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landsatlinkr.js
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landsatlinkr.js
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/**
* @license
* Copyright 2020 Justin Braaten
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Ideas for correcting sensors: https://ieeexplore.ieee.org/abstract/document/9093966
var msslib = require('users/jstnbraaten/modules:msslib/msslib.js');
var ltgee = require('users/emaprlab/public:Modules/LandTrendr.js');
var animation = require('users/gena/packages:animation');
/**
* Returns a filtered TM WRS-2 T1 surface reflectance image collection.
*
* @param {ee.Geometry | ee.Feature} aoi An ee.Filter to filter TM image collection.
*
* @return {ee.ImageCollection} An MSS WRS-2 image collection filtered by
* bounds and quality.
*/
function getTmWrs2Col(aoi){
var tm4 = ee.ImageCollection("LANDSAT/LT04/C01/T1_SR")
.filterBounds(aoi);
var tm5 = ee.ImageCollection("LANDSAT/LT05/C01/T1_SR")
.filterBounds(aoi);
return tm4.merge(tm5);
}
exports.getTmWrs2Col = getTmWrs2Col;
/**
* Add unique path, row, orbit ID as image property for joining TM and MSS collections.
*
* @param {ee.Image} tmWrs2Col A TM image collection.
* @param {ee.Image} mssWrs2Col A MSS image collection.
*
* @return {ee.ImageCollection} An image collection ____WAH_____.
*/
function coincidentTmMssCol(tmWrs2Col, mssWrs2Col){
var filter = ee.Filter.equals({leftField: 'imgID', rightField: 'imgID'});
var join = ee.Join.saveFirst('coincidentTmMss');
return ee.ImageCollection(join.apply(tmWrs2Col, mssWrs2Col, filter));
}
exports.coincidentTmMssCol = coincidentTmMssCol;
/**
* Add unique path, row, orbit ID as image property for joining TM and MSS collections.
*
* @param {ee.Image} img A TM or MSS image.
*
* @return {ee.ImageCollection} A copy of the input image with an 'imgID'
* property added to the image describing the unique path, row, orbit.
*/
function addTmToMssJoinId(img){
//return col.map(function(img) {
var date = ee.Image(img).date();
var year = ee.Algorithms.String(date.get('year'));
var doy = ee.Algorithms.String(date.getRelative('day', 'year'));
var path = ee.Algorithms.String(img.getNumber('WRS_PATH').toInt());
var row = ee.Algorithms.String(img.getNumber('WRS_ROW').toInt());
var yearDoy = year.cat(doy).cat(path).cat(row);
return img.set({'imgID': yearDoy,
'path': path,
'row': row
});
//});
}
exports.addTmToMssJoinId = addTmToMssJoinId;
/**
* Returns the footprint of an image as a ee.Geometry.Polygon.
*
* @param {ee.Image} img The image to get the footprint for.
*
* @return {ee.Geometry.Polygon} The ee.Geometry.Polygon representation of
* an image's footprint.
*/
function getFootprint(img){
return ee.Geometry.Polygon(ee.Geometry(img.get('system:footprint')).coordinates())}
exports.getFootprint = getFootprint;
/**
* Generates an ee.Filter for filtering MSS and TM image collection by
* intersection with a given geometry.
*
* @param {ee.Geometry | ee.Feature} aoi Area of interest to filter collection to.
* Include images less than given value.
*
* @return {ee.Filter} A filter to be passed as an argument to the .filter()
* ee.ImageCollection method.
*/
function filterBounds(aoi) {
return ee.Filter.bounds(aoi);
}
exports.filterBounds = filterBounds;
/**
* Returns a cloud and cloud shadow mask from CFmask.
* @param {ee.Image} img Landsat SR image.
* @return {ee.Image} A 0/1 mask image to be used with .updateMask().
*/
function getCfmask(img) {
var cloudShadowBitMask = 1 << 3;
var cloudsBitMask = 1 << 5;
var qa = img.select('pixel_qa');
var mask = qa.bitwiseAnd(cloudShadowBitMask)
.eq(0)
.and(qa.bitwiseAnd(cloudsBitMask).eq(0));
return mask;
}
exports.getCfmask = getCfmask;
function applyCfmask(img) {
var mask = getCfmask(img);
return img.updateMask(mask);
}
exports.getCfmask = getCfmask;
// #############################################################################
// ### Process steps ###
// #############################################################################
/**
* Display the series of WRS-1 images for a given WRS-1 granule.
*/
function viewWrs1Col(params) {
print('Displaying WRS-1 images to the console');
var granuleGeom = msslib.getWrs1GranuleGeom(params.wrs1);
params.aoi = ee.Geometry(granuleGeom.get('centroid'));
params.wrs = '1';
var mssDnCol = msslib.getCol(params)
.filter(ee.Filter.eq('pr', params.wrs1));
msslib.viewThumbnails(mssDnCol);
}
exports.viewWrs1Col = viewWrs1Col;
/**
* Display the WRS-1 grid to the map.
