Mapping landuse with Landsat-8

Who said landuse classification is difficult?

In the next exercise we will use the Google Earth Engine to create a landuse map using landsat-8. This time we use the spectral signature, whereas we used the temporal signature in the previous exercise.

Step 1: Import the landsat-8 imagecollection.

Step 2: define the time range of interest:

// define start of time series
var startdate = ee.Date.fromYMD(2013,1,1);
// define end of time series
var enddate = ee.Date.fromYMD(2016,12,1);

Step 3: Import a fusion table with your area of interest.

// the ca basin
var CaBasin = ee.FeatureCollection('ft:1ILFPzWUf4dL_tKNY0Rd3EhGtRQoLfzmk_00i9xC3');

Step 4: filter the image collection based on date and location.

// filter on date and location
var l8images = l8.filterDate(startdate,enddate).filterBounds(CaBasin);

Step 5: apply the cloud function to filter the clouds:

// set cloud threshold
var cloud_thresh = 40;

// the cloud function
var cloudfunction = function(image) {
  //use add the cloud likelihood band to the image
  var CloudScore = ee.Algorithms.Landsat.simpleCloudScore(image);
  //isolate the cloud likelihood band
  var quality ='cloud');
  //get pixels above the threshold
  var cloud01 =;
  //create a mask from high likelihood pixels
  var cloudmask = image.mask().and(cloud01.not());
  //mask those pixels from the image
  return image.updateMask(cloudmask);

// filter clouds for all images in imagecollection
l8images =;

Step 6: Add a map with the visual bands to the map for visual inspection

var ls8_viz = {bands:["B4","B3","B2"], min:0.0, max:0.3};
Map.addLayer(l8images.median().clip(CaBasin), ls8_viz, "ls8_nocloud");

Step 7: Create a list with landsat-8 bands

// list with l8 bands
var LS8_bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7'];//, 'B10', 'B11'];

Step 8: Create a training image with the median of the 8 landsat bands.

// make the training image
var TrainingImage = ee.Image(1)

Step 9: Set the selection bands.

// set the selection bands
var predictionBands = TrainingImage.bandNames();

we need to create training areas to tell the earth engine what type of landuse classes we have in the different areas.

Step 10: Select draw a shape.


Step 11: Draw a polygon on a know landuse class (Rice is our case).


Note: Do note make your training areas to large, this will result in a computational time out.

Step 12: repeat this for two other areas with the same landuse.

Step 13: Go to the settings of the layer


Step 14: change the selection to FeatureCollection and add the property class with value 1.


Step 15: Click add layer under geometry imports.

Step 16: Repeat this procedure (from step 10) for a number of other landuse classes. Chose a different value for properties each time you create a new layer.

Step 17: Merge all training features into a new imagecollection.

// merge all into a single ImageCollection
var trainingFeatures = rice  //1
.merge(urban)                //2
.merge(water)                //3
.merge(rich_forest)          //4
.merge(secondary_forest)     //5

Step 18: Include the code below to create your classified image

// sample the regions
var classifierTraining ={collection: trainingFeatures, properties: ['class'], scale: 30 });

// train the classifier
var classifier = ee.Classifier.cart()
.train({features:classifierTraining, classProperty:'class', inputProperties: predictionBands});

// get the classified image
var classified =;

Step 19: add the classified image to the map

// add layer to map
Map.addLayer(classified, {min: 1, max: 6,
palette: ['fffa00' //1
,'#ff0004' //2
,'0000ff' //3
, '003f15' //4
, '6bf442' //5
, 'f402ec']}, //6

Step 20: There are a large number of classifiers. Also try some of the classifiers below.

var classifier = ee.Classifier.cart()
var classifier = ee.Classifier.continuousNaiveBayes()
var classifier = ee.Classifier.gmoMaxEnt()
var classifier = ee.Classifier.ikpamir() .
var classifier = ee.Classifier.minimumDistance()
var classifier = ee.Classifier.naiveBayes()
var classifier = ee.Classifier.pegasosGaussian()
var classifier = ee.Classifier.pegasosLinear()
var classifier = ee.Classifier.pegasosPolynomial()
var classifier = ee.Classifier.perceptron()
var classifier = ee.Classifier.svm()
var classifier = ee.Classifier.winnow()
See an example here


  1. Hi, I was just wondering if the codes mentioned in this exercise can also be used for Landsat 7 and/or Landsat 5 or 4?


      1. thanks, yeah i got that. i saw that you also put a variable for sentinel 2 when i opened your sample link. what did you use if for?


      1. another question, is there another way to sharpen the images to make it clearer? I am finding it hard to classify different land Use


      2. No. The spatial resolution of Landsat is 30 meter. You can use sentinel-2 for a higher spatial resolution.
        Also, you can do the identification on the Google Maps Satellite view. Zoom in and you will get layers with aerial images.


  2. what if i want to map from previous years (e.g. i want to to do a land use change analysis between the year 2007 and 2017) can i still use the google maps satellite view? (i know its a stupid question but i just want to put it out there)


    1. Only if the land cover is the same. That’s why field measurements are so important. You might wanna have a look at the collect earth online initiative where crowd source observations are integrated with rs technologies to create landuse maps


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