ee.Image.reduceRegion
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Einen Reducer auf alle Pixel in einer bestimmten Region anwenden.
Entweder muss der Reducer dieselbe Anzahl an Eingaben wie das Eingabebild an Bändern haben oder er muss eine einzelne Eingabe haben und wird für jedes Band wiederholt.
Gibt ein Dictionary mit den Ausgaben des Reducer zurück.
| Nutzung | Ausgabe |
|---|
Image.reduceRegion(reducer, geometry, scale, crs, crsTransform, bestEffort, maxPixels, tileScale) | Wörterbuch |
| Argument | Typ | Details |
|---|
So gehts: image | Bild | Das zu verkleinernde Bild. |
reducer | Reducer | Der anzuwendende Reducer. |
geometry | Geometrie, Standardwert: null | Die Region, für die Daten reduziert werden sollen. Standardmäßig wird der Footprint des ersten Bands des Bilds verwendet. |
scale | Gleitkommazahl, Standardwert: null | Ein nominaler Maßstab in Metern für die Projektion, in der gearbeitet werden soll. |
crs | Projektion, Standardwert: null | Die Projektion, in der gearbeitet werden soll. Wenn nichts angegeben ist, wird die Projektion des ersten Bands des Bildes verwendet. Wird zusätzlich zur Skalierung angegeben und auf die angegebene Skalierung skaliert. |
crsTransform | Liste, Standard: null | Die Liste der Werte für die CRS-Transformation. Dies ist eine zeilenweise Anordnung der 3 × 2-Transformationsmatrix. Diese Option schließt sich gegenseitig mit „scale“ aus und ersetzt alle Transformationen, die bereits für die Projektion festgelegt sind. |
bestEffort | Boolescher Wert, Standard: „false“ | Wenn das Polygon bei der angegebenen Skalierung zu viele Pixel enthalten würde, berechnen und verwenden Sie eine größere Skalierung, damit der Vorgang erfolgreich ausgeführt werden kann. |
maxPixels | Lang, Standardwert: 10000000 | Die maximale Anzahl der zu reduzierenden Pixel. |
tileScale | Gleitkommazahl, Standardwert: 1 | Ein Skalierungsfaktor zwischen 0,1 und 16, mit dem die Größe von Aggregationskacheln angepasst wird.Wenn Sie einen größeren Wert für „tileScale“ festlegen (z.B. 2 oder 4) werden kleinere Kacheln verwendet. Dadurch können Berechnungen möglich sein, die mit der Standardeinstellung nicht durchgeführt werden können, weil der Arbeitsspeicher nicht ausreicht. |
Beispiele
Code-Editor (JavaScript)
// A Landsat 8 surface reflectance 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 ecoregion geometry.
var geom = ee.FeatureCollection('EPA/Ecoregions/2013/L4')
.filter('us_l4name == "Santa Cruz Mountains"').geometry();
// Display layers on the map.
Map.setCenter(-122.08, 37.22, 9);
Map.addLayer(img, {min: 10000, max: 20000}, 'Landsat image');
Map.addLayer(geom, {color: 'white'}, 'Santa Cruz Mountains ecoregion');
// Calculate median band values within Santa Cruz Mountains ecoregion. 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.reduceRegion({
reducer: ee.Reducer.median(),
geometry: geom,
scale: 30, // meters
crs: 'EPSG:3310', // California Albers projection
});
// A dictionary is returned; keys are band names, values are the statistic.
print('Median band values, Santa Cruz Mountains ecoregion', stats);
// You can combine reducers to calculate e.g. mean and standard deviation
// simultaneously. The output dictionary keys 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.reduceRegion({
reducer: reducer,
geometry: geom,
scale: 30,
crs: 'EPSG:3310',
});
print('Mean & SD band values, Santa Cruz Mountains ecoregion', multiStats);
Python einrichten
Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite
Python-Umgebung.
import ee
import geemap.core as geemap
Colab (Python)
# A Landsat 8 surface reflectance 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 ecoregion geometry.
geom = (
ee.FeatureCollection('EPA/Ecoregions/2013/L4')
.filter('us_l4name == "Santa Cruz Mountains"')
.geometry()
)
# 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(geom, {'color': 'white'}, 'Santa Cruz Mountains ecoregion')
display(m)
# Calculate median band values within Santa Cruz Mountains ecoregion. 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.reduceRegion(
reducer=ee.Reducer.median(),
geometry=geom,
scale=30, # meters
crs='EPSG:3310', # California Albers projection
)
# A dictionary is returned keys are band names, values are the statistic.
display('Median band values, Santa Cruz Mountains ecoregion', stats)
# You can combine reducers to calculate e.g. mean and standard deviation
# simultaneously. The output dictionary keys 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.reduceRegion(
reducer=reducer,
geometry=geom,
scale=30,
crs='EPSG:3310',
)
display('Mean & SD band values, Santa Cruz Mountains ecoregion', multi_stats)
Sofern nicht anders angegeben, sind die Inhalte dieser Seite unter der Creative Commons Attribution 4.0 License und Codebeispiele unter der Apache 2.0 License lizenziert. Weitere Informationen finden Sie in den Websiterichtlinien von Google Developers. Java ist eine eingetragene Marke von Oracle und/oder seinen Partnern.
