Welcome to the Harris Geospatial product documentation center. Here you will find reference guides, help documents, and product libraries.


Harris Geospatial / Docs Center / Using ENVI / Spectral Indices

ENVI

Spectral Indices

Spectral Indices

Spectral indices are combinations of surface reflectance at two or more wavelengths that indicate relative abundance of features of interest. Vegetation indices are the most popular type, but other indices are available for burned areas, man-made (built-up) features, water, and geologic features.

The following topics provide definitions and formulas of the indices, grouped by feature type:

You can also write a script to compute spectral indices using the ENVISpectralIndicesTask or ENVISpectralIndexTask routine.

You can also write your own mathematical expressions for custom indices using the Band Math tool. Or, use the Band Ratios tool to create color composites of band ratios.

The Spectral Indices tool creates an image that consists of one or more spectral indices, each as a separate band. Follow these steps:

  1. From the Toolbox, select Band Algebra > Spectral Indices.
  2. Select an input image. The image must contain wavelength metadata.
  3. The Spectral Indices dialog lists the indices that are available to compute, based on the wavelengths of the input raster. (See Band Assignments for details.) Select one or more indices to compute, using the Ctrl or Shift key.
  4. Select an output filename and location.
  5. Enable the Preview check box to see a preview of the settings before you click OK to process the data. The preview is calculated only on the area in the Image window and uses the resolution level at which you are viewing the image. See Preview for details on the results.
  6. Select the Display result option to display the output image when processing is complete.
  7. Click OK. ENVI adds the resulting output to the Data Manager and, if the Display Result check box was enabled, adds the layer to the Layer Manager and displays the output in the Image window. By default, the first spectral index in the output image is displayed. Open the Data Manager to list and display individual spectral index bands.

Preprocessing


Before applying spectral indices to imagery, raw pixel values (also called digital numbers or DN values) must be calibrated into physically meaningful units. The three most common radiometric corrections are radiance, top-of-atmosphere (TOA) reflectance, and apparent surface reflectance. Some literature suggests that spectral indices computed from any of these data types are technically correct, although each will yield different index results for the same surface conditions. However, the general consensus is that calibration to apparent surface reflectance yields the most accurate results with spectral indices. This is especially important for hyperspectral sensors such as AVIRIS and EO-1 Hyperion. Calibrating imagery to surface reflectance also ensures consistency when comparing indices over time and from different sensors.

The ENVI Radiometric Calibration tool calibrates imagery from most modern spaceborne sensors to radiance and TOA reflectance. An atmospheric correction tool such as FLAASH® or QUAC can further remove the effects of atmospheric scattering and gas absorptions to produce surface reflectance data, which typically range from 0 to 1. FLAASH and QUAC automatically scale reflectance data by 10,000 to produce integer data, which consumes less disk space.

Tip: Take the output image from FLAASH or QUAC and import it into the Apply Gain and Offset tool (or ENVIApplyGainOffsetTask in the ENVI API). Set the Gain Values for all bands to 0.0001. Keep the default value of 0 for Offset Values for all bands. Save this as a new raster. The result will have reflectance values that range from 0 to 1.

See the following tutorials for instructions on using FLAASH to correct hyperspectral and multispectral imagery:

Other tools such as Dark Subtraction, Empirical Line Correction, Flat Field Correction, and IAR Reflectance Correction provide a quick way to correct for atmospheric effects; these may be sufficient for preparing multispectral data for spectral indices.

Unless otherwise noted, ENVI's spectral indices assume that images have been calibrated to surface reflectance. Data typically range from 0 to 1.0, unless a different scale factor has been applied. In most cases, higher pixel values generally represent a higher abundance of the feature of interest (for example, burned areas or vegetation). Exceptions are noted in the descriptions of each spectral index.

