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# Contributed by CEOS SEO/AMA

​Spectral Index algorithms calculate the relative magnitudes of wavelength components. The particular wavelength components used determine the product calculated. The example Notebook and tutorial here cover the following algorithms:

Normalized Difference Vegetation Index (NDVI)

Represents the density of green (vegetation) on a patch of land. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected, respectively. NDVI = (NIR – Red) / (NIR + Red).

Normalized Difference Buildup Index (NDBI)

A common index for detecting urbanization, where NDBI = (SWIR1 – NIR) / (SWIR1 + NIR). This index is similar to NBR (below).

Normalized Burn Ratio (NBR)

Similar to NDBI (reverse sign), this index is used to estimate burn severity. NBR = (NIR – SWIR1) / (NIR + SWIR1). Typical values are: <-0.1 for post fire regrowth, -0.1 to +0.1 for unburned, 0.1 to 0.27 for low-severity burn, 0.27 to 0.66 for moderate severity burn, and >0.66 for high severity burn.

Normalized Difference Water Index (NDWI)

A common index for detecting water, where NDWI = (Green – NIR) / (Green + NIR).

Modified Normalized Difference Water Index (mNDWI) or Normalized Difference Snow Index (NDSI)

A common index for detecting water (mNDWI) or snow (NDSI), where mNDWI = NDSI = (Green – SWIR1) / (Green + SWIR1).

A vegetation index adjusted for soil response, where SAVI = (NIR – Red) / (NIR + Red + 0.5)*1.5.

Enhanced Vegetation Index (EVI)

An "optimized" vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions. Landsat EVI = 2.5 * (NIR – Red) / ( NIR + 6*Red – 7.5*Blue + 1).

Fractional Cover (FC)

Estimates the average vegetation fractional cover using a linear un-mixing technique developed by Juan P. Guerschman (CSIRO). Each pixel is classified as a fraction of bare soil (BS), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV).

Spectral Variability Vegetation Index (SVVI)

An index by Lloyd Coulter (San Diego State University) based on the standard deviation of a Landsat 7 time series mosaic, where SVVI = STD (1,2,3,4,5,7) – STD (4,5,7). This algorithm has been used to detect forests and agriculture. Explore the Application

View the Notebook and sample outputs here.

Want to use the example Notebook on your own Data Cube and data?

Download the Notebook from GitHub here and run it on your own Cube in a Box installation.

Tutorial

The video provides a run through of the example Notebook linked above, with the steps listed here:​

1. All DCAL notebooks begin by specifying a location, time window, and product of interest.

2. The extents and products are examined and selected in the first 6 cells of the notebook, with user options available in 2nd and 4th cells, marked by "# CHANGE HERE >>>>>>>>>>>>>>>>>>".

3. Any values within the bounds detailed in the 3rd cell will work, and it is suggested to keep the region and time period small. The resulting area is shown in red in cell 5.

4. You can click on the map to query the coordinates under the cursor.

5. In cell 6, you load a region of the Data Cube using the selected extents, products and measurements. The resulting data are summarised in cell 7.

6. In cells 8-11, a cloud free mosaic is created. For more information on this process, see our dedicated cloud free mosaic DCAL entry.

7. Land Fractional Cover is calculated in cells 12 and 13, and Land Spectral Indices are calculated in cells 14-20. Various methods of visualising the results are shown in cells 21-28. No changes need to be made here.

8. Water Spectral Indices (NDWI and WOfS) are demonstrated in cells 29-32.

9. The remaining cells follow the customisation of a Threshold Plot. In these, you can define levels at which sharp colour changes occur in images, which can be used to highlight different features.

10. Finally, the results can be exported as GeoTIFF files for download.

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