Über eine ImageCollection iterieren
Mit Sammlungen den Überblick behalten
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map() wendet zwar eine Funktion auf jedes Bild in einer Sammlung an, die Funktion wird jedoch für jedes Bild in der Sammlung unabhängig aufgerufen. Angenommen, Sie möchten beispielsweise eine kumulative Anomalie (At) zum Zeitpunkt t aus einer Zeitreihe berechnen. Wenn Sie eine rekursiv definierte Reihe vom Typ At = f(Bildt, At-1) erhalten möchten, funktioniert die Zuordnung nicht, da die Funktion (f) vom vorherigen Ergebnis (At-1) abhängt. Angenommen, Sie möchten eine Reihe von kumulativen Bildern mit Anomalien des normalisierten Differenz-Vegetationsindexes (NDVI) im Vergleich zu einem Referenzwert berechnen. Dabei ist A0 = 0 und f(Imaget, At-1) = Imaget + At-1, wobei At-1 die kumulative Anomalie bis zum Zeitpunkt t-1 und Imaget die Anomalie zum Zeitpunkt t ist. Verwenden Sie imageCollection.iterate(), um diese rekursive Definition von ImageCollection zu erstellen. Im folgenden Beispiel nimmt die Funktion accumulate() zwei Parameter an: ein Bild in der Sammlung und eine Liste aller vorherigen Ausgaben. Bei jedem Aufruf von iterate() wird die Anomalie der laufenden Summe hinzugefügt und das Ergebnis der Liste. Das Endergebnis wird an den ImageCollection-Konstruktor übergeben, um eine neue Bildsequenz zu erhalten:
Code-Editor (JavaScript)
// Load MODIS EVI imagery.
var collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI');
// Define reference conditions from the first 10 years of data.
var reference = collection.filterDate('2001-01-01', '2010-12-31')
// Sort chronologically in descending order.
.sort('system:time_start', false);
// Compute the mean of the first 10 years.
var mean = reference.mean();
// Compute anomalies by subtracting the 2001-2010 mean from each image in a
// collection of 2011-2014 images. Copy the date metadata over to the
// computed anomaly images in the new collection.
var series = collection.filterDate('2011-01-01', '2014-12-31').map(function(image) {
return image.subtract(mean).set('system:time_start', image.get('system:time_start'));
});
// Display cumulative anomalies.
Map.setCenter(-100.811, 40.2, 5);
Map.addLayer(series.sum(),
{min: -60000, max: 60000, palette: ['FF0000', '000000', '00FF00']}, 'EVI anomaly');
// Get the timestamp from the most recent image in the reference collection.
var time0 = reference.first().get('system:time_start');
// Use imageCollection.iterate() to make a collection of cumulative anomaly over time.
// The initial value for iterate() is a list of anomaly images already processed.
// The first anomaly image in the list is just 0, with the time0 timestamp.
var first = ee.List([
// Rename the first band 'EVI'.
ee.Image(0).set('system:time_start', time0).select([0], ['EVI'])
]);
// This is a function to pass to Iterate().
// As anomaly images are computed, add them to the list.
var accumulate = function(image, list) {
// Get the latest cumulative anomaly image from the end of the list with
// get(-1). Since the type of the list argument to the function is unknown,
// it needs to be cast to a List. Since the return type of get() is unknown,
// cast it to Image.
var previous = ee.Image(ee.List(list).get(-1));
// Add the current anomaly to make a new cumulative anomaly image.
var added = image.add(previous)
// Propagate metadata to the new image.
.set('system:time_start', image.get('system:time_start'));
// Return the list with the cumulative anomaly inserted.
return ee.List(list).add(added);
};
// Create an ImageCollection of cumulative anomaly images by iterating.
