L'ordinamento dell'array è utile per ottenere mosaici di qualità personalizzata che richiedono la riduzione di un insieme di bande di immagini in base ai valori di un'altra banda. L'esempio seguente ordina in base all'indice NDVI, quindi ottiene la media di un sottoinsieme di osservazioni nella raccolta con i valori NDVI più elevati:
Editor di codice (JavaScript)
// 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
Colab (Python)
# 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
Come nell'esempio di creazione di modelli lineari, separa le bande di interesse dall'indice di ordinamento (NDVI)
utilizzando arraySlice() lungo l'asse della banda. Quindi ordina le bande di interesse in base all'
indicizzatore di ordinamento utilizzando arraySort(). Dopo aver ordinato i pixel in base al valore NDVI in ordine decrescente, utilizza arraySlice() lungo imageAxis per ottenere il 20% dei pixel con il valore NDVI più alto. Infine, applica arrayReduce() lungo
imageAxis con un riduttore medio per ottenere la media dei pixel NDVI
più elevati. Il passaggio finale converte nuovamente l'immagine dell'array in un'immagine multibanda per la visualizzazione.