Die Arraysortierung ist nützlich, um Mosaike mit benutzerdefinierter Qualität zu erhalten, bei denen eine Teilmenge von Bildbändern anhand der Werte in einem anderen Band reduziert wird. Im folgenden Beispiel werden die Daten nach NDVI sortiert und dann der Mittelwert einer Teilmenge der Beobachtungen in der Sammlung mit den höchsten NDVI-Werten ermittelt:
// Define a function that scales and masks Landsat 8 surface reflectance images // and adds an NDVI band. function prepSrL8(image) { // Develop masks for unwanted pixels (fill, cloud, cloud shadow). var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0); var saturationMask = image.select('QA_RADSAT').eq(0); // Apply the scaling factors to the appropriate bands. var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2); var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0); // Calculate NDVI. var ndvi = opticalBands.normalizedDifference(['SR_B5', 'SR_B4']) .rename('NDVI'); // Replace original bands with scaled bands, add NDVI band, and apply masks. return image.addBands(opticalBands, null, true) .addBands(thermalBands, null, true) .addBands(ndvi) .updateMask(qaMask) .updateMask(saturationMask); } // Define an arbitrary region of interest as a point. var roi = ee.Geometry.Point(-122.26032, 37.87187); // Load a Landsat 8 surface reflectance collection. var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') // Filter to get only imagery at a point of interest. .filterBounds(roi) // Filter to get only six months of data. .filterDate('2021-01-01', '2021-07-01') // Prepare images by mapping the prepSrL8 function over the collection. .map(prepSrL8) // Select the bands of interest to avoid taking up unneeded memory. .select('SR_B.|NDVI'); // Convert the collection to an array. var array = collection.toArray(); // Label of the axes. var imageAxis = 0; var bandAxis = 1; // Get the NDVI slice and the bands of interest. var bandNames = collection.first().bandNames(); var bands = array.arraySlice(bandAxis, 0, bandNames.length()); var ndvi = array.arraySlice(bandAxis, -1); // Sort by descending NDVI. var sorted = bands.arraySort(ndvi.multiply(-1)); // Get the highest 20% NDVI observations per pixel. var numImages = sorted.arrayLength(imageAxis).multiply(0.2).int(); var highestNdvi = sorted.arraySlice(imageAxis, 0, numImages); // Get the mean of the highest 20% NDVI observations by reducing // along the image axis. var mean = highestNdvi.arrayReduce({ reducer: ee.Reducer.mean(), axes: [imageAxis] }); // Turn the reduced array image into a multi-band image for display. var meanImage = mean.arrayProject([bandAxis]).arrayFlatten([bandNames]); Map.centerObject(roi, 12); Map.addLayer(meanImage, {bands: ['SR_B6', 'SR_B5', 'SR_B4'], min: 0, max: 0.4});
import ee import geemap.core as geemap
# Define a function that scales and masks Landsat 8 surface reflectance images # and adds an NDVI band. def prep_sr_l8(image): # Develop masks for unwanted pixels (fill, cloud, cloud shadow). qa_mask = image.select('QA_PIXEL').bitwiseAnd(int('11111', 2)).eq(0) saturation_mask = image.select('QA_RADSAT').eq(0) # Apply the scaling factors to the appropriate bands. optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2) thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0) # Calculate NDVI. ndvi = optical_bands.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI') # Replace the original bands with the scaled ones and apply the masks. return ( image.addBands(optical_bands, None, True) .addBands(thermal_bands, None, True) .addBands(ndvi) .updateMask(qa_mask) .updateMask(saturation_mask) ) # Define an arbitrary region of interest as a point. roi = ee.Geometry.Point(-122.26032, 37.87187) # Load a Landsat 8 surface reflectance collection. collection = ( ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') # Filter to get only imagery at a point of interest. .filterBounds(roi) # Filter to get only six months of data. .filterDate('2021-01-01', '2021-07-01') # Prepare images by mapping the prep_sr_l8 function over the collection. .map(prep_sr_l8) # Select the bands of interest to avoid taking up unneeded memory. .select('SR_B.|NDVI') ) # Convert the collection to an array. array = collection.toArray() # Label of the axes. image_axis = 0 band_axis = 1 # Get the NDVI slice and the bands of interest. band_names = collection.first().bandNames() bands = array.arraySlice(band_axis, 0, band_names.length()) ndvi = array.arraySlice(band_axis, -1) # Sort by descending NDVI. sorted = bands.arraySort(ndvi.multiply(-1)) # Get the highest 20% NDVI observations per pixel. num_images = sorted.arrayLength(image_axis).multiply(0.2).int() highest_ndvi = sorted.arraySlice(image_axis, 0, num_images) # Get the mean of the highest 20% NDVI observations by reducing # along the image axis. mean = highest_ndvi.arrayReduce(reducer=ee.Reducer.mean(), axes=[image_axis]) # Turn the reduced array image into a multi-band image for display. mean_image = mean.arrayProject([band_axis]).arrayFlatten([band_names]) m = geemap.Map() m.center_object(roi, 12) m.add_layer( mean_image, {'bands': ['SR_B6', 'SR_B5', 'SR_B4'], 'min': 0, 'max': 0.4} ) m
Trennen Sie die gewünschten Bänder wie im Beispiel für die lineare Modellierung mithilfe von arraySlice() entlang der Bandachse vom Sortierindex (NDVI). Sortieren Sie die gewünschten Bänder dann mit arraySort() nach dem Sortierindex. Nachdem die Pixel nach absteigendem NDVI sortiert wurden, verwenden Sie arraySlice() entlang der imageAxis, um 20% der Pixel mit dem höchsten NDVI zu erhalten. Wenden Sie abschließend arrayReduce() entlang der imageAxis mit einem Mittelwertminderer an, um den Mittelwert der höchsten NDVI-Pixel zu erhalten. Im letzten Schritt wird das Arraybild wieder in ein Mehrbandbild für die Anzeige konvertiert.