การทำซ้ำใน ImageCollection
จัดทุกอย่างให้เป็นระเบียบอยู่เสมอด้วยคอลเล็กชัน
บันทึกและจัดหมวดหมู่เนื้อหาตามค่ากำหนดของคุณ
แม้ว่า map() จะใช้ฟังก์ชันกับรูปภาพทุกรูปในคอลเล็กชัน แต่ฟังก์ชันดังกล่าวจะเรียกใช้รูปภาพทุกรูปในคอลเล็กชันแยกกัน ตัวอย่างเช่น สมมติว่าคุณต้องการคํานวณความผิดปกติสะสม (At) ณ เวลา t จากอนุกรมเวลา หากต้องการรับชุดค่ายําที่กําหนดแบบซ้ำซ้อนของรูปแบบ At =
f(Imaget, At-1) การแมปจะไม่ทํางานเนื่องจากฟังก์ชัน (f) ขึ้นอยู่กับผลลัพธ์ก่อนหน้า (At-1) ตัวอย่างเช่น สมมติว่าคุณต้องการคํานวณชุดรูปภาพความผิดปกติของดัชนีความแตกต่างของพืช (NDVI) ที่ปรับให้เป็นมาตรฐานแบบสะสม (Cumulative Normalized Difference Vegetation Index) เทียบกับภาพฐาน สมมติให้ A0 = 0 และ
f(Imaget, At-1) = Imaget + At-1
โดยที่ At-1 คือความผิดปกติสะสมจนถึงเวลา t-1 และ
Imaget คือความผิดปกติ ณ เวลา t ใช้
imageCollection.iterate() เพื่อทำให้นิยามนี้ทำงานแบบซ้ำ
ImageCollection ในตัวอย่างต่อไปนี้ ฟังก์ชัน accumulate() จะใช้พารามิเตอร์ 2 รายการ ได้แก่ รูปภาพในคอลเล็กชันและรายการเอาต์พุตก่อนหน้าทั้งหมด เมื่อเรียกใช้ iterate() แต่ละครั้ง ระบบจะเพิ่มความผิดปกติลงในผลรวมที่สะสมอยู่และเพิ่มผลลัพธ์ลงในรายการ ผลลัพธ์สุดท้ายจะส่งผ่านไปยังตัวสร้าง ImageCollection เพื่อรับลำดับรูปภาพใหม่
เครื่องมือแก้ไขโค้ด (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));
การตั้งค่า Python
ดูข้อมูลเกี่ยวกับ Python API และการใช้ geemap สําหรับการพัฒนาแบบอินเทอร์แอกทีฟได้ที่หน้า
สภาพแวดล้อม Python
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)
แผนภูมิของลำดับเหล่านี้จะระบุว่า NDVI มีแนวโน้มที่จะคงที่เมื่อเทียบกับความผันผวนก่อนหน้า หรือ NDVI มีแนวโน้มที่จะเข้าสู่สถานะใหม่ ดูข้อมูลเพิ่มเติมเกี่ยวกับแผนภูมิใน Earth Engine ได้จากส่วนแผนภูมิ
ฟังก์ชันที่วนซ้ำจะจำกัดการดำเนินการที่ทำได้ กล่าวโดยละเอียดคือ ไม่สามารถแก้ไขตัวแปรที่อยู่นอกฟังก์ชัน ไม่สามารถพิมพ์ข้อมูลใดๆ และใช้คำสั่ง "if" หรือ "for" ของ JavaScript ไม่ได้ ผลลัพธ์ที่คุณต้องการรวบรวมหรือข้อมูลกลางที่ต้องการนําไปใช้กับการวนซ้ำครั้งถัดไปต้องอยู่ในค่าส่งกลับของฟังก์ชัน คุณสามารถใช้ ee.Algorithms.If() เพื่อดำเนินการแบบมีเงื่อนไขได้
เนื้อหาของหน้าเว็บนี้ได้รับอนุญาตภายใต้ใบอนุญาตที่ต้องระบุที่มาของครีเอทีฟคอมมอนส์ 4.0 และตัวอย่างโค้ดได้รับอนุญาตภายใต้ใบอนุญาต Apache 2.0 เว้นแต่จะระบุไว้เป็นอย่างอื่น โปรดดูรายละเอียดที่นโยบายเว็บไซต์ Google Developers Java เป็นเครื่องหมายการค้าจดทะเบียนของ Oracle และ/หรือบริษัทในเครือ
อัปเดตล่าสุด 2025-07-25 UTC
[null,null,["อัปเดตล่าสุด 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."]]