Band Assignments


Many spectral index equations list categories of wavelength ranges such as "Red" or "NIR" (near-infrared). For example, NDVI uses the following equation:

This allows for some flexibility in applying the indices across a wide variety of sensors, since each sensor may have slightly different band centers for red and NIR. Unless otherwise noted, ENVI uses the following definitions for each wavelength range:

 

Minimum

Center

Maximum

Blue

400 nm

470 nm

500 nm

Green

500 nm

550 nm

600 nm

Red

600 nm

650 nm

700 nm

NIR

760 nm

860 nm

960 nm

SWIR1

1550 nm

1650 nm

1750 nm

SWIR2

2080 nm

2220 nm

2350 nm

Imagery used for spectral indices must include definitions of the center wavelengths for each band. Using NDVI as an example, ENVI:

  1. Finds the center wavelengths for each band of the input file
  2. Determines if the center wavelengths fall within the ranges listed above (600-700 nm for red, 760-960 nm for NIR)
  3. If more than one band meets the criteria for Step 2, it chooses the band nearest 650 nm for the Red term and the band nearest 860 nm for the NIR term.

Some spectral indices were designed for specific sensors. For example, the WorldView Built-Up Index uses WorldView-2 Coastal and Red Edge bands. In these cases, ENVI uses the appropriate bands from each sensor to compute the indices.

Narrowband Definitions

Other indices are designed for use with imaging spectrometers and therefore use specific wavelengths. An example is the Modified Red Edge NDVI:

In this case, ENVI assigns the band whose center wavelength is closest to each term of the equation. It allows a narrow tolerance of values for each wavelength term. If no bands fall within the allowable ranges for each wavelength, ENVI issues an error message saying the image does not contain the appropriate wavelengths for that index. The following table lists the ranges that ENVI allows for specific wavelengths used in equations. Values are in nanometers (nm).

 

Minimum

Maximum

ρ445

435

448

ρ450

425

475

ρ500

480

520

ρ510

500

515

ρ531

525

550

ρ550

540

560

ρ570

560

575

ρ670

650

690

ρ699

650

735

ρ700

680

730

ρ705

697

708

ρ715

714

716

ρ715 wide (VREI2)

710

719

ρ720

718

722

ρ726

725

727

ρ734

730

736

ρ740

730

750

ρ747

742

748

ρ750

730

780

ρ750 narrow (RENDVI)

730

760

ρ795 720 800

ρ800

780

865

ρ800 wide (ARVI)

750

870

ρ819

815

824

ρ857

854

860

ρ860

841

876

ρ900

860

910

ρ970

965

975

ρ990 830 995

ρ1241

1230

1250

ρ1510

1500

1515

ρ1599

1590

1620

ρ1640

1628

1652

ρ1649

1645

1655

ρ1680

1670

1690

ρ1754

1750

1758

ρ2000

1980

2040

ρ2100

2085

2110

ρ2130

2105

2155

ρ2200

2170

2220

References


Gu, Z., et al. "Using Multiple Radiometric Correction Images to Estimate Leaf Area Index." International Journal of Remote Sensing 32 (2011): 9441-9454.

Guyot, G., and G. Xing-Fa. "Effect of Radiometric Corrections on NDVI-Determined from SPOT-HRV and Landsat-TM Data." Remote Sensing of Environment 49, no. 3 (1994): 169-180.

Hadjimitsis, D., et al. "Atmospheric Correction for Satellite Remotely Sensed Data Intended for Agricultural Applications: Impact on Vegetation Indices." Natural Hazards Earth System Science 10 (2010): 89-95.

Jackson, R., and A. Huete. "Interpreting Vegetation Indices." Preventive Veterinary Medicine 11 (1991): 185-200.

Price, J. "Calibration of Satellite Radiometers and the Comparison of Vegetation Indices." Remote Sensing of Environment 21 (1987): 15-27.

Ray, T. "A FAQ on Vegetation in Remote Sensing." http://www.yale.edu/ceo/Documentation/rsvegfaq.html, updated 13 October 1994. Accessed February 2014.

Steven, M., et al. "Intercalibration of Vegetation Indices from Different Sensor Systems." Remote Sensing of Environment 88 (2003): 412-422.

Related Topics


Band Math, Band Ratios



© 2017 Exelis Visual Information Solutions, Inc. |  Legal
My Account    |    Buy    |    Contact Us