Module 3: Time Series Processing
+
+
+
+01. Mapping a Function
+ +// Let's see how to take a list of numbers and add 1 to each element
+var myList = ee.List.sequence(1, 10);
+
+// Define a function that takes a number and adds 1 to it
+var myFunction = function(number) {
+ return number + 1;
+}
+print(myFunction(1));
+
+//Re-Define a function using Earth Engine API
+var myFunction = function(number) {
+ return ee.Number(number).add(1);
+}
+
+// Map the function of the list
+var newList = myList.map(myFunction);
+print(newList);
+
+// Let's write another function to square a number
+var squareNumber = function(number) {
+ return ee.Number(number).pow(2)
+}
+var squares = myList.map(squareNumber)
+print(squares)
+
+// Extracting value from a list
+
+var value = newList.get(0)
+print(value)
+
+// Casting
+
+// Let's try to do some computation on the extracted value
+//var newValue = value.add(1)
+//print(newValue)
+
+// You get an error because Earth Engine doesn't know what is the type of 'value'
+// We need to cast it to appropriate type first
+var value = ee.Number(value)
+var newValue = value.add(1)
+print(newValue)
+
+// Dates
+// For any date computation, you should use ee.Date module
+
+var date = ee.Date.fromYMD(2019, 1, 1)
+var futureDate = date.advance(1, 'year')
+print(futureDate)
+
+
+Exercise
+ +var probabilityBands = ee.List([
+ 'water', 'trees', 'grass', 'flooded_vegetation', 'crops',
+ 'shrub_and_scrub', 'built', 'bare', 'snow_and_ice'
+ ]);
+print(probabilityBands);
+
+// Exercise
+// Sort the list 'probabilityBands' in alphabetical order
+// Hint: Look for the function under ee.List module
+
+
+02. Reducers
+ +// Computing stats on a list
+var myList = ee.List.sequence(1, 10);
+print(myList)
+
+// Use a reducer to compute min and max in the list
+var mean = myList.reduce(ee.Reducer.mean());
+print(mean);
+
+var geometry = ee.Geometry.Polygon([[
+ [36.62513180012346, -1.2332928138897847],
+ [36.62499232525469, -1.2339685738967323],
+ [36.62524981732012, -1.234011479288199],
+ [36.62541074986101, -1.2334322564449516]
+]]);
+
+var s2 = ee.ImageCollection("COPERNICUS/S2_HARMONIZED");
+Map.centerObject(geometry)
+
+// Apply a reducer on a image collection
+var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
+ .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
+ .filter(ee.Filter.bounds(geometry))
+ .select('B.*')
+
+print(filtered.size())
+var collMean = filtered.reduce(ee.Reducer.mean());
+print('Reducer on Collection', collMean);
+
+var image = ee.Image('COPERNICUS/S2_HARMONIZED/20190109T074301_20190109T074303_T36MZD')
+var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
+Map.addLayer(image, rgbVis, 'Image')
+Map.addLayer(geometry, {color: 'red'}, 'Farm')
+// If we want to compute the average value in each band,
+// we can use reduceRegion instead
+var stats = image.reduceRegion({
+ reducer: ee.Reducer.mean(),
+ geometry: geometry,
+ scale: 100,
+ maxPixels: 1e10
+ })
+print(stats);
+
+// Result of reduceRegion is a dictionary.