*/
function wrs1GranuleSelector() {
var wrs1Granules = ee.FeatureCollection('users/jstnbraaten/wrs/wrs1_descending_land');
Map.addLayer(wrs1Granules, {color: 'grey'}, null, null, 0.5);
var message = ui.Label({value: 'Click granules to print ID. Wait patiently after clicking. Repeat as needed.',
style: {position: 'top-center'}});
var holder = ui.Panel({style: {width: '170px', height: '220px', position: 'top-left'}});
var label = ui.Label({value: 'WRS-1 Granule ID(s):'});
var ids = ui.Panel({style: {backgroundColor: '#DCDCDC'}});
holder.add(label);
holder.add(ids);
Map.add(message);
Map.add(holder);
Map.style().set('cursor', 'crosshair');
Map.onClick(function(e) {
ids.clear();
var nLayers = Map.layers().length();
for(var i=0; i < nLayers-1; i++) {
Map.layers().remove(Map.layers().get(1));
}
var point = ee.Geometry.Point(e.lon, e.lat);
var joinFilter = ee.Filter.intersects({leftField: '.geo', rightField: '.geo', maxError: 500});
var join = ee.Join.simple();
var intersectingFeatures = join.apply(wrs1Granules, ee.FeatureCollection(point), joinFilter);
intersectingFeatures.toList(intersectingFeatures.size()).evaluate(function(fList) {
var colors = ['red', 'blue', 'green', 'yellow', 'orange', 'pink', 'purple'];
for(var i in fList) {
var f = ee.Feature(fList[i]);
var outline = ee.Image().byte().paint({
featureCollection: ee.FeatureCollection(f),
color: 1,
width: 3
});
var pr = f.get('PR').getInfo();
var title = pr + ' ' + colors[i];
Map.addLayer(outline, {palette: colors[i]}, title);
ids.add(ui.Label({value: pr, style: {color: colors[i], backgroundColor: '#DCDCDC'}}));
}
});
});
}
exports.wrs1GranuleSelector = wrs1GranuleSelector;
// #############################################################################
// ### Reference prep ###
// #############################################################################
/**
* calculate the medoid of a collection.
*
*/
function getMedoid(col, bands) {
col = col.select(bands);
var median = col.median();
var difFromMedian = col.map(function(img) {
var dif = ee.Image(img).subtract(median).pow(ee.Image.constant(2));
return dif.reduce(ee.Reducer.sum())
.addBands(img);
});
var bandNames = difFromMedian.first().bandNames();
var len = bandNames.length();
var bandsPos = ee.List.sequence(1, len.subtract(1));
var bandNamesSub = bandNames.slice(1);
return difFromMedian.reduce(ee.Reducer.min(len)).select(bandsPos, bandNamesSub);
}
exports.getMedoid = getMedoid;
function getRefImg(params) {
var granuleGeoms = msslib.getWrs1GranuleGeom(params.wrs1);
var centroid = ee.Geometry(granuleGeoms.get('centroid'));
var bounds = ee.Geometry(granuleGeoms.get('bounds'));
var refCol = msslib.getCol({
aoi: bounds,
wrs: '2',
yearRange: [1983, 1987], // Use five early years - want good coverage, but near to MSS WRS-1 window.
doyRange: params.doyRange,
}).map(addTmToMssJoinId);
var tmCol = getTmWrs2Col(bounds)
.filterDate('1983-01-01', '1988-01-01')
.map(addTmToMssJoinId);
var coincident = coincidentTmMssCol(refCol, tmCol)
.map(function(img) {
var mask = getCfmask(ee.Image(img.get('coincidentTmMss')));
var imgToa = msslib.calcToa(img);
return imgToa.updateMask(mask);
});
return msslib.addTc(msslib.addNdvi(getMedoid(coincident, ['green', 'red', 'red_edge', 'nir'])))
.select(['green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca']) // TODO: scale these data to get them to int16
.set('bounds', bounds);
}
exports.getRefImg = getRefImg;
function exportMssRefImg(params) {
print('Preparing reference image export task, please wait');
var refImg = getRefImg(params);
Export.image.toAsset({
image: refImg,
description: 'MSS-reference-image',
assetId: params.baseDir + '/ref',
region: ee.Geometry(refImg.get('bounds')),
scale: 60,
crs: params.crs,
maxPixels: 1e13
});
}
exports.exportMssRefImg = exportMssRefImg;
// #############################################################################
/**
* Returns an example Tm image.
*
* @return {ee.Image} Example TM image.
*/
function exampleTmImg() {
return ee.Image('LANDSAT/LT05/C01/T1_SR/LT05_045029_19840728');
}
exports.exampleTmImg = exampleTmImg;
// Example AOIs
var wrs2045029 = ee.Geometry.Point([-121.454, 44.47]);
exports.wrs2045029 = wrs2045029;
// #############################################################################
// ### Process MSS WRS-1 images ###
// #############################################################################
// Create a new image that is the concatenation of three images: a constant,
// the SWIR1 band, and the SWIR2 band.
