Modular landcover system step-7: sampling

Add covariates to your composite and create a training sample

1. Create a new script called covariate_module with the code below and save in your repository

var elevation = ee.Image("USGS/SRTMGL1_003")
var jrcImage = ee.Image("JRC/GSW1_0/GlobalSurfaceWater")

var ndCovariatesList = [
['blue', 'green'],
['blue', 'red'],
['blue', 'nir'],
['blue', 'swir1'],
['blue', 'swir2'],
['green', 'red'],
['green', 'nir'],
['green', 'swir1'],
['green', 'swir2'],
['red', 'swir1'],
['red', 'swir2'],
['nir', 'red'],
['nir', 'swir1'],
['nir', 'swir2'],
['swir1', 'swir2']
];
var rCovariatesList = [
['swir1', 'nir'],
['red', 'swir1']
];

var ComputeNDCovariatesList = function () {
var list = [];
for (var index in ndCovariatesList) {
var list_ = [ndCovariatesList[index][0], ndCovariatesList[index][1]];
list.push(list_);
}
return list;
};

var addNDCovariates = function (image){
var list = ComputeNDCovariatesList();
print(list)
for (var index in list) {
image = image.addBands(image.normalizedDifference(list[index]).rename('ND_'+ ndCovariatesList[index][0] + '_' + ndCovariatesList[index][1]));
}
return image;
};

var ComputeRCovariatesList = function () {
var list = [];
for (var index in rCovariatesList) {
var list_ = [rCovariatesList[index][0], rCovariatesList[index][1]];
list.push(list_);
}
return list;
};

var addRCovariates = function (image) {
var list = ComputeRCovariatesList();
for (var index in list) {
image = image.addBands(image.select(list[index][0]).divide(image.select(list[index][1]))
.rename('_R_' + rCovariatesList[index][0] + '_' + rCovariatesList[index][1]));
}
return image;
};

var addEVI = function (image) {
var evi = image.expression('2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {
'NIR' : image.select('nir'),
'RED' : image.select('red'),
'BLUE': image.select('blue')
}).float();
return image.addBands(evi.rename('EVI'));
};

var addSAVI = function (image) {
// Add Soil Adjust Vegetation Index (SAVI)
// using L = 0.5;
var savi = image.expression('(NIR - RED) * (1 + 0.5)/(NIR + RED + 0.5)', {
'NIR': image.select('nir'),
'RED': image.select('red')
}).float();
return image.addBands(savi.rename('SAVI'));
};

var addIBI = function (image) {
// Add Index-Based Built-Up Index (IBI)
var ibiA = image.expression('2 * SWIR1 / (SWIR1 + NIR)', {
'SWIR1': image.select('swir1'),
'NIR' : image.select('nir')
}).rename(['IBI_A']);

var ibiB = image.expression('(NIR / (NIR + RED)) + (GREEN / (GREEN + SWIR1))', {
'NIR' : image.select('nir'),
'RED' : image.select('red'),
'GREEN': image.select('green'),
'SWIR1': image.select('swir1')
}).rename(['IBI_B']);

var ibiAB = ibiA.addBands(ibiB);
var ibi = ibiAB.normalizedDifference(['IBI_A', 'IBI_B']);
return image.addBands(ibi.rename(['IBI']));
};

// Function to compute the Tasseled Cap transformation and return an image
var getTassledCapComponents = function (image) {
var coefficients = ee.Array([
[0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863],
[-0.2848, -0.2435, -0.5436, 0.7243, 0.0840, -0.1800],
[0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572],
[-0.8242, 0.0849, 0.4392, -0.0580, 0.2012, -0.2768],
[-0.3280, 0.0549, 0.1075, 0.1855, -0.4357, 0.8085],
[0.1084, -0.9022, 0.4120, 0.0573, -0.0251, 0.0238]
]);
var bands = ee.List(['blue', 'green', 'red', 'nir', 'swir1', 'swir2']);

// Make an Array Image, with a 1-D Array per pixel.
var arrayImage1D = image.select(bands).toArray();