// Since the return type of iterate is unknown, it needs to be cast to a List.
var cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)));
// Predefine the chart titles.
var title = {
title: 'Cumulative EVI anomaly over time',
hAxis: {title: 'Time'},
vAxis: {title: 'Cumulative EVI anomaly'},
};
// Chart some interesting locations.
var pt1 = ee.Geometry.Point(-65.544, -4.894);
print('Amazon rainforest:',
ui.Chart.image.series(
cumulative, pt1, ee.Reducer.first(), 500).setOptions(title));
var pt2 = ee.Geometry.Point(116.4647, 40.1054);
print('Beijing urbanization:',
ui.Chart.image.series(
cumulative, pt2, ee.Reducer.first(), 500).setOptions(title));
var pt3 = ee.Geometry.Point(-110.3412, 34.1982);
print('Arizona forest disturbance and recovery:',
ui.Chart.image.series(
cumulative, pt3, ee.Reducer.first(), 500).setOptions(title));
Python einrichten
Auf der Seite
Python-Umgebung finden Sie Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung.
import ee
import geemap.core as geemap
Colab (Python)
import altair as alt
# Load MODIS EVI imagery.
collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI')
# Define reference conditions from the first 10 years of data.
reference = collection.filterDate('2001-01-01', '2010-12-31').sort(
# Sort chronologically in descending order.
'system:time_start',
False,
)
# Compute the mean of the first 10 years.
mean = reference.mean()
# Compute anomalies by subtracting the 2001-2010 mean from each image in a
# collection of 2011-2014 images. Copy the date metadata over to the
# computed anomaly images in the new collection.
series = collection.filterDate('2011-01-01', '2014-12-31').map(
lambda image: image.subtract(mean).set(
'system:time_start', image.get('system:time_start')
)
)
# Display cumulative anomalies.
m = geemap.Map()
m.set_center(-100.811, 40.2, 5)
m.add_layer(
series.sum(),
{'min': -60000, 'max': 60000, 'palette': ['FF0000', '000000', '00FF00']},
'EVI anomaly',
)
display(m)
# Get the timestamp from the most recent image in the reference collection.
time_0 = reference.first().get('system:time_start')
# Use imageCollection.iterate() to make a collection of cumulative anomaly over time.
# The initial value for iterate() is a list of anomaly images already processed.
# The first anomaly image in the list is just 0, with the time_0 timestamp.
first = ee.List([
# Rename the first band 'EVI'.
ee.Image(0)
.set('system:time_start', time_0)
.select([0], ['EVI'])
])
# This is a function to pass to Iterate().
# As anomaly images are computed, add them to the list.
def accumulate(image, list):
# Get the latest cumulative anomaly image from the end of the list with
# get(-1). Since the type of the list argument to the function is unknown,
# it needs to be cast to a List. Since the return type of get() is unknown,
# cast it to Image.
previous = ee.Image(ee.List(list).get(-1))
# Add the current anomaly to make a new cumulative anomaly image.
added = image.add(previous).set(
# Propagate metadata to the new image.
'system:time_start',
image.get('system:time_start'),
)
# Return the list with the cumulative anomaly inserted.
return ee.List(list).add(added)
# Create an ImageCollection of cumulative anomaly images by iterating.
# Since the return type of iterate is unknown, it needs to be cast to a List.
cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)))
# Predefine the chart titles.
title = 'Cumulative EVI anomaly over time'
# Chart some interesting locations.
def display_chart(region, collection):
reduced = (
collection.filterBounds(region)
.sort('system:time_start')
.map(
lambda image: ee.Feature(
None,
image.reduceRegion(ee.Reducer.first(), region, 500).set(
'time', image.get('system:time_start')
),
)
)
)
reduced_dataframe = ee.data.computeFeatures(
{'expression': reduced, 'fileFormat': 'PANDAS_DATAFRAME'}
)
alt.Chart(reduced_dataframe).mark_line().encode(
alt.X('time:T').title('Time'),
alt.Y('EVI:Q').title('Cumulative EVI anomaly'),
).properties(title=title).display()
pt_1 = ee.Geometry.Point(-65.544, -4.894)
display('Amazon rainforest:')
display_chart(pt_1, cumulative)
pt_2 = ee.Geometry.Point(116.4647, 40.1054)
display('Beijing urbanization:')
display_chart(pt_2, cumulative)
pt_3 = ee.Geometry.Point(-110.3412, 34.1982)
display('Arizona forest disturbance and recovery:')
display_chart(pt_3, cumulative)
Wenn Sie diese Sequenzen in Diagrammen darstellen, sehen Sie, ob sich der NDVI im Vergleich zu früheren Störungen stabilisiert oder ob er sich in Richtung eines neuen Zustands bewegt. Weitere Informationen zu Diagrammen in Earth Engine finden Sie im Abschnitt „Diagramme“.
