The soil-adjusted total vegetation index (SATVI) is a modification of several earlier vegetation indices that correlates with the amount of green and senescent vegetation present on the ground. Commonly used vegetation indices like NDVI and SAVI are sensitive to the amount of green (i.e., photosynthesizing) vegetation, but generally do not correlate well with the amount of senescent or dead vegetation. This prompted Qi et al. (see Marsett et al. 2006) to develop the normalized difference senescent vegetation index (NDSVI) to map dried (non-photosynthesizing) vegetation. They then combined the NDSVI with SAVI to create the SATVI to index both green and senescent vegetation.
The output of SATVI calculation is an index of the amount of green AND senescent (i.e., brown) vegetation for each pixel. SATVI values range from -1 (no green or senescent vegetation) to +1 (complete coverage by green vegetation).
The SATVI has not seen as widespread adoption as the other vegetation indices (perhaps because of the requirement of short-wave infrared bands which restrict it to just a couple of existing sensors). Time-series calculations of SATVI have been developed as one of several products in a couple of prototype online decision support systems for looking at the changes in green-up and biomass accumulation over time. For more information, see Marsett et al. (2006) below as well as http://www.rangesllc.com and this NASA grant summary.
The biggest limitation of SATVI for rangeland applications is that it requires a short-wave infrared band in order to calculate it. The restricts it's application to moderate to coarse-resolution sensors like Landsat TM, ASTER (30m resolution only, not 15m), and MODIS. Thus getting a high-resolution SATVI is usually not possible.
Another limitation of SATVI is that some types of rock that reflect a high proportion of short-wave infrared light will have high SATVI values suggesting that there is vegetation there when in fact these areas are barren (see example below). You can generally work around this problem by first using an index like MSAVI to identify and filter out unvegetated areas and then calculate SATVI for only the vegetated portions of the image.
The SATVI can be calculated from only image information without any empirical field data. With field data, though, the SATVI can be converted into percent cover estimates of green and senescent vegetation (see Fractional Cover). The SATVI requires bands from three different wavelength regions: red (~630 to 690nm), short-wave infrared #1 (~1,550 to 1,750nm) and short-wave infrared #2 (~2,090 to 2,350nm). These correspond to Landsat TM bands 3, 5, and 7, respectively. The formula for calculating SATVI from Landsat data is:
where ρ is the reflectance value for the TM bands and L is constant (related to the slope of the soil-line in a feature-space plot) that is usually set to 0.5.
The only special requirement for calculating SATVI is an image processing program that will allow you to do mathematical operations on an image in a cell-by-cell fashion. Programs like ERDAS, ENVI, IDRISI, or Spring all allow you to do this easily. Scripts can be written for GIS programs like ArcGIS to calculate indices like SATVI without too much effort as well.
Few existing applications have implemented SATVI to date.
*Applied Geosolutions is developing a prototype decision support system that implements frequently updated SATVI layers calculated from MODIS images to monitor changes in vegetation cover in Arizona over a growing season.
* The Idaho Chapter of The Nature Conservancy is also implementing a similar system of multi-temporal SATVI layers for southern Idaho as part of it's Landscape Toolbox project.
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* Marsett, R.C., Qi, J., Heilman, P., Biedenbender, S.H., Watson, M.C., Amer, S., Weltz, M., Goodrich, D., Marsett, R. 2006. Remote sensing for grassland management in the arid southwest. Rangeland Ecology and Management 59:530-540.
* Qi, J., Marsett, R., Heilman, P., Beidenbender, S., Moran, S., Goodrich, D. 2002. RANGES improves satellite-based information and land cover assessments in southwest United States. Eos, Transactions, American Geophysical Union. 83:601-606.
* Qi, J., Marsett, R., Moran, M.S., Goodrich, D.C., Heilman, P., Kerr, Y.H., Dedieu, G., Chehbouni, A., Zhang, X.X. 2000. Spatial and temporal dynamics of vegetation in the San Pedro River basin area. Agricultural and Forest Meteorology. 105:55-68.
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