Vegetation Index Calculator
Calculate the Normalized Difference Vegetation Index (NDVI) from near-infrared and red reflectance values. Used in remote sensing and ecology to assess plant health, biomass, and land cover change.
About this calculator
The Normalized Difference Vegetation Index (NDVI) is one of the most widely used remote sensing indices for quantifying vegetation health and density. It exploits the fact that healthy vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared (NIR) light. The formula is: NDVI = (NIR − Red) / (NIR + Red). Values range from −1 to +1. Bare soil typically yields values near 0, water and snow produce negative values, and dense healthy vegetation produces values between 0.6 and 0.9. Sparse or stressed vegetation falls in the 0.2–0.5 range. By normalizing the difference against the sum, NDVI corrects for variations in illumination and surface slope, making it comparable across different images and sensors.
How to use
Suppose a satellite pixel returns a near-infrared reflectance of 0.45 and a red reflectance of 0.10. Step 1 — Subtract: 0.45 − 0.10 = 0.35. Step 2 — Add: 0.45 + 0.10 = 0.55. Step 3 — Divide: 0.35 / 0.55 ≈ 0.636. An NDVI of 0.636 indicates dense, healthy vegetation cover at that location. Compare this to a bare-soil pixel with NIR = 0.20 and Red = 0.18, which gives NDVI = 0.02/0.38 ≈ 0.053.
Frequently asked questions
What does an NDVI value tell me about vegetation health?
NDVI values close to +1 indicate dense, vigorous green vegetation, while values near 0 suggest bare soil or very sparse cover. Negative values typically indicate water bodies, snow, or clouds. Values between 0.2 and 0.5 often represent grasslands, shrublands, or stressed crops. Monitoring changes in NDVI over time allows ecologists and farmers to detect drought stress, disease, or land degradation early.
How is NDVI used in agriculture and crop monitoring?
Farmers and agronomists use NDVI derived from drone, satellite, or aerial imagery to create variability maps of their fields. Areas with low NDVI receive targeted inputs such as irrigation, fertilizer, or pesticide application, a practice known as precision agriculture. Temporal NDVI profiles can track crop growth stages and predict yields. Many platforms like Sentinel Hub or Google Earth Engine provide free NDVI layers for large-scale agricultural monitoring.
What are the limitations of using NDVI for vegetation assessment?
NDVI can saturate in very dense canopies, meaning differences between highly vegetated areas may not be captured accurately. It is also sensitive to soil background effects when vegetation cover is sparse. Atmospheric conditions, sensor calibration, and sun angle can introduce errors if reflectance values are not properly corrected. Alternative indices such as EVI (Enhanced Vegetation Index) or SAVI (Soil-Adjusted Vegetation Index) were developed specifically to address some of these limitations.