Die iterierte Funktion ist in den ausführbaren Vorgängen eingeschränkt. Insbesondere können damit keine Variablen außerhalb der Funktion geändert, nichts ausgegeben und keine JavaScript-Wenn- oder For-Anweisungen verwendet werden. Alle Ergebnisse, die Sie erfassen möchten, oder Zwischeninformationen, die Sie in die nächste Iteration übernehmen möchten, müssen im Rückgabewert der Funktion enthalten sein. Sie können ee.Algorithms.If() verwenden, um bedingte Vorgänge auszuführen.
Sofern nicht anders angegeben, sind die Inhalte dieser Seite unter der Creative Commons Attribution 4.0 License und Codebeispiele unter der Apache 2.0 License lizenziert. Weitere Informationen finden Sie in den Websiterichtlinien von Google Developers. Java ist eine eingetragene Marke von Oracle und/oder seinen Partnern.
Zuletzt aktualisiert: 2025-07-25 (UTC).
[null,null,["Zuletzt aktualisiert: 2025-07-25 (UTC)."],[[["\u003cp\u003e\u003ccode\u003eimageCollection.iterate()\u003c/code\u003e is used to perform calculations that depend on previous results within an ImageCollection, unlike \u003ccode\u003emap()\u003c/code\u003e which processes each image independently.\u003c/p\u003e\n"],["\u003cp\u003eThis example demonstrates using \u003ccode\u003eiterate()\u003c/code\u003e to calculate cumulative anomalies of the Normalized Difference Vegetation Index (NDVI) over time.\u003c/p\u003e\n"],["\u003cp\u003eThe function passed to \u003ccode\u003eiterate()\u003c/code\u003e accumulates anomalies, adding the current image's anomaly to the running sum from previous iterations.\u003c/p\u003e\n"],["\u003cp\u003eResulting cumulative anomaly ImageCollection can be charted to analyze NDVI trends and disturbances over time in different locations.\u003c/p\u003e\n"],["\u003cp\u003eThe iterated function has limitations: it can't modify external variables, print, or use JavaScript 'if'/'for' statements, requiring results and intermediate information to be within its return value.\u003c/p\u003e\n"]]],["`imageCollection.iterate()` computes cumulative anomalies, unlike `map()`, which processes images independently. It defines a series *A~t~* = *f(Image~t~, A~t-1~)*, using a function to add the current anomaly to the previous cumulative anomaly. An example shows calculating cumulative Normalized Difference Vegetation Index (NDVI) anomalies, where `accumulate()` adds the current anomaly to a running sum. The result is stored in a list and then converted to a new `ImageCollection`. The function used in iterate cannot modify variables outside itself or use `if` or `for` statements.\n"],null,["# Iterating over an ImageCollection\n\nAlthough `map()` applies a function to every image in a collection, the\nfunction visits every image in the collection independently. For example, suppose you\nwant to compute a cumulative anomaly (*A~t~* ) at time *t* from a time\nseries. To obtain a recursively defined series of the form *A~t~ =\nf(Image~t~, A~t-1~)* , mapping won't work because the function\n(*f* ) depends on the previous result (*A~t-1~* ). For example, suppose\nyou want to compute a series of cumulative Normalized Difference Vegetation Index (NDVI)\nanomaly images relative to a baseline. Let *A~0~* = 0 and\n*f(Image~t~, A~t-1~)* = *Image~t~ + A~t-1~*\nwhere *A~t-1~* is the cumulative anomaly up to time *t-1* and\n*Image~t~* is the anomaly at time *t* . Use\n`imageCollection.iterate()` to make this recursively defined\n`ImageCollection`. In the following example, the function\n`accumulate()` takes two parameters: an image in the collection, and a list\nof all the previous outputs. With each call to `iterate()`, the anomaly is\nadded to the running sum and the result is added to the list. The final result is\npassed to the `ImageCollection` constructor to get a new sequence of images:\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load MODIS EVI imagery.