This is part of a series on tips for getting the most out of your geospatial applications. Check back regularly or follow HySpeed Computing to see the latest examples and demonstrations.
Objective: Calculate a collection of vegetation indices for hyperspectral and multispectral imagery using ENVI’s Vegetation Index Calculator.
Scenario: In this tip, vegetation indices are calculated for two variants of AVIRIS data from Jasper Ridge, California: one version using the full range of 224 possible hyperspectral bands (400-2500 nm); and the other using a version that has been spectrally convolved to match 8 of the 11 possible multispectral bands of Landsat 8 OLI (i.e., all bands except the thermal and panchromatic).
The AVIRIS data (JasperRidge98av_flaash_refl; shown below) was obtained from the ENVI Classic Tutorial Data available from the Exelis website, and has already been corrected to surface reflectance using FLAASH.
Vegetation Indices: There are numerous vegetation indices included in ENVI, so in most cases there is already a vegetation tool available that meets your needs. These indices can be found in three main locations within the ENVI Toolbox: (1) Spectral > Vegetation; (2) SPEAR > SPEAR Vegetation Delineation; and (3) THOR > THOR Stressed Vegetation.
The core functionality for deriving vegetation properties in ENVI is the Vegetation Index Calculator (located in Toolbox > Spectral > Vegetation). This tool provides access to 27 different vegetation indices, and will conveniently pre-select the indices that can be calculated for a given input image dependent on the spectral characteristics of the data. Despite this bit of assistance, however, properly implementing and interpreting the various vegetation indices still requires thorough understanding of what is being calculated. To obtain this information, details and references for each index are provided in the ENVI help documentation.
- Broadband Greenness [5 indices]: Normalized Difference Vegetation Index, Simple Ratio Index, Enhanced Vegetation Index, Atmospherically Resistant Vegetation Index, Sum Green Index.
- Narrowband Greenness [7 indices]: Red Edge Normalized Difference Vegetation Index, Modified Red Edge Simple Ratio Index, Modified Red Edge Normalized Difference Vegetation Index, Vogelmann Red Edge Index 1, Vogelmann Red Edge Index 2, Vogelmann Red Edge Index 3, Red Edge Position Index.
- Light Use Efficiency [3 indices]: Photochemical Reflectance Index, Structure Insensitive Pigment Index, Red Green Ratio Index.
- Canopy Nitrogen [1 index]: Normalized Difference Nitrogen Index
- Dry or Senescent Carbon [3 indices]: Normalized Difference Lignin Index, Cellulose Absorption Index, Plant Senescence Reflectance Index.
- Leaf Pigment [4 indices]: Carotenoid Reflectance Index 1, Carotenoid Reflectance Index 2, Anthocyanin Reflectance Index 1, Anthocyanin Reflectance Index 2.
- Canopy Water Content [4 indices]: Water Band Index, Normalized Difference Water Index, Moisture Stress Index, Normalized Difference Infrared Index.
There are also five additional vegetation tools included in Toolbox > Spectral Vegetation. The Vegetation Suppression Tool essentially removes the spectral contributions of vegetation from the image. The NDVI tool simply provides direct access to the commonly used Normalized Difference Vegetation Index. And the three other tools consolidate select subsets of the above vegetation indices into specific application categories: Agricultural Stress Tool, Fire Fuel Tool, and Forest Health Tool.
Two additional vegetation tools are also available as part of the THOR and SPEAR toolboxes. The THOR Stressed Vegetation and the SPEAR Vegetation Delineation tools both provide workflow approaches to calculating vegetation indices, inclusive of options such as atmospheric correction, mask definition, and spatial filtering. The SPEAR Vegetation Delineation tool uses NDVI to assess the presence and relative vigor of vegetation, whereas the THOR Stressed Vegetation tool provides a step-by-step methodology for processing imagery using the same suite of vegetation indices as defined for the Spectral toolbox.
It is important to note that input images should be atmospherically corrected prior to running the vegetation tools, or in the case of the SPEAR and THOR tools atmospherically corrected as part of the image processing workflow.
The Tip: This example demonstrates the steps used for running ENVI’s Vegetation Index Calculator. Interested users are also encouraged to download the tutorial data from Exelis, or use their own data, and explore what the other vegetation tools have to offer.
- As specified above, two sets of imagery are used in this example: one is the full AVIRIS hyperspectral dataset, and the other is a spectrally convolved Landsat 8 OLI multispectral dataset of the same image.
- After opening the images in ENVI, the vegetation tool is started by selecting Spectral > Vegetation > Vegetation Index Calculator.
- The opening dialog window is used to specify the Input File along with any desired Spatial Subset and/or Mask Band.
- Next is the main dialog for selecting Vegetation Indices and specifying the Output Filename. There is also an option for Biophysical Cross Checking, which compares results from different indices and masks out pixels with conflicting data values. Using Biophysical Cross Checking is application dependent, but can be useful for removing anomalous pixels from your analysis.
- As illustrated below, the general process for calculating Vegetation Indices is always the same for any given dataset; the only difference is the list of vegetation indices that are actually available for a particular set of bands. In our example, the full AVIRIS hyperspectral dataset allows for 25 different indices to be calculated, whereas the Landsat 8 LOI multispectral dataset allows only 6 indices.
- Once you have selected the relevant vegetation indices for your application, simply select OK and the Vegetation Index Calculator will generate an output file with individual bands corresponding to each of the selected vegetation indices.
Shown below is the output data from our two images, along with example quicklooks demonstrating the variability in the various output indices. The reason for this variability is that each index derives different, but related, biophysical information. Thus, be sure to look at the definitions and references for each index to help guide interpretation of the output.