ee.Reducer.mean().combine({
reducer2: ee.Reducer.stdDev(),
sharedInputs: true
});
var multiStats = img.reduceRegions({
collection: regionCol,
reducer: reducer,
scale: 30,
crs: 'EPSG:3310',
});
print('Mean & SD band values, Santa Cruz Mountains ecoregions', multiStats);
Configuration de Python
Consultez la page
Environnement Python pour en savoir plus sur l'API Python et l'utilisation de geemap pour le développement interactif.
import ee
import geemap.core as geemap
Colab (Python)
# A Landsat 8 SR image with SWIR1, NIR, and green bands.
img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508').select(
['SR_B6', 'SR_B5', 'SR_B3']
)
# Santa Cruz Mountains ecoregions feature collection.
region_col = ee.FeatureCollection('EPA/Ecoregions/2013/L4').filter(
'us_l4name == "Santa Cruz Mountains" || '
+ 'us_l4name == "San Mateo Coastal Hills" || '
+ 'us_l4name == "Leeward Hills"'
)
# Display layers on the map.
m = geemap.Map()
m.set_center(-122.08, 37.22, 9)
m.add_layer(img, {'min': 10000, 'max': 20000}, 'Landsat image')
m.add_layer(
region_col, {'color': 'white'}, 'Santa Cruz Mountains ecoregions'
)
display(m)
# Calculate median band values within Santa Cruz Mountains ecoregions. It is
# good practice to explicitly define "scale" (or "crsTransform") and "crs"
# parameters of the analysis to avoid unexpected results from undesired
# defaults when e.g. reducing a composite image.
stats = img.reduceRegions(
collection=region_col,
reducer=ee.Reducer.median(),
scale=30, # meters
crs='EPSG:3310', # California Albers projection
)
# The input feature collection is returned with new properties appended.
# The new properties are the outcome of the region reduction per image band,
# for each feature in the collection. Region reduction property names
# are the same as the input image band names.
display('Median band values, Santa Cruz Mountains ecoregions', stats)
# You can combine reducers to calculate e.g. mean and standard deviation
# simultaneously. The resulting property names are the concatenation of the
# band names and statistic names, separated by an underscore.
reducer = ee.Reducer.mean().combine(
reducer2=ee.Reducer.stdDev(), sharedInputs=True
)
multi_stats = img.reduceRegions(
collection=region_col,
reducer=reducer,
scale=30,
crs='EPSG:3310',
)
display('Mean & SD band values, Santa Cruz Mountains ecoregions', multi_stats)
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2025/07/25 (UTC).
[null,null,["Dernière mise à jour le 2025/07/25 (UTC)."],[[["\u003cp\u003e\u003ccode\u003eImage.reduceRegions\u003c/code\u003e applies a reducer function to an image within the boundaries of each feature in a feature collection.\u003c/p\u003e\n"],["\u003cp\u003eThe reducer output is added as new properties to the input features, with property names corresponding to the image band names.\u003c/p\u003e\n"],["\u003cp\u003eUsers can specify the scale, projection (CRS), and tile scaling for the reduction operation to ensure accurate and efficient processing.\u003c/p\u003e\n"],["\u003cp\u003eMultiple reducers can be combined to calculate different statistics simultaneously, resulting in property names that reflect both the band and the statistic.\u003c/p\u003e\n"]]],[],null,["# ee.Image.reduceRegions\n\nApply a reducer over the area of each feature in the given collection.\n\n\u003cbr /\u003e\n\nThe reducer must have the same number of inputs as the input image has bands.\n\nReturns the input features, each augmented with the corresponding reducer outputs.\n\n| Usage | Returns |\n|-----------------------------------------------------------------------------------------------------------------------------|-------------------|\n| Image.reduceRegions`(collection, reducer, `*scale* `, `*crs* `, `*crsTransform* `, `*tileScale* `, `*maxPixelsPerRegion*`)` | FeatureCollection |\n\n| Argument | Type | Details |\n|----------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `image` | Image | The image to reduce. |\n| `collection` | FeatureCollection | The features to reduce over. |\n| `reducer` | Reducer | The reducer to apply. |\n| `scale` | Float, default: null | A nominal scale in meters of the projection to work in. |\n| `crs` | Projection, default: null | The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale. |\n| `crsTransform` | List, default: null | The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and will replace any transform already set on the projection. |\n| `tileScale` | Float, default: 1 | A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g., 2 or 4) may enable computations that run out of memory with the default. |\n| `maxPixelsPerRegion` | Long, default: null | The maximum number of pixels to reduce per region. