Google Earth Engine Tutorial: Track Deforestation with Sentinel-2A

Google Earth Engine (GEE) is a powerful platform for analyzing geospatial data, and Sentinel-2A provides high-resolution optical imagery ideal for tracking changes in land cover, such as deforestation. This tutorial outlines the process of using GEE to monitor deforestation using Sentinel-2A data.

1. Accessing Google Earth Engine

To begin, ensure you have a Google account and access to the GEE platform. Visit the GEE Code Editor (https://code.earthengine.google.com) and sign in. Familiarize yourself with the interface, which includes a scripting area, map view, and console.

2. Setting Up the Environment

Load the GEE library in your script using:

var ee = require('ee');

Initialize the library and authenticate your account by running:

ee.Authenticate();

Follow the prompts to grant access if not already authorized.

3. Loading Sentinel-2A Data

Start by loading the Sentinel-2A dataset from the GEE catalog:

var sentinel2 = ee.ImageCollection('COPERNICUS/S2_SR');

Filter the dataset by date and location. For example, to load data for a specific region and time range:

var region = geometry; // Define your study area as a geometry object
var startDate = '2020-01-01';
var endDate = '2023-12-31';
var filtered = sentinel2.filterDate(startDate, endDate).filterBounds(region);

4. Preprocessing the Data

Preprocess the images to ensure consistent analysis:

• Apply cloud masking using the Sentinel-2 quality assessment band.
• Resample the data to a common resolution (e.g., 10 meters for Sentinel-2A).
• Compute vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to highlight forested areas.

Example code for cloud masking:

function maskS2clouds(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask);
}

5. Analyzing Deforestation

Create a composite image for a time period (e.g., pre and post-deforestation) and compare changes:

var preDeforestation = filtered.filterDate('2020-01-01', '2020-12-31').mean();
var postDeforestation = filtered.filterDate('2023-01-01', '2023-12-31').mean();

Use the difference between these composites to identify areas of change:

var diff = postDeforestation.select('B11').subtract(preDeforestation.select('B11'));
Map.addLayer(diff, {min: -100, max: 100, palette: ['red', 'white', 'green']}, 'Deforestation Difference');

6. Visualization and Export

Visualize the deforestation highlights on the map. Adjust parameters like min, max, and palette for clarity. Export the results as a GeoTIFF:

Export.image.toDrive({
  image: diff,
  description: 'deforestation_diff',
  folder: 'GEE_Export',
  maxPixels: 1e9
});

Monitor the export process via the GEE console.

7. Advanced Techniques

For more precise analysis, consider training a machine learning classifier (e.g., Random Forest or Logistic Regression) to distinguish forested from non-forested areas. Use historical deforestation data as training samples and apply the classifier to the most recent imagery.

FAQ

Q: How do I access Sentinel-2A data in GEE?

A: Use the dataset ID 'COPERNICUS/S2_SR' in the GEE catalog. Filter by date, location, and apply preprocessing steps to handle clouds and resampling.

Q: Can I track deforestation at a higher resolution?

A: Yes, Sentinel-2A has 10m resolution for bands like B4, B3, and B2. Use relevant bands to enhance spatial accuracy.

Q: What if my study area is large?

A: Filter the ImageCollection by the region of interest and use aggregated methods like mean() or median() to reduce computational load.

Q: How long does it take to process the data?

A: Processing time depends on the size of the dataset, your internet connection, and GEE's computational capacity. Use the GEE task manager to monitor progress.

Q: What other datasets can I use alongside Sentinel-2A?

A: Combine Sentinel-2A with datasets like Landsat or MODIS for time series analysis. Climate data or land use/cover layers can also improve accuracy.

Q: How do I handle false positives in deforestation detection?

A: Refine your analysis by using multiple bands, applying thresholding for vegetation indices, or incorporating ancillary data (e.g., elevation, slope) for validation.