The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see vegetation as the colour green. If we could see near-infrared, then it would be strong for vegetation too. It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as well. The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the intensities of reflected light in the red and infrared range divided by the sum of these intensities. 푁퐷푉퐼=((푁퐼푅−푅퐸퐷))/((푁퐼푅+푅퐸퐷)) The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.

1. NORMALIZED DIFFERENCE
VEGETATION INDEX
KAMLESH KUMAR

2. The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near-
infrared (NIR) bands of the electromagnetic spectrum to analyze whether the target (image) being observed
contains green vegetation or not. Healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green
light compared to other wavelengths. But it absorbs more red and blue light. This is why our eyes see
vegetation as the colour green. If we could see near-infrared, then it would be strong for vegetation too.
It is basically measured through the use of Intensity, Hue and saturation of an image and through pixels as
well.
The density of vegetation (NDVI) at a certain point on the image is equal to the difference in the
intensities of reflected light in the red and infrared range divided by the sum of these intensities.
𝑁𝐷𝑉𝐼 =
(𝑁𝐼𝑅 − 𝑅𝐸𝐷)
(𝑁𝐼𝑅 + 𝑅𝐸𝐷)
The result of this formula generates a value between -1 and +1. If you have low reflectance (low values) in the
red band and high reflectance in the NIR, this will yield a high NDVI value. And vice versa.

3. 1. Run the app. Right click
on the image list on the
content tab to add an
image. Go to Open
Raster Layer.
2. Browse the file and
load it.
STEPS

4. 3. Go to Raster after the
image has been loaded.
Click on the Unsupervised
tab and select NDVI.
4. Make the
changes in the
Indices table as
shown in the Band
selection section
and set the
destination path
and click OKAY.

5. 5. Load the exported image alongside
the original one.
6. Go to Home and link the two images
as shown.

6. 7. Select the Inquire option in Home
tab.
8. You can move the cursor all
over the image to have at least 15
points for further observations.
Click the go to next linked
viewer as shown to change the
viewer table for both the images
alternatively. And note the File
Pixel for each separate point from
the NDVI image on the right.

7. Serial Pixel Value
1 -0.125
2 0.101
3 0.290
4 -0.198
5 0.467
6 0.579
7 0.400
8 0.109
9 0.000
10 0.667
11 0.565
12 0.391
13 0.429
14 0.132
15 0.493
Total 4.3
Average =
 𝑃𝑖𝑥𝑒𝑙 𝑣𝑎𝑙𝑢𝑒
𝑁
=
4.3
15
=0.287
CALCULATION OF THRESHOLD OF VEGETATION IN THE NDVI IMAGE
 The value being closer
to 0 pointing towards low
vegetation richness.

8. NDVI IMAGE
15 POINT MARKS

24. NDVI
MODEL MAKING

25. STEPS
1. Select the second image
ie. Panchromatic NDVI
image and go to Toolbox
and select Model Maker.
A dialogue box would pop
up as shown below.

26. 2. Make a model using a raster
object a function model
And linkages to inputs and outputs
3. Select each of the three models:
the input raster, function, commands
and finally the output image as
shown.

27. 4

28. 5

29. NOTE: 1 represents richness of vegetation while 0 represents the scarce
vegetation and 0.2 being the threshold for vegetation richness.

30. Finally export the file output
and load it alongside the
original image.

31. The area
represented for 0
and 1 represents
the values for
vegetation
concentration.

32. The area represented for 0 and 1 represents the values for vegetation concentration. Given the
resolution of the image in context i.e. 30X30. We can calculate the area covered under rich and
sparse vegetation as follows:
Richness (1):
= 433046 X 30 X 30
= 389741400 sq. mtrs.
= 389.74 sq. km
Sparse (0):
= 54154 X 30 X 30
= 48738600 sq. mtrs.
= 48.74 sq. km.

33. Signing off..