How to use NDVI in ArcGIS
In agriculture, forestry and ecology, these fields use Normalized Difference Vegetation Index or NDVI maps to assess vegetation. But what is NDVI?
In general, NDVI uses two properties to quantify healthy vegetation. It uses near-infrared (NIR) because vegetation strongly reflects it. And it also uses red light, because plants strongly absorb it. For this same reason, this is why our eyes see vegetation as the color green.
Now that you have a bit of background on NDVI, you can easily create NDVI maps in ArcGIS.
Simply, follow these 4 steps.
How to create NDVI maps in ArcMap
You can create NDVI maps with the image analysis toolbar in ArcGIS 10. All you need is imagery with red and NIR bands. For example, you can download Sentinel-2 or Landsat data from this list of 15 free satellite imagery sources.
In this tutorial, we want to classify high and low vegetation. Pixels with high NDVI values indicate high vegetation or chlorophyll. Whereas, low NDVI values generally means less vegetation. Furthermore, negative NDVI values is a good indicator that it’s water.
In this NDVI example, we use Worldview-2 (WV-2) which is multispectral imagery. We say multispectral because it has multiple bands like red, green, blue and near-infrared (NIR). The band configuration for WV-2 is:
Worldview-2 Band Configuration
Coastal as Band 1
Blue as Band 2
Green as Band 3
Yellow as Band 4
Red as Band 5
Red Edge as Band 6
Near Infrared 1 (NIR-1) as Band 7
Near Infrared 2 (NIR-2) as Band 8
Let’s examine this agricultural area with center pivot irrigation. As this type of irrigation rotates on a pivot in a circle, it creates these circular crop patterns.
If you want to display true color, select the red, green and blue bands in layer properties. We say true color because it is the same as how our eyes see. In the case of Worldview-2, this is would be band 5 as red, band 3 as green and band 2 as blue.
But if you want to display color infrared, you need band 7 as the red channel, band 5 as the green channel and band 3 as the blue channel. We say color infrared because near infrared is in the red channel. As you can see below, the pivot irrigation vegetation should already be shouting out at you in bright red!
Step 1: Enable Image Analysis Toolbar
Enable the Image Analysis Toolbar (Windows > Image Analysis). The image analysis window will be displayed in ArcMap.
Step 2: Check Scientific Output Properties
Under image analysis options, select the red band and the near infrared band.
For Worldview-2 imagery, under the NDVI tab – the red band is “band 5” and the NIR band is “band 7”.
Optionally select “Scientific Output” so your values range from -1 to 1.
Step 3: Click NDVI Icon
Highlight your layer by clicking it.
Under properties, Select the NDVI icon which looks like a leaf.
This will create temporary layer in the table of contents. Bright green indicates high NDVI. Whereas red has low NDVI.
Step 4: Export Raster
Highlight the new NDVI layer that you want to export by selecting it in the image analysis toolbar.
Right click layer, and export raster to save into memory.
High positive NDVI values (green) means high vegetation. While water usually has negative NDVI values (yellow and red). In general, urban features usually are near zero.
How do you use NDVI maps?
Normalized Difference Vegetation Index (NDVI) uses the NIR and red channels to measure healthy vegetation. If you want to calculate it manually, this is the formula you can use.
NDVI always generates a value between -1 and +1. It’s really just a standardized way to measure healthy vegetation. But how do you apply NDVI as a remote sensing application?
In the example above, agriculture uses NDVI for precision farming. If you look very closely in the NDVI image, you can see where crop growth isn’t as good. In this case, farmers can increase crop yield by knowing exactly where unhealthy vegetation is. Here are other GIS applications on the farm.
For over 20 years, AVHRR has recorded NDVI. Because we know how NDVI changes over time, this helps us understand about vegetation growth on Earth. In forestry, this helps them understand about leaf area index, forest supply and fire danger