ee.FeatureCollection.reduceToImage

با اعمال یک کاهنده بر روی خصوصیات انتخاب شده همه ویژگی هایی که هر پیکسل را قطع می کنند، یک تصویر از یک مجموعه ویژگی ایجاد می کند.

استفاده	برمی گرداند
FeatureCollection. reduceToImage (properties, reducer)	تصویر

استدلال	تایپ کنید	جزئیات
این: collection	مجموعه ویژگی ها	مجموعه ویژگی برای تقاطع با هر پیکسل خروجی.
properties	فهرست کنید	ویژگی هایی برای انتخاب از هر ویژگی و انتقال به کاهنده.
reducer	کاهنده	یک Reducer برای ترکیب خصوصیات هر ویژگی متقاطع در نتیجه نهایی برای ذخیره در پیکسل.

نمونه ها

ویرایشگر کد (جاوا اسکریپت)

// 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');

راه اندازی پایتون

برای اطلاعات در مورد API پایتون و استفاده از geemap برای توسعه تعاملی به صفحه محیط پایتون مراجعه کنید.

import ee
import geemap.core as geemap

کولب (پایتون)

# 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