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Using the NDVI process
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About the NDVI process

The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing you to generate an image displaying greenness (relative biomass). This index takes advantage of the contrast of the characteristics of two bands from a multispectral raster dataset—the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared (NIR) band.
An NDVI is often used worldwide to monitor drought, monitor and predict agricultural production, assist in predicting hazardous fire zones, and map desert encroachment. The NDVI is preferred for global vegetation monitoring because it helps to compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors (Lillesand 2004).

The differential reflection in the red and infrared (IR) bands enables you to monitor density and intensity of green vegetation growth using the spectral reflectivity of solar radiation. Green leaves commonly show better reflection in the near-infrared wavelength range than in visible wavelength ranges. When leaves are water stressed, diseased, or dead, they become more yellow and reflect significantly less in the near-infrared range. Clouds, water, and snow show better reflection in the visible range than in the near-infrared range, while the difference is almost zero for rock and bare soil. The NDVI process creates a single-band dataset that mainly represents greenery. The negative values represent clouds, water, and snow, and values near zero represent rock and bare soil.

The equation ArcGIS Image Server uses to generate the output is as follows:

NDVI = arctangent((IR – R)/(IR+R))
IR = pixel values from the infrared band
R = pixel values from the red band


This produces a single-band dataset, mostly representing greenness, where any negative values are mainly generated from clouds, water, and snow, and values near zero are mainly generated from rock and bare soil. This index outputs values between -1.0 and 1.0. Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8) (Ref: http://earthobservatory.nasa.gov/Library/MeasuringVegetation).

Below are examples of a Landsat 7,4,3 band combination (left) and an NDVI using a color map that highlights the agricultural activity of the area (right).

Landsat 7,4,3 band combination NDVI example

Adding the arctangent to the standard NDVI equation allows ArcGIS Image Server to scale the output NDVI pixel values to improve the appearance of the displayed image.

Even though the NDVI formula has an output of -1.0 to 1.0, the NDVI process scales this output to 0 to 255, thereby creating a one-band 8-bit output image. You might need to enter scale and offset values to create the value range you will be using with a color map.

References


Chen, Zhikang, et al., "Vegetation Change Detection Using High Spectral Resolution Vegetation Indices," Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, R. S. Lunetta and C. D. Elvidge (Eds.), pp. 181–190. Ann Arbor Press, Chelsea, Michigan, 1998.
Lillesand, T. M., et al., Remote Sensing and Image Interpretation, fifth edition. John Wiley & Sons, Inc., New York, New York, 2004.
Piwowar, J. M., "Digital Image Analysis," Remote Sensing for GIS Managers, S. Aronoff (Ed.), pp. 287–335. ESRI Press, Redlands, California, 2005.


How to apply the NDVI process

Applying the NDVI process

Below is a workflow you can use to apply the NDVI process to a Landsat scene. You can easily adapt this to any raster dataset with red and near-infrared bands.

  1. Create a new image service definition using the defaults—three bands, unsigned 8-bit integer pixel type, 8 bits, RGB—and specify the spatial reference system specific to your dataset.

  2. Add the raster data by clicking the Image Service drop-down menu on the Image Service Definition Editor toolbar, pointing to Advanced, then clicking Add Raster Dataset and adding the Landsat scenes using the Landsat-7 Level 1G raster type in the Georeferenced Imagery folder. Specify the input for band 1 as Band-3 and the input for band 2 as Band-4; the inputs for the other bands do not matter, since they are not being used. Also uncheck Pan-sharpening, leave Sharpen Method as None, and set Stretch Method on the Enhancement tab to None.

  3. Add the NDVI process to the service by opening the Image Service Properties dialog box and clicking the Service Processes node from the list on the left. This displays the interface for adding processes to the service. Add the NDVI process to the selected list and open it using the Edit button Edit Process button. Type "1" in the Red band text box and "2" in the Near-infrared band text box, because when adding the Landsat data to the image service, you defined band 1 as red (3) and band 2 as NIR (4). Next, specify an offset of 50 and leave the scale as 1. The result of this process is a single-band NDVI computation. The next process will enable visualizing this output as an RGB image.

  4. Add the Colormap process to the selected list and open it using the Edit button Edit Process button. Specify a predefined Color Table Source file.
    5. You have completed the required process chain and can now close the Image Service Properties dialog box.

  5. Build the image service definition with the Compute Output Properties options checked on, as the output image properties may have changed from an image of two or more bands to a one-band image.

  6. Preview the image service.

  7. Compile the image service definition.


Creating a .clr file for the NDVI process


To create a .clr file for an NDVI, copy the text below to a text file and save the file with a .clr extension.

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