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Lab 4_ Spectral indices and transformations.docx

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Lab 4: Spectral indices and

transformations

Purpose: The purpose of this lab is to give you a tour of spectral indices that can be used to

enhance phenomena of interest in remotely sensed images. You will be introduced to methods

for creating vegetation, water, snow, bare and burned area indices. You will explore the

Tasseled-cap and principal components transforms. At the completion of the lab, you will be

able to implement spectral indices and transforms to accentuate the information of interest in

your study area.

Prerequisites: Lab 3

1. Spectral indices

Spectral indices are based on the fact that reflectance spectra of different land covers are

different. The indices are designed to exploit these differences to accentuate particular land

cover types. Consider the following chart of reflectance spectra for various targets:

Observe that the land covers are separable at one or more wavelengths. Note, in particular,

that vegetation curves (green) have relatively high reflectance in the NIR range, where radiant

energy is scattered by cell walls (Bowker et al. 1985). Also note that vegetation has low

reflectance in the red range, where radiant energy is absorbed by chlorophyll. These

observations motivate the formulation of vegetation indices, for example:

a. NDVI.

The Normalized Difference Vegetation Index (NDVI) has a long history in remote sensing. The

typical formulation is

NDVI = (NIR - red) / (NIR + red)

Where NIR and red refer to reflectance, radiance or DN at the respective wavelength.

Implement indices of this form in Earth Engine with the normalizedDifference() method.

First, get an image of interest by drawing a Point named point, importing the Landsat 8

Collection 1 TOA as landsat8 and sorting the collection by cloud cover metadata:

var image = ee.Image(landsat8

.filterBounds(point)

.filterDate('2015-06-01', '2015-09-01')

.sort('CLOUD_COVER')

.first());

var trueColor = {bands: ['B4', 'B3', 'B2'], min: 0, max: 0.3};

Map.addLayer(image, trueColor, 'image');

The NDVI computation is one line:

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

Display the NDVI image with a color palette (feel free to make a better one):

var vegPalette = ['white', 'green'];

Map.addLayer(ndvi, {min: -1, max: 1, palette: vegPalette}, 'NDVI');

Use the Inspector to check pixel values in areas of vegetation and non-vegetation.

b. EVI.

The Enhanced Vegetation Index (EVI) is designed to minimize saturation and background

effects in NDVI (Huete et al. 2002). Since it is not a normalized difference index, compute it

with an expression:

var evi = image.expression(

'2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {

'NIR': image.select('B5'),

'RED': image.select('B4'),

'BLUE': image.select('B2')

});

Observe that bands are referenced with the help of an object that is passed as the second

argument to image.expression(). Display EVI:

Map.addLayer(evi, {min: -1, max: 1, palette: vegPalette}, 'EVI');

Compare EVI to NDVI. What do you observe?

c. NDWI.

The Normalized Difference Water Index (NDWI) was developed by Gao (1996) as an index of

vegetation water content:

NDWI = (NIR - SWIR) / (NIR + SWIR)

Compute NDWI in Earth Engine with:

var ndwi = image.normalizedDifference(['B5', 'B6']);

And display:

var waterPalette = ['white', 'blue'];

Map.addLayer(ndwi, {min: -0.5, max: 1, palette: waterPalette},

'NDWI');

Note that this is not an exact implementation of NDWI, according to the OLI spectral response,

since OLI does not have a band in the right position (1.26 m).𝛍

d. NDWBI.

It's unfortunate that two different NDWI indices were independently invented in 1996. To

distinguish, define the Normalized Difference Water Body Index (NDWBI) as the index

described in McFeeters (1996):

NDWBI = (green - NIR) / (green + NIR )

As previously, implement NDWBI with normalizedDifference() and display the result:

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