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Landsat 8 SR image with SWIR1, NIR, and green bands.\nvar img = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508')\n .select(['SR_B6', 'SR_B5', 'SR_B3']);\n\n// Santa Cruz Mountains ecoregions feature collection.\nvar regionCol = ee.FeatureCollection('EPA/Ecoregions/2013/L4')\n .filter('us_l4name == \"Santa Cruz Mountains\" || ' +\n 'us_l4name == \"San Mateo Coastal Hills\" || ' +\n 'us_l4name == \"Leeward Hills\"');\n\n// Display layers on the map.\nMap.setCenter(-122.08, 37.22, 9);\nMap.addLayer(img, {min: 10000, max: 20000}, 'Landsat image');\nMap.addLayer(regionCol, {color: 'white'}, 'Santa Cruz Mountains ecoregions');\n\n// Calculate median band values within Santa Cruz Mountains ecoregions. It is\n// good practice to explicitly define \"scale\" (or \"crsTransform\") and \"crs\"\n// parameters of the analysis to avoid unexpected results from undesired\n// defaults when e.g. reducing a composite image.\nvar stats = img.reduceRegions({\n collection: regionCol,\n reducer: ee.Reducer.median(),\n scale: 30, // meters\n crs: 'EPSG:3310', // California Albers projection\n});\n\n// The input feature collection is returned with new properties appended.\n// The new properties are the outcome of the region reduction per image band,\n// for each feature in the collection. Region reduction property names\n// are the same as the input image band names.\nprint('Median band values, Santa Cruz Mountains ecoregions', stats);\n\n// You can combine reducers to calculate e.g. mean and standard deviation\n// simultaneously. The resulting property names are the concatenation of the\n// band names and statistic names, separated by an underscore.\nvar reducer = ee.Reducer.mean().combine({\n reducer2: ee.Reducer.stdDev(),\n sharedInputs: true\n});\nvar multiStats = img.reduceRegions({\n collection: regionCol,\n reducer: reducer,\n scale: 30,\n crs: 'EPSG:3310',\n});\nprint('Mean & SD band values, Santa Cruz Mountains ecoregions', multiStats);\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# A Landsat 8 SR image with SWIR1, NIR, and green bands.\nimg = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20210508').select(\n ['SR_B6', 'SR_B5', 'SR_B3']\n)\n\n# Santa Cruz Mountains ecoregions feature collection.\nregion_col = ee.FeatureCollection('EPA/Ecoregions/2013/L4').filter(\n 'us_l4name == \"Santa Cruz Mountains\" || '\n + 'us_l4name == \"San Mateo Coastal Hills\" || '\n + 'us_l4name == \"Leeward Hills\"'\n)\n\n# Display layers on the map.\nm = geemap.Map()\nm.set_center(-122.08, 37.22, 9)\nm.add_layer(img, {'min': 10000, 'max': 20000}, 'Landsat image')\nm.add_layer(\n region_col, {'color': 'white'}, 'Santa Cruz Mountains ecoregions'\n)\ndisplay(m)\n\n# Calculate median band values within Santa Cruz Mountains ecoregions. It is\n# good practice to explicitly define \"scale\" (or \"crsTransform\") and \"crs\"\n# parameters of the analysis to avoid unexpected results from undesired\n# defaults when e.g. reducing a composite image.\nstats = img.reduceRegions(\n collection=region_col,\n reducer=ee.Reducer.median(),\n scale=30, # meters\n crs='EPSG:3310', # California Albers projection\n)\n\n# The input feature collection is returned with new properties appended.\n# The new properties are the outcome of the region reduction per image band,\n# for each feature in the collection. Region reduction property names\n# are the same as the input image band names.\ndisplay('Median band values, Santa Cruz Mountains ecoregions', stats)\n\n# You can combine reducers to calculate e.g. mean and standard deviation\n# simultaneously. The resulting property names are the concatenation of the\n# band names and statistic names, separated by an underscore.\nreducer = ee.Reducer.mean().combine(\n reducer2=ee.Reducer.stdDev(), sharedInputs=True\n)\nmulti_stats = img.reduceRegions(\n collection=region_col,\n reducer=reducer,\n scale=30,\n crs='EPSG:3310',\n)\ndisplay('Mean & SD band values, Santa Cruz Mountains ecoregions', multi_stats)\n```"]]
