NDVI (Normalized Difference Vegetation Index)
Definition
The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator used to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation. It is calculated from the visible (red) and near-infrared (NIR) light reflected by vegetation. Healthy vegetation absorbs most of the visible red light for photosynthesis and reflects a large portion of the near-infrared light, while sparse or stressed vegetation reflects more visible red light and less near-infrared light.
Application
NDVI is extensively used in agriculture for crop health monitoring, yield prediction, and early detection of stress from drought, pests, or disease. It is also widely applied in forestry for assessing forest cover, phenology, and health, in environmental science for tracking changes in vegetation due to climate change or land use, and in disaster management for monitoring drought conditions and wildfire recovery.
FAQ
What is the formula for calculating NDVI?
NDVI is calculated using the formula: (NIR - Red) / (NIR + Red), where NIR is the reflectance in the near-infrared band and Red is the reflectance in the red visible band. The resulting values range from -1 to 1. Values close to 1 indicate dense, healthy green vegetation, values near 0 represent bare soil or non-vegetated surfaces, and negative values typically correspond to water, snow, or clouds.
What are the typical NDVI values for different land covers?
Typical NDVI value ranges are: Water bodies: -0.1 to 0.1; Bare soil, rock, or urban areas: 0.1 to 0.2; Sparse vegetation (grasslands, shrubs): 0.2 to 0.5; Dense, healthy vegetation (forests, active crops): 0.5 to 0.9. These ranges can vary based on sensor characteristics, atmospheric conditions, and season.
What are the main limitations of NDVI?
The primary limitations of NDVI include:
1) Susceptibility to saturation in areas of very high biomass or dense canopy, where it fails to differentiate further increases in leaf area;
2) Sensitivity to the underlying soil brightness, especially in areas with low vegetation cover;
3) Influence from atmospheric conditions (e.g., aerosols, water vapor) that can affect reflectance values;
4) Dependence on sensor-specific band characteristics, making direct comparisons between different satellite sensors challenging without calibration.
How is NDVI used in precision agriculture?
In precision agriculture, NDVI maps derived from satellites (e.g., Landsat, Sentinel-2) or drones allow farmers to identify spatial variability in crop health within a field. This enables variable-rate application of water, fertilizers, and pesticides—applying inputs only where and when needed. It is used for tasks like zoning fields for differential harvest, assessing crop damage after weather events, and monitoring irrigation efficiency, ultimately improving yield, reducing costs, and minimizing environmental impact.
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