Sortowanie tablic jest przydatne do uzyskiwania niestandardowych mozaikowych obrazów, które wymagają zmniejszenia podzbioru pasm obrazu zgodnie z wartościami w innym paśmie. W tym przykładzie dane są sortowane według wartości NDVI, a następnie obliczana jest średnia wartości podzbioru obserwacji w zbiorze zbiorów z najwyższymi wartościami NDVI:
Edytor kodu (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
Podobnie jak w przykładzie modelowania liniowego, oddziel interesujące nas pasma od indeksu sortowania (NDVI) za pomocą funkcji arraySlice() wzdłuż osi pasma. Następnie posortuj interesujące pasma według indeksu sortowania za pomocą funkcji arraySort(). Po posortowaniu pikseli według malejącego NDVI użyj wartości arraySlice() w przypadku imageAxis, aby uzyskać 20% najwyższych pikseli NDVI. Na koniec zastosuj funkcję arrayReduce() wzdłuż imageAxis z redukcją średniej, aby uzyskać średnią najwyższych pikseli NDVI. Ostatnim krokiem jest przekształcenie obrazu macierzy z powrotem w obraz wielopasmowy do wyświetlania.