 This video will show you how to compute the Normalized Difference Vegetation Index, or NDVI, using ArcGIS. NDVI can be computed from multispectral, remotely sensed data using the formula of the near-infrared band minus the red band, divided by the near-infrared band plus the red band. This yields values that range from negative 1 to positive 1, with negative 1 values indicating low vegetation content, and values closer to 1 indicating high vegetation content. For the demonstration we're going to be working with a Landsat 8 scene of western Vermont, there's Lake Champlain, there's the Green Mountains over on the east, covered by clouds, and there's Burlington Vermont, Vermont's largest city. I created this image by stacking together bands 1 through 7 from Landsat 8. Now let's create a color infrared composite by going to the layer symbology and displaying the near-infrared band to the red color gun, the red band to the green color gun, and the green band to the blue color gun, creating a 5-4-3 composite. There are a number of ways to compute NDVI in ArcGIS, but the quickest and easiest is to go to the Windows menu and activate the image analysis tools. First thing to do with the image analysis tools is to click on the button to open the image analysis options. Once in the image analysis options window, click on the tab for NDVI. We're going to uncheck the use wavelength button because we don't have wavelength metadata, and we're going to check the box for scientific output because we want our values to range from negative 1 to 1. We're then going to adjust the band combinations. Band 5 in this particular Landsat image corresponds to the near-infrared band, and band 4 corresponds to the red band. So we'll set band 5 and 4 as the infrared band and red band respectively, and then click OK to activate those settings. Now we're ready to compute NDVI. First confirm that the image layer we want to use is selected, and then we'll scroll down and under Processing and click on the NDVI icon, which looks like a leaf. Using the image analysis functions, we can compute NDVI in a matter of seconds, but it's important to note that the NDVI layer that we've created is only temporary. To improve the display of the NDVI layer, we're going to go into our layer properties and under the Symbology tab, adjust the color ramp. We use a red to green color ramp, where green represents high vegetation content and red low vegetation content. Let's examine our output. Lakes in urbanized areas that have very low amounts of vegetation have corresponding low NDVI values. Clouds also have low NDVI values. If we look in the shattered areas of the clouds, we can see that NDVI values are impacted, meaning that within the shattered areas, the NDVI values tend to be lower than similar vegetation in non-shattered areas. Zooming into downtown Burlington gives us a chance to look at NDVI values in an urbanized area. We see that the city center, which has very few large patches of vegetation, has low NDVI values, but surrounding areas, even residential areas with large tree canopy, do have higher NDVI values. Now let's move south to a landscape that's dominated more by agricultural ranges. These fields with active healthy crops have high NDVI values. NDVI values tend to be low in those fields where the crops have either been recently harvested, exposing the bare soil, or the crops are in poor health. Finally, we'll take a look at some wetland vegetation adjacent to a river. We see a mix of NDVI values. The lower NDVI values in those areas where we have predominantly water, and the higher NDVI values are those areas where the vegetation is obscuring the water. To make the NDVI layer a permanent raster, we'll go back into the image analysis tools and click on the export icon. This launches the export raster data window. The first step is going to be to specify the location for this new raster file. After we've reset the location, we'll go through and give it a file name. In this particular case, I've used the .tif extension to export the raster as a geotiff. Once we're set, we'll click save and a new raster layer is produced, which we're adding to our ArcMap document. I'm going to remove the temporary NDVI raster and go into the symbology for our newly added raster and give it a red-green color ramp. Now we'll take a look at how you can use layer symbology to determine a threshold to differentiate vegetated areas from unvegetated areas. Within the layer symbology, I'm going to select the classified option for raster display, changing the number of clauses to two, and applying a red-green color ramp so that red means not vegetated and green means vegetated. Clicking on the classify button will bring up the histogram, and I can use the slider to attempt to determine the appropriate threshold value for non-vegetated and vegetated pixels. In addition to using the slider bar, I also have the option to enter the break value manually by typing it in under break values. We can now use this threshold value in the raster calculator to create a new raster layer that consists of vegetation and non-vegetation. Opening up the raster calculator, we're going to use our NDVI layer in the expression and simply say greater than 0.25, which we determined to be the threshold value. We're going to output this to a new raster layer in geotiff format, so we're adding the .tif extension at the end. Clicking OK will execute the raster calculator operation. The pixels in our resulting raster layer have values of 0 and 1. Zero means the NDVI was less than or equal to 0.25, and one means the NDVI value was greater than 0.25. To evaluate how well we did with this threshold, we can use the effects toolbar and the swipe tool to swipe our binary layer over the original image dataset.