\nvar collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI');\n\n// Define reference conditions from the first 10 years of data.\nvar reference = collection.filterDate('2001-01-01', '2010-12-31')\n // Sort chronologically in descending order.\n .sort('system:time_start', false);\n\n// Compute the mean of the first 10 years.\nvar mean = reference.mean();\n\n// Compute anomalies by subtracting the 2001-2010 mean from each image in a\n// collection of 2011-2014 images. Copy the date metadata over to the\n// computed anomaly images in the new collection.\nvar series = collection.filterDate('2011-01-01', '2014-12-31').map(function(image) {\n return image.subtract(mean).set('system:time_start', image.get('system:time_start'));\n});\n\n// Display cumulative anomalies.\nMap.setCenter(-100.811, 40.2, 5);\nMap.addLayer(series.sum(),\n {min: -60000, max: 60000, palette: ['FF0000', '000000', '00FF00']}, 'EVI anomaly');\n\n// Get the timestamp from the most recent image in the reference collection.\nvar time0 = reference.first().get('system:time_start');\n\n// Use imageCollection.iterate() to make a collection of cumulative anomaly over time.\n// The initial value for iterate() is a list of anomaly images already processed.\n// The first anomaly image in the list is just 0, with the time0 timestamp.\nvar first = ee.List([\n // Rename the first band 'EVI'.\n ee.Image(0).set('system:time_start', time0).select([0], ['EVI'])\n]);\n\n// This is a function to pass to Iterate().\n// As anomaly images are computed, add them to the list.\nvar accumulate = function(image, list) {\n // Get the latest cumulative anomaly image from the end of the list with\n // get(-1). Since the type of the list argument to the function is unknown,\n // it needs to be cast to a List. Since the return type of get() is unknown,\n // cast it to Image.\n var previous = ee.Image(ee.List(list).get(-1));\n // Add the current anomaly to make a new cumulative anomaly image.\n var added = image.add(previous)\n // Propagate metadata to the new image.\n .set('system:time_start', image.get('system:time_start'));\n // Return the list with the cumulative anomaly inserted.\n return ee.List(list).add(added);\n};\n\n// Create an ImageCollection of cumulative anomaly images by iterating.\n// Since the return type of iterate is unknown, it needs to be cast to a List.\nvar cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)));\n\n// Predefine the chart titles.\nvar title = {\n title: 'Cumulative EVI anomaly over time',\n hAxis: {title: 'Time'},\n vAxis: {title: 'Cumulative EVI anomaly'},\n};\n\n// Chart some interesting locations.\nvar pt1 = ee.Geometry.Point(-65.544, -4.894);\nprint('Amazon rainforest:',\n ui.Chart.image.series(\n cumulative, pt1, ee.Reducer.first(), 500).setOptions(title));\n\nvar pt2 = ee.Geometry.Point(116.4647, 40.1054);\nprint('Beijing urbanization:',\n ui.Chart.image.series(\n cumulative, pt2, ee.Reducer.first(), 500).setOptions(title));\n\nvar pt3 = ee.Geometry.Point(-110.3412, 34.1982);\nprint('Arizona forest disturbance and recovery:',\n ui.Chart.image.series(\n cumulative, pt3, ee.Reducer.first(), 500).setOptions(title));\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\nimport altair as alt\n# Load MODIS EVI imagery.\ncollection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI')\n\n# Define reference conditions from the first 10 years of data.\nreference = collection.filterDate('2001-01-01', '2010-12-31').sort(\n # Sort chronologically in descending order.\n 'system:time_start',\n False,\n)\n\n# Compute the mean of the first 10 years.