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productivity.js
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// Required Data Inputs
// ===================
// * USGS/NASA's Landsat 4 surface reflectance tier 1 dataset (August 1982 - December 1993)
// * USGS/NASA's Landsat 5 surface reflectance tier 1 dataset (January 1, 1984 - May 5, 2012)
// * USGS/NASA's Landsat 7 surface reflectance tier 1 dataset (January 1, 1999 - December 31, 2019)
// * USGS/NASA's Landsat 8 surface reflectance tier 1 dataset (April 11, 2013 - December 31, 2019)
// * Study Area Polygon
var countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017");
var country = 'Egypt';
var ALGO = "MEAN"; // PERCENTILE75 // PERCENTILE65 // PERCENTILE60 // MEDIAN
var cloudCoveragePercentage = 80;
var studyArea = countries.filter(ee.Filter.eq('country_na',country ))
if (country === 'Egypt'){
studyArea = ee.FeatureCollection("users/derickongeri/EgyptGrids")//.filter(ee.Filter.eq('id',3))
var tileList = studyArea.aggregate_array('id').distinct();
tileList = tileList.getInfo();
print(tileList)
}
Map.centerObject(studyArea);
var trend_baselinePeriod = ee.List.sequence(2000, 2015, 1).getInfo(); //baseline years for reporting trend 2000-2015 (16 years)
var trend_reportingPeriod = ee.List.sequence(2005, 2020, 1).getInfo(); //reporting period for trend 2005 - 2020 (16 years)
var state_baselinePeriod = ee.List.sequence(2000, 2012, 1).getInfo(); // first 13year for reporting state 2000 - 2012
var state_baselinePeriod_final = ee.List.sequence(2013, 2015, 1).getInfo(); // final 3 yars of the reporing period 2013 - 2015
var state_reportingPeriod = ee.List.sequence(2005, 2017, 1).getInfo(); // first 13year of the reporting period
var state_reportingPeriod_final = ee.List.sequence(2018, 2020, 1).getInfo(); // last 3 years of the roporting period
function yearlyNDVI(year){
var start_date = year+ '-01-01';
var end_date = year+ '-12-31';
//--------------------------------------------------------------------
// Landsat 4, 5, 7 cloudmask
//--------------------------------------------------------------------
// If the cloud bit (5) is set and the cloud confidence (7) is high
// or the cloud shadow bit is set (3), then it's a bad pixel.
var cloudMaskL7 = function(image) {
var qa = image.select('pixel_qa');
var cloud = qa.bitwiseAnd(1 << 5)
.and(qa.bitwiseAnd(1 << 7))
.or(qa.bitwiseAnd(1 << 3));
// Remove edge pixels that don't occur in all bands
//var mask2 = image.mask().reduce(ee.Reducer.min())//.focal_min(300,'square','meters').eq(0);
//var mask2 = image.select('B4').reduce(ee.Reducer.min()).gt(0)//.focal_min(500,'square','meters');
// Remove edge pixels that don't occur in all bands
var mask3 =
(image.select('B3').gt(100))
.and(image.select('B4').gt(100))
.and(image.select('B4').lt(10000))
.and(image.select('B3').lt(10000))
return image.updateMask(cloud.not()).updateMask(mask3)//.updateMask(mask2)//.clip(image.geometry().buffer(-5000))//.or(mask3));
};
var cloudMaskL45 = function(image) {
var qa = image.select('pixel_qa');
var cloud = qa.bitwiseAnd(1 << 5)
.and(qa.bitwiseAnd(1 << 7))
.or(qa.bitwiseAnd(1 << 3));
// Remove edge pixels that don't occur in all bands
//var mask2 = image.mask().reduce(ee.Reducer.min());
var mask2 =
(image.select('B3').gt(100))
.and(image.select('B4').gt(100))
.and(image.select('B4').lt(10000))
.and(image.select('B3').lt(10000))
return (image.updateMask(cloud.not()).updateMask(mask2))//.clip(image.geometry().buffer(-5000))//.updateMask(mask2);
};
//--------------------------------------------------------------------
// Landsat 8 cloudmask
//--------------------------------------------------------------------
// Bits 3 and 5 are cloud shadow and cloud, respectively.
function maskL8sr(image) {
var cloudShadowBitMask = (1 << 3);
var cloudsBitMask = (1 << 5);
// Get the pixel QA band.