Zuletzt aktualisiert: 2025-07-26 (UTC).
[null,null,["Zuletzt aktualisiert: 2025-07-26 (UTC)."],[[["\u003cp\u003e\u003ccode\u003eImage.reduceRegion()\u003c/code\u003e applies a reducer function to all pixels within a specified region of an image.\u003c/p\u003e\n"],["\u003cp\u003eThe reducer can either accept the same number of inputs as the image bands or a single input to be applied to each band.\u003c/p\u003e\n"],["\u003cp\u003eIt returns a dictionary containing the reducer's output, with keys representing band names and values corresponding to the calculated statistic.\u003c/p\u003e\n"],["\u003cp\u003eUsers can define parameters like scale, projection, and geometry to control the region and resolution of the reduction operation.\u003c/p\u003e\n"],["\u003cp\u003eMultiple reducers can be combined to calculate multiple statistics simultaneously, with output dictionary keys reflecting both band and statistic names.\u003c/p\u003e\n"]]],[],null,["# ee.Image.reduceRegion\n\nApply a reducer to all the pixels in a specific region.\n\n\u003cbr /\u003e\n\nEither the reducer must have the same number of inputs as the input image has bands, or it must have a single input and will be repeated for each band.\n\nReturns a dictionary of the reducer's outputs.\n\n| Usage | Returns |\n|---------------------------------------------------------------------------------------------------------------------------------------|------------|\n| Image.reduceRegion`(reducer, `*geometry* `, `*scale* `, `*crs* `, `*crsTransform* `, `*bestEffort* `, `*maxPixels* `, `*tileScale*`)` | Dictionary |\n\n| Argument | Type | Details |\n|----------------|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `image` | Image | The image to reduce. |\n| `reducer` | Reducer | The reducer to apply. |\n| `geometry` | Geometry, default: null | The region over which to reduce data. Defaults to the footprint of the image's first band. |\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 replaces any transform already set on the projection. |\n| `bestEffort` | Boolean, default: false | If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed. |\n| `maxPixels` | Long, default: 10000000 | The maximum number of pixels to reduce. |\n| `tileScale` | Float, default: 1 | A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// A Landsat 8 surface reflectance 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 ecoregion geometry.\nvar geom = ee.FeatureCollection('EPA/Ecoregions/2013/L4')\n .filter('us_l4name == \"Santa Cruz Mountains\"').geometry();\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(geom, {color: 'white'}, 'Santa Cruz Mountains ecoregion');\n\n// Calculate median band values within Santa Cruz Mountains ecoregion. 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.reduceRegion({\n reducer: ee.Reducer.median(),\n geometry: geom,\n scale: 30, // meters\n crs: 'EPSG:3310', // California Albers projection\n});\n\n// A dictionary is returned; keys are band names, values are the statistic.\nprint('Median band values, Santa Cruz Mountains ecoregion', stats);\n\n// You can combine reducers to calculate e.g. mean and standard deviation\n// simultaneously. The output dictionary keys are the concatenation of the band\n// 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.reduceRegion({\n reducer: reducer,\n geometry: geom,\n scale: 30,\n crs: 'EPSG:3310',\n});\nprint('Mean & SD band values, Santa Cruz Mountains ecoregion', 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 surface reflectance 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 ecoregion geometry.\ngeom = (\n ee.FeatureCollection('EPA/Ecoregions/2013/L4')\n .filter('us_l4name == \"Santa Cruz Mountains\"')\n .geometry()\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(geom, {'color': 'white'}, 'Santa Cruz Mountains ecoregion')\ndisplay(m)\n\n# Calculate median band values within Santa Cruz Mountains ecoregion. 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.reduceRegion(\n reducer=ee.Reducer.median(),\n geometry=geom,\n scale=30, # meters\n crs='EPSG:3310', # California Albers projection\n)\n\n# A dictionary is returned keys are band names, values are the statistic.\ndisplay('Median band values, Santa Cruz Mountains ecoregion', stats)\n\n# You can combine reducers to calculate e.g. mean and standard deviation\n# simultaneously. The output dictionary keys are the concatenation of the band\n# names and statistic names, separated by an underscore.\nreducer = ee.Reducer.mean().combine(\n reducer2=ee.Reducer.stdDev(), sharedInputs=True\n)\nmulti_stats = img.reduceRegion(\n reducer=reducer,\n geometry=geom,\n scale=30,\n crs='EPSG:3310',\n)\ndisplay('Mean & SD band values, Santa Cruz Mountains ecoregion', multi_stats)\n```"]]
