Deciduous forest mapping in Cambodia I

sampling your composite

The code snippets below describe how to import the data, calculate the covariates, sample the data and export the data.

step 1: copy the functions to calculate the covariates to your script.

/*Copyright (c) 2019 SERVIR-Mekong

Permission is hereby granted, free of charge, to any person obtaining a copy
of the data and associated documentation files, to deal in the data
without restriction, including without limitation the rights to use, copy, modify,
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to whom the data is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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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 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;
};

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']));
};

Step 2: import the data

// import data
var elevation = ee.Image("USGS/SRTMGL1_003");
var jrcImage = ee.Image("JRC/GSW1_0/GlobalSurfaceWater");
var composites = ee.ImageCollection("projects/servir-mekong/yearlyComposites");
var deciduousData = ee.FeatureCollection("ee.FeatureCollection("projects/servir-mekong/ReferenceData/CambodiaDecidiousForest");
var tccCollection = ee.ImageCollection("projects/servir-mekong/UMD/tree_canopy");

Step 3: Show data for deciduous and other.

// select deciduous and no deciduous
var deciduous = deciduousData.filter(ee.Filter.eq("land_class",1));
var noDeciduous = deciduousData.filter(ee.Filter.eq("land_class",0));

Map.addLayer(deciduous,{},"deciduous");
Map.addLayer(noDeciduous.draw("red"),{},"no deciduous");

Step 4: make a list containing all years with reference data

// create a list with years
var years = ee.List.sequence(2000,2018,1);

Step 5: pull the image composite for each year, add the covariates and sample the composite with reference data of that year.

// sample the composite for each year
var trainingData = ee.FeatureCollection(years.map(function(y){
    var start = ee.Date.fromYMD(y,1,1);
    var end = ee.Date.fromYMD(y,12,31);

    var image = ee.Image(composites.filterDate(start,end).first()).divide(10000);
    var tcc = ee.Image(tccCollection.filterDate(start,end).first()).rename("tcc");

    image = image.addBands(tcc);

    image = addEVI(image);
    image = addSAVI(image);
    image = addIBI(image);
    image = addTopography(image);
    image = addJRCDataset(image);
    image = addNDCovariates(image);

    var trainingData  = deciduousData.filter(ee.Filter.eq("year",y));
    var training = image.sampleRegions({collection:trainingData,properties:["land_class"],scale:30,geometries:true});
    return training.toList(training.size());
}).flatten());

Step 6:  Export the data.

Export.table.toAsset({collection:trainingData,description:"deciduous",assetId:"your/asset/Training"});