My Ssec Capstone Project Three indices should be covered in the detection of desertification through remote sensing which are the study of vegetation variation

Three indices should be covered in the detection of desertification through remote sensing which are the study of vegetation variation

Three indices should be covered in the detection of desertification through remote sensing which are the study of vegetation variation (normalized difference vegetation index (NDVI)), range of sand movement (CI), and determining the type of topsoil grain size index (GSI), which should be calculated respectively according to the equation of each index.
-NDVI: The normalized difference vegetation index(NDVI) is the most common form of vegetation indices. The normalized difference vegetation index is basically the difference between the red and near infrared band combination divided by the sum of the red and near infrared band combination or:
NDVI=(NIR-R)/(NIR+R)
where R and NIR are the red and near infrared bands respectively.
-Crust index: In order to study a practicable indicator (fine sand content in topsoil) for monitoring the variation of surface soil using remote sensing technology, soil index covered in this study, the crust index, was tested for topsoil cover variation.
The crest index algorithm was run and a new dataset was produced. A spectral crest index is developed based on the normalized difference between the red and the blue spectral weight. Applying the index to a sand soil region, it has been known that the crest index can be used to detect and to map, from remote sensing imagery, different lithological/morphological units such as active crusted sand regions, which are expressed in the topography. As a mapping tool, the crest index image is much more sensitive to ground features than the original image.
CI=1-(R-B)/(R+B)
The allocation of soil crust is a vital information for vegetation degradation and climate variation investigations. They are also important information tools for increasing agricultural regions and/or infrastructures in location studies since soil crusts is related to soil stability, soil build-up, and soil fertility. Applying the suggested crust index can be performed with imagery gathered by any sensor which has the blue band. Nowadays, Landsat TM and Landsat ETM are the most common data sources for colored images.
-Topsoil GSI: Topsoil grain size index (GSI) is developed according to field survey of soil surface spectral reflectance and laboratory interpretation of soil grain composition. The grain size index found has close correlation to the fine sand or clay–silt-sized grain content of the topsoil in sparsely vegetated arid land. High grain size index values correspond to the region with high content of fine sand in topsoil or low content of clay–silt grains. The GSI can be simply calculated by:
GSI=(R-B)/(R+B+G)
where R, B, and G are the red, blue, and green bands respectively.
Grain size index value is close to zero in the vegetated regions, and a negative value for water body.

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