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Normalized Difference Vegetation Index (NDVI)

By Nikita Godemani

Normalized Difference Vegetation Index:

  • The Normalized Difference Vegetation Index is a simple measure of the amount and health of vegetation, used in remote sensing and various fields like agriculture and ecology. It uses the difference between reflectance in the near-in-frared (NIR) and red light bands to determine vegetation density and vigor.
  • NDVI is a dimensionless index that quantifies vegetation greenness and health.
  • It ranges from -1 to +1, with higher values indicating denser, healthier vegetation and lower values indicating less vegetation or even water bodies.
  • It’s calculated by measuring the difference in reflectance of light in the NIR and red bands.
  • Plants strongly reflect NIR light and absorb red light, making the difference a good indicator of their presence and health.

Formula of NDVI

(NIR – RED) ÷ (NIR + RED)

  • How it works:
  • Healthy plants with lots of chlorophyll reflect NIR light and absorb red light, leading to a high NDVI value.
  • Unhealthy plants, or areas with little vegetation, reflect more red light and less NIR light, resulting in a lower NDVI value.
  • Negative NDVI values usually indicate water bodies or snow.
  • Interpreting NDVI values:
  • -1 to -0.1: Likely water bodies, snow, or thick cloud cover.
  • 0 to 0.1: Bare soil, rocky areas, or deserts.
  • 0.2 to 0.5: Sparse or stressed vegetation, like grasslands or meadows.
  • 0.6 to 1: Dense, healthy vegetation, such as mature forests or dense crop canopies.

 GNDVI (Green Normalized Difference Vegetation Index)

Green Normalized Difference Vegetation Index is a vegetation index used to assess the “greenness” or photosynthetic activity of plants. It’s particularly useful for monitoring plant health,  stress levels, and assessing water and nitrogen uptake, especially in later stages of plant development. Unlike NDVI, GNDVI is more sensitive to chlorophyll concentration and saturates later.

The GNDVI is a vegetation index that uses near- infrared (NIR) and green light (540-570nm) bands to assess plant “greenness” and “photosynthetic” activity.

  • Formula

(NIR – GREEN) ÷ (NIR + GREEN)

  • FUNCTIONALITY
  • Satellite or drone sensors capture light reflected from the Earth’s surface.
  • The NIR and green band values are extracted.
  • The GNDVI formula is applied to generate a vegetation index map.
  • Areas with higher GNDVI values typically indicate healthier, more vigorous vegetation, while lower values suggest stress, disease, or poor growth.
  • Interpretation
  • Higher GNDVI value (closer to +1) indicates healthy, dense vegetation with high chlorophyll content.
  • Lower GNDVI value (closer to -1) suggest unhealthy vegetation, sparse vegetation, or the presence of water.

NDRE (Normalizes Difference Red Edge)

The Normalizes Difference Red Edge index is a vegetation index used in remote sensing to assess plant health and chlorophyll content, particularly in mature or near-harvest crops. It works by analyzing the difference between near-infrared and red-edge reflectance, which helps identify subtle changes in crop condition that might not be detectable with other indices like NDVI.

The Normalized Difference Red Edge index is an index sensitive to chlorophyll content in leaves. It is better to use this index when plants are mature or ready to be harvested.

  • Formula

NDRE = (NIR – RedEdge) ÷ (NIR + RedEdge)

FUNCTIONALITY

1. Sensors on satellites, drones, or handheld devices measure the light reflected from crops in the NIR and red edge bands.

2. The NDRE formula is applied to these values.

3. The result is a numerical index (usually between -1 and 1) that highlights vegetation condition.

4. Higher NDRE values indicate healthy, chlorophyll-rich plants; lower values may suggest stress, nutrient deficiency, or disease.

Interpretation

1 to 0.2: bare soil or a developing crop;

0.2 to 0.6: unhealthy plant or not yet mature;

0.6 to 1: healthy, mature and maturing crops.

A low NDRE value may indicate damages, disease, pests, or lack of nutrients in crops

Research Contributors:

Nikita Arjun Godemani

(Shivaji University Student)

Linkedin:

https://www.linkedin.com/in/nikita-godemani-01937534b

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