/**
* Have found that the best regression is robustLinear on entire image
* with scale set as 60.
* Also tried: robustLinear, scale 300
* linear, scale 300
* stratified sample based on image segmentation k-means - worst - could be poor sampling
* Yet to try using all bands to predicit a given band - multiple regression
* Yest to try using different threshold and scale for masking dif to ref in `correctMssImg`
*/
function calcRegression(xImg, yImg, xBand, yBand, aoi, scale) {
var constant = ee.Image(1);
var xVar = xImg.select(xBand);
var yVar = yImg.select(yBand);
var imgRegress = ee.Image.cat(constant, xVar, yVar);
var linearRegression = imgRegress.reduceRegion({
reducer: ee.Reducer.robustLinearRegression({
numX: 2,
numY: 1
}),
geometry: aoi,
scale: scale,
maxPixels: 1e13
});
var coefList = ee.Array(linearRegression.get('coefficients')).toList();
var intercept = ee.List(coefList.get(0)).get(0);
var slope = ee.List(coefList.get(1)).get(0);
var rmse = ee.Array(linearRegression.get('residuals')).toList().get(0);
return ee.Dictionary({slope: slope, intercept: intercept, rmse: rmse});
}
exports.calcRegression = calcRegression;
// Function to apply correction to reference image.
function applyCoef(img, band, coef) {
coef = ee.Dictionary(coef);
return img.select(band)
.multiply(ee.Image.constant(coef.getNumber('slope')))
.add(ee.Image.constant(coef.getNumber('intercept')));
}
exports.applyCoef = applyCoef;
function getSampleImg(img, ref, band) {
var dif = img.select(band)
.subtract(ref.select(band)).rename('dif');
var difThresh = dif.reduceRegion({
reducer: ee.Reducer.percentile({
percentiles: [40, 60],
maxRaw: 1000000,
maxBuckets: 1000000,
minBucketWidth: 0.00000000001
}),
geometry: img.geometry(),
scale: 60,
maxPixels: 1e13
});
var mask = dif.gt(difThresh.getNumber('dif_p40'))
.and(dif.lt(difThresh.getNumber('dif_p60')));
return img.updateMask(mask);
}
exports.getSampleImg = getSampleImg;
// Function to make normalization function.
function correctMssImg(img) {
var ref = ee.Image(img.get('ref_img'));
var granuleGeoms = msslib.getWrs1GranuleGeom(img.getString('pr'));
var granule = ee.Feature(granuleGeoms.get('granule')).geometry();
// // ** Use three bands for mask
// var allMask = getSampleImg(img, ref, 'green').mask().multiply(
// getSampleImg(img, ref, 'red').mask()).multiply(
// getSampleImg(img, ref, 'nir').mask());
// var sampleImg = img.updateMask(allMask);
// var greenCoef = calcRegression(sampleImg, ref, 'green', 'green', granule, 60);
// var redCoef = calcRegression(sampleImg, ref, 'red', 'red', granule, 60);
// var nirCoef = calcRegression(sampleImg, ref, 'nir', 'nir', granule, 60);
// var ndviCoef = calcRegression(sampleImg, ref, 'ndvi', 'ndvi', granule, 60);
// var tcbCoef = calcRegression(sampleImg, ref, 'tcb', 'tcb', granule, 60);
// var tcgCoef = calcRegression(sampleImg, ref, 'tcg', 'tcg', granule, 60);
// var tcaCoef = calcRegression(sampleImg, ref, 'tca', 'tca', granule, 60);
// // ** Use three bands for mask
var greenCoef = calcRegression(getSampleImg(img, ref, 'green'), ref, 'green', 'green', granule, 60);
var redCoef = calcRegression(getSampleImg(img, ref, 'red'), ref, 'red', 'red', granule, 60);
var nirCoef = calcRegression(getSampleImg(img, ref, 'nir'), ref, 'nir', 'nir', granule, 60);
var ndviCoef = calcRegression(getSampleImg(img, ref, 'ndvi'), ref, 'ndvi', 'ndvi', granule, 60);
var tcbCoef = calcRegression(getSampleImg(img, ref, 'tcb'), ref, 'tcb', 'tcb', granule, 60);
var tcgCoef = calcRegression(getSampleImg(img, ref, 'tcg'), ref, 'tcg', 'tcg', granule, 60);
var tcaCoef = calcRegression(getSampleImg(img, ref, 'tca'), ref, 'tca', 'tca', granule, 60);
return ee.Image(ee.Image.cat(
applyCoef(img, 'green', greenCoef).toFloat(),
applyCoef(img, 'red', redCoef).toFloat(),
applyCoef(img, 'nir', nirCoef).toFloat(),
applyCoef(img, 'ndvi', nirCoef).toFloat(),
applyCoef(img, 'tcb', tcbCoef).toFloat(),
applyCoef(img, 'tcg', tcgCoef).toFloat(),
applyCoef(img, 'tca', tcaCoef).toFloat())
.rename(['green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.copyProperties(img, img.propertyNames()));
}
function prepMss(img) {
var toa = msslib.calcToa(img);
var toaAddBands = msslib.addTc(msslib.addNdvi(toa));
var toaAddBandsMask = msslib.applyQaMask(toaAddBands);
return msslib.applyMsscvm(toaAddBandsMask);
}
exports.prepMss = prepMss;
function processMssWrs1Img(img) {
var toaAddBandsMsscvmMask = prepMss(img);
var corrected = correctMssImg(toaAddBandsMsscvmMask);
return corrected;
}
