Introduction to Google Earth Engine

Estéban Hamel & Arthur de Granpré

January 2021

Summary

This workshop is an introduction to Google Earth Engine (GEE).

Earth Engine is online software from Google that allows users to manipulate and analyze lots of geographical layers (satellite imagery, climate data, topographic layers, etc.). An advantage of using GEE is that most of the calculations are done online on Google cloud and not on a personal computer. The following manipulations are an introduction to GEE tools with coding chunks that are easy to reproduce and adapt to users needs.

Objectives

Step 1: Interface overview

First, you need to have a Google account to access the GEE plateform.

You can sign up to GEE with your Gmail identifiant to the following link: https://signup.earthengine.google.com/#!/.

Once you register to GEE, you can access the code editor to this link: https://code.earthengine.google.com/.

Here’s some information about the interface.

Left Panel

Center Panel

Right Pannel

Search Bar

Map

Step 2: Select a region of interest with geometry tools

Start by navigating to La Mauricie National Park. You can search “Shawinigan” in the search bar to help you to locate the place.

  1. With the geometry tool, add a landmark by clicking on the landmark pictogram and put a point in the middle of the National Park.
  2. Click on “Exit” once the landmark is in place to avoid adding other points.
  3. Rename the landmark “pnm” in the script.

An alternative to geometry tools is to type this code to place a landmark. To complete the landmark you would have to click on the import option given by the code after typing it.

var pnm = /* color: #d63000 */ee.Geometry.Point([-72.97, 46.74])

Do not forget to save you script with the Save button.

Step 3: Load and display elevation layer

To import a dataset, you have to import it from Earth Engine database. To do so, you can use the search bar to search for the dataset needed. Once it is imported, the dataset will not be displayed automatically. You will have to define visualization parameters and boundaries around where you want to display the dataset, otherwise it will display the dataset for all its entire area.

// Print information from the dataset in the right console
print(srtm);

// Display the elevation layer
Map.addLayer(srtm);


// Give a range of value to display the elevation layer
Map.addLayer(srtm, {min: 0, max: 400},"Elevation");

// Add elevation layer with visualisation parameter to create a nice map
Map.addLayer(srtm, {min: 0, max: 400, palette: ['blue', 'yellow', 'red']},"Elevation with colour");


// Create a hillshade layer
var hillshade = ee.Terrain.hillshade(srtm);

// Display hillshade map
Map.addLayer(hillshade, {min: 150, max:255}, 'Hillshade');

// Create a slope layer
var slope = ee.Terrain.slope(srtm);

//Display slope map
Map.addLayer(slope, {min: 0, max: 30}, 'Slope');

Step 4 Work with satellite imagery for a region of interest

Google Earth Engine dataset allows us to work with tons of satellite images. To focus on a region of interest you will have to apply some filters. Begin by importing images from Sentinel 2 satellite. It would be also possible to work with Landsat images.

-The following lines define some filters for the region of interest selected earlier in La Mauricie National Park: between September and October 2020, and for the least cloudy image possible. You can also print the result of your query in the console with the function .size to get an idea of the number of images available depending on your filter.

// Create a variable for the satellite image
var pnm_s2=sent2
    .filterBounds(pnm) // Geographical filter
    .filterDate("2020-07-01", "2020-08-30") // Temporal filter
    .filterMetadata('CLOUDY_PIXEL_PERCENTAGE','less_than',10);// Filter for image clearness

// Query for number of picture corresponding to all filters
print(pnm_s2.size(),"n. images");

The previous code allows us to display many images at the same time. The next one will be preferencially select the best images according to filter applied.

// Create an object that contain the satellite image selected
var pnm_i = sent2
    .filterBounds(pnm) // Geographical filter
    .filterDate("2020-07-01", "2020-09-30") // Temporal filter
    .sort("CLOUD_COVERAGE_ASSESSMENT") // Filter for image clearness
    .first(); // Select the less cloudy image

print(pnm_i,"Best image");


//extra : 2nd best?
var pnm_i2 = sent2
    .filterDate("2020-07-01", "2020-09-30")
    .filterBounds(pnm)
    .sort("CLOUD_COVERAGE_ASSESSMENT")
    .select(1);  //first index is 0, so the 2nd best image will be selected by 1
print(pnm_i2, "2nd best image");


// Define visualization parameters
var rgb_colour = {
        bands: ["B4", "B3", "B2"],
        min: 0,
        max: 1850
        };

Map.addLayer(pnm_i,rgb_colour,"Sentinel-2 Image");

Step 5: Display a satellite image with the boundaries of a shapefile

In the previous step you worked with the landmark placed in La Mauricie National Park. It would be also possible to import a shapefile in order to work within a specific area. In this step you will have to import a shapefile and associate it to the National Park in order to display an image exclusively in this area.

