ee.ImageCollection.aggregate_stats

Aggregiert die Werte einer bestimmten Eigenschaft der Objekte in einer Sammlung und berechnet die Summe, den Minimalwert, den Maximalwert, den Mittelwert, die Standardabweichung der Stichprobe, die Varianz der Stichprobe, die Gesamtstandardabweichung und die Gesamtvarianz der ausgewählten Eigenschaft.

Nutzung	Ausgabe
ImageCollection.aggregate_stats(property)	Wörterbuch

Argument	Typ	Details
So gehts: collection	FeatureCollection	Die Sammlung, über die aggregiert werden soll.
property	String	Die Property, die für jedes Element der Sammlung verwendet werden soll.

Beispiele

Code-Editor (JavaScript)

// A Lansat 8 TOA image collection for a specific year and location.
var col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
  .filterBounds(ee.Geometry.Point([-122.073, 37.188]))
  .filterDate('2018', '2019');

// An image property of interest, percent cloud cover in this case.
var prop = 'CLOUD_COVER';

// Use ee.ImageCollection.aggregate_* functions to fetch information about
// values of a selected property across all images in the collection. For
// example, produce a list of all values, get counts, and calculate statistics.
print('List of property values', col.aggregate_array(prop));
print('Count of property values', col.aggregate_count(prop));
print('Count of distinct property values', col.aggregate_count_distinct(prop));
print('First collection element property value', col.aggregate_first(prop));
print('Histogram of property values', col.aggregate_histogram(prop));
print('Min of property values', col.aggregate_min(prop));
print('Max of property values', col.aggregate_max(prop));

// The following methods are applicable to numerical properties only.
print('Mean of property values', col.aggregate_mean(prop));
print('Sum of property values', col.aggregate_sum(prop));
print('Product of property values', col.aggregate_product(prop));
print('Std dev (sample) of property values', col.aggregate_sample_sd(prop));
print('Variance (sample) of property values', col.aggregate_sample_var(prop));
print('Std dev (total) of property values', col.aggregate_total_sd(prop));
print('Variance (total) of property values', col.aggregate_total_var(prop));
print('Summary stats of property values', col.aggregate_stats(prop));

// Note that if the property is formatted as a string, min and max will
// respectively return the first and last values according to alphanumeric
// order of the property values.
var propString = 'LANDSAT_SCENE_ID';
print('List of property values (string)', col.aggregate_array(propString));
print('Min of property values (string)', col.aggregate_min(propString));
print('Max of property values (string)', col.aggregate_max(propString));

Python einrichten

Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite Python-Umgebung.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

# A Lansat 8 TOA image collection for a specific year and location.
col = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterBounds(
    ee.Geometry.Point([-122.073, 37.188])).filterDate('2018', '2019')

# An image property of interest, percent cloud cover in this case.
prop = 'CLOUD_COVER'

# Use ee.ImageCollection.aggregate_* functions to fetch information about
# values of a selected property across all images in the collection. For
# example, produce a list of all values, get counts, and calculate statistics.
print('List of property values:', col.aggregate_array(prop).getInfo())
print('Count of property values:', col.aggregate_count(prop).getInfo())
print('Count of distinct property values:',
      col.aggregate_count_distinct(prop).getInfo())
print('First collection element property value:',
      col.aggregate_first(prop).getInfo())
print('Histogram of property values:')
pprint(col.aggregate_histogram(prop).getInfo())
print('Min of property values:', col.aggregate_min(prop).getInfo())
print('Max of property values:', col.aggregate_max(prop).getInfo())

# The following methods are applicable to numerical properties only.
print('Mean of property values:', col.aggregate_mean(prop).getInfo())
print('Sum of property values:', col.aggregate_sum(prop).getInfo())
print('Product of property values:', col.aggregate_product(prop).getInfo())
print('Std dev (sample) of property values:',
      col.aggregate_sample_sd(prop).getInfo())
print('Variance (sample) of property values:',
      col.aggregate_sample_var(prop).getInfo())
print('Std dev (total) of property values:',
      col.aggregate_total_sd(prop).getInfo())
print('Variance (total) of property values:',
      col.aggregate_total_var(prop).getInfo())
print('Summary stats of property values:')
pprint(col.aggregate_stats(prop).getInfo())

# Note that if the property is formatted as a string, min and max will
# respectively return the first and last values according to alphanumeric
# order of the property values.
prop_string = 'LANDSAT_SCENE_ID'
print('List of property values (string):',
      col.aggregate_array(prop_string).getInfo())
print('Min of property values (string):',
      col.aggregate_min(prop_string).getInfo())
print('Max of property values (string):',
      col.aggregate_max(prop_string).getInfo())