exports.processMssWrs1Img = processMssWrs1Img;
function processMssWrs1Imgs(params) {
print('Preparing MSS WRS-1 image processing tasks, please wait');
var granuleGeom = msslib.getWrs1GranuleGeom(params.wrs1);
params.aoi = ee.Geometry(granuleGeom.get('centroid'));
params.wrs = '1';
var mssCol = msslib.getCol(params)
.filter(ee.Filter.eq('pr', params.wrs1))
.map(function(img) {
return img.set('ref_img', ee.Image(params.baseDir + '/ref'));
});
//print(mssCol);
//var years = ee.List(mssCol.aggregate_array('year').distinct()).sort().getInfo(); // TODO: make this so that all year are written out - include dummies - the script that reads them in assumes that all years exist
var dummy = ee.Image([0, 0, 0, 0, 0, 0, 0, 0]).selfMask().toShort()
.rename(['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca']);
var outImg;
for(var y = 1972; y <= 1982; y++) {
var yrCol = mssCol.filter(ee.Filter.eq('year', y));
if(yrCol.size().getInfo() === 0) { // Try to use ee.Algorithms.If - so that the browser does not hang.
outImg = dummy.set({
dummy: true,
year: y,
'system:time_start': ee.Date.fromYMD(y, 1, 1)
});
} else {
yrCol = yrCol.map(processMssWrs1Img);
outImg = getMedoid(yrCol, ['green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.set({
dummy: false,
year: y,
'system:time_start': ee.Date.fromYMD(y, 1, 1)
});
}
Export.image.toAsset({
image: outImg,
description: y.toString(),
assetId: params.baseDir + '/WRS1_to_WRS2/' + y.toString(),
region: ee.Feature(granuleGeom.get('granule')).geometry(),
scale: 60,
crs: params.crs
});
}
}
exports.processMssWrs1Imgs = processMssWrs1Imgs;
function correctMssWrs2(params) { // NOTE: this is just grabbing 1983 for now.
var aoi = ee.Feature(
msslib.getWrs1GranuleGeom(params.wrs1).get('granule')).geometry();
var mssCol = msslib.getCol({
aoi: aoi,
wrs: '2',
doyRange: params.doyRange
}).filterDate('1983-01-01', '1984-01-01')
.map(prepMss);
return correctMssImgToMedianTm(mssCol, params);
}
exports.correctMssWrs2 = correctMssWrs2;
// #############################################################################
// ### Process TM images ###
// #############################################################################
// Function to get and rename bands of interest from OLI.
function renameOli(img) {
return img.select(
['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'pixel_qa'],
['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'pixel_qa']);
}
// Function to get and rename bands of interest from ETM+.
function renameTm(img) {
return img.select(
['B1', 'B2', 'B3', 'B4', 'B5', 'B7', 'pixel_qa'],
['blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'pixel_qa']);
}
function tmAddIndices(img) {
var b = ee.Image(img).select(['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);
var brt_coeffs = ee.Image.constant([0.2043, 0.4158, 0.5524, 0.5741, 0.3124, 0.2303]);
var grn_coeffs = ee.Image.constant([-0.1603, -0.2819, -0.4934, 0.7940, -0.0002, -0.1446]);
var brightness = b.multiply(brt_coeffs).reduce(ee.Reducer.sum()).round().toShort();
var greenness = b.multiply(grn_coeffs).reduce(ee.Reducer.sum()).round().toShort();
var angle = (greenness.divide(brightness)).atan().multiply(180 / Math.PI).multiply(100).round().toShort();
var ndvi = img.normalizedDifference(['nir', 'red']).rename('ndvi').multiply(1000).round().toShort();
var tc = ee.Image.cat(ndvi, brightness, greenness, angle).rename(['ndvi', 'tcb', 'tcg', 'tca']);
return img.addBands(tc);
}
function gatherTmCol(params) {
var granuleGeom = msslib.getWrs1GranuleGeom(params.wrs1);
var aoi = ee.Feature(granuleGeom.get('granule')).geometry();
var dateFilter = ee.Filter.calendarRange(params.doyRange[0], params.doyRange[1], 'day_of_year');
var startDate = ee.Date.fromYMD(params.yearRange[0], 1, 1);
var endDate = startDate.advance(1, 'year');
var oliCol = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterBounds(aoi).filterDate(startDate, endDate).filter(dateFilter).map(prepOli);
var etmCol = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterBounds(aoi).filterDate(startDate, endDate).filter(dateFilter).map(prepTm);
var tm5Col = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR')
.filterBounds(aoi).filterDate(startDate, endDate).filter(dateFilter).map(prepTm);
var tm4Col = ee.ImageCollection('LANDSAT/LT04/C01/T1_SR')
.filterBounds(aoi).filterDate(startDate, endDate).filter(dateFilter).map(prepTm);
return tm4Col.merge(tm5Col).merge(etmCol).merge(oliCol);
}
exports.gatherTmCol = gatherTmCol;
// Define function to prepare OLI images.
