Google Earth Engine Tutorial: Beginners Guide 5 NDVI Analysis with Landsat 8

Google Earth Engine Tutorial: Beginners Guide 5 NDVI Analysis with Landsat 8

Welcome to this comprehensive tutorial on performing NDVI analysis using Landsat 8 data in Google Earth Engine. This guide will walk you through the entire process from loading data to visualizing results.

What is NDVI?

The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used indices for vegetation analysis. It uses the red and near-infrared spectral bands to quantify vegetation health and density.

Getting Started with Google Earth Engine

Before we begin, ensure you have a Google account and access to Google Earth Engine Code Editor at code.earthengine.google.com.

Loading Landsat 8 Data

First, we need to load the Landsat 8 Surface Reflectance collection:

// Load Landsat 8 Surface Reflectance collection
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2020-01-01', '2020-12-31')
  .filterBounds(geometry)
  .filter(ee.Filter.lt('CLOUD_COVER', 20));

Selecting the Best Image

We’ll use the median composite approach to reduce cloud cover effects:

// Create a median composite
var image = collection.median();

// Select the required bands (Red and NIR)
var red = image.select('SR_B4');
var nir = image.select('SR_B5');

Calculating NDVI

The NDVI formula is: (NIR – Red) / (NIR + Red)

// Calculate NDVI
var ndvi = nir.subtract(red).divide(nir.add(red)).rename('NDVI');

// Alternative method using normalizedDifference
var ndviAlt = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI');

Visualizing Results

Let’s set up visualization parameters for our NDVI results:

// Define visualization parameters
var ndviParams = {
  min: -1,
  max: 1,
  palette: ['blue', 'white', 'green']
};

// Add the NDVI layer to the map
Map.addLayer(ndvi, ndviParams, 'NDVI');

// Center the map
Map.centerObject(geometry, 10);

Creating Statistics

We can calculate basic statistics for our NDVI results:

// Calculate NDVI statistics
var stats = ndvi.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e9
});

print('Mean NDVI:', stats.get('NDVI'));

Advanced Analysis: Time Series

For more comprehensive analysis, let’s create a time series chart:

// Create time series chart
var chart = ui.Chart.image.series({
  imageCollection: collection.select('SR_B5', 'SR_B4'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 30,
  xProperty: 'system:time_start'
}).setOptions({title: 'Landsat 8 NDVI Time Series'});

print(chart);

Exporting Results

Finally, let’s export our NDVI results:

// Export the result to Google Drive
Export.image.toDrive({
  image: ndvi,
  description: 'NDVI_Landsat8',
  folder: 'GEE_Export',
  region: geometry,
  scale: 30,
  maxPixels: 1e13
});

Complete Script Summary

Here’s the complete working script:

// Define your area of interest
var geometry = ee.Geometry.Rectangle([-122.5, 37.5, -122.0, 38.0]);

// Load Landsat 8 collection
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  .filterDate('2020-06-01', '2020-09-30')
  .filterBounds(geometry)
  .filter(ee.Filter.lt('CLOUD_COVER', 20));

// Create median composite
var image = collection.median();

// Calculate NDVI
var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI');

// Visualization parameters
var ndviParams = {
  min: -1,
  max: 1,
  palette: ['blue', 'white', 'green']
};

// Add layers to map
Map.addLayer(image, {bands: ['SR_B4', 'SR_B3', 'SR_B2'], min: 0, max: 0.3}, 'RGB');
Map.addLayer(ndvi, ndviParams, 'NDVI');
Map.centerObject(geometry, 10);

// Print statistics
var stats = ndvi.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e9
});

print('Mean NDVI:', stats.get('NDVI'));

Tips for Success

• Always filter by cloud cover to get better quality images
• Use median composites to reduce noise and cloud effects
• Consider the temporal resolution of Landsat 8 (16 days)
• Choose appropriate scale values for your analysis area
• Test different visualization palettes to best represent your data