Previously, you learned how to get individual Landsat scenes by doing something like this, where l8 and point are imports representing the Landsat 8 TOA collection and an area-of-interest geometry:

var point = ee.Geometry.Point([-85.341796875, 38.66835610151506]);

var l8 = ee.ImageCollection('LANDSAT/LC8_L1T_TOA');

// Get the least cloudy image in 2015.

var image = ee.Image(

  l8.filterBounds(point)

    .filterDate('2015-01-01', '2015-12-31')

    .sort('CLOUD_COVER')

    .first()

);

Suppose now that you want to compute a Normalized Difference Vegetation Index (NDVI) image from the Landsat image. Vegetation reflects light in the near-infrared (NIR) part of the electromagnetic spectrum and absorbs light in the red part (Learn more about NIR reflectance from vegetation). NDVI uses this to create a single value roughly reflecting the photosynthetic activity occurring at a pixel. The calculation is (NIR - red) / (NIR + red). This results in a number between 1 and -1, where pixels with high photosynthetic activity have a high NDVI.

The normalized difference operation is so ubiquitous in remote sensing that we have a shortcut function to perform it an ee.Image: ee.Image.normalizedDifference([nirBandName, redBandName]).

// Compute the Normalized Difference Vegetation Index (NDVI).

// Landsat 8 Band 5 is Near Infrared, Band 4 is Red.

var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');

// Display the result.

Map.centerObject(image, 9);

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

Map.addLayer(ndvi, ndviParams, 'NDVI image');

Mapping a Function over a Collection

Suppose now that you want to add NDVI to every image in an image collection. The way to do that in Earth Engine is to map() a function over the collection.

Let's make a function to compute NDVI on an input image and return the input image with a new NDVI band:

var point = ee.Geometry.Point([-85.341796875, 38.66835610151506]);

var l8 = ee.ImageCollection('LANDSAT/LC8_L1T_TOA');

// Get the least cloudy image in 2015.

var image = ee.Image(

  l8.filterBounds(point)

    .filterDate('2015-01-01', '2015-12-31')

    .sort('CLOUD_COVER')

    .first()

);

var addNDVI = function(image) {

  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');

  return image.addBands(ndvi);

};

// Test the addNDVI function on a single image.

var ndvi = addNDVI(image).select('NDVI');

Once you've tested the function on an individual image and have determined that it does what you want, you can map it over the collection:

var withNDVI = l8.map(addNDVI);

Make a greenest pixel composite

Now that you've made an image collection in which each image has an NDVI band, we can explore a new way to make composites: qualityMosaic().

// Make a "greenest" pixel composite.

var greenest = withNDVI.qualityMosaic('NDVI');

// Display the result.

var visParams = {bands: ['B4', 'B3', 'B2'], max: 0.3};

Map.addLayer(greenest, visParams, 'Greenest pixel composite');

The result of this code should look something like Figure 9. Comparing Figure 9 to the median composite shown in Figure 6, observe that the greenest pixel composite is indeed much greener. However, close examination of water bodies should make a different problem apparent. Specifically, water bodies now appear cloudy. This is due to the way the qualityMosaic() method works: at each location, the entire time series is examined and the pixel with the maximum value in the NDVI band is set as the composite value. Because NDVI is higher over clouds than water, water areas get cloudy pixels, while vegetated areas all appear green because NDVI is highest when the vegetation in the pixel is photosynthetically active.

Where to go next?

Chart NDVI over time at a given location

Export the result of your analysis, including GeoTiffs, videos, Static map tiles, static images, and vector feature collections.

Import data to work with in Earth Engine: raster data and vector data