Lab 8 - Band Arithmetic: NDVI

Lab Instructions

1. Manual Method for NDVI

   As you remember, NDVI is calculated by doing arithmetic on bands using the following formula:

   

   To compute the NDVI, we will introduce Earth Engine's implementation of band arithmetic. Cloud-based band arithmetic is one of the most powerful aspects of Earth Engine because the platform's computers are optimized for this type of heavy processing. Earth Engine automatically partitions calculations across many computers as needed.

   Copy and paste the following code into Earth Engine:

   /////
   // Band Arithmetic
   /////
   
   // Calculate NDVI using Sentinel 2
   
   // Import and filter imagery by location and date.
   var sfoPoint = ee.Geometry.Point(-122.3774, 37.6194); // long lat of San Francisco
   var sfoImage = ee.ImageCollection('COPERNICUS/S2')
       .filterBounds(sfoPoint)
       .filterDate('2020-02-01', '2020-04-01')
       .first();
   
   // Display the image as a false color composite.
   Map.centerObject(sfoImage, 11);
   Map.addLayer(sfoImage, {
       bands: ['B8', 'B4', 'B3'],
       min: 0,
       max: 2000
   }, 'False color');

   The above code creates a false color image, as we have done in previous labs. Now we will look at using band arithmetic to create the NDVI.

   The simplest mathematical operations in Earth Engine are the add, subtract, multiply, and divide methods. Let's select the near-infrared and red bands and use these operations to calculate NDVI:

   // Extract the near infrared and red bands.
   var nir = sfoImage.select('B8');
   var red = sfoImage.select('B4');
   
   // Calculate the numerator and denominator using subtraction and addition.
   var numerator = nir.subtract(red);
   var denominator = nir.add(red);
   
   // Now calculate NDVI.
   var ndvi = numerator.divide(denominator);
   
   // Add the layer to our map with a palette.
   var vegPalette = ['red', 'white', 'green'];
   Map.addLayer(ndvi, {
       min: -1,
       max: 1,
       palette: vegPalette
   }, 'NDVI Manual');

   Examine the resulting index using the Inspector to pick out pixel values in areas of vegetation and non-vegetation. NDVI outputs vary between −1 and 1. High amounts of green vegetation have values around 0.8–0.9. Absence of green leaves gives values near 0, and water gives values near −1.

   Question 1 (10 Points):

   Explore the image. Find a location that contrasts healthy vegetation and non-vegetation. Screenshot that area and write a caption explaining the differences in NDVI values.

2. Single-Operation Computation: normalizedDifference

   Normalized differences like NDVI are so common in remote sensing that Earth Engine provides the ability to do that particular sequence of subtraction, addition, and division in a single step, using the normalizedDifference method.

   The NDVI computation can be replaced with one line of code:

   // Use the built-in normalizedDifference function.
   var ndviND = sfoImage.normalizedDifference(['B8', 'B4']);
   Map.addLayer(ndviND, {
       min: -1,
       max: 1,
       palette: vegPalette
   }, 'NDVI normalizedDiff');

   Important: Note that the order in which you provide the two bands to normalizedDifference is important. We use B8 (near-infrared) as the first parameter, and B4 (red) as the second. If your two computations of NDVI do not look identical, check the order of your bands.

3. Using Normalized Difference for NDWI

   The normalized difference approach is used for many different indices. Let's apply the same method to another index.

   The Normalized Difference Water Index (NDWI) was developed by Gao (1996) as an index of vegetation water content. The index is sensitive to changes in the liquid content of vegetation canopies. This means it can be used to detect vegetation experiencing drought conditions or differentiate crop irrigation levels. It is also sometimes called the Normalized Difference Moisture Index (NDMI).

   

   // Use normalizedDifference to calculate NDWI
   var ndwi = sfoImage.normalizedDifference(['B8', 'B11']);
   var waterPalette = ['white', 'blue'];
   Map.addLayer(ndwi, {
       min: -0.5,
       max: 1,
       palette: waterPalette
   }, 'NDWI');

   Troubleshooting help: Full code link

   Question 2 (10 Points):

   Look at the same area that you looked at in Question 1. Look at it now with NDWI. What does this index tell you about the previous interpretation you had? Include a screenshot.

4. Additional Resources

   Read the following articles to learn about more spectral indices:

   • Remote Sensing Indices (Medium)
   • 4 Useful Remote Sensing Indices (Medium)

   Question 3 (20 Points):

   Select a different index from the articles in Medium or another source. Using either the normalizedDifference method or the manual method, write code that:

   • Calculates the index
   • Adds the layer to Earth Engine with a good palette for visualization
   • Interprets what this new index tells you about your site

   Turn in a screenshot of your AOI from Questions 1 and 2 with the new index. Include screenshots.

   Question 4 (10 Points):

   Add your URL for your code to your document with your screenshots and submit.