function prepOli(img) {
var orig = img;
img = renameOli(img);
img = tmAddIndices(img);
img = applyCfmask(img);
return ee.Image(img.copyProperties(orig, orig.propertyNames()));
}
// Define function to prepare ETM+ images.
function prepTm(img) {
var orig = img;
img = renameTm(img);
img = tmAddIndices(img);
img = applyCfmask(img);
return ee.Image(img.copyProperties(orig, orig.propertyNames()));
}
exports.prepTm = prepTm;
function getCoincidentTmMssCol(params) {
var aoi = ee.Feature(
msslib.getWrs1GranuleGeom(params.wrs1).get('granule')).geometry();
var mssCol = msslib.getCol({
aoi: aoi,
wrs: '2',
doyRange: params.doyRange,
excludeIds: params.excludeIds
})
.map(addTmToMssJoinId);
var tmCol = getTmWrs2Col(aoi).map(addTmToMssJoinId);
var coincident = coincidentTmMssCol(mssCol, tmCol);
return coincident;
}
exports.getCoincidentTmMssCol = getCoincidentTmMssCol;
// Function to make normalization function.
function getMss2TmCoefCol(img) {
var sampleMask = ee.Image.random().gt(0.90);
var xImg = msslib.addTc(msslib.addNdvi(msslib.calcToa(img)));
var xImgSamp = xImg.updateMask(sampleMask);
var yImg = prepTm(ee.Image(xImg.get('coincidentTmMss')));
var granule= ee.Feature(ee.FeatureCollection('users/jstnbraaten/wrs/wrs2_descending_land')
.filter(ee.Filter.eq('PR', xImg.getString('pr'))).first()).geometry();
var blueCoef = calcRegression(xImgSamp, yImg, 'green', 'blue', granule, 150); // TODO: this 300 is maybe not ideal - could try a small image sample like 10% or 5% or 1%.
var greenCoef = calcRegression(xImgSamp, yImg, 'green', 'green', granule, 150);
var redCoef = calcRegression(xImgSamp, yImg, 'red', 'red', granule, 150);
var nirCoef = calcRegression(xImgSamp, yImg, 'nir', 'nir', granule, 150);
var ndviCoef = calcRegression(xImg, yImg, 'ndvi', 'ndvi', granule, 150);
var tcbCoef = calcRegression(xImg, yImg, 'tcb', 'tcb', granule, 150);
var tcgCoef = calcRegression(xImg, yImg, 'tcg', 'tcg', granule, 150);
var tcaCoef = calcRegression(xImg, yImg, 'tca', 'tca', granule, 150);
var coef = {
blue_coef: blueCoef,
green_coef: greenCoef,
red_coef: redCoef,
nir_coef: nirCoef,
ndvi_coef: ndviCoef,
tcb_coef: tcbCoef,
tcg_coef: tcgCoef,
tca_coef: tcaCoef,
};
xImg = xImg.set('mss_2_tm_coef', coef);
var xImgCor = _correctMssImg(xImg);
return yImg.select(xImgCor.bandNames()).subtract(xImgCor.select(xImgCor.bandNames()))
.copyProperties(xImgCor, xImgCor.propertyNames());
}
exports.getMss2TmCoefCol = getMss2TmCoefCol;
function exportTm2mssCoefCol(params) {
var col = getCoincidentTmMssCol(params);
var coefFc = col.map(getTm2mssCoefCol);
Export.table.toAsset({
collection: coefFc,
description: 'tm2MssCoefCol',
assetId: params.baseDir + '/tm2MssCoefCol'
});
}
exports.exportTm2mssCoefCol = exportTm2mssCoefCol;
function exportMss2TmCoefCol(params) {
var col = getCoincidentTmMssCol(params);
var mssOffsetCol = col.map(getMss2TmCoefCol);
var coefFc = mssOffsetCol.map(function(img) {
var coefs = ee.Dictionary(img.get('mss_2_tm_coef'));
var blueCoef = ee.Dictionary(coefs.get('blue_coef'));
var greenCoef = ee.Dictionary(coefs.get('green_coef'));
var redCoef = ee.Dictionary(coefs.get('red_coef'));
var nirCoef = ee.Dictionary(coefs.get('nir_coef'));
var ndviCoef = ee.Dictionary(coefs.get('ndvi_coef'));
var tcbCoef = ee.Dictionary(coefs.get('tcb_coef'));
var tcgCoef = ee.Dictionary(coefs.get('tcg_coef'));
var tcaCoef = ee.Dictionary(coefs.get('tca_coef'));
var coef = {
'blue_slope': blueCoef.getNumber('slope'),
'blue_intercept': blueCoef.getNumber('intercept'),
'blue_rmse': blueCoef.getNumber('rmse'),
'green_slope': greenCoef.getNumber('slope'),
'green_intercept': greenCoef.getNumber('intercept'),
'green_rmse': greenCoef.getNumber('rmse'),
'red_slope': redCoef.getNumber('slope'),
'red_intercept': redCoef.getNumber('intercept'),
'red_rmse': redCoef.getNumber('rmse'),
'nir_slope': nirCoef.getNumber('slope'),
'nir_intercept': nirCoef.getNumber('intercept'),
'nir_rmse': nirCoef.getNumber('rmse'),
