AI-generated Key Takeaways
-
The
ImageCollection.getRegionmethod outputs an array of values for each pixel, band, and image tuple in an ImageCollection. -
The output is a list where each row includes the ID, longitude, latitude, time, and all band values for each image that intersects a given pixel.
-
This method accepts various geometry types including points, lines, and polygons to define the region for data extraction.
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An error will occur if you attempt to extract more than 1048576 values using this method.
| Usage | Returns |
|---|---|
ImageCollection.
getRegion
(geometry, scale
, crs
, crsTransform
)
|
List |
| Argument | Type | Details |
|---|---|---|
|
this:
collection
|
ImageCollection | The image collection to extract data from. |
geometry
|
Geometry | The region over which to extract data. |
scale
|
Float, default: null | A nominal scale in meters of the projection to work in. |
crs
|
Projection, optional | The projection to work in. If unspecified, defaults to EPSG:4326. If specified in addition to scale, the projection is rescaled to the specified scale. |
crsTransform
|
List, default: null | The array of CRS transform values. This is a row-major ordering of a 3x2 affine transform. This option is mutually exclusive with the scale option, and will replace any transform already set on the given projection. |
Examples
Code Editor (JavaScript)
// A Landsat 8 TOA image collection (3 months at a specific point, RGB bands). var col = ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' ) . filterBounds ( ee . Geometry . Point ( - 90.70 , 34.71 )) . filterDate ( '2020-07-01' , '2020-10-01' ) . select ( 'B[2-4]' ); print ( 'Collection' , col ); // Define a region to get pixel values for. This is a small rectangle region // that intersects 2 image pixels at 30-meter scale. var roi = ee . Geometry . BBox ( - 90.496353 , 34.851971 , - 90.495749 , 34.852197 ); // Display the region of interest overlaid on an image representative. Note // the ROI intersection with 2 pixels. var visParams = { bands : [ 'B4' , 'B3' , 'B2' ], min : 0.128 , max : 0.163 }; Map . setCenter ( - 90.49605 , 34.85211 , 19 ); Map . addLayer ( col . first (), visParams , 'Image representative' ); Map . addLayer ( roi , { color : 'white' }, 'ROI' ); // Fetch pixel-level information from all images in the collection for the // pixels intersecting the ROI. var pixelInfoBbox = col . getRegion ({ geometry : roi , scale : 30 }); // The result is a table (a list of lists) where the first row is column // labels and subsequent rows are image pixels. Columns contain values for // the image ID ('system:index'), pixel longitude and latitude, image // observation time ('system:time_start'), and bands. In this example, note // that there are 5 images and the region intersects 2 pixels, so n rows // equals 11 (5 * 2 + 1). All collection images must have the same number of // bands with the same names. print ( 'Extracted pixel info' , pixelInfoBbox ); // The function accepts all geometry types (e.g., points, lines, polygons). // Here, a multi-point geometry with two points is used. var points = ee . Geometry . MultiPoint ([[ - 90.49 , 34.85 ], [ - 90.48 , 34.84 ]]); var pixelInfoPoints = col . getRegion ({ geometry : points , scale : 30 }); print ( 'Point geometry example' , pixelInfoPoints );
import ee import geemap.core as geemap
Colab (Python)
# A Landsat 8 TOA image collection (3 months at a specific point, RGB bands). col = ( ee . ImageCollection ( 'LANDSAT/LC08/C02/T1_TOA' ) . filterBounds ( ee . Geometry . Point ( - 90.70 , 34.71 )) . filterDate ( '2020-07-01' , '2020-10-01' ) . select ( 'B[2-4]' ) ) display ( 'Collection' , col ) # Define a region to get pixel values for. This is a small rectangle region # that intersects 2 image pixels at 30-meter scale. roi = ee . Geometry . BBox ( - 90.496353 , 34.851971 , - 90.495749 , 34.852197 ) # Display the region of interest overlaid on an image representative. Note # the ROI intersection with 2 pixels. vis_params = { 'bands' : [ 'B4' , 'B3' , 'B2' ], 'min' : 0.128 , 'max' : 0.163 } m = geemap . Map () m . set_center ( - 90.49605 , 34.85211 , 19 ) m . add_layer ( col . first (), vis_params , 'Image representative' ) m . add_layer ( roi , { 'color' : 'white' }, 'ROI' ) display ( m ) # Fetch pixel-level information from all images in the collection for the # pixels intersecting the ROI. pixel_info_bbox = col . getRegion ( geometry = roi , scale = 30 ) # The result is a table (a list of lists) where the first row is column # labels and subsequent rows are image pixels. Columns contain values for # the image ID ('system:index'), pixel longitude and latitude, image # observation time ('system:time_start'), and bands. In this example, note # that there are 5 images and the region intersects 2 pixels, so n rows # equals 11 (5 * 2 + 1). All collection images must have the same number of # bands with the same names. display ( 'Extracted pixel info' , pixel_info_bbox ) # The function accepts all geometry types (e.g., points, lines, polygons). # Here, a multi-point geometry with two points is used. points = ee . Geometry . MultiPoint ([[ - 90.49 , 34.85 ], [ - 90.48 , 34.84 ]]) pixel_info_points = col . getRegion ( geometry = points , scale = 30 ) display ( 'Point geometry example' , pixel_info_points )
