plot_doy_by_regions#

geetools.ee_image_collection.ImageCollectionAccessor.plot_doy_by_regions(band, regions, label='system:index', spatialReducer='mean', timeReducer='mean', dateProperty='system:time_start', colors=[], ax=None, scale=10000, crs=None, crsTransform=None, tileScale=1)#

Plot the reduced data for each image in the collection by regions for a single band.

This method is plotting the reduced data for each image in the collection by regions for a single band.

Parameters:

  • band (str) – The band to reduce.

  • regions (ee.FeatureCollection) – The regions to reduce the data on.

  • label (str) – The property to use as label for each region. Default is "system:index".

  • spatialReducer (str | ee.Reducer) – The name of the reducer or a reducer object to use. Default is "mean".

  • timeReducer (str | ee.Reducer) – The name of the reducer or a reducer object to use. Default is "mean".

  • dateProperty (str) – The property to use as date for each image. Default is "system:time_start".

  • colors (list) – The colors to use for the regions. If empty, the default colors are used.

  • ax (matplotlib.axes.Axes | None) – The matplotlib axes to plot the data on. If None, a new figure is created.

  • scale (int) – The scale in meters to use for the reduction. default is 10000m

  • crs (str | None) – The projection to work in. If unspecified, the projection of the image’s first band is used. If specified in addition to scale, rescaled to the specified scale.

  • crsTransform (list | None) – The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with ‘scale’, and replaces any transform already set on the projection.

  • tileScale (float) – A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.

Returns:

A matplotlib axes with the reduced values for each region and each day.

Return type:

matplotlib.axes.Axes

See also

  • doyByBands: Aggregate the images that occurs on the same day and then reduce each band on a single region.

  • doyByRegions: Aggregate the images that occurs on the same day and then reduce a single band on multiple regions.

  • doyBySeasons: Aggregate for each year on a single region a single band.

  • doyByYears: Aggregate for each year on a single region a single band.

  • plot_doy_by_bands: Plot the reduced data for each image in the collection by bands on a specific region.

  • plot_doy_by_seasons: Plot the reduced data for each image in the collection by years for a single band.

  • plot_doy_by_years: Plot the reduced data for each image in the collection by years for a single band.

Examples

import ee, geetools

ee.Initialize()

collection = (
    ee.ImageCollection("LANDSAT/LC08/C01/T1_TOA")
    .filterBounds(ee.Geometry.Point(-122.262, 37.8719))
    .filterDate("2014-01-01", "2014-12-31")
)

regions = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(10000), {"name": "region1"}),
    ee.Feature(ee.Geometry.Point(-122.262, 37.8719).buffer(20000), {"name": "region2"})
])

collection.geetools.plot_doy_by_regions("B1", regions, "name", "mean", "mean", 10000, "system:time_start")