added ws202425 courses

2024-11-14 13:11:04 +01:00
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+/////// NDVI time series over Landsat 5,7 and 8
+
+
+// to be defined
+Map.centerObject(roi, 12)
+
+var startyear = '1984-01-01'
+var endyear = '2021-12-31'
+
+var startmonth = 9
+var endmonth = 9
+
+//////////////////////////////////////////////////////////////////////////////////////////////////////
+
+//// Functions that are applied to every image of a collection
+// Due to the specific sensor characteristics of TM and OLI, specific function are needed for the sensors
+
+// 1) Cloud masking
+// 2) Rename Bands
+// 3) Add NDVI and two bands to save the result of the linear regression (gain and constant)
+
+// 1) Cloud masking
+// Function to cloud mask from the pixel_qa band of Landsat 5 and 7 SR data.
+// (From the Code Editor Examples > Cloud Masking)
+function maskL457sr(image) {
+
+  // Bit 0 - Fill
+  // Bit 1 - Dilated Cloud
+  // Bit 2 - Cirrus
+  // Bit 3 - Cloud
+  // Bit 4 - Cloud Shadow
+  var qa = image.select('QA_PIXEL');
+  
+  var cloud = qa.bitwiseAnd(1 << 0)
+          .or(qa.bitwiseAnd(1 << 1))
+          .or(qa.bitwiseAnd(1 << 2))
+          .or(qa.bitwiseAnd(1 << 3))
+          .or(qa.bitwiseAnd(1 << 4));
+
+
+  // The flags should equal zero, indicating clear conditions.
+  var mask = cloud.eq(0);
+ 
+    // Apply the scaling factors to the appropriate bands.
+  var opticalBands = image.select('SR_B1',"SR_B2", "SR_B3","SR_B4","SR_B5","SR_B7").multiply(0.0000275).add(-0.2);
+
+  // Replace the original bands with the scaled ones and apply the masks.
+  return image.addBands(opticalBands, null, true)
+      .updateMask(mask)
+      .copyProperties(image, ['system:time_start']);
+}
+  
+
+
+// Function to cloud mask from the pixel_qa band of Landsat 8 SR data.
+
+function maskL8sr(image) {
+  // Bit 0 - Fill
+  // Bit 1 - Dilated Cloud
+  // Bit 2 - Cirrus
+  // Bit 3 - Cloud
+  // Bit 4 - Cloud Shadow
+  var qa = image.select('QA_PIXEL');
+  
+  var cloud = qa.bitwiseAnd(1 << 0)
+          .or(qa.bitwiseAnd(1 << 1))
+          .or(qa.bitwiseAnd(1 << 2))
+          .or(qa.bitwiseAnd(1 << 3))
+          .or(qa.bitwiseAnd(1 << 4));
+
+
+  // The flags should equal zero, indicating clear conditions.
+  var mask = cloud.eq(0);
+ 
+    // Apply the scaling factors to the appropriate bands.
+  var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
+
+  // Replace the original bands with the scaled ones and apply the masks.
+  return image.addBands(opticalBands, null, true)
+      .updateMask(mask)
+      .copyProperties(image, ['system:time_start']);
+}
+
+
+// 2) Rename Bands
+
+// Function to rename bands of Landsat 5 and 7 to match Landsat 8
+function renameBandsTM(image) {
+    var bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B7'];
+    var new_bands = ['B', 'G', 'R', 'NIR', 'SWIR1', 'SWIR2'];
+    return image.select(bands).rename(new_bands).copyProperties(image, ['system:time_start']);
+}
+
+// Function to rename bands of Landsat 8 to match Landsat 5 and 8
+function renameBandsOLI(image){
+    var bands = ['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'];
+    var new_bands = ['B', 'G', 'R', 'NIR', 'SWIR1', 'SWIR2'];
+    return image.select(bands).rename(new_bands).copyProperties(image, ['system:time_start']);
+}
+
+
+// 3) Add NDVI and two bands to save the result of the linear regression (gain and constant)
+
+// This field contains UNIX time in milliseconds.
+var timeField = 'system:time_start';
+
+
+function addVariables(image) {
+  // Compute time in fractional years since the epoch.
+  var date = ee.Date(image.get(timeField));
+  var years = date.difference(ee.Date('1970-01-01'), 'year');
+  // Return the image with the added bands.