'ndvi_slope': ndviCoef.getNumber('slope'),
'ndvi_intercept': ndviCoef.getNumber('intercept'),
'ndvi_rmse': ndviCoef.getNumber('rmse'),
'tcb_slope': tcbCoef.getNumber('slope'),
'tcb_intercept': tcbCoef.getNumber('intercept'),
'tcb_rmse': tcbCoef.getNumber('rmse'),
'tcg_slope': tcgCoef.getNumber('slope'),
'tcg_intercept': tcgCoef.getNumber('intercept'),
'tcg_rmse': tcgCoef.getNumber('rmse'),
'tca_slope': tcaCoef.getNumber('slope'),
'tca_intercept': tcaCoef.getNumber('intercept'),
'tca_rmse': tcaCoef.getNumber('rmse'),
};
return ee.Feature(ee.Geometry.Point(0, 0)).set(coef)
.copyProperties(img, ['imgID', 'year', 'path', 'row', 'pr']);
});
var medianOffset = mssOffsetCol.median().round().toShort();
var granuleGeom = msslib.getWrs1GranuleGeom(params.wrs1);
Export.image.toAsset({
image: medianOffset,
description: 'medianOffset',
assetId: params.baseDir + '/mss_offset',
region: ee.Feature(granuleGeom.get('granule')).geometry(),
scale: 60,
crs: params.crs
});
Export.table.toAsset({
collection: coefFc,
description: 'mss2TmCoefCol',
assetId: params.baseDir + '/mss2TmCoefCol'
});
}
exports.exportMss2TmCoefCol = exportMss2TmCoefCol;
function _getMedianCoef(table, coef) {
return ee.List(table.aggregate_array(coef))
.reduce(ee.Reducer.median());
}
function getMedianCoef(table) {
return ee.Dictionary({
blue_coef: {
slope: _getMedianCoef(table, 'blue_slope'),
intercept: _getMedianCoef(table, 'blue_intercept')
},
green_coef: {
slope: _getMedianCoef(table, 'green_slope'),
intercept: _getMedianCoef(table, 'green_intercept')
},
red_coef: {
slope: _getMedianCoef(table, 'red_slope'),
intercept: _getMedianCoef(table, 'red_intercept')
},
nir_coef: {
slope: _getMedianCoef(table, 'nir_slope'),
intercept: _getMedianCoef(table, 'nir_intercept')
},
ndvi_coef: {
slope: _getMedianCoef(table, 'ndvi_slope'),
intercept: _getMedianCoef(table, 'ndvi_intercept')
},
tcb_coef: {
slope: _getMedianCoef(table, 'tcb_slope'),
intercept: _getMedianCoef(table, 'tcb_intercept')
},
tcg_coef: {
slope: _getMedianCoef(table, 'tcg_slope'),
intercept: _getMedianCoef(table, 'tcg_intercept')
},
tca_coef: {
slope: _getMedianCoef(table, 'tca_slope'),
intercept: _getMedianCoef(table, 'tca_intercept')
}
});
}
exports.getMedianCoef = getMedianCoef;
function _correctMssImg(img) {
var coefs = ee.Dictionary(img.get('mss_2_tm_coef'));
return ee.Image(ee.Image.cat(
applyCoef(img, 'green', ee.Dictionary(coefs.get('blue_coef'))).toFloat(),
applyCoef(img, 'green', ee.Dictionary(coefs.get('green_coef'))).toFloat(),
applyCoef(img, 'red', ee.Dictionary(coefs.get('red_coef'))).toFloat(),
applyCoef(img, 'nir', ee.Dictionary(coefs.get('nir_coef'))).toFloat(),
applyCoef(img, 'ndvi', ee.Dictionary(coefs.get('ndvi_coef'))).toFloat(),
applyCoef(img, 'tcb', ee.Dictionary(coefs.get('tcb_coef'))).toFloat(),
applyCoef(img, 'tcg', ee.Dictionary(coefs.get('tcg_coef'))).toFloat(),
applyCoef(img, 'tca', ee.Dictionary(coefs.get('tca_coef'))).toFloat())
.rename(['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.copyProperties(img, img.propertyNames()));
}
exports._correctMssImg = _correctMssImg;
function correctMssImgToMedianTm(col, params) {
var table = ee.FeatureCollection(params.baseDir + '/mss2TmCoefCol');
var offset = ee.Image(params.baseDir + '/mss_offset');
var coefs = getMedianCoef(table);
return col.map(function(img) {
return img
.set('mss_2_tm_coef', coefs);
})
.map(_correctMssImg)
.map(function(img) {
return img.add(offset).round().toShort().copyProperties(img, img.propertyNames()); // NOTE: no offset - img.round().toShort().copyProperties(img, img.propertyNames());
});
}
exports.correctMssImgToMedianTm = correctMssImgToMedianTm;
function getFinalCorrectedMssCol(params) {
var mssCol = ee.ImageCollection([]);
for(var y = 1972; y <= 1982; y++) {
var img = ee.Image(params.baseDir + '/WRS1_to_WRS2/' + y.toString())
.set('system:time_start', ee.Date.fromYMD(y, 1 ,1).millis());
mssCol = mssCol.merge(ee.ImageCollection(img));
