ee.FeatureCollection.reduceToImage

تنشئ هذه الدالة صورة من مجموعة عناصر من خلال تطبيق أداة تقليل على السمات المحدّدة لجميع العناصر التي تتقاطع مع كل بكسل.

الاستخدام	المرتجعات
FeatureCollection.reduceToImage(properties, reducer)	صورة

الوسيطة	النوع	التفاصيل
هذا: collection	FeatureCollection	مجموعة العناصر الجغرافية التي سيتم تقاطعها مع كل بكسل من بكسلات الناتج
properties	قائمة	السمات التي سيتم الاختيار من كل ميزة وتمريرها إلى أداة الاختزال.
reducer	Reducer	دالة Reducer لدمج خصائص كل عنصر متقاطع في نتيجة نهائية يتم تخزينها في البكسل

أمثلة

محرّر الرموز البرمجية (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');

إعداد Python

راجِع صفحة بيئة Python للحصول على معلومات حول واجهة برمجة التطبيقات Python واستخدام geemap للتطوير التفاعلي.

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