+  return image
+    // Add an NDVI band.
+    .addBands(image.normalizedDifference(['NIR', 'R']).rename('NDVI'))
+    // Add a time band.
+    .addBands(ee.Image(years).rename('gain'))
+    .float()
+    // Add a constant band.
+    .addBands(ee.Image.constant(1));
+}
+
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+// Create collections of Landsat 5, 7 and 8
+
+// a) using filters (calendar range and ROI)
+// b) using the three functions (cloud masking, rename bands, add NDVI)
+// c) Merge the collections --> combine the three collections to form a time series over the entire time span
+
+// Landat 5 surface reflection data
+var L5coll = ee.ImageCollection('LANDSAT/LT05/C02/T1_L2')
+.filterBounds(roi)
+.filter(ee.Filter.calendarRange(startmonth,endmonth, 'month'))
+.filterDate(startyear, endyear)
+.map(maskL457sr)
+.map(renameBandsTM)
+.map(addVariables);
+
+// Landat 7 surface reflection data
+var L7coll = ee.ImageCollection('LANDSAT/LE07/C02/T1_L2')
+.filter(ee.Filter.calendarRange(startmonth,endmonth, 'month'))
+.filterDate(startyear, endyear)
+.filterBounds(roi)
+.map(maskL457sr)
+.map(renameBandsTM)
+.map(addVariables);
+
+
+// Landat 8 surface reflection data, rename the band names. See USGS pages for more info
+var L8coll = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
+.filter(ee.Filter.calendarRange(startmonth,endmonth, 'month'))
+.filterDate(startyear, endyear)
+.filterBounds(roi)
+.map(maskL8sr)
+.map(renameBandsOLI)
+.map(addVariables)
+
+
+
+// merge L5, L7 & L8
+var collection_merge = ee.ImageCollection(L5coll.merge(L7coll.merge(L8coll)));
+print (collection_merge)
+
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////
+///////////////////////////////////////////////////////////////////////////////////////////////////////
+
+// Plot time series for an ROI
+
+// Plot a time series of NDVI at a single location.
+var l8Chart = ui.Chart.image.series(collection_merge.select('NDVI'), roi)
+    .setChartType('ScatterChart')
+    .setOptions({
+      title: 'Landsat NDVI time series at ROI',
+      trendlines: {0: {
+        color: 'CC0000'
+      }},
+      lineWidth: 1,
+      pointSize: 3,
+    });
+print(l8Chart);
+
+
+// Display the NDVI of the time series (mean, first image, last image)
+Map.addLayer(collection_merge.select("NDVI").mean(),{min:0, max:1}, "Mean NDVI Landsat 8")
+Map.addLayer(collection_merge.select("NDVI").first(),{min:0, max:1}, "Oldest image within time series")
+Map.addLayer(collection_merge.sort("system:time_start",false).select("NDVI").first(),{min:0, max:1}, "Newest image within time series")
+
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////
+
+// Trend analysis 
+// Calculate the constant and gain of the linear regression of the NDVI on a per-pixel basis
+
+Map.centerObject(roi, 11)
+
+// List of the independent variable names
+var independents = ee.List(['constant', 'gain']);
+
+// Name of the dependent variable.
+var dependent = ee.String('NDVI');
+
+// Compute a linear trend.  This will have two bands: 'residuals' and 
+// a 2x1 band called 'coefficients' (columns are for dependent variables).
+var trend = collection_merge.select(independents.add(dependent))
+    .reduce(ee.Reducer.linearRegression(independents.length(), 1));
+// Map.addLayer(trend, {}, 'trend array image');
+
+// Flatten the coefficients into a 2-band image
+var coefficients = trend.select('coefficients')
+  .arrayProject([0])
+  .arrayFlatten([independents]);
+
+// Plot the two bands as gray scale
+Map.addLayer(coefficients.select('gain'), {min:-0.01, max: 0.01}, "gain")
+Map.addLayer(coefficients.select('constant'), {},"constant")
+
+// Plot the two bands in a RGB-False colour Composite (positive trends = greenish, negative trends = reddish)
+Map.addLayer(coefficients,
+    {min: 0, max: [-0.02, 0.02, 0.7], bands: ['gain', 'gain', 'constant']},
+    'Trend analysis')