Mengurangi FeatureCollection

Untuk menggabungkan data dalam properti FeatureCollection, gunakan featureCollection.reduceColumns(). Misalnya, untuk memeriksa properti area di watershed FeatureCollection, kode ini menghitung Error Akar Rataan Kuadrat (RMSE) relatif terhadap area yang dihitung Earth Engine:

Editor Kode (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);

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

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)

Dalam contoh ini, perhatikan bahwa nilai yang ditampilkan reduceColumns() adalah kamus dengan kunci ‘mean’. Untuk mendapatkan nilai rata-rata, transmisikan hasil dictionary.get() ke angka dengan ee.Number() sebelum mencoba memanggil sqrt(). Untuk informasi selengkapnya tentang struktur data tambahan di Earth Engine, lihat tutorial ini.

Untuk menempatkan fitur pada gambar, gunakan featureCollection.reduceRegions(). Misalnya, untuk menghitung volume presipitasi di DAS Amerika Serikat kontinental, gunakan reduceRegions(), diikuti dengan map():

Editor Kode (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);

Penyiapan Python

Lihat halaman Lingkungan Python untuk mengetahui informasi tentang Python API dan penggunaan geemap untuk pengembangan interaktif.

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)

Untuk informasi selengkapnya tentang cara mengurangi koleksi fitur, lihat Statistik Kolom FeatureCollection dan Konversi Vektor ke Raster.