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The output of NDVI is a new image file/layer. Values of NDVI can range from -1.0 to +1.0, but values less than zero typically do not have any ecological meaning, so the range of the index is truncated to 0.0 to +1.0. Higher values signify a larger difference between the red and near infrared radiation recorded by the sensor - a condition associated with highly photosynthetically-active vegetation. Low NDVI values mean there is little difference between the red and NIR signals. This happens when there is little photosynthetic activity, or when there is just very little NIR light reflectance (i.e., water reflects very little NIR light).
Because of its ease of use and relationship to many ecosystem parameters, NDVI has seen widespread use in rangeland ecosystems. The uses include assessing or monitoring:
NDVI has been applied to many different aspects of rangeland ecology and management. Below is a partial listing of application references arranged by topic.
Vegetation Dynamics / Phenology change over time
Grazing Impacts / Grazing Management
Vegetation / Land Cover Classification
Soil Moisture Estimation
Carbon Sequestration / CO2 flux
The following technical references describe the theory behind NDVI and how it was developed.
The NDVI is correlated with a number of attributes that are of interest to rangeland ecologist and managers (e.g., percent cover of bare ground and vegetation, biomass). It is not, however, a direct measure of any of these things - it is a measure of “greenness” produced by the ratio of infrared and red light that is reflected from the surface. While the biophysical interpretation of NDVI is the fraction of photosynthetically active radiation (fPAR)absorbed by the surface, there are a lot of factors that influence the strength of the relationship between NDVI and rangeland ecosystem attributes. These can include: atmospheric conditions, scale of the imagery, vegetation moisture, soil moisture, overall vegetative cover, differences in soil type, management, etc… It is important when using NDVI data in analyses that steps be taken to understand and, to the extent possible, control for factors that might be affecting NDVI values before interpretations of differences in NDVI between areas of within the same area over time can be made.
Light from the soil surface can influence the NDVI values by a large degree. This is of concern in rangeland applications because many semi-arid and arid environments tend to have higher cover of bare ground and exposed rock than other temperate or tropical habitats. Heute and Jackson (1988) found that the soil surface impact on NDVI values was greatest in areas with between 45% and 70% vegetative cover. This limitation was the reason for the development of the several different soil-adjusted vegetation indices (e.g., Soil-adjusted Vegetation Index, Modified Soil-adjusted Vegetation Index), and these indices tend to be preferred for rangeland applications.
In addition to the influence of soil surface at the low-end of vegetation cover, NDVI also suffers from a loss of sensitivity to changes in amount of vegetation at the high-cover/biomass end. This means that as the amount of green vegetation increases, the change in NDVI gets smaller and smaller. So at very high NDVI values, a small change in NDVI may actually represent a very large change in vegetation. This type of sensitivity change is problematic for analysis of areas with a high amount of photosynthetically active vegetation. This could be an issue in rangeland ecosystems if you were interested in assessing changes in riparian areas. In these situations, it may be advisable to use another vegetation index with better sensitivity to high-vegetation cover situations like the Enhanced Vegetation Index or the Wide Dynamic Range Vegetation Index.
The inputs for NDVI are pretty simple. You need an image with a red band and a near infrared band.
The software and hardware requirements for calculating and working with NDVI data are generally low. To calculate NDVI, you need some sort of image processing program (e.g., ERDAS Imagine, ENVI, IDRISI) or GIS program that can handle raster calculations (e.g., ESRI ArcGIS, GRASS). Many programs have functions specifically designed to calculate NDVI, but if not, it is fairly easy to implement the equation to calculate it manually. The hardware requirements, in terms of processing capability and disk space, will largely be determined by the type and size of imagery you have.
Image courtesy of the Idaho Chapter of The Nature Conservancy
Example of an NDVI image calculated from an Ikonos image on an approximately 1 mi2 area in Owyhee County, Idaho. The “true” color image (upper-left) shows encroaching juniper woodlands grading into a mosaic of montane sagebrush and semi-wet meadows. The Red (upper-right) and Near Infrared (lower-left) bands for this area each highlight different aspects of the area. From the NDVI image (lower-right), however, the junipers and semi-wet meadows are easily distinguishable.
Image courtesy of the Idaho Chapter of The Nature Conservancy
An example of the change in NDVI over a growing season with changes in plant phenology. Top images are false-color composites of Landsat TM5 images. Bottom images are NDVI calculated from the Landsat TM5 image (bands 4 and 3). Example is for the 45-Ranch on the South Fork of the Owyhee River, Owyhee County, Idaho.
NDVI is easy to calculate from a wide range of different image sources. In terms of already prepared NDVI data, MODIS data are processed into several different vegetation indices and made available on 16-day, monthly, and yearly intervals at different resolutions. MODIS NDVI data can be downloaded from NASA's Warehouse Inventory Search Tool (WIST) or from the Global Land Cover Facility.
Prepared MODIS and AVHRR NDVI datasets are also used in online tools like RangeView. While these NDVI datasets cannot be downloaded directly, they can be viewed and used for simple analyses via the website.
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