TVI (Triangular Vegetation Index)
Definition
The Triangular Vegetation Index (TVI) quantifies vegetation amount by measuring the area of the triangle formed by the green peak, the red chlorophyll absorption minimum, and the NIR plateau in reflectance spectra. This geometric approach captures the overall shape of vegetation reflectance curves rather than relying on specific band ratios.
Application
TVI is used for estimating leaf area index (LAI), biomass, and vegetation fraction in both agricultural and natural ecosystems. Its sensitivity to the overall spectral shape makes it valuable for monitoring vegetation development, assessing crop growth stages, and studying phenological changes. It's particularly useful in research requiring comprehensive spectral characterization.
FAQ
What is the mathematical formulation of TVI?
A common TVI formula is: TVI = 0.5 * [120 * (NIR - Green) - 200 * (Red - Green)]. This formulation approximates the area of the triangle formed by the green peak (around 550 nm), red minimum (around 670 nm), and NIR plateau (around 800 nm) in typical vegetation reflectance spectra.
What are the main advantages of the triangular approach?
Advantages include:
1) Utilizes information from three key spectral regions simultaneously;
2) Captures the overall spectral shape rather than just band differences;
3) Generally shows good correlation with biophysical parameters like LAI and biomass;
4) Less sensitive to absolute calibration errors than ratio-based indices;
5) Provides a more holistic representation of vegetation spectral properties.
What types of sensors are required for TVI calculation?
TVI requires sensors with at least three bands:
1) Green band (typically 540-560 nm);
2) Red band (typically 660-680 nm);
3) NIR band (typically 770-900 nm). Most modern multispectral sensors and some broad-band sensors meet these requirements.
How does TVI perform compared to traditional vegetation indices?
TVI generally:
1) Shows stronger correlation with LAI and biomass than many simple indices;
2) Is less prone to saturation at high vegetation densities;
3) May be more affected by soil background in sparse vegetation;
4) Requires more careful atmospheric correction due to multiple bands;
5) Is computationally more complex than simple ratio indices like NDVI.

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