statgis.gee.zonal_statistics#

Reduce images in a region of interest

Module Contents#

Functions#

zonal_statistics_image(→ pandas.DataFrame)

Function to calculate a statistic in the specified region for one image.

zonal_statistics_collection(→ pandas.DataFrame)

Function to calculate a statistic in the specified region for all Image in an image
statgis.gee.zonal_statistics.zonal_statistics_image(image: ee.Image, geom: ee.Geometry, scale: float, bands: Union[Sequence[str], str] = 'all', reducer: Union[ee.Reducer, str] = 'all', tile_scale: int = 16) → pandas.DataFrame#

Function to calculate a statistic in the specified region for one image.

Parameters:

  • image (ee.Image) – Image of interest.

  • geom (ee.Geometry) – Region of interest to reduce the image.

  • scale (float) – Pixel size for the sample to perform the zonal statistics.

  • bands (Sequence | str (optional)) – List, tuple with the bands of interest or, if you only want one band, the name of the band. By default, the process takes into consideration all bands.

  • reducer (ee.Reducer | str (optional)) – Reducer to apply to the image. By default, image are reduced to its, mean, standard deviation, maximum, minimum, and count.

  • tile_scale (int (optional)) – Scale of the mosaic to allow EarthEngine to split the task to more cores.

Returns:

data – DataFrame with all the stats for all specified bands.

Return type:

pandas.DataFrame

Example

>>> import ee
>>> from statgis.gee import zonal_statistics
>>> ee.Initialize()
>>> roi = ee.Geometry.BBox(-75.2671803, 4.4104561 ,-75.2691803, 4.4124561)
>>> mean_precipitation_2022 = (
...     ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY")
...     .filterDate("2022-01-01", "2022-12-31")
...     .mean()
... )
>>> stats = zonal_statistics.zonal_statistics_image(mean_precipitation_2022, roi, 30, "precipitation")
statgis.gee.zonal_statistics.zonal_statistics_collection(image_collection: ee.ImageCollection, geom: ee.Geometry, scale: float, bands: Union[Sequence[str], str] = 'all', reducer: Union[ee.Reducer, str] = 'all', tile_scale: int = 16) → pandas.DataFrame#

Function to calculate a statistic in the specified region for all Image in an image collection.

Parameters:

  • image_collection (ee.ImageCollection) – Image Collection with the image to reduce.

  • geom (ee.Geometry) – Region of interest to reduce the images.

  • scale (float) – Pixel size for the sample to perform the zonal statistics.

  • bands (Sequence | str (optional)) – List, tuple with the bands of interest or, if you only want one band, the name of the band. By default, the process takes into consideration all bands.

  • reducer (ee.Reducer | str (optional)) – Reducer to apply to all image. By default, image are reduced to its, mean, standard deviation, maximum, minimum, and count.

  • tile_scale (int (optional)) – Scale of the mosaic to allow EarthEngine to split the task to more cores.

Returns:

data – DataFrame with all the stats for all specified bands.

Return type:

pandas.DataFrame

Example

>>> import ee
>>> from statgis.gee import zonal_statistics
>>> ee.Initialize()
>>> roi = ee.Geometry.BBox(-75.2671803, 4.4104561 ,-75.2691803, 4.4124561)
>>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("2022-01-01", "2022-12-31")
>>> daily_mean_precipitation = zonal_statistics.zonal_statistics_collection(
...     chirps, roi, 30, "precipitation", ee.Reducer.mean()
... )