ee.ImageCollection.fromImages

Devuelve la colección de imágenes que contiene las imágenes proporcionadas.

Uso	Muestra
ee.ImageCollection.fromImages(images)	ImageCollection

Argumento	Tipo	Detalles
images	Lista	Son las imágenes que se incluirán en la colección.

Ejemplos

Editor de código (JavaScript)

// A series of images.
var img1 = ee.Image(0);
var img2 = ee.Image(1);
var img3 = ee.Image(2);

// Convert the list of images into an image collection.
var col = ee.ImageCollection.fromImages([img1, img2, img3]);
print('Collection from list of images', col);

// The ee.ImageCollection.fromImages function is intended to coerce the image
// list to a collection when the list is an ambiguous, computed object fetched
// from the properties of a server-side object. For instance, a list
// of images retrieved from a ee.Feature property. Here, we set an image
// list as a property of a feature, retrieve it, and convert it to
// a collection. Notice that the ee.ImageCollection constructor fails to coerce
// the image list to a collection, but ee.ImageCollection.fromImages does.
var feature = ee.Feature(null).set('img_list', [img1, img2, img3]);
var ambiguousImgList = feature.get('img_list');
print('Coerced to collection', ee.ImageCollection.fromImages(ambiguousImgList));
print('NOT coerced to collection', ee.ImageCollection(ambiguousImgList));

// A common use case is coercing an image list from a saveAll join to a
// image collection, like in this example of building a collection of mean
// annual NDVI images from a MODIS collection.
var modisCol = ee.ImageCollection('MODIS/006/MOD13A2')
  .filterDate('2017', '2021')
  .select('NDVI')
  .map(function(img) {return img.set('year', img.date().get('year'))});

var distinctYearCol = modisCol.distinct('year');

var joinedCol = ee.Join.saveAll('img_list').apply({
  primary: distinctYearCol,
  secondary: modisCol,
  condition: ee.Filter.equals({'leftField': 'year', 'rightField': 'year'})
});

var annualNdviMean = joinedCol.map(function(img) {
  return ee.ImageCollection.fromImages(img.get('img_list')).mean()
    .copyProperties(img, ['year']);
});
print('Mean annual NDVI collection', annualNdviMean);

Configuración de Python

Consulta la página Entorno de Python para obtener información sobre la API de Python y el uso de geemap para el desarrollo interactivo.

import ee
import geemap.core as geemap

Colab (Python)

# A series of images.
img1 = ee.Image(0)
img2 = ee.Image(1)
img3 = ee.Image(2)

# Convert the list of images into an image collection.
col = ee.ImageCollection.fromImages([img1, img2, img3])
print('Collection from list of images:', col.getInfo())

# The ee.ImageCollection.fromImages function is intended to coerce the image
# list to a collection when the list is an ambiguous, computed object fetched
# from the properties of a server-side object. For instance, a list
# of images retrieved from a ee.Feature property. Here, we set an image
# list as a property of a feature, retrieve it, and convert it to
# a collection. Notice that the ee.ImageCollection constructor fails to coerce
# the image list to a collection, but ee.ImageCollection.fromImages does.
feature = ee.Feature(None).set('img_list', [img1, img2, img3])
ambiguous_img_list = feature.get('img_list')
print(
    'Coerced to collection:',
    ee.ImageCollection.fromImages(ambiguous_img_list).getInfo(),
)
print(
    'NOT coerced to collection:',
    ee.ImageCollection(ambiguous_img_list).getInfo(),
)

# A common use case is coercing an image list from a saveAll join to a
# image collection, like in this example of building a collection of mean
# annual NDVI images from a MODIS collection.
modis_col = (
    ee.ImageCollection('MODIS/006/MOD13A2')
    .filterDate('2017', '2021')
    .select('NDVI')
    .map(lambda img: img.set('year', img.date().get('year')))
)

distinct_year_col = modis_col.distinct('year')

joined_col = ee.Join.saveAll('img_list').apply(
    primary=distinct_year_col,
    secondary=modis_col,
    condition=ee.Filter.equals(leftField='year', rightField='year'),
)

annual_ndvi_mean = joined_col.map(
    lambda img: ee.ImageCollection.fromImages(img.get('img_list'))
    .mean()
    .copyProperties(img, ['year'])
)
print('Mean annual NDVI collection:', annual_ndvi_mean.getInfo())