Wildlife Conservation Efforts

Normalized Difference Vegetation Index (NDVI)


From UPSC perspective, the following things are important :

Prelims level : NDVI

Mains level : Utility of NDVI

  • A field study by researchers from Bengaluru shows that a popularly used index that remotely estimates density of vegetation does not yield a reliable estimate of food abundance for elephants in tropical forests.
  • In fact, researchers show that this index has a negative correlation with graminoids (grassy food – grasses, sedges and rushes – preferentially consumed by elephants) in tropical forests.

Normalized Difference Vegetation Index (NDVI)

  • For both academic and practical purposes, there is the practice of remotely monitoring vegetation in an area and representing it in terms of maps and parameters.
  • One such parameter used is the normalized difference vegetation index (NDVI) which is measured remotely from satellite data.
  • The NDVI is a simple indicator which tells how much of the ground is covered with vegetation.
  • It basically calculates the difference between the red and near infrared (NIR) components of light reflected by objects, from, say, a satellite.
  • Since healthy vegetation strongly absorbs red and reflects near infrared light, this difference can indicate the presence of healthy vegetation and map it into a colour code.

Using NDVI

  • 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 aren’t green leaves and it could even be an urbanized area.

Why NDVI isn’t a good measure of vegetation cover?

  • NDVI was negatively correlated to grasses. This means grass abundance tends to be low in locations where NDVI is high and vice-versa.
  • While canopy cover and shrub abundance contribute positively to NDVI, they negatively affect grass abundance.
  • Because of the poor correlation, NDVI cannot be reliably used as a measure of forage abundance in a multi-storeyed forest with a low proportional abundance of food species.
  • Grasses form a large component of food of elephants and also ungulates (hoofed animals) like deer, sambar and gaur.

Misleading Elephants data

  • This has been used to estimate the amount of food abundance available to herbivorous animals, for example, elephants.
  • The NDVI is used, for instance, in attempts to track the presence of elephants using the vegetation they consume.
  • However, this work clearly establishes that this can be misleading, and field-based studies are the ones which can yield definitive results.
  • Researchers in India have found that the abundance of food plants is not correlated with NDVI.
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