ee.ImageCollection.reduceToImage

Membuat gambar dari kumpulan fitur dengan menerapkan peredam pada properti yang dipilih dari semua fitur yang berpotongan dengan setiap piksel.

Penggunaan	Hasil
ImageCollection.reduceToImage(properties, reducer)	Gambar

Argumen	Jenis	Detail
ini: collection	FeatureCollection	Kumpulan fitur untuk berpotongan dengan setiap piksel output.
properties	Daftar	Properti yang akan dipilih dari setiap fitur dan diteruskan ke reducer.
reducer	Pengurang	Reducer untuk menggabungkan properti setiap fitur yang berpotongan ke dalam hasil akhir untuk disimpan dalam piksel.

Contoh

Code Editor (JavaScript)

var col = ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
  .filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))
  .filterDate('2021', '2022');

// Image visualization settings.
var visParams = {
  bands: ['B4', 'B3', 'B2'],
  min: 0.01,
  max: 0.25
};
Map.addLayer(col.mean(), visParams, 'RGB mean');

// Reduce the geometry (footprint) of images in the collection to an image.
// Image property values are applied to the pixels intersecting each
// image's geometry and then a per-pixel reduction is performed according
// to the selected reducer. Here, the image cloud cover property is assigned
// to the pixels intersecting image geometry and then reduced to a single
// image representing the per-pixel mean image cloud cover.
var meanCloudCover = col.reduceToImage({
  properties: ['CLOUD_COVER'],
  reducer: ee.Reducer.mean()
});

Map.setCenter(-119.87, 44.76, 6);
Map.addLayer(meanCloudCover, {min: 0, max: 50}, 'Cloud cover mean');

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

import ee
import geemap.core as geemap

Colab (Python)

col = (
    ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA')
    .filterBounds(ee.Geometry.BBox(-124.0, 43.2, -116.5, 46.3))
    .filterDate('2021', '2022')
)

# Image visualization settings.
vis_params = {'bands': ['B4', 'B3', 'B2'], 'min': 0.01, 'max': 0.25}
m = geemap.Map()
m.add_layer(col.mean(), vis_params, 'RGB mean')

# Reduce the geometry (footprint) of images in the collection to an image.
# Image property values are applied to the pixels intersecting each
# image's geometry and then a per-pixel reduction is performed according
# to the selected reducer. Here, the image cloud cover property is assigned
# to the pixels intersecting image geometry and then reduced to a single
# image representing the per-pixel mean image cloud cover.
mean_cloud_cover = col.reduceToImage(
    properties=['CLOUD_COVER'], reducer=ee.Reducer.mean()
)

m.set_center(-119.87, 44.76, 6)
m.add_layer(mean_cloud_cover, {'min': 0, 'max': 50}, 'Cloud cover mean')
m