var qa = image.select('pixel_qa');
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
.and(qa.bitwiseAnd(cloudsBitMask).eq(0));
var mask2 =
(image.select('B5').gt(100))
.and(image.select('B4').gt(100))
.and(image.select('B5').lt(10000))
.and(image.select('B4').lt(10000))
//var mask2 = image.mask().reduce(ee.Reducer.min()).focal_min(500,'square','meters');
//return image
return image.updateMask(mask).updateMask(mask2)//.clip(image.geometry().buffer(-5000));
}
// Apply Cloudmask to L4.5.7
var L4 = ee.ImageCollection("LANDSAT/LT04/C01/T1_SR")
.filterDate(start_date, end_date)
.filter(ee.Filter.lessThan('CLOUD_COVER_LAND', cloudCoveragePercentage))
.filterBounds(studyArea)
.map(cloudMaskL45)
.select(['B3', 'B4'], ['RED', 'NIR']);
var L5 = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR')
.filterDate(start_date, end_date)
.filter(ee.Filter.lessThan('CLOUD_COVER_LAND', cloudCoveragePercentage))
.filterBounds(studyArea)
.map(cloudMaskL45)
.select(['B3', 'B4'], ['RED', 'NIR']);
var L7a = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterDate('1999-01-01', '2003-04-01')
.filterDate(start_date, end_date)
.filter(ee.Filter.lessThan('CLOUD_COVER_LAND', 100))
.filterBounds(studyArea)
.map(cloudMaskL7)
.select(['B3', 'B4'], ['RED', 'NIR']);
var L7b = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterDate('2012-01-01', '2013-12-31')
.filterDate(start_date, end_date)
.filter(ee.Filter.lessThan('CLOUD_COVER_LAND', 100))
.filterBounds(studyArea)
.map(cloudMaskL7)
.select(['B3', 'B4'], ['RED', 'NIR']);
var L7 = L7a.merge(L7b);
var L8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterDate(start_date, end_date)
.filter(ee.Filter.lessThan('CLOUD_COVER', cloudCoveragePercentage))
.filterBounds(studyArea)
//.filterBounds(AOI)
.map(maskL8sr)
.select(['B4', 'B5'], ['RED', 'NIR']);
//--------------------------------------------------------------------
// Merge Landsat 4, 5, 8 imagery collections and filter all by date/place
//--------------------------------------------------------------------
var L4578 = L4.merge(L5).merge(L7).merge(L8);
//--------------------------------------------------------------------
// Create NDVI Collection
//--------------------------------------------------------------------
var NDVI = function(image) {
return image.normalizedDifference(['NIR', 'RED']).rename('NDVI'+year);
//return image.addBands(ndvi);
};
if (ALGO=='MEDIAN'){
var suffix = 'median';
var annualNDVI = L4578.map(NDVI).median().clip(studyArea).rename(suffix+'NDVI');
}
if (ALGO=='MEAN'){
var suffix = 'mean';
var annualNDVI = L4578.map(NDVI).mean().clip(studyArea).rename(suffix+'NDVI');
}
if (ALGO=='PERCENTILE75'){
var suffix = '75pc';
var annualNDVI = L4578.map(NDVI).reduce(ee.Reducer.percentile([75])).clip(studyArea).rename(suffix+'NDVI'+'_'+year);
}
if (ALGO=='PERCENTILE65'){
var suffix = '65pc';
var annualNDVI = L4578.map(NDVI).reduce(ee.Reducer.percentile([65])).clip(studyArea).rename(suffix+'NDVI'+'_'+year);
}
//mask out permanent water bodies
var surfaceWater_dataset = ee.ImageCollection('JRC/GSW1_3/YearlyHistory').filterMetadata('year', 'equals', year);
var surfaceWaterYearly = ee.Image(surfaceWater_dataset.first());
var swater_mask = surfaceWaterYearly.updateMask(surfaceWaterYearly.eq(3));
var annualNDVI = annualNDVI.where(swater_mask,0)
return annualNDVI
}
//pass computed yearlyNDVI to collection
function ndviCollection(listYears){
var ImgColl_NDVI = ee.ImageCollection.fromImages(listYears.map(yearlyNDVI))
return ImgColl_NDVI
}
//coputing NDVI trends for reporting and baseline periods.
