The normalized difference vegetation index (NDVI) is a powerful remote sensing technique that helps in identifying vegetation and assessing its health and vitality. NDVI has shown a positive correlation with various measures such as leaf area index and foliage projective cover.

To grasp the concept of NDVI, we need to understand the spectral signature of vegetation. This involves looking at the wavelength on the x-axis and reflectance on the y-axis. The presence of chlorophyll and the process of photosynthesis absorb light in the red region of the spectrum, while the internal cellular structure or biomass of the vegetation is indicated in the near-infrared region.

In general, healthy and dense vegetation reflects a significant amount of near-infrared light while reflecting very little red light, which is absorbed. Conversely, when vegetation is sparse or unhealthy, we observe a decrease in near-infrared reflectance and an increase in red reflectance due to lower chlorophyll levels.

The NDVI combines information from the red and near-infrared bands to create a single representative value. It is calculated by subtracting the reflectance in the red spectral band from that in the near-infrared band, then dividing this by the sum of the near-infrared and red reflectance values.

The negative sign in the numerator ensures that the result will always yield a value less than that of the denominator. Therefore, NDVI values will always range between -1 and +1.

 

\[
\text{NDVI} = \frac{\text{NIR} – \text{Red}}{\text{NIR} + \text{Red}}
\]

where **NIR** is the near-infrared light reflected by vegetation, and **Red** is the red light reflected. Since healthy vegetation reflects more NIR and absorbs more Red light, the NDVI values tend to be positive, typically ranging from 0.2 to 0.8. 

When the Red reflectance is higher than the NIR, such as in cases of water or barren land, NDVI values turn negative or closer to zero, respectively. The numerator’s potential to be negative or positive ensures the NDVI remains within the -1 to +1 range.

For healthy and dense vegetation, the high reflectance in the near-infrared band results in NDVI values tending towards +1. In contrast, for less healthy vegetation, the red reflectance has a more significant impact, reducing the overall NDVI value, although it remains positive.

While NDVI is primarily utilized for vegetation analysis, it can also help identify other features in an image. For instance, water has a very distinct NDVI value. Since almost all near-infrared light is absorbed by water, the red reflectance value becomes higher than that of the near-infrared, leading to a negative NDVI value.

NDVI serves as a crucial tool in remote sensing, providing valuable insights into vegetation health and other land features. Its ability to quantify vegetation based on reflectance makes it an essential index for researchers and environmental scientists.

 

Here are some key uses of NDVI

Monitoring Vegetation Health

NDVI measures vegetation health. Higher values mean healthy, dense plants, while lower values indicate stressed or sparse vegetation. It helps track changes in plant health, which can signal issues like pests, diseases, drought, or nutrient problems.

Agricultural Applications

  Crop Monitoring: Farmers use NDVI to track crop growth, identify poor performance areas, and evaluate field health. It supports precision agriculture by showing where to apply fertilizers and water.
Yield Prediction: NDVI data, along with climate and soil information, helps estimate crop yields by linking vegetation indices to past yield data.
Drought Assessment: NDVI assesses drought effects on crops by monitoring decreases in vegetation cover and photosynthesis. 

Land Cover Classification

NDVI is used in remote sensing to classify land cover types like forests, grasslands, water, urban areas, and barren land. It helps map and monitor changes in land use over time, including deforestation, urban expansion, and desertification.

Environmental and Ecological Studies

 Biodiversity Assessment: NDVI helps evaluate habitat quality and biodiversity by showing vegetation density and diversity.
Wildlife Management: Wildlife managers use NDVI to track vegetation in habitats for food availability and habitat suitability for various species.
Carbon Sequestration Studies: NDVI data aids in estimating biomass and carbon sequestration potential of forests and vegetation types.

Forestry Management

 NDVI is used to check forest health, identify degradation, evaluate reforestation, and estimate biomass. It also helps track post-fire recovery by observing changes in vegetation cover over time.

Disaster Management

NDVI is useful for evaluating how natural disasters such as floods, fires, droughts, and hurricanes affect plants and ecosystems. It also supports recovery efforts by tracking vegetation regrowth and the success of restoration activities.

Climate Change Studies

NDVI is used to study how climate change affects vegetation around the world. It helps monitor issues like desertification, glacial retreats, and seasonal changes in plant life.

Soil Erosion and Degradation Monitoring

NDVI helps identify areas at risk of soil erosion by monitoring vegetation cover. Declining NDVI values may indicate loss of vegetation due to soil erosion.

Urban Planning and Green Space Management

 Urban planners use NDVI to monitor green spaces, evaluate urban heat effects, and promote sustainable development by preserving vegetation. It aids in designing and managing parks and gardens in cities.

Hydrology and Watershed Management

 NDVI helps manage watersheds by monitoring vegetation cover, which is important for water quality, reducing erosion, and managing water flow.