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.