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NDVI - Landsat 8

Now, we are going to look at how to calculate the NDVI using Landsat 8 data of the same area at the same time.

To do so, we will access the Landsat 8 Tier 1 Surface Reflectance dataset.

Preparing the data

The process is almost identical to that of Sentinel-2, however we will have to adjust the correct bands. Check the data documentation to find out which bands store which information and how to adress them.

// Compute Normalized Difference Vegetation Index over LS8 product.
// NDVI = (NIR - RED) / (NIR + RED), where
// RED is B4, 636-673 nm
// NIR is B5, 851-879 nm

//Step 1: Access your boundary-defining geometry
var extent_lebanon = ee.FeatureCollection("FAO/GAUL/2015/level0")
                  .filter(ee.Filter.eq('ADM0_NAME', 'Lebanon')); //filter for entry that equals the UN country name 'Lebanon'



//Step 2: Access the Landsat 8 Tier 1 Surface Reflectance data and filter it for all the the images of the year 2020 that lie within the geometries boundaries. Keep only the relevant bands and filter for cloud coverage.
var ls8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
                  .filterBounds(extent_lebanon)
                  .filterDate('2020-07-01', '2020-07-31')
                  .select('B1','B2','B3','B4','B5','B6','B7')
                  .filter(ee.Filter.lt('CLOUD_COVER', 10));

//Print your ImageCollection to your console tab to inspect it
print(ls8, 'Image Collection Lebanon July 2020');
Map.centerObject(ls8,9)


//Step 3: Create a single Image by reducing by Median and clip it to the extent of the geometry
var ls8_median = ls8.median()
                    .clip(extent_lebanon);

//Print your Image to your console tab to inspect it
print(ls8_median, 'Median reduced Image Lebanon July 2020');

//Add your Image as a map layer
var visParams = {'min': 400,'max': [4000,3000,3000],   'bands':'B5,B4,B3'};
Map.addLayer(ls8_median, visParams, 'S2 Lebanon July 2020 Median');

Calculating the NDVI

//Step 4: Calculate the NDVI manually: NDVI = (B5 - B4) / (B5 + B4)
//this can be achieved using either simple band operations, .expression or .normalizedDifference
//Variant 1: Simple band operations
var nir = ls8_median.select('B5');
var red = ls8_median.select('B4');
var ndvi = nir.subtract(red).divide(nir.add(red)).rename('NDVI');
print(ndvi, 'NDVI Lebanon July 2020 V1')

// Display the result.
var ndviParams = {min: -1, max: 1, palette: ['blue', 'white', 'green']};
Map.addLayer(ndvi, ndviParams, 'NDVI Lebanon July 2020 V1');

//Variant 2: .expression
var ndvi_2 = ls8_median.expression(
                      '(NIR-RED)/(NIR+RED)', {
                        'NIR' : ls8_median.select('B5'),
                        'RED' : ls8_median.select('B4')
                      })
                      .rename('NDVI');

print(ndvi_2, 'NDVI Lebanon July 2020 V2')

//define special visualization parameters for the ndvi
Map.addLayer(ndvi_2, ndviParams , 'NDVI Lebanon July 2020 V2');

//Variant 3: .normalizedDifference(NIR, RED)
//find out how .normalizedDifference works by checking Docs -> ee.Image -> normalizedDifference
var ndvi_3 = ls8_median.normalizedDifference(['B5', 'B4'])
                      .rename('NDVI');
print(ndvi_3, 'NDVI Lebanon July 2020 V3');

//You can also create more complex colour palettes via hex strings.
//this color combination is taken from the Examples script Image -> Normalized Difference:
var palette = ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718',
               '74A901', '66A000', '529400', '3E8601', '207401', '056201',
               '004C00', '023B01', '012E01', '011D01', '011301'];
//Please keep in mind that for this palette, you should set your minimum visible value to 0, as it s designed for this purpose.
//This is due to it being a gradient from brown to green tones, with a heavy focus on the green side. If we would set min: -1, NDVI = 0 would already be displayed in a dark green tone.
//You can recognize this by checking the palette-section of your layer information for ndvi_3.

// Display the input image and the NDVI derived from it.
Map.addLayer(ndvi_3, {min: 0, max: 1, palette: palette}, 'NDVI Lebanon July 2020 V3')

Now that we have calculated and displayed the NDVI in three different variants, we can again use the Inspector to see that the pixel values are always identical!


We can now compare the NDVI results of S2 and LS8. If you zoom in close enough, you can easily see how the spatial resolution of Sentinel-2 is superior to that of Landsat 8.