Para agregar datos en las propiedades de un FeatureCollection, usa featureCollection.reduceColumns(). Por ejemplo, para verificar las propiedades del área en las cuencas hidrográficas FeatureCollection, este código calcula la raíz cuadrada del error cuadrático medio (RMSE) en relación con el área calculada de Earth Engine:
// 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);
import ee import geemap.core as geemap
# 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)
En este ejemplo, ten en cuenta que el valor que se muestra de reduceColumns() es un diccionario con la clave ‘mean’. Para obtener la media, convierte el resultado de
dictionary.get() en un número con ee.Number() antes de intentar llamar a
sqrt() en él. Para obtener más información sobre las estructuras de datos complementarias en Earth Engine, consulta este instructivo.
Para superponer componentes en las imágenes, usa featureCollection.reduceRegions(). Por ejemplo, para calcular el volumen de precipitación en las cuencas continentales de EE.UU., usa reduceRegions() seguido de un map():
// 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);
import ee import geemap.core as geemap
# 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)
Para obtener más información sobre cómo reducir las colecciones de componentes, consulta Estadísticas de las columnas de FeatureCollection y Conversión de vectores a tramas.