ee.Image.pixelArea

Erstelle ein Bild, in dem der Wert jedes Pixels der Fläche dieses Pixels in Quadratmetern entspricht. Das zurückgegebene Bild hat ein einzelnes Band namens „area“.

Nutzung	Ausgabe
ee.Image.pixelArea()	Bild

Keine Argumente.

Beispiele

Code-Editor (JavaScript)

// Create a pixel area image. Pixel values are square meters based on
// a given CRS and scale (or CRS transform).
var pixelArea = ee.Image.pixelArea();

// The default projection is WGS84 with 1-degree scale.
print('Pixel area default projection', pixelArea.projection());

// When inspecting the output in the Code Editor map, the scale of analysis is
// determined by the zoom level. As you zoom in and out, you'll notice that the
// area of the clicked pixel changes. To set a specific pixel scale when
// performing a computation, provide an argument to the `scale` or
// `crsTransform` parameters whenever a function gives you the option.
Map.addLayer(pixelArea, null, 'Pixel area for inspection', false);

// The "area" band produced by the `pixelArea` function can be useful for
// calculating the area of a certain condition of another image. For example,
// here we use the sum reducer to determine the area above 2250m in the North
// Cascades ecoregion, according to a 30m digital elevation model.

// Import a DEM and subset the "elevation" band.
var elev = ee.Image('NASA/NASADEM_HGT/001').select('elevation');

// Define a high elevation mask where pixels with elevation greater than 2250m
// are set to 1, otherwise 0.
var highElevMask = elev.gt(2250);

// Apply the high elevation mask to the pixel area image.
var highElevArea = pixelArea.updateMask(highElevMask);

// Import an ecoregion feature collection and filter it by ecoregion name.
var ecoregion = ee.FeatureCollection('RESOLVE/ECOREGIONS/2017')
  .filter('ECO_NAME == "North Cascades conifer forests"');

// Display the ecoregion and high elevation area.
Map.setCenter(-121.127, 48.389, 7);
Map.addLayer(ecoregion, null, 'North Cascades ecoregion');
Map.addLayer(highElevArea.clip(ecoregion),
             {palette: 'yellow'}, 'High elevation area');

// Sum the area of high elevation pixels in the North Cascades ecoregion.
var area = highElevArea.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: ecoregion,
  crs: elev.projection(),  // DEM coordinate reference system
  crsTransform: elev.projection().getInfo().transform,  // DEM grid alignment
  maxPixels: 1e8
});

// Fetch the summed area property from the resulting dictionary and convert
// square meters to square kilometers.
var squareMeters = area.getNumber('area');
var squareKilometers = squareMeters.divide(1e6);

print('Square meters above 2250m elevation', squareMeters);
print('Square kilometers above 2250m elevation', squareKilometers);

Python einrichten

Informationen zur Python API und zur Verwendung von geemap für die interaktive Entwicklung finden Sie auf der Seite Python-Umgebung.

import ee
import geemap.core as geemap

Colab (Python)

# Create a pixel area image. Pixel values are square meters based on
# a given CRS and scale (or CRS transform).
pixel_area = ee.Image.pixelArea()

# The default projection is WGS84 with 1-degree scale.
display('Pixel area default projection', pixel_area.projection())

# When inspecting the output in the Code Editor map, the scale of analysis is
# determined by the zoom level. As you zoom in and out, you'll notice that the
# area of the clicked pixel changes. To set a specific pixel scale when
# performing a computation, provide an argument to the `scale` or
# `crsTransform` parameters whenever a function gives you the option.
m = geemap.Map()
m.add_layer(pixel_area, None, 'Pixel area for inspection', False)

# The "area" band produced by the `pixel_area` function can be useful for
# calculating the area of a certain condition of another image. For example,
# here we use the sum reducer to determine the area above 2250m in the North
# Cascades ecoregion, according to a 30m digital elevation model.

# Import a DEM and subset the "elevation" band.
elev = ee.Image('NASA/NASADEM_HGT/001').select('elevation')

# Define a high elevation mask where pixels with elevation greater than 2250m
# are set to 1, otherwise 0.
high_elev_mask = elev.gt(2250)

# Apply the high elevation mask to the pixel area image.
high_elev_area = pixel_area.updateMask(high_elev_mask)

# Import an ecoregion feature collection and filter it by ecoregion name.
ecoregion = ee.FeatureCollection('RESOLVE/ECOREGIONS/2017').filter(
    'ECO_NAME == "North Cascades conifer forests"'
)

# Display the ecoregion and high elevation area.
m.set_center(-121.127, 48.389, 7)
m.add_layer(ecoregion, None, 'North Cascades ecoregion')
m.add_layer(
    high_elev_area.clip(ecoregion), {'palette': 'yellow'}, 'High elevation area'
)
display(m)

# Sum the area of high elevation pixels in the North Cascades ecoregion.
area = high_elev_area.reduceRegion(
    reducer=ee.Reducer.sum(),
    geometry=ecoregion,
    crs=elev.projection(),  # DEM coordinate reference system
    crsTransform=elev.projection().getInfo()['transform'],  # DEM grid alignment
    maxPixels=1e8,
)

# Fetch the summed area property from the resulting dictionary and convert
# square meters to square kilometers.
square_meters = area.getNumber('area')
square_kilometers = square_meters.divide(1e6)

display('Square meters above 2250m elevation', square_meters)
display('Square kilometers above 2250m elevation', square_kilometers)