ee.Image.reduceRegions
संग्रह की मदद से व्यवस्थित रहें
अपनी प्राथमिकताओं के आधार पर, कॉन्टेंट को सेव करें और कैटगरी में बांटें.
दिए गए कलेक्शन में हर सुविधा के क्षेत्र पर रिड्यूसर लागू करें.
रीड्यूसर में उतने ही इनपुट होने चाहिए जितने इनपुट इमेज में बैंड हैं.
इनपुट फ़ीचर दिखाता है. हर फ़ीचर को, उससे जुड़े रिड्यूसर आउटपुट के साथ दिखाया जाता है.
| इस्तेमाल | रिटर्न |
|---|
Image.reduceRegions(collection, reducer, scale, crs, crsTransform, tileScale, maxPixelsPerRegion) | FeatureCollection |
| आर्ग्यूमेंट | टाइप | विवरण |
|---|
यह: image | इमेज | वह इमेज जिसे छोटा करना है. |
collection | FeatureCollection | वे सुविधाएं जिनका इस्तेमाल कम किया जाना है. |
reducer | रिड्यूसर | लागू किया जाने वाला रिड्यूसर. |
scale | फ़्लोट, डिफ़ॉल्ट: null | प्रोजेक्शन में काम करने के लिए, मीटर में नाममात्र स्केल. |
crs | प्रोजेक्शन, डिफ़ॉल्ट: null | जिस प्रोजेक्शन में काम करना है. अगर कोई वैल्यू नहीं दी गई है, तो इमेज के पहले बैंड के प्रोजेक्शन का इस्तेमाल किया जाता है. अगर स्केल के अलावा कोई अन्य वैल्यू भी दी गई है, तो उसे तय किए गए स्केल के हिसाब से फिर से स्केल किया जाता है. |
crsTransform | सूची, डिफ़ॉल्ट: null | सीआरएस ट्रांसफ़ॉर्म वैल्यू की सूची. यह 3x2 ट्रांसफ़ॉर्म मैट्रिक का लाइन-मेजर ऑर्डर है. यह विकल्प, 'स्केल' विकल्प के साथ काम नहीं करता. साथ ही, यह प्रोजेक्शन पर पहले से सेट किए गए किसी भी ट्रांसफ़ॉर्म को बदल देगा. |
tileScale | फ़्लोट, डिफ़ॉल्ट: 1 | एग्रीगेशन टाइल का साइज़ कम करने के लिए इस्तेमाल किया जाने वाला स्केलिंग फ़ैक्टर. बड़े tileScale (उदाहरण के लिए, 2 या 4) से, ऐसे कैलकुलेशन चालू हो सकते हैं जो डिफ़ॉल्ट सेटिंग में मेमोरी खत्म होने की वजह से नहीं चल पाते. |
maxPixelsPerRegion | लंबी, डिफ़ॉल्ट: null | हर इलाके में कम से कम पिक्सल की संख्या. |
उदाहरण
कोड एडिटर (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);
Python सेटअप
Python API के बारे में जानकारी पाने और इंटरैक्टिव डेवलपमेंट के लिए geemap का इस्तेमाल करने के लिए,
Python एनवायरमेंट पेज देखें.
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)
जब तक कुछ अलग से न बताया जाए, तब तक इस पेज की सामग्री को Creative Commons Attribution 4.0 License के तहत और कोड के नमूनों को Apache 2.0 License के तहत लाइसेंस मिला है. ज़्यादा जानकारी के लिए, Google Developers साइट नीतियां देखें. Oracle और/या इससे जुड़ी हुई कंपनियों का, Java एक रजिस्टर किया हुआ ट्रेडमार्क है.
आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया.
[null,null,["आखिरी बार 2025-07-25 (UTC) को अपडेट किया गया."],[[["\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```"]]
