LM Google Earth Engine basics

Learn the essentials of Google Earth Engine, the Earth Engine Data Catalog, and JavaScript.

What is Google Earth Engine?

Google Earth Engine is a powerful platform designed for analyzing and visualizing environmental data. Imagine you have a supercomputer that can access a huge library of satellite images of Earth taken over the last few decades. This is what Google Earth Engine provides.

Why is GEE relevant to us?

Massive Satellite Imagery Database: Google Earth Engine has a vast collection of satellite images. This database is constantly updated and includes historical data, which is great for observing changes over time.

Powerful Computing Resources: You don’t need a high-end computer to use it. Google Earth Engine does the heavy lifting on its servers, so you can run complex analyses and algorithms through a web interface without needing powerful hardware.

Environmental Monitoring and Analysis: It’s widely used for environmental research like tracking deforestation, monitoring water levels in lakes and rivers, observing the melting of glaciers, and analyzing urban development.

Accessible to Non-Experts: The platform is designed to be user-friendly, so even if you’re not an expert in remote sensing or geographical information systems (GIS), you can still use it. There’s a bit of a learning curve, but there are plenty of tutorials to help you get started.

Collaboration and Sharing: You can collaborate with others and share your findings. It’s a great tool for group projects or research work.

Real-World Applications: The data and insights gained from Google Earth Engine can have real-world implications, such as helping to inform policy decisions on climate change or natural resource management.

Introduction to the GEE Code Editor

Once you have signed up for a GEE account, you can start exploring satellite data using the GEE Code Editor. The Code Editor is a web-based integrated development environment (IDE) for writing and executing JavaScript code for Earth Engine. A very well documented guide for working with GEE can be found here.

Image: Diagram of components of the Earth Engine Code Editor at code.earthengine.google.com

Earth Engine Data Catalog

The EE data catalog provides a large variety of datasets.

Image: Example from the catalog dataset showing details for Sentinel-2

Image: Example from the catalog dataset showing script to access Sentinel-2 data

JavaScript in GEE

To get started with GEE we need to first learn some basics of JavaScript. Following are a few necessary functions we will need, but explore here for further functions.

• Variables: to store data values.

  var city = 'Moshi';
  

• Lists: store multiple values in a single variable.

  var cities = ['Moshi', 'Marburg'];
  

• Objects: store key-value pairs, where each value can be referred to by its key.

  // Earth Engine uses [longitude, latitude]; south of the equator, latitude is negative
  var cityData = {'city': 'Moshi', 'coordinates': [37.3507, -3.3430], 'population_2017': 201150};
  print(cityData);
  

• Writing a function: give an input, do some computation and get an output.

  var greetings = function(name) { return 'Hello ' + name; };
  print(greetings('World'));
  print(greetings('People'));
  

• Loading an area of study: ee.Geometry.Point(), ee.Geometry.Polygon(), ee.Feature(), ee.FeatureCollection()

  var cityLocation = ee.Geometry.Point(cityData.coordinates);
  

• Mapping

  // Add the point as a layer to the map
  Map.addLayer(cityLocation, {color: 'blue'}, 'Moshi Location');
  // Center the map on the city location
  Map.centerObject(cityLocation, 9); // '9' is the zoom level, you can adjust it as needed
  

• Loading datasets: ee.Image() and ee.ImageCollection()

  var dataset = ee.Image('USGS/SRTMGL1_003'); // elevation raster
  var sentinel_collection = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED');
  

• Filtering data: filter(), filterDate(), and filterBounds()

  var filteredCollection = sentinel_collection
    .filterDate('2020-01-01', '2020-12-31')
    .filterBounds(ee.Geometry.Point(cityData.coordinates))
    .filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', 10));
  

• Image processing: select(), median()

  var selectedBands = filteredCollection.select(['B4', 'B3', 'B2']);
  Map.addLayer(selectedBands.median(), {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']}, 'RGB Composite');
  var medianComposite = filteredCollection.median();
  // Display the composite
  Map.addLayer(medianComposite, {bands: ['B4', 'B3', 'B2'], max: 3000}, 'Median composite');
  

• Exporting images

  // Buffer the point to create an area for export (in metres)
  var bufferedRegion = cityLocation.buffer(5000);
  // Export script
  Export.image.toDrive({
    image: medianComposite, // the image you want to export
    description: 'Sentinel2_CityLocation_Export',
    scale: 10, // Sentinel-2 resolution for RGB bands
    region: bufferedRegion,
    fileFormat: 'GeoTIFF',
    folder: 'GEE_Exports', // optional
    maxPixels: 1e9 // adjust if needed
  });