var trendBaseline_NDVIcollection = ndviCollection(trend_baselinePeriod);
var trendReporting_NDVIcollection = ndviCollection(trend_reportingPeriod);
//Computing NDVI state for baseline and reporting periods
var stateBline_NDVIcollection = ndviCollection(state_baselinePeriod);
var stateBline_NDVIcollection_final = ndviCollection(state_baselinePeriod_final);
var stateReporting_NDVIcollection = ndviCollection(state_baselinePeriod);
var stateReporting_NDVIcollection_final = ndviCollection(state_baselinePeriod_final);
//=============================================================================================================
// Function to compute state
function productivityState(coll1, coll2){
//compute NDVI mean and standard deviation for baseline period
var coll_1_mean = coll1.mean().rename('mean');
var coll_1_stdDev = coll1.reduce(ee.Reducer.stdDev()).rename('stdDev');
var coll_2_mean = coll2.mean().rename('mean');
//compute z-statistics
var zstats = coll_2_mean.expression(
'( X - U )/(SD/3**0.5)',{
'X':coll_2_mean.select('mean'),
'U':coll_1_mean.select('mean'),
'SD':coll_1_stdDev.select('stdDev'),
}).rename('zstats')
return zstats
}
//===============================================================================================================
var palette = ['red', 'white', 'green'];
var productivityState_baseline = productivityState(stateBline_NDVIcollection, stateBline_NDVIcollection_final);
var productivityState_reporting = productivityState(stateReporting_NDVIcollection, stateReporting_NDVIcollection_final)
//var trendBaselinePeriod = trendBaseline_NDVIcollection.reduce(ee.Reducer.kendallsCorrelation());
//var trendReportingPeriod = trendReporting_NDVIcollection.reduce(ee.Reducer.kendallsCorrelation());
//print(trendReportingPeriod)
// ============================================================================================================================
// adding the layers to map
// Map.addLayer(productivityState_baseline, {palette:palette}, 'Baseline State');
// Map.addLayer(productivityState_reporting, {palette:palette}, 'reporting State');
// Map.addLayer( trendBaselinePeriod.select('meanNDVI_p-value'), {palette:palette}, 'Baseline p-value')
// Map.addLayer( trendReportingPeriod.select('meanNDVI_p-value'), {palette:palette}, 'Reporting p-value')
// Map.addLayer( trendBaselinePeriod.select('meanNDVI_tau'), {palette:palette}, 'Baseline tau')
// Map.addLayer( trendReportingPeriod.select('meanNDVI_tau'), {palette:palette}, 'Reporting tau')
// =========================================================================================================================
/*
// Seperate result into 5 classes
var thresholds = ee.Image([-1.96, -1.28, 1.28, 1.96, 3]);
var classified = zstats.lt(thresholds).reduce('sum').toInt();
//Map.addLayer(classified, {}, 'state classified');
var ndvi_visualization = {
min: -0.22789797020331423,
max: 0.6575894075894075,
palette: 'FFFFFF, CE7E45, DF923D, F1B555, FCD163, 99B718, 74A901, 66A000, 529400,' +
'3E8601, 207401, 056201, 004C00, 023B01, 012E01, 011D01, 011301'
};*/
//======================================MannKendall test=========================================
function mannKendall(imgCollection){
var afterFilter = ee.Filter.lessThan({
leftField: 'system:index',
rightField: 'system:index'
});
var joined = ee.ImageCollection(ee.Join.saveAll('after').apply({
primary: imgCollection,
secondary: imgCollection,
condition: afterFilter
}));
var sign = function(i, j) { // i and j are images
return ee.Image(j).neq(i) // Zero case
.multiply(ee.Image(j).subtract(i));
};
var kendall = ee.ImageCollection(joined.map(function(current) {
var afterCollection = ee.ImageCollection.fromImages(current.get('after'));
return afterCollection.map(function(image) {
// The unmask is to prevent accumulation of masked pixels that
// result from the undefined case of when either current or image
// is masked. It won't affect the sum, since it's unmasked to zero.
return ee.Image(sign(current, image))
});
// Set parallelScale to avoid User memory limit exceeded.