}
return correctMssImgToMedianTm(mssCol, params);
}
function exportFinalCorrectedMssCol(params) {
var mssCol = getFinalCorrectedMssCol(params);
var granuleGeom = msslib.getWrs1GranuleGeom(params.wrs1);
// var dummy = ee.Image([0, 0, 0, 0, 0, 0, 0, 0]).selfMask().toShort()
// .rename(['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca']);
for(var y = 1972; y <= 1982; y++) {
var yrCol = mssCol.filter(ee.Filter.eq('year', y));
// // Deal with missing years - provide a dummy.
// var outImg = ee.Algorithms.If({
// condition: yrCol.size(),
// trueCase: yrCol.first().resample('bicubic'), // NOTE: not sure about the resample?,
// falseCase: dummy.set('system:time_start', ee.Date.fromYMD(y, 1 ,1).millis())
// });
Export.image.toAsset({
image: yrCol.first().resample('bicubic'), //outImg,
description: y.toString(),
assetId: params.baseDir + '/WRS1_to_TM/' + y.toString(),
region: ee.Feature(granuleGeom.get('granule')).geometry(),
scale: 30,
crs: params.crs,
maxPixels: 1e13
});
}
}
exports.exportFinalCorrectedMssCol = exportFinalCorrectedMssCol;
// #############################################################################
// ### Final collection assembly ###
// #############################################################################
function getColForLandTrendrOnTheFly(params) { // Does not rely on WRS1_to_TM assets
var mssCol = getFinalCorrectedMssCol(params);
var tmCol = ee.ImageCollection([]);
for(var y=1983; y<=2020; y++) {
params.yearRange = [y, y];
var thisYearCol = getMedoid(gatherTmCol(params), ['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.set('system:time_start', ee.Date.fromYMD(y, 1 ,1).millis());
tmCol = tmCol.merge(ee.ImageCollection(thisYearCol.toShort()));
}
var combinedCol = mssCol.merge(tmCol).map(function(img) {
return img.select('ndvi').multiply(-1).rename('LTndvi') // TODO: move this into the run landtrandr function - get the fitting index from params
.addBands(img.select(['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])) // TODO: move this into the run landtrandr function - what indices should be FTV
.set('system:time_start', img.get('system:time_start'));
}).sort('system:time_start');
return combinedCol;
}
exports.getColForLandTrendrOnTheFly = getColForLandTrendrOnTheFly;
function getColForLandTrendrFromAsset(params) { // Relies on WRS1_to_TM assets
var mssCol = ee.ImageCollection([]);
for(var y=1972; y<=1982; y++) {
var img = ee.Image(params.baseDir + '/WRS1_to_TM/' + y.toString())
.set('system:time_start', ee.Date.fromYMD(y, 1 ,1).millis());
mssCol = mssCol.merge(ee.ImageCollection(img));
}
var mss1983 = ee.ImageCollection(getMedoid(correctMssWrs2(params), ['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.set('system:time_start', ee.Date.fromYMD(1983, 1 ,1).millis()));
var tmCol = ee.ImageCollection([]);
for(var y=1984; y<=2020; y++) {
params.yearRange = [y, y];
var thisYearCol = getMedoid(gatherTmCol(params), ['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])
.set('system:time_start', ee.Date.fromYMD(y, 1 ,1).millis());
tmCol = tmCol.merge(ee.ImageCollection(thisYearCol.toShort()));
}
var combinedCol = mssCol.merge(mss1983).merge(tmCol).map(function(img) {
return img.select('ndvi').multiply(-1).rename('LTndvi') // TODO: move this into the run landtrandr function - get the fitting index from params
.addBands(img.select(['blue', 'green', 'red', 'nir', 'ndvi', 'tcb', 'tcg', 'tca'])) // TODO: move this into the run landtrandr function - what indices should be FTV
.set('system:time_start', img.get('system:time_start'));
}).sort('system:time_start');
return combinedCol;
}
exports.getColForLandTrendrFromAsset = getColForLandTrendrFromAsset;
function runLandTrendrMss2Tm(params) {
var ltCol = getColForLandTrendrFromAsset(params); // alternative: getColForLandTrendrOnTheFly(params)
var lt = ee.Algorithms.TemporalSegmentation.LandTrendr({
timeSeries: ltCol,
maxSegments: 10,
spikeThreshold: 0.7,