// Make an Array Image with a 2-D Array per pixel, 6 x 1
var arrayImage2D = arrayImage1D.toArray(1);

var componentsImage = ee.Image(coefficients).matrixMultiply(arrayImage2D).arrayProject([0])
.arrayFlatten([['brightness', 'greenness', 'wetness', 'fourth', 'fifth', 'sixth']]).float();
// Get a multi-band image with TC-named bands
return image.addBands(componentsImage);
};

// Function to add Tasseled Cap angles and distances to an image. Assumes image has bands: 'brightness', 'greenness', and 'wetness'.
var getTassledCapAngleAndDistance = function (image) {
var brightness = image.select('brightness');
var greenness = image.select('greenness');
var wetness = image.select('wetness');

// Calculate tassled cap angles and distances
var tcAngleBG = brightness.atan2(greenness).divide(Math.PI).rename(['tcAngleBG']);
var tcAngleGW = greenness.atan2(wetness).divide(Math.PI).rename(['tcAngleGW']);
var tcAngleBW = brightness.atan2(wetness).divide(Math.PI).rename(['tcAngleBW']);

var tcDistanceBG = brightness.hypot(greenness).rename(['tcDistanceBG']);
var tcDistanceGW = greenness.hypot(wetness).rename(['tcDistanceGW']);
var tcDistanceBW = brightness.hypot(wetness).rename(['tcDistanceBW']);

image = image.addBands(tcAngleBG).addBands(tcAngleGW).addBands(tcAngleBW).addBands(tcDistanceBG).addBands(tcDistanceGW).addBands(tcDistanceBW);

return image;
};

var computeTassledCap = function (image) {
image = getTassledCapComponents(image);
image = getTassledCapAngleAndDistance(image);
return image;
};

var addTopography = function (image) {

// Calculate slope, aspect and hillshade
var topo = ee.Algorithms.Terrain(elevation);

// From aspect (a), calculate eastness (sin a), northness (cos a)
var deg2rad = ee.Number(Math.PI).divide(180);
var aspect = topo.select(['aspect']);
var aspect_rad = aspect.multiply(deg2rad);
var eastness = aspect_rad.sin().rename(['eastness']).float();
var northness = aspect_rad.cos().rename(['northness']).float();

// Add topography bands to image
topo = topo.select(['elevation','slope','aspect']).addBands(eastness).addBands(northness);
image = image.addBands(topo);
return image;
};

var addJRCDataset = function (image) {
// Update the mask.
jrcImage = jrcImage.unmask(0);

image = image.addBands(jrcImage.select(['occurrence']).rename(['occurrence']));
image = image.addBands(jrcImage.select(['change_abs']).rename(['change_abs']));
image = image.addBands(jrcImage.select(['change_norm']).rename(['change_norm']));
image = image.addBands(jrcImage.select(['seasonality']).rename(['seasonality']));
image = image.addBands(jrcImage.select(['transition']).rename(['transition']));
image = image.addBands(jrcImage.select(['max_extent']).rename(['max_extent']));

return image;
};
exports.addCovariates = function (image) {
image = addNDCovariates(image);
image = addEVI(image);
image = addSAVI(image);
image = addIBI(image);
image = computeTassledCap(image);
return image;
};

var addJRCAndTopo = function (image) {
image = addTopography(image);
image = addJRCDataset(image);
return image;
};

exports.addJRCAndTopo = addJRCAndTopo;

link
 

2. Create a new script called sample and add reference data. You can also click here:

var covariates = require("users/user/landcoverS2:covariate_module");
var img = ee.Image("projects/servir-mekong/NgheAnComposite2017")

img = covariates.addJRCAndTopo(img)
img = covariates.addCovariates(img)

print(img)
Map.addLayer(img,{min:0,max:0.3000,bands:"red,green,blue"})
var TrainingData = urban.merge(paddyrice).merge(water).merge(crop).merge(forest);

var sample = img.sampleRegions(TrainingData,["land_class"],30).randomColumn();

Export.table.toDrive({
  collection: sample,
  description: "train_nghean",
  fileFormat: 'csv',
});