Google Earth Engine – Showing Land Cover Class Area in Charts

google-earth-enginegoogle-earth-engine-javascript-apiland-classification

I've produced a classified map in GEE and trying to create a chart showing the area for each landcover class. The code I'm using is as follows,

var classNames = ['vegetation Covers', 'Water Bodies', 'Urban Areas', 'Barren Lands'];
var create_chart = function(classifiedImage, mumbai, classNames){ // for classNames, create a list of your classes as strings
  var options = {
    hAxis: {title: 'Land Cover Class'},
    vAxis: {title: 'Area in Kilometers'},
    title: 'Area in KM by Land Cover Class',
    series: { // set color for each class
      0: {color: 'green'}, // Vegetation covers
      1: {color: 'blue'}, // Water bodies
      2: {color: 'black'}, // Urban areas
      3: {color: 'yellow'}} // Barren lands
  };
  var areaChart = ui.Chart.image.byClass({
    image: ee.Image.pixelArea().addBands(classifiedImage),
    classBand: 'classification', 
    scale: 30,
    region: mumbai,
    reducer: ee.Reducer.sum()

  }).setSeriesNames(classNames)
  .setOptions(options)
  ;
  print(areaChart);
};

create_chart(classifiedImage, mumbai, classNames);

classifiedImage is the output image of the classification. mumbai is the area of interest.

The chart should show the landcover classes on the x-axis and the corresponding area in kilometers on the y-axis. But the chart produce by this code is showing the number of pixels on the y-axis.

I understand, I have not specified any rule for area calculation in the code. I have tried changing the code but always get errors. Can anyone suggest, how to get the desired output here (area in kilometers on the y-axis)?

I'm using the Landsat images here.

Best Answer

It's not showing the number of pixels, its showing the area in meters, since that's the units on pixelArea. Divide that image by 1,000,000 to get sqkm.

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[GIS] Google Earth Engine, Supervised Classification

I'd do something like this:

// Landsat 5 TM Raw Image Collection
var lt5 = ee.ImageCollection("LANDSAT/LT5_L1T");

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Early = lt5.filterDate('1985-01-01', '1986-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud = ee.Image(lakeTiticaca_Early.sort('CLOUD_COVER').first());

// Filter by date and location (Landsat Path and Row Designation)
var lakeTiticaca_Later = lt5.filterDate('2011-01-01', '2012-01-01')
.filter(ee.Filter.eq('WRS_PATH', 2))
.filter(ee.Filter.eq('WRS_ROW', 71));

// Selecting Images with least cloud cover
var leastCloud2 = ee.Image(lakeTiticaca_Later.sort('CLOUD_COVER').first());

// Applying MNDWI Function to Least Cloudy Images 
var mNDWI_Early = leastCloud.expression(
  '(Green - MIR) / (Green + MIR)', {
  'Green': leastCloud.select('B2'),
  'MIR': leastCloud.select('B5')
 });

var mNDWI_Later = leastCloud2.expression(
 '(Green - MIR) / (Green + MIR)', {
 'Green': leastCloud2.select('B2'),
 'MIR': leastCloud2.select('B5')
 });

// Creating an image collection from the MNDWI calculated Images 
var constant1 = ee.Image(mNDWI_Early).select([0],["mNDWI"]);
Map.addLayer(leastCloud,{bands:["B4","B5","B3"]},"before")
var constant2 = ee.Image(mNDWI_Later).select([0],["mNDWI"]);
Map.addLayer(leastCloud2,{bands:["B4","B5","B3"]},"after")

var dif = constant2.subtract(constant1)
Map.addLayer(dif,{},"dif")

var img1 = leastCloud.addBands(constant1)
var img2 = leastCloud2.addBands(constant2)

var final = ee.Image.cat(img1,img2)
Map.addLayer(final,{},"final")

//* Supervised Image Classification *//

var training = ee.FeatureCollection([water_nochange, water_gain, water_loss, land_nochange]).flatten()

var sample = final.sampleRegions(training, ["class"], 30)

var trained = ee.Classifier.cart().train(sample, "class")

var classified = final.classify(trained)
Map.addLayer(classified,{min:1, max:4, palette:["#b3b3ff","#0000e6", "#ff0000", "#009900"]},"classified")

You can access the feature collections in the following link: https://code.earthengine.google.com/3f4c2908fcf7c469357b80783f3ef26f

You should mask clouds, use another path/row to include the missing part of the lake, and make more training points. You can also try another classifier like svm, randomForest, etc.

Google Earth Engine – Troubleshooting LULC-2021 Layer Error: User Memory Limit Exceeded

You run out of memory because your training data set has 2,656,173 features. sampleRegions() where each region is a polygon will sample all points within those polygons, at the scale you specified (10m).

To control the number of pixels sampled, you can use ee.Image.sample() instead. Here below, I've sampled 200 pixels from each of your seven feature collections. I pick up the class value from the first feature in each collection, assuming all features represent within a collection only represents a single class (I didn't bother to validate that assumption).

var classProperty = 'landcover'
var samplesPerClass = 200
var filtered = filtered.median()

var training = ee.FeatureCollection(
  [
    builtup, 
    water, 
    agriculture, 
    fallowland, 
    barrenland, 
    otherVegetation, 
    wetlands
  ].map(function (featureCollection) {
    return filtered.select(bands)
      .sample({
        region: featureCollection.geometry(), 
        scale: 10, 
        numPixels: samplesPerClass
      })
      .map(function (point) {
        return point.copyProperties(featureCollection.first(), [classProperty])
      })
  })
).flatten()

var classifier = ee.Classifier.smileRandomForest(50).train({
  features: training,
  classProperty: classProperty,
  inputProperties: bands
})
var classified = filtered.classify(classifier)

https://code.earthengine.google.com/16614ad867a90219f2633953a0a6faab

Best Answer

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[GIS] Google Earth Engine, Supervised Classification

Google Earth Engine – Troubleshooting LULC-2021 Layer Error: User Memory Limit Exceeded

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