A vegetation index that uses the triangular area formed by green, red, and red-edge reflectance values. It is sensitive to leaf chlorophyll content and is particularly useful for estimating green LAI.

Used in crop monitoring, and forest monitoring.

When to use

  • Time-series monitoring of crop health, growth stages, and stress detection
  • Land cover classification and vegetation type discrimination
  • Biomass estimation and net primary productivity studies
  • Drought impact assessment over agricultural and forest areas
  • Phenology tracking — green-up, peak season, and senescence
  • Chlorophyll content estimation
  • Green LAI prediction

Limitations

  • Saturates in dense canopies (LAI > 3) — values plateau and lose discrimination ability
  • Sensitive to atmospheric scattering, especially blue-band haze
  • Soil background contaminates measurements in sparsely vegetated areas
  • Sun-sensor geometry (BRDF effects) introduces variability across acquisitions
  • Cloud cover and shadows produce invalid pixels that need masking

What the values mean

-1 Water / Snow
-0.1 Bare ground / Built-up
0.1 Sparse / Stressed
0.3 Moderate vegetation
0.5 Healthy vegetation
0.7 Dense canopy
Surface typeTypical TVI
Open water, snow-0.3 to -0.1
Bare soil, urban-0.1 to 0.2
Sparse or stressed crops0.2 to 0.4
Healthy crops, grassland0.4 to 0.7
Dense forest, peak season0.7 to 0.9

General Formula

green 550
red 670
re1 750

Sensor-Specific Formulas

Most-used sensors — click to show code below

SensorProviderFormulaBand Mapping
Wyvern0.5 * (120 * (Band 20 - Band 5) - 200 * (Band 13 - Band 5))green→Band 5, red→Band 13, re1→Band 20
ESA0.5 * (120 * (B6 - B3) - 200 * (B4 - B3))green→B3, red→B4, re1→B6
MAXAR0.5 * (120 * (Red Edge - Green) - 200 * (Red - Green))green→Green, red→Red, re1→Red Edge
MAXAR0.5 * (120 * (Red_Edge - Green) - 200 * (Red - Green))green→Green, red→Red, re1→Red_Edge

Spectral Band Visualization — Dragonette-1

Code Examples

Adapted for Dragonette-1 bands —

tvi_triangular_dragonette-001.py

Frequently Asked Questions

What is the TVI (Triangular Vegetation Index) and when should I use it?

A vegetation index that uses the triangular area formed by green, red, and red-edge reflectance values. It is sensitive to leaf chlorophyll content and is particularly useful for estimating green LAI. Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. TVI is particularly suited for chlorophyll content estimation, green lai prediction, crop canopy analysis. The general formula is 0.5 * (120 * (RE1 - Green) - 200 * (Red - Green)), which requires green and red and re1 spectral bands.

Which satellite sensors can I use to calculate TVI?

TVI is supported by 14 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Gaofen-1, Gaofen-2, GeoEye-1 and 9 more. Each sensor uses different band designations — for example, Dragonette-1 uses the formula 0.5 * (120 * (Band 20 - Band 5) - 200 * (Band 13 - Band 5)), while Dragonette-2/3 uses 0.5 * (120 * (Band24 - Band9) - 200 * (Band17 - Band9)). Select a sensor above to see its specific band mapping.

What spectral bands does TVI require and why?

TVI requires green (550), red (670), re1 (750). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

How do I calculate TVI in Python or R?

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

How does TVI compare to NDVI and other vegetation indices?

While NDVI is the most common vegetation index, TVI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.

TVI vs other vegetation indices

IndexNameHow it differs
ARIAnthocyanin Reflectance IndexAlternative vegetation index — different band combination
mARIModified Anthocyanin Reflectance IndexRefined formulation for specific conditions
ARVIAtmospherically Resistant Vegetation IndexAtmospherically corrected version
ARVI2Atmospherically Resistant Vegetation Index 2Atmospherically corrected version

Related Vegetation Indices

References

Broge & Leblanc (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices.
Haboudane et al. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies.

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