+// We can extract the values using .get() function
+print('Average value in B4', stats.get('B4'))
+
+
+Exercise
+ +var geometry = ee.Geometry.Polygon([[
+ [82.60642647743225, 27.16350437805251],
+ [82.60984897613525, 27.1618529901377],
+ [82.61088967323303, 27.163695288375266],
+ [82.60757446289062, 27.16517483230927]
+]]);
+
+var cropVis = {min: 0, max: 1, bands: ['crops']};
+var dwImage = ee.Image('GOOGLE/DYNAMICWORLD/V1/20190223T050811_20190223T051829_T44RPR')
+var probabilityBands = [
+ 'water', 'trees', 'grass', 'flooded_vegetation', 'crops',
+ 'shrub_and_scrub', 'built', 'bare', 'snow_and_ice'
+ ];
+var image = dwImage.select(probabilityBands);
+
+Map.addLayer(image, cropVis, 'DW Crop Probabilities')
+Map.addLayer(geometry, {color: 'red'}, 'Farm')
+Map.centerObject(geometry)
+
+
+// Exercise
+// The following code calculates the average probability
+// value of each band within the farm
+// Change it to calculate the minimum and maximum values
+var stats = image.reduceRegion({
+ reducer: ee.Reducer.mean(),
+ geometry: geometry,
+ scale: 10,
+ maxPixels: 1e10
+ })
+print(stats)
+
+
+03. Computation on ImageCollections
+ +var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
+var lsib = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
+
+var country = 'Kenya';
+var selected = lsib.filter(ee.Filter.eq('country_na', country));
+var geometry = selected.geometry();
+Map.centerObject(geometry);
+
+var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
+
+var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
+ .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
+ .filter(ee.Filter.bounds(geometry));
+
+var composite = filtered.median();
+Map.addLayer(composite.clip(geometry), rgbVis, 'Kenya Composite');
+
+
+// Write a function that computes NDVI for an image and adds it as a band
+function addNDVI(image) {
+ var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
+ return image.addBands(ndvi);
+}
+
+// Map the function over the collection
+var withNdvi = filtered.map(addNDVI);
+
+var composite = withNdvi.median();
+
+var ndviComposite = composite.select('ndvi');
+
+var palette = [
+ 'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
+ '74A901', '66A000', '529400', '3E8601', '207401', '056201',
+ '004C00', '023B01', '012E01', '011D01', '011301'];
+
+var ndviVis = {min:0, max:0.5, palette: palette };
+Map.addLayer(ndviComposite.clip(geometry), ndviVis, 'ndvi');
+
+
+Exercise
+ +var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
+var lsib = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
+
+var country = 'Kenya';
+var selected = lsib.filter(ee.Filter.eq('country_na', country));
+var geometry = selected.geometry();
+Map.centerObject(geometry);
+
+var rgbVis = {min: 0.0, max: 3000, bands: ['B4', 'B3', 'B2']};
+
+var filtered = s2.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))
+ .filter(ee.Filter.date('2019-01-01', '2020-01-01'))
+ .filter(ee.Filter.bounds(geometry));
+
+
+var composite = filtered.median();
+Map.addLayer(composite.clip(geometry), rgbVis, 'Kenya Composite');
+
+// This function calculates both NDVI an d NDWI indices
+// and returns an image with 2 new bands added to the original image.
+function addIndices(image) {
+ var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
+ var ndwi = image.normalizedDifference(['B3', 'B8']).rename('ndwi');
+ return image.addBands(ndvi).addBands(ndwi);
+}
+
+// Map the function over the collection
+var withIndices = filtered.map(addIndices);
+
+// Composite
+var composite = withIndices.median();
+print(composite);
+
+// Extract the 'ndwi' band
+// Clip and display a NDWI map
+// use the palette ['white', 'blue']
+// Hint: Use .select() function to select a band
+
+
+04. Time-Series Charts
+ +var s2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED');
