Aunque map() aplica una función a cada imagen de una colección, la función visita cada imagen de la colección de forma independiente. Por ejemplo, supongamos que quieres calcular una anomalía acumulativa (At) en el momento t a partir de una serie temporal. Para obtener una serie definida recursivamente del tipo At =
f(Imaget, At-1), la asignación no funcionará porque la función
(f) depende del resultado anterior (At-1). Por ejemplo, supongamos que quieres calcular una serie de imágenes de anomalías del índice de vegetación normalizada (NDVI) acumulativas en relación con un modelo de referencia. Sea A0 = 0 y f(Imaget, At-1) = Imaget + At-1, en la que At-1 es la anomalía acumulada hasta el momento t-1 y Imaget es la anomalía en el momento t. Usa imageCollection.iterate() para hacer que esta ImageCollection se defina de forma recursiva. En el siguiente ejemplo, la función accumulate() toma dos parámetros: una imagen en la colección y una lista de todos los resultados anteriores. Con cada llamada a iterate(), la anomalía se agrega a la suma en curso y el resultado se agrega a la lista. El resultado final se pasa al constructor ImageCollection para obtener una nueva secuencia de imágenes:
// 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
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)
La creación de gráficos con estas secuencias indica si el NDVI se estabiliza en relación con las perturbaciones anteriores o si tiene una tendencia a un estado nuevo. Obtén más información sobre los gráficos en Earth Engine en la sección Gráficos.
La función iterada está limitada en las operaciones que puede realizar. Específicamente, no puede modificar variables fuera de la función, no puede imprimir nada ni usar las instrucciones “if” ni “for” de JavaScript. Cualquier resultado que desees recopilar o información intermedia que desees llevar a la siguiente iteración debe estar en el valor que muestra la función. Puedes usar "ee.Algorithms.If()` para realizar operaciones condicionales.