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