}).flatten()).reduce('sum', 2);
var threshold_z_score = [5, 1.96, 1.28, -1.28, -1.96]
var classiffied_Kendall = kendall.lt(threshold_z_score).reduce('sum').toInt()
return classiffied_Kendall
}
var trendBaselinePeriod = mannKendall(trendBaseline_NDVIcollection);
//var reportingKendall = mannKendall(collReporting);
Map.addLayer(trendBaselinePeriod, {min:0, max:5, palette:['1b8607','b8ff68','ffffff','ffc443','ff1919']}, 'Baseline p-value');
/*
//==========================================================================================
function kendall_test(imageCollection){
var TimeSeriesList = imageCollection.toList(13)
var NumberOfItems = TimeSeriesList.length().getInfo()
var ConcordantArray = ee.List([])
var DiscordantArray = ee.List([])
for (var k = 0; k < 14; k++){
var CurrentImage = ee.Image(TimeSeriesList.get(k))
for (var l = k+1; l < NumberOfItems-1; l++){
var nextImage = ee.Image(TimeSeriesList.get(l))
var Concordant = CurrentImage.lt(nextImage)
var ConcordantArray = ConcordantArray.add(Concordant)
var Discordant = CurrentImage.gt(nextImage)
var DiscordantArray = DiscordantArray.add(Discordant)}
}
var ConcordantSum = ee.ImageCollection(ConcordantArray).sum().rename('csum')
var DiscordantSum = ee.ImageCollection(DiscordantArray).sum().rename('dsum')
var MKSstat = ConcordantSum.subtract(DiscordantSum)//.addBands([ConcordantSum, DiscordantSum])
return MKSstat
}
var kendall_stats = kendall_test(ndviCollection);
// print(kendall_stats)
// Stretch this as necessary.
ui.root.clear()
var map1 = ui.Map()
var map2 = ui.Map()
var map3 = ui.Map()
var map4 = ui.Map()
map1.addLayer(trend.select('meanNDVI_tau'), {min:-1, max:1, palette: palette}, 'trend'); //palette: palette
map2.addLayer(kendall_stats, {min:-1, max:1, palette: palette}, 'MKtrend');
map1.addLayer(baseKendall, {min:-1, max:1,palette: palette}, 'kendall'); //palette: palette
map2.addLayer(reportingKendall, {min:-1, max:1, palette: palette}, 'kendall reporting')
var maps = [map1, map2, map3, map4]
var linker = ui.Map.Linker(maps);
// Create a grid of maps.
var mapGrid = ui.Panel(
[
ui.Panel([maps[0], maps[1]], null, {stretch: 'both'}),
ui.Panel([maps[2], maps[3]], null, {stretch: 'both'})
],
ui.Panel.Layout.Flow('horizontal'), {stretch: 'both'});
ui.root.widgets().reset([mapGrid]);
ui.root.setLayout(ui.Panel.Layout.Flow('horizontal'));
// var trajectory = collReporting.reduce(ee.Reducer.kendallsCorrelation());
// Map.addLayer(trajectory.select('meanNDVI_tau'), {palette: palette}, 'trendReporting'); //palette: palette
// Map.addLayer(kendall, {palette: palette}, 'kendall'); //palette: palette
// Map.addLayer(kendallReporting, {palette:palette}, 'kendallReporting')
// // ================= ADD Sen's Slope ==============
// var slope = function(i, j) { // i and j are images
// return ee.Image(j).subtract(i)
// .divide(ee.Image(j).metadata('index').difference(ee.Image(i).metadata('index'), 'years'))
// .rename('slope')
// .float();
// };
// var slopes = ee.ImageCollection(joined.map(function(current) {
// var afterCollection = ee.ImageCollection.fromImages(current.get('after'));
// return afterCollection.map(function(image) {
// return ee.Image(slope(current, image));
// });
// }).flatten());
// var sensSlope = slopes.reduce(ee.Reducer.median(), 2); // Set parallelScale.
// Map.addLayer(sensSlope, {palette: palette}, 'sensSlope');
// ============== Get Sen's Intercept ==================
// var epochDate = ee.Date('1970-01-01');
// var sensIntercept = ndviCollection.map(function(image) {
// var epochDays = image.date().difference(epochDate, 'days').float();
// return image.subtract(sensSlope.multiply(epochDays)).float();
// }).reduce(ee.Reducer.median(), 2);
// Map.addLayer(sensIntercept, {}, 'sensIntercept');
// // ============== Kendall Variance ==================
// // Values that are in a group (ties). Set all else to zero.