+
+var geometry = ee.Geometry.Polygon([[
+ [36.62513180012346, -1.2332928138897847],
+ [36.62499232525469, -1.2339685738967323],
+ [36.62524981732012, -1.234011479288199],
+ [36.62541074986101, -1.2334322564449516]]
+]);
+
+Map.addLayer(geometry, {color: 'red'}, 'Farm');
+Map.centerObject(geometry);
+
+var startDate = ee.Date.fromYMD(2019, 1, 1);
+var endDate = startDate.advance(1, 'year');
+
+var filtered = s2
+ .filter(ee.Filter.date(startDate, endDate))
+ .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 35))
+ .filter(ee.Filter.bounds(geometry));
+
+// Write a function for Cloud masking
+function maskS2clouds(image) {
+ var qa = image.select('QA60');
+ var cloudBitMask = 1 << 10;
+ var cirrusBitMask = 1 << 11;
+ var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(
+ qa.bitwiseAnd(cirrusBitMask).eq(0));
+ return image.updateMask(mask)
+ .select('B.*')
+ .copyProperties(image, ['system:time_start']);
+}
+
+var filtered = filtered.map(maskS2clouds);
+// Write a function that computes NDVI for an image and adds it as a band
+function addNDVI(image) {
+ var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
+ return image.addBands(ndvi);
+}
+
+// Map the function over the collection
+var withNdvi = filtered.map(addNDVI);
+
+// Display a time-series chart
+var chart = ui.Chart.image.series({
+ imageCollection: withNdvi.select('ndvi'),
+ region: geometry,
+ reducer: ee.Reducer.mean(),
+ scale: 10
+}).setOptions({
+ lineWidth: 0.5,
+ pointSize: 1,
+ title: 'NDVI Time Series',
+ interpolateNulls: true,
+ vAxis: {title: 'NDVI', viewWindow: {min:0, max:1}},
+ hAxis: {title: '', format: 'YYYY-MMM'}
+ })
+print(chart);
+
+Exercise
+ + +
+
+
+05. Dynamic World Time-Series charts
+
+
+
+
+
++Dynamic World Time-Series +
+// Charting Class Probabilities Over Time
+
+var geometry = ee.Geometry.Polygon([[
+ [36.62513180012346, -1.2332928138897847],
+ [36.62499232525469, -1.2339685738967323],
+ [36.62524981732012, -1.234011479288199],
+ [36.62541074986101, -1.2334322564449516]]
+]);
+
+Map.addLayer(geometry, {color: 'red'}, 'Selected Location')
+Map.centerObject(geometry, 16)
+
+// Filter the Dynamic World collection for the time period and
+// location of interest.
+var startDate = ee.Date.fromYMD(2019, 1, 1);
+var endDate = startDate.advance(1, 'year');
+
+var dw = ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')
+ .filter(ee.Filter.date(startDate, endDate))
+ .filter(ee.Filter.bounds(geometry))
+
+var probabilityBands = [
+ 'water', 'trees', 'grass', 'flooded_vegetation', 'crops',
+ 'shrub_and_scrub', 'built', 'bare', 'snow_and_ice'
+ ];
+
+// Select all probability bands
+var dwTimeSeries = dw.select(probabilityBands)
+
+// Plot the time series for a single class
+var chart = ui.Chart.image.series({
+ imageCollection: dwTimeSeries.select('crops'),
+ region: geometry,
+ scale: 10
+}).setOptions({
+ lineWidth: 0.5,
+ pointSize: 1,
+ title: 'Dynamic World Class Probability (Crops)',
+ interpolateNulls: true,
+ vAxis: {title: 'Probability', viewWindow: {min:0, max:1}},
+ hAxis: {title: '', format: 'YYYY-MMM'},
+ series: {
+ 0: {color: 'green'}
+ }
+ })
+print(chart)
+
+// Plot the time series for a multiple classes
+
+var chart = ui.Chart.image.series({
+ imageCollection: dwTimeSeries.select(['crops', 'shrub_and_scrub']),
+ region: geometry,
+ scale: 10
+}).setOptions({
+ lineWidth: 0.5,
+ pointSize: 1,
+ title: 'Dynamic World Class Probabilities',
+ interpolateNulls: true,
+ vAxis: {title: 'Probability', viewWindow: {min:0, max:1}},
+ hAxis: {title: '', format: 'YYYY-MMM'},
+ series: {
+ 0: {color: 'green'},
+ 1: {color: 'orange'}
+ },
+ legend: {
+ position: 'top'
+ }
+ });
+print(chart);
+
+Exercise
+ + +
+
+
+Quiz - Module 3
+This is a short quiz to test your understanding of the Module 3 +concepts.
+ ++