\nmean = reference.mean()\n\n# Compute anomalies by subtracting the 2001-2010 mean from each image in a\n# collection of 2011-2014 images. Copy the date metadata over to the\n# computed anomaly images in the new collection.\nseries = collection.filterDate('2011-01-01', '2014-12-31').map(\n lambda image: image.subtract(mean).set(\n 'system:time_start', image.get('system:time_start')\n )\n)\n\n# Display cumulative anomalies.\nm = geemap.Map()\nm.set_center(-100.811, 40.2, 5)\nm.add_layer(\n series.sum(),\n {'min': -60000, 'max': 60000, 'palette': ['FF0000', '000000', '00FF00']},\n 'EVI anomaly',\n)\ndisplay(m)\n\n# Get the timestamp from the most recent image in the reference collection.\ntime_0 = reference.first().get('system:time_start')\n\n# Use imageCollection.iterate() to make a collection of cumulative anomaly over time.\n# The initial value for iterate() is a list of anomaly images already processed.\n# The first anomaly image in the list is just 0, with the time_0 timestamp.\nfirst = ee.List([\n # Rename the first band 'EVI'.\n ee.Image(0)\n .set('system:time_start', time_0)\n .select([0], ['EVI'])\n])\n\n# This is a function to pass to Iterate().\n# As anomaly images are computed, add them to the list.\ndef accumulate(image, list):\n # Get the latest cumulative anomaly image from the end of the list with\n # get(-1). Since the type of the list argument to the function is unknown,\n # it needs to be cast to a List. Since the return type of get() is unknown,\n # cast it to Image.\n previous = ee.Image(ee.List(list).get(-1))\n # Add the current anomaly to make a new cumulative anomaly image.\n added = image.add(previous).set(\n # Propagate metadata to the new image.\n 'system:time_start',\n image.get('system:time_start'),\n )\n # Return the list with the cumulative anomaly inserted.\n return ee.List(list).add(added)\n\n# Create an ImageCollection of cumulative anomaly images by iterating.\n# Since the return type of iterate is unknown, it needs to be cast to a List.\ncumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first)))\n\n# Predefine the chart titles.\ntitle = 'Cumulative EVI anomaly over time'\n\n# Chart some interesting locations.\ndef display_chart(region, collection):\n reduced = (\n collection.filterBounds(region)\n .sort('system:time_start')\n .map(\n lambda image: ee.Feature(\n None,\n image.reduceRegion(ee.Reducer.first(), region, 500).set(\n 'time', image.get('system:time_start')\n ),\n )\n )\n )\n reduced_dataframe = ee.data.computeFeatures(\n {'expression': reduced, 'fileFormat': 'PANDAS_DATAFRAME'}\n )\n alt.Chart(reduced_dataframe).mark_line().encode(\n alt.X('time:T').title('Time'),\n alt.Y('EVI:Q').title('Cumulative EVI anomaly'),\n ).properties(title=title).display()\n\npt_1 = ee.Geometry.Point(-65.544, -4.894)\ndisplay('Amazon rainforest:')\ndisplay_chart(pt_1, cumulative)\n\npt_2 = ee.Geometry.Point(116.4647, 40.1054)\ndisplay('Beijing urbanization:')\ndisplay_chart(pt_2, cumulative)\n\npt_3 = ee.Geometry.Point(-110.3412, 34.1982)\ndisplay('Arizona forest disturbance and recovery:')\ndisplay_chart(pt_3, cumulative)\n```\n\nCharting these sequences indicates whether NDVI is stabilizing relative to previous\ndisturbances or whether NDVI is trending to a new state. Learn more about charts in\nEarth Engine from the [Charts section](/earth-engine/guides/charts).\n\nThe iterated function is limited in the operations it can perform. Specifically, it can't\nmodify variables outside the function; it can't print anything; it can't use JavaScript 'if'\nor 'for' statements. Any results you wish to collect or intermediate information you wish to\ncarry over to the next iteration must be in the function's return value. You can use\n\\`ee.Algorithms.If()\\` to perform conditional operations."]]