// var groups = ndviCollection.map(function(i) {
// var matches = ndviCollection.map(function(j) {
// return i.eq(j); // i and j are images.
// }).sum();
// return i.multiply(matches.gt(1));
// });
// // Compute tie group sizes in a sequence. The first group is discarded.
// var group = function(array) {
// var length = array.arrayLength(0);
// // Array of indices. These are 1-indexed.
// var indices = ee.Image([1])
// .arrayRepeat(0, length)
// .arrayAccum(0, ee.Reducer.sum())
// .toArray(1);
// var sorted = array.arraySort();
// var left = sorted.arraySlice(0, 1);
// var right = sorted.arraySlice(0, 0, -1);
// // Indices of the end of runs.
// var mask = left.neq(right)
// // Always keep the last index, the end of the sequence.
// .arrayCat(ee.Image(ee.Array([[1]])), 0);
// var runIndices = indices.arrayMask(mask);
// // Subtract the indices to get run lengths.
// var groupSizes = runIndices.arraySlice(0, 1)
// .subtract(runIndices.arraySlice(0, 0, -1));
// return groupSizes;
// };
// // See equation 2.6 in Sen (1968).
// var factors = function(image) {
// return image.expression('b() * (b() - 1) * (b() * 2 + 5)');
// };
// var groupSizes = group(groups.toArray());
// var groupFactors = factors(groupSizes);
// var groupFactorSum = groupFactors.arrayReduce('sum', [0])
// .arrayGet([0, 0]);
// var count = joined.count();
// var kendallVariance = factors(count)
// .subtract(groupFactorSum)
// .divide(18)
// .float();
// //Map.addLayer(kendallVariance, {}, 'kendallVariance');
// // =========== Compute Z-statistics ============
// var zero = kendall.multiply(kendall.eq(0));
// var pos = kendall.multiply(kendall.gt(0)).subtract(1);
// var neg = kendall.multiply(kendall.lt(0)).add(1);
// var z = zero
// .add(pos.divide(kendallVariance.sqrt()))
// .add(neg.divide(kendallVariance.sqrt()));
// //Map.addLayer(z, {min: -2, max: 2}, 'z');
// // https://en.wikipedia.org/wiki/Error_function#Cumulative_distribution_function
// function eeCdf(z) {
// return ee.Image(0.5)
// .multiply(ee.Image(1).add(ee.Image(z).divide(ee.Image(2).sqrt()).erf()));
// }
// function invCdf(p) {
// return ee.Image(2).sqrt()
// .multiply(ee.Image(p).multiply(2).subtract(1).erfInv());
// }
// // ============== Compute P-values ============
// var p = ee.Image(1).subtract(eeCdf(z.abs()));
// //Map.addLayer(p, {min: 0, max: 1}, 'p');
// // Pixels that can have the null hypothesis (there is no trend) rejected.
// // Specifically, if the true trend is zero, there would be less than 5%
// // chance of randomly obtaining the observed result (that there is a trend).
// //Map.addLayer(p, {}, 'significant trends');
//------------------------------------------------------------------------------------------
// Export as GeoTIFF
//------------------------------------------------------------------------------------------
// if (country === 'Tunisia'){
// Export.image.toDrive({
// image: annualNDVI,
// description: country + '_NDVI_' + suffix + '_' + Year,
// scale: 30,
// region: studyArea,
// maxPixels: 1e13,
// fileFormat: 'GeoTIFF',
// folder:'GEE_classification',
// formatOptions: {
// cloudOptimized: true
// },
// skipEmptyTiles: true
// });
// }
// if (country === 'Egypt'){
// tileList.map(function(tile){
// var tmpAOI = studyArea.filter(ee.Filter.eq('id', tile));
// //print(tile)
// Map.addLayer(tmpAOI,{},'ID',1);
// Export.image.toDrive({
// image: annualNDVI,
// description: country +'_'+tile+'_NDVI_' + suffix + '_' + Year,
// scale: 30,
// region: tmpAOI,
// maxPixels: 1e13,
// fileFormat: 'GeoTIFF',
// folder:'GEE_classification',
// formatOptions: {
// cloudOptimized: true
// },
// skipEmptyTiles: true
// })
// //return 0
// })
// }
//Map.addLayer(L7.first(), {}, 'L7');
*/