NDVI — the Normalized Difference Vegetation Index — is the standard way to measure plant health from satellite or drone imagery. It's used everywhere from precision agriculture to deforestation monitoring, and the math behind it is simple. Getting the right bands, in the right order, out of a raw multi-band file is usually where people get stuck.
The formula, briefly
NDVI compares how much red light a surface absorbs against how much near-infrared (NIR) light it reflects. Healthy vegetation absorbs red light for photosynthesis but strongly reflects NIR, so the formula is:
The result ranges from −1 to 1. Dense healthy vegetation typically scores 0.6–0.9. Bare soil sits near 0. Water usually comes out negative.
Why band order trips people up
A multi-band GeoTIFF is just a stack of grayscale images — the file itself doesn't necessarily say which band is Red and which is NIR. Landsat, Sentinel-2, and a typical drone camera don't all order their bands the same way. Manually guessing wrong silently produces an NDVI map that looks plausible but is measuring the wrong thing.
How to do it
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Upload your multi-band GeoTIFF
Satellite imagery (Landsat, Sentinel-2, etc.) or drone-captured multispectral imagery both work, as long as it includes a Red and a Near-Infrared band.
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Confirm the band mapping
Review the auto-detected Red/NIR assignment (and Green/Blue/SWIR if present for other indices), correct it if needed.
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Run NDVI and export
Get a color-mapped PNG for quick visualization, plus the underlying GeoTIFF with the real NDVI values for further analysis in whatever software you use downstream.
Beyond NDVI
The same band-detection approach powers NDWI (water index) and NDBI (built-up/urban index), plus a False Color Composite for general visual interpretation — all in the same tool.
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