Meskipun map() menerapkan fungsi ke setiap gambar dalam koleksi, fungsi ini mengunjungi setiap gambar dalam koleksi secara independen. Misalnya, Anda
ingin menghitung anomali kumulatif (At) pada waktu t dari deret
waktu. Untuk mendapatkan deret yang ditentukan secara rekursif dalam bentuk At =
f(Imaget, At-1), pemetaan tidak akan berfungsi karena fungsi
(f) bergantung pada hasil sebelumnya (At-1). Misalnya, Anda ingin menghitung serangkaian gambar anomali Normalized Difference Vegetation Index (NDVI) kumulatif yang relatif terhadap dasar pengukuran. Misalkan A0 = 0 dan
f(Imaget, At-1) = Imaget + At-1
dengan At-1 adalah anomali kumulatif hingga waktu t-1 dan
Imaget adalah anomali pada waktu t. Gunakan
imageCollection.iterate() untuk membuat ImageCollection yang ditentukan secara rekursif. Dalam contoh berikut, fungsi
accumulate() menggunakan dua parameter: gambar dalam koleksi, dan daftar
semua output sebelumnya. Dengan setiap panggilan ke iterate(), anomali
ditambahkan ke jumlah yang sedang berjalan dan hasilnya ditambahkan ke daftar. Hasil akhir
diteruskan ke konstruktor ImageCollection untuk mendapatkan urutan gambar baru:
Editor Kode (JavaScript)
// Load MODIS EVI imagery. var collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI'); // Define reference conditions from the first 10 years of data. var reference = collection.filterDate('2001-01-01', '2010-12-31') // Sort chronologically in descending order. .sort('system:time_start', false); // Compute the mean of the first 10 years. var mean = reference.mean(); // Compute anomalies by subtracting the 2001-2010 mean from each image in a // collection of 2011-2014 images. Copy the date metadata over to the // computed anomaly images in the new collection. var series = collection.filterDate('2011-01-01', '2014-12-31').map(function(image) { return image.subtract(mean).set('system:time_start', image.get('system:time_start')); }); // Display cumulative anomalies. Map.setCenter(-100.811, 40.2, 5); Map.addLayer(series.sum(), {min: -60000, max: 60000, palette: ['FF0000', '000000', '00FF00']}, 'EVI anomaly'); // Get the timestamp from the most recent image in the reference collection. var time0 = reference.first().get('system:time_start'); // Use imageCollection.iterate() to make a collection of cumulative anomaly over time. // The initial value for iterate() is a list of anomaly images already processed. // The first anomaly image in the list is just 0, with the time0 timestamp. var first = ee.List([ // Rename the first band 'EVI'. ee.Image(0).set('system:time_start', time0).select([0], ['EVI']) ]); // This is a function to pass to Iterate(). // As anomaly images are computed, add them to the list. var accumulate = function(image, list) { // Get the latest cumulative anomaly image from the end of the list with // get(-1). Since the type of the list argument to the function is unknown, // it needs to be cast to a List. Since the return type of get() is unknown, // cast it to Image. var previous = ee.Image(ee.List(list).get(-1)); // Add the current anomaly to make a new cumulative anomaly image. var added = image.add(previous) // Propagate metadata to the new image. .set('system:time_start', image.get('system:time_start')); // Return the list with the cumulative anomaly inserted. return ee.List(list).add(added); }; // Create an ImageCollection of cumulative anomaly images by iterating. // Since the return type of iterate is unknown, it needs to be cast to a List. var cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first))); // Predefine the chart titles. var title = { title: 'Cumulative EVI anomaly over time', hAxis: {title: 'Time'}, vAxis: {title: 'Cumulative EVI anomaly'}, }; // Chart some interesting locations. var pt1 = ee.Geometry.Point(-65.544, -4.894); print('Amazon rainforest:', ui.Chart.image.series( cumulative, pt1, ee.Reducer.first(), 500).setOptions(title)); var pt2 = ee.Geometry.Point(116.4647, 40.1054); print('Beijing urbanization:', ui.Chart.image.series( cumulative, pt2, ee.Reducer.first(), 500).setOptions(title)); var pt3 = ee.Geometry.Point(-110.3412, 34.1982); print('Arizona forest disturbance and recovery:', ui.Chart.image.series( cumulative, pt3, ee.Reducer.first(), 500).setOptions(title));