// Add a variable corresponding to a shapefile
var pnm_poly = ee.FeatureCollection('users/XXX/PNM_poly');

// Display satellite image for the shapefile area of the National Park
Map.addLayer(pnm_i.clip(shape_pnm),rgb_colour,"Satellite Image cropped");

Step 6: Calculate a spectral index (ex: NDVI)

Another exercise with Google Earth Engine is to create a new layer which contains a calculated band like NDVI index (Normalized Difference Vegetation Index).

This type of index is often used to highlight some element of an existing image. In the case of NDVI, it is a ratio between reflectance of red and near infrared bands that highlight the presence of vegetation in an image. The formula to calculate NDVI is shown here.

\(NDVI = \frac{NIR-RED}{NIR+RED}\)

  1. By using a function.
// Creating the function
var ndvi = function (x) {
  var result=x.normalizedDifference(["B8", "B4"]).rename("NDVI");
  return x.addBands(result);
};

// Apply the function to an image
var ndvi1 = ndvi(pnm_i);

// Display the result
Map.addLayer(ndvi1, {bands:['NDVI'],min:0,max:1,palette:['red','yellow','green']},"NDVI Method1");
  1. By using an expression that calculate difference between bands.
// Create the expression to calculate
var ndvi2 = pnm_i.expression(
     "(NIR - RED) / (NIR + RED)",
    {
      RED: pnm_i.select("B4"),
      NIR: pnm_i.select("B8"),
 }).rename('NDVI'); // Give a name to the band you created

// Display the result
Map.addLayer(ndvi2, {min: 0, max: 1,palette:['cyan','green','orange'] }, "NDVI Method 2");

Step 7: Image classification with Google Earth Engine

This exercise is about image classification with GEE. The following code makes a supervised classification of an image for different type of landscape. First, you need to build a training dataset where landscapes are known. After you can reuse the training data to extend the analysis to a bigger scale.

// Merge polygons together
var classNames = forest.merge(agriculture).merge(water).merge(city);

print(classNames);


var bands = ['B2', 'B3', 'B4', 'B8']; // Define the bands to use for the classification

// Create the training dataset
var training = pnm_i.select(bands).sampleRegions({
  collection: classNames,
  properties: ['landcover'],
  scale: 30
});

print(training, 'training dataset');

//The classification algorithm (Here we use .cart tool, but there are others)
var classifier = ee.Classifier.smileCart().train({
  features: training,
  classProperty: 'landcover',
  inputProperties: bands
});

// Apply classification to "pnm_i" and display the result
var classification = pnm_i.select(bands).classify(classifier);
Map.addLayer(classification, {min: 0, max: 3, palette: ['green', 'yellow','blue','red']}, 'classification');


// Add other bands
var bands2 = ['B2', 'B3', 'B4', 'B8', 'NDVI']; // pour définir les bandes a utiliser

// Create a new training dataset with the new bands of ndvi1 object
var training2 = ndvi1.select(bands2).sampleRegions({
  collection: classNames,
  properties: ['landcover'],
  scale: 30
});


// Classification algorithm
var classifier2 = ee.Classifier.smileCart().train({
  features: training2,
  classProperty: 'landcover',
  inputProperties: bands2
});

// Apply classification to ndvi1 object and display the result
var classification2 = ndvi1.select(bands2).classify(classifier2);

Map.addLayer(classification2, {min: 0, max: 3, palette: ['green', 'yellow','blue','red']}, 'classification +ndvi');

Step 8: Export raster layer to Google Drive

It is possible to export the layer you created in GEE via Google Drive. From there, you can continue to modify those layers in a GIS software for example. In this step, you will export only a small region of the classification because the entire image would be too heavy to export.

// Create an export zone with a buffer
var zone=zone.buffer(1500);
Map.addLayer(zone,rgb_colour,"Export zone");

//Export raster
Export.image.toDrive({
  image: classification,
  description: 'image_classified',
  scale: 10,
  region: zone
});

Step 9: Draw graph with GEE

GEE also allows you to create different type of charts to look at the distribution of the data. In this step, you will take the elevation layer that you will clip to La Mauricie National Park to create a chart about height distribution in this area.

// Clip the elevation layer for the National Park
var elevation1= srtm.clip(shape_pnm);
print(elevation1,"Elevation 1");

// Check the result
Map.addLayer(elevation1, {min: 0, max: 400, palette: ['blue', 'yellow', 'red']},"Elevation colour pnm");

// Draw a graph and print it in the console
var Chart1 = ui.Chart.image.histogram(
    elevation1,shape_pnm,30);
print(Chart1);

// Set some visualisation option to improve the look of the graph
var options = {
  title: "Height frequency distribution for La Mauricie National Park",
  fontSize: 15,
  hAxis: {title: "Elevation (m)"},
  vAxis: {title: 'Frequency'},
  series: {
    0: {color: 'magenta'}
  }};

// Create the improved graph
var Chart2 = ui.Chart.image.histogram(
    elevation1,shape_pnm,30)
    .setSeriesNames(['Height'])
    .setOptions(options);
print(Chart2);

End of the workshop