The process of dividing the brightness values in one band of a multispectral dataset by a second band image is known as Band Ratioing. Ratioing is one of the most common image transformations in remote sensing because:

1. It can be used to emphasize certain aspects of the shape of the spectral signatures of different land covers, and;

2. It can be used to de-emphasize the effects of variable illumination within a scene.

The first of these characteristics makes band ratioing particularly useful for creating image products that are derived from the original multispectral dataset and are suited to identifying specific land-cover types. This is the reason why so many of the remote-sensing based vegetation indices are essentially ratios of two bands. However, it is the second property of band ratios listed above that we are most interested in for this lab.

The purpose of an image classification (see Part 2) is to identify meaningful land-cover classes within a scene. Differentiating between deciduous and coniferous forest classes is meaningful. It is, however, not particularly meaningful to differentiate between coniferous forest that is situated on a well-illuminated, sun-facing slope versus the in-shadow opposing slope side. However, these two illumination-varying classes areas will have apparently different radiometric properties in a multispectral data set. Band ratio images can be used to lessen the effect of uneven illumination caused by varying topography, based on the assumption that the ratio of two bands for areas of equivalent land-cover type is the same regardless of what direction the slope faces. Ratioing can help to reduce other causes of varying illumination as well, including the shadows cast by clouds.

After you have downloaded the data associated with this lab assignment from the CourseLink page, decompress (unzip) the data into a working directory that you have created to dedicate to this assignment. Open the contents of this folder and examine the files contained within. These data are the 30 m resolution bands, in GeoTIFF image format, of a subsection of a Landsat 8 scene acquired June 21, 2016. These data should contain six bands (i.e. bands 2 through 7) of image data, for an area of Southern Ontario between Kitchener-Waterloo, Cambridge, and Guelph. If you are unfamiliar with the southern Ontario area, you may also want to explore the area using Google Maps to familiarize yourself with the type of terrain and land-use/land-covers in the area of the image.

We will calculate a number of common normalized difference indices, a type of standardized band ratio, to serve as inputs for a later image classification performed in Part 2 of the lab assignment. Calculate the following image-derived products:

1. The normalized difference vegetation index: NDVI = (NIR - RED) / (NIR + RED)

2. The normalized difference water index version 1: NDWI1 = (NIR - SWIR1) / (NIR + SWIR1)

3. The normalized difference water index version 2: NDWI2 = (GREEN - NIR) / (GREEN + NIR)

4. The normalized burn ratio: NBR = (NIR - SWIR2) / (NIR + SWIR2)

5. The normalized blue-red ratio: NBRR = (BLUE - RED) / (BLUE + RED)

For Landsat 8, the spectral designations in the above equations relate to bands in the following way:

BLUE = Band 2

GREEN = Band 3

RED = Band 4

Near infrared (NIR) = Band 5

Shortwave infrared 1 (SWIR1) = Band 6

Shortwave infrared 2 (SWIR2) = Band 7

Write a script to use the WhiteboxTools' **NormalizedDifferenceRatio** tool to create each of the above normalized difference indices. Be sure to clip the distribution tails by 0.5%, and use a correction value of 0.0.

1.1. Include screenshots of each of the five indices, being sure to label each carefully and indicating the minimum and maximum values of each. (15 marks)

1.2. Include a copy of your Python script used to create the five normalized difference indices. (2 marks)

1.3. Compare each of the five indices to the natural-colour composite image. To what extent was the use of the band-ratioing technique able to lessen the apparent effects of cloud shadows in the image? (2 marks)