import ee import geemap.core as geemap
Colab (Python)
import altair as alt # Load MODIS EVI imagery. collection = ee.ImageCollection('MODIS/006/MYD13A1').select('EVI') # Define reference conditions from the first 10 years of data. reference = collection.filterDate('2001-01-01', '2010-12-31').sort( # Sort chronologically in descending order. 'system:time_start', False, ) # Compute the mean of the first 10 years. mean = reference.mean() # Compute anomalies by subtracting the 2001-2010 mean from each image in a # collection of 2011-2014 images. Copy the date metadata over to the # computed anomaly images in the new collection. series = collection.filterDate('2011-01-01', '2014-12-31').map( lambda image: image.subtract(mean).set( 'system:time_start', image.get('system:time_start') ) ) # Display cumulative anomalies. m = geemap.Map() m.set_center(-100.811, 40.2, 5) m.add_layer( series.sum(), {'min': -60000, 'max': 60000, 'palette': ['FF0000', '000000', '00FF00']}, 'EVI anomaly', ) display(m) # Get the timestamp from the most recent image in the reference collection. time_0 = reference.first().get('system:time_start') # Use imageCollection.iterate() to make a collection of cumulative anomaly over time. # The initial value for iterate() is a list of anomaly images already processed. # The first anomaly image in the list is just 0, with the time_0 timestamp. first = ee.List([ # Rename the first band 'EVI'. ee.Image(0) .set('system:time_start', time_0) .select([0], ['EVI']) ]) # This is a function to pass to Iterate(). # As anomaly images are computed, add them to the list. def accumulate(image, list): # Get the latest cumulative anomaly image from the end of the list with # get(-1). Since the type of the list argument to the function is unknown, # it needs to be cast to a List. Since the return type of get() is unknown, # cast it to Image. previous = ee.Image(ee.List(list).get(-1)) # Add the current anomaly to make a new cumulative anomaly image. added = image.add(previous).set( # Propagate metadata to the new image. 'system:time_start', image.get('system:time_start'), ) # Return the list with the cumulative anomaly inserted. return ee.List(list).add(added) # Create an ImageCollection of cumulative anomaly images by iterating. # Since the return type of iterate is unknown, it needs to be cast to a List. cumulative = ee.ImageCollection(ee.List(series.iterate(accumulate, first))) # Predefine the chart titles. title = 'Cumulative EVI anomaly over time' # Chart some interesting locations. def display_chart(region, collection): reduced = ( collection.filterBounds(region) .sort('system:time_start') .map( lambda image: ee.Feature( None, image.reduceRegion(ee.Reducer.first(), region, 500).set( 'time', image.get('system:time_start') ), ) ) ) reduced_dataframe = ee.data.computeFeatures( {'expression': reduced, 'fileFormat': 'PANDAS_DATAFRAME'} ) alt.Chart(reduced_dataframe).mark_line().encode( alt.X('time:T').title('Time'), alt.Y('EVI:Q').title('Cumulative EVI anomaly'), ).properties(title=title).display() pt_1 = ee.Geometry.Point(-65.544, -4.894) display('Amazon rainforest:') display_chart(pt_1, cumulative) pt_2 = ee.Geometry.Point(116.4647, 40.1054) display('Beijing urbanization:') display_chart(pt_2, cumulative) pt_3 = ee.Geometry.Point(-110.3412, 34.1982) display('Arizona forest disturbance and recovery:') display_chart(pt_3, cumulative)
Membuat diagram urutan ini menunjukkan apakah NDVI stabil dibandingkan gangguan sebelumnya atau apakah NDVI cenderung ke status baru. Pelajari diagram di Earth Engine lebih lanjut dari bagian Diagram.
Fungsi yang di-iterasi dibatasi dalam operasi yang dapat dilakukannya. Secara khusus, fungsi ini tidak dapat mengubah variabel di luar fungsi; tidak dapat mencetak apa pun; tidak dapat menggunakan pernyataan 'if' atau 'for' JavaScript. Setiap hasil yang ingin Anda kumpulkan atau informasi perantara yang ingin Anda bawa ke iterasi berikutnya harus berada dalam nilai return fungsi. Anda dapat menggunakan `ee.Algorithms.If()` untuk melakukan operasi kondisional.