vertexCountOvershoot: 3,
preventOneYearRecovery: true,
recoveryThreshold: 0.5,
pvalThreshold: 0.05,
bestModelProportion: 0.75,
minObservationsNeeded: 6
});
return lt;
}
exports.runLandTrendrMss2Tm = runLandTrendrMss2Tm;
// #############################################################################
// ### Functions under development (Annie Taylor) ###
// #############################################################################
function displayCollection(col) {
var rgbviz = {
bands: ['red','green','blue'],
min: 100,
max: 2000,
gamma: [1.2]
};
Map.centerObject(col.first(), 8);
Map.addLayer(col, rgbviz, 'Full Landsat Collection',false);
}
exports.displayCollection = displayCollection;
function animateCollection(col) {
var rgbviz = {
bands: ['red','green','blue'],
min: 100,
max: 2000,
gamma: [1.2]
};
// TODO: add year of image as label in animation
// col = col.map(function(img) {
// img = img.set({label: ee.String(img.get('system:id'))})
// return img
// })
Map.centerObject(col.first(), 8);
// run the animation
animation.animate(col, {
vis: rgbviz,
timeStep: 1500,
maxFrames: col.size()
})
}
exports.animateCollection = animateCollection;
function displayGreatestDisturbance(lt, params) {
var granuleGeom = ee.Feature(msslib.getWrs1GranuleGeom(params.wrs1)
.get('granule')).geometry();
var currentYear = new Date().getFullYear(); // TODO: make sure there is not a better way to get year from image metadata eg
var changeParams = { // TODO: allow a person to override these params
delta: 'loss',
sort: 'greatest',
year: {checked:true, start:1972, end:currentYear}, // TODO: make sure there is not a better way to get years from image metadata eg
mag: {checked:true, value:200, operator:'>'},
dur: {checked:true, value:4, operator:'<'},
preval: {checked:true, value:300, operator:'>'},
mmu: {checked:true, value:11},
};
// Note: add index to changeParams object this is hard coded to NDVI because currently that is the only option.
changeParams.index = 'NDVI';
var changeImg = ltgee.getChangeMap(lt, changeParams);
var palette = ['#9400D3', '#4B0082', '#0000FF', '#00FF00',
'#FFFF00', '#FF7F00', '#FF0000'];
var yodVizParms = {
min: 1972, // TODO: make sure there is not a better way to get year from image metadata eg
max: currentYear, // TODO: make sure there is not a better way to get year from image metadata eg
palette: palette
};
var magVizParms = {
min: 200,
max: 800,
palette: palette
};
Map.centerObject(granuleGeom, 12); // Zoom in pretty far otherwise the mmu filter is going to take forever (probably crash)
// display two change attributes to map
Map.addLayer(changeImg.select(['mag']), magVizParms, 'Magnitude of Change');
Map.addLayer(changeImg.select(['yod']), yodVizParms, 'Year of Detection');
}
exports.displayGreatestDisturbance = displayGreatestDisturbance;
// #############################################################################
// ### TM to MSS functions ###
// #############################################################################
// // Function to make normalization function.
// function getTm2mssCoefCol(img) { //function makeCorrectionFun(refImgPath) {
// //var img = coCol.first();
// var yImg = msslib.addNdvi(msslib.calcToa(img));
// var xImg = ee.Image(yImg.get('coincidentTmMss'));
// var xImgNdvi = xImg.normalizedDifference(['B4', 'B3']).rename(['ndvi']);
// var mask = getCfmask(xImg);
// xImg = xImg.addBands(xImgNdvi).updateMask(mask);
// var greenCoef = calcRegression(xImg, yImg, 'B2', 'green');
// var redCoef = calcRegression(xImg, yImg, 'B3', 'red');
// var nirCoef = calcRegression(xImg, yImg, 'B4', 'nir');
// var ndviCoef = calcRegression(xImg, yImg, 'ndvi', 'ndvi');
// var granuleGeoms = msslib.getWrs1GranuleGeom('049029'); // TODO - NEED TO GET THIS FROM THE PARAMS - could really just make this a dummy geom at 0, 0 - it's not needed.
// var centroid = ee.Geometry(granuleGeoms.get('centroid'));
// return ee.Feature(centroid).set(
// {