Normalized Difference Vegetation Index {LSRS}

Normalized Difference Vegetation Index

Description

Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs). NDVI always ranges from -1 to +1. But there isn’t a distinct boundary for each type of land cover. For example, when you have negative values, it’s highly likely that it’s water. On the other hand, if you have a NDVI value close to +1, there’s a high possibility that it’s dense green leaves. But when NDVI is close to zero, there isn’t green leaves and it could even be an urbanized area.As shown below, Normalized Difference Vegetation Index (NDVI) uses the NIR and red channels in its formula. NDVI=NIR-Red/NIR+Red

The result of this formula generates a value between -1 and +1. If you have low reflectance (or low values) in the red channel and high reflectance in the NIR channel, this will yield a high NDVI value. And vice versa.Overall, NDVI is a standardized way to measure healthy vegetation. When you have high NDVI values, you have healthier vegetation. When you have low NDVI, you have less or no vegetation. satellites like Sentinel-2, Landsat and SPOT produce red and near infrared images.

Usage

NDVI(a="NIR",b="Red")

Arguments

a

NIR satellite band (format:TIF)

b

Red satellite band (format:TIF)

Author(s)

Mehdi Sarparast

References

[1] http://gisgeography.com/ndvi-normalized-difference-vegetation-index/

Examples

## The function is currently defined as
function (a, b){
    name <- NDVI(a,b)
areaXY <-c(xmin, xmax, ymin, ymax)
cropXY <- crop(name, areaXY)
plot(cropXY,lwd=4,
     main="NDVI",
     xlab="easting", ylab="northing")
hist(cropXY,
     main="NDVI",

     xlab="NDVI",col="red", ylab="Frequency of Pixels")
}