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I have a composite image collection which I used for land use classification. I want to calculate the area of the feature collections. The code I have used is:

// Composite an image collection and clip it to a boundary.

// Load Landsat 7 raw imagery and filter it to Jan-Dec 2018.
var collection = ee.ImageCollection("LANDSAT/LC08/C01/T1")
    .filterDate('2018-01-01', '2018-12-31');

// Reduce the collection by taking the median.
var median = ee.Algorithms.Landsat.simpleComposite({collection: collection});

// Load a table of boundaries and filter.
var fc = ee.FeatureCollection('users/njsnigdha42/Asia');

// Clip to the output image to the Asia boundaries.
var composite = median.clipToCollection(fc);

// Display the result.
var visParams = {bands: ['B3', 'B2', 'B1'], gain: [1.4, 1.4, 1.1]};
Map.addLayer(composite, visParams, 'clipped composite');

// Merge the five geometry layers into a single FeatureCollection.
var newfc = Urban.merge(Vegetation).merge(Water).merge(Barren_Land).merge(Snow_and_Ice);
print(newfc);

// Use these bands for classification.
var bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7'];
// The name of the property on the points storing the class label.
var classProperty = 'landcover';

// Sample the composite to generate training data.  Note that the
// class label is stored in the 'landcover' property.
var training = composite.select(bands).sampleRegions({
  collection: newfc,
  properties: [classProperty],
  scale: 30
});

// Train a CART classifier.
var classifier = ee.Classifier.smileCart().train({
  features: training,
  classProperty: classProperty,
});

// Print some info about the classifier (specific to CART).
print('CART, explained', classifier.explain());

// Classify the composite.
var classified = composite.classify(classifier);
Map.centerObject(newfc);
Map.addLayer(classified, {min: 0, max: 4, palette: ['red', 'green', 'blue','yellow','white']});

// Optionally, do some accuracy assessment.  Fist, add a column of
// random uniforms to the training dataset.
var withRandom = training.randomColumn('random');

// We want to reserve some of the data for testing, to avoid overfitting the model.
var split = 0.7;  // Roughly 70% training, 30% testing.
var trainingPartition = withRandom.filter(ee.Filter.lt('random', split));
var testingPartition = withRandom.filter(ee.Filter.gte('random', split));

// Trained with 70% of our data.
var trainedClassifier = ee.Classifier.smileRandomForest(5).train({
  features: trainingPartition,
  classProperty: classProperty,
  inputProperties: bands
});

// Classify the test FeatureCollection.
var test = testingPartition.classify(trainedClassifier);

// Print the confusion matrix.
var confusionMatrix = test.errorMatrix(classProperty, 'classification');
print('Confusion Matrix', confusionMatrix);

//Converting the data into Binary data


//Cartographic way to calculate area
var area_pxa = image.multiply(ee.Image.pixelArea())
                         .reduceRegion(ee.Reducer.sum(), Urban,30,null,null,false,1e13)
                         .get('constant');

print('Estimated urbanization (km2)',area_pxa);

Number (Error) Dictionary.get: Dictionary does not contain key: constant.

How do I solve this issue? Also, I think I have not converted the data into binary data and I don't know how to do that.

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  • Well, even with the formatting, it doesn't work Apr 29, 2020 at 23:36

1 Answer 1

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This all depends on how classified looks. If classified contained a band called urban which was either 0 or 1, this should work. The error message is pretty clear in this case: The dictionary returned by reduceRegion() doesn't contain the key urban. Print the resulting dictionary to see which keys it contain:

var areaDict = classified.multiply(ee.Image.pixelArea())
  .reduceRegion(ee.Reducer.sum(), urban, 30, null, null, false, 1e13)

print(areaDict)

You haven't provided enough details in your question to give much more advice than this.

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  • It contains Classification: 0 Apr 30, 2020 at 14:28

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