Normalized Difference Vegetation Index (NDVI) | Remote Sensing | Methods in Field Biology

Question: In remote sensing, Which one of the following formulae is used for the calculation of normalized difference vegetation Index (NDVI)?
1. RED / (NIR+RED)
2. RED / (NIR-RED)
3.(NIR+RED) / (NIR-RED)
4. (NIR-RED) / (NIR+RED)

Answer: 4. (NIR-RED) / (NIR+RED) 

🌿What is NDVI?

• NDVI has become the most commonly used vegetation index in remote sensing

NDVI (Normalized Difference Vegetation Index) is a remote sensing method that was first developed in the 1970s. The concept of using the reflectance of light in the visible and near-infrared (NIR) wavelengths to determine the amount and health of vegetation was first proposed by a scientist named Rouse, in 1973.

 It aids in detecting and quantifying the presence of live green vegetation based on how objects interact with light. To understand the plant’s health condition, one needs to compare the absorption and reflection values of red and NIR (near-infrared).

 
Plants have a unique reflectance characteristic, they reflect more near-infrared (NIR) light and absorb more visible light. When plants are healthy, they have a high chlorophyll content, which allows them to absorb more light in the red region of the spectrum and reflect more light in the NIR region. So Normalized Difference Vegetation Index (NDVI) uses this characteristic of plants to differentiate healthy vegetation from  unhealthy vegetation.

🌿How NDVI is Calculated?
NDVI is calculated by subtracting the reflectance of the NIR band from the reflectance of the red band and then dividing that value by the sum of the reflectance of the NIR and red bands. NDVI values range from -1 to 1, with -1 indicating no vegetation, 0 indicating bare soil or water, and values closer to 1 indicating greater amounts and healthier vegetation.

🌿Applications of NDVI

NDVI is widely used in many applications such as monitoring of crops, vegetation health, precision agriculture, land use and land cover mapping, and monitoring of ecosystem health.