Riduzione di una raccolta di elementi

Per aggregare i dati nelle proprietà di un FeatureCollection, utilizza featureCollection.reduceColumns(). Ad esempio, per controllare le proprietà dell'area nei bacini idrografici FeatureCollection, questo codice calcola l'errore quadratico medio (RMSE) rispetto all'area calcolata di Earth Engine:

Editor di codice (JavaScript)

// Load watersheds from a data table and filter to the continental US.
var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
  .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29));

// This function computes the squared difference between an area property
// and area computed directly from the feature's geometry.
var areaDiff = function(feature) {
  // Compute area in sq. km directly from the geometry.
  var area = feature.geometry().area().divide(1000 * 1000);
  // Compute the difference between computed area and the area property.
  var diff = area.subtract(ee.Number.parse(feature.get('areasqkm')));
  // Return the feature with the squared difference set to the 'diff' property.
  return feature.set('diff', diff.pow(2));
};

// Calculate RMSE for population of difference pairs.
var rmse = ee.Number(
  // Map the difference function over the collection.
  sheds.map(areaDiff)
  // Reduce to get the mean squared difference.
  .reduceColumns(ee.Reducer.mean(), ['diff'])
  .get('mean')
)
// Compute the square root of the mean square to get RMSE.
.sqrt();

// Print the result.
print('RMSE=', rmse);

Configurazione di Python

Per informazioni sull'API Python e sull'utilizzo di geemap per lo sviluppo interattivo, consulta la pagina Ambiente Python.

import ee
import geemap.core as geemap

Colab (Python)

# Load watersheds from a data table and filter to the continental US.
sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds(
    ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)
)

# This function computes the squared difference between an area property
# and area computed directly from the feature's geometry.
def area_diff(feature):
  # Compute area in sq. km directly from the geometry.
  area = feature.geometry().area().divide(1000 * 1000)
  # Compute the difference between computed area and the area property.
  diff = area.subtract(ee.Number.parse(feature.get('areasqkm')))
  # Return the feature with the squared difference set to the 'diff' property.
  return feature.set('diff', diff.pow(2))

# Calculate RMSE for population of difference pairs.
rmse = (
    ee.Number(
        # Map the difference function over the collection.
        sheds.map(area_diff)
        # Reduce to get the mean squared difference.
        .reduceColumns(ee.Reducer.mean(), ['diff']).get('mean')
    )
    # Compute the square root of the mean square to get RMSE.
    .sqrt()
)

# Print the result.
display('RMSE=', rmse)

In questo esempio, tieni presente che il valore restituito di reduceColumns() è un dizionario con chiave ‘mean’. Per ottenere la media, trasmetti il risultato di dictionary.get() a un numero con ee.Number() prima di provare a chiamare sqrt(). Per ulteriori informazioni sulle strutture di dati aggiuntivi in Earth Engine, consulta questo tutorial.

Per sovrapporre elementi alle immagini, utilizza featureCollection.reduceRegions(). Ad esempio, per calcolare il volume delle precipitazioni nei bacini idrografici continentali degli Stati Uniti, utilizza reduceRegions() seguito da map():

Editor di codice (JavaScript)

// Load an image of daily precipitation in mm/day.
var precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first());

// Load watersheds from a data table and filter to the continental US.
var sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
  .filterBounds(ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29));

// Add the mean of each image as new properties of each feature.
var withPrecip = precip.reduceRegions(sheds, ee.Reducer.mean())
  .filter(ee.Filter.notNull(['prcp']));

// This function computes total rainfall in cubic meters.
var prcpVolume = function(feature) {
  // Precipitation in mm/day -> meters -> sq. meters.
  var volume = ee.Number(feature.get('prcp'))
    .divide(1000).multiply(feature.geometry().area());
  return feature.set('volume', volume);
};

var highVolume = withPrecip
  // Map the function over the collection.
  .map(prcpVolume)
  // Sort descending.
  .sort('volume', false)
  // Get only the 5 highest volume watersheds.
  .limit(5)
  // Extract the names to a list.
  .reduceColumns(ee.Reducer.toList(), ['name']).get('list');

// Print the resulting FeatureCollection.
print(highVolume);

Configurazione di Python

Per informazioni sull'API Python e sull'utilizzo di geemap per lo sviluppo interattivo, consulta la pagina Ambiente Python.

import ee
import geemap.core as geemap

Colab (Python)

# Load an image of daily precipitation in mm/day.
precip = ee.Image(ee.ImageCollection('NASA/ORNL/DAYMET_V3').first())

# Load watersheds from a data table and filter to the continental US.
sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06').filterBounds(
    ee.Geometry.Rectangle(-127.18, 19.39, -62.75, 51.29)
)

# Add the mean of each image as new properties of each feature.
with_precip = precip.reduceRegions(sheds, ee.Reducer.mean()).filter(
    ee.Filter.notNull(['prcp'])
)


# This function computes total rainfall in cubic meters.
def prcp_volume(feature):
  # Precipitation in mm/day -> meters -> sq. meters.
  volume = (
      ee.Number(feature.get('prcp'))
      .divide(1000)
      .multiply(feature.geometry().area())
  )
  return feature.set('volume', volume)

high_volume = (
    # Map the function over the collection.
    with_precip.map(prcp_volume)
    # Sort descending and get only the 5 highest volume watersheds.
    .sort('volume', False).limit(5)
    # Extract the names to a list.
    .reduceColumns(ee.Reducer.toList(), ['name']).get('list')
)

# Print the resulting FeatureCollection.
display(high_volume)

Per ulteriori informazioni sulla riduzione delle raccolte di elementi, consulta Statistiche delle colonne FeatureCollection e Conversione da vettore a raster.