By Soundarya Dayanand Kamble
Information:
The Transformed Soil Adjusted Vegetation Index (TSAVI) is a vegetation index used in remote sensing to estimate vegetation cover and health while minimizing the influence of soil brightness on the measurements. It’s an improvement over the standard Soil Adjusted Vegetation Index (SAVI) and other vegetation indices, designed to be more robust in areas with low vegetation cover and varying soil conditions.
How it works?
The Transformed Soil Adjusted Vegetation Index (TSAVI) is a vegetation index that aims to reduce the influence of soil brightness on vegetation estimations, particularly when vegetation cover is low. It achieves this by incorporating a soil line, characterized by a slope and intercept, into its calculation.
Here’s how it works:
1. Soil Line:
The TSAVI assumes that soil reflectance in the red and near-infrared (NIR) bands follows a linear relationship, forming a “soil line”.
2. Input Data:
The calculation utilizes pixel values from the red and NIR bands, along with the soil line’s slope and intercept.
Formula:
The TSAVI is calculated using a formula that incorporates these parameters to adjust for soil effects.
Formula :
TSAVI=(s(NIR-s*Red-a))/(a*NIR+Red-a*s+X*(1+s2))
NIR = pixel values from the near-infrared band
R = pixel values from the red band
s = the soil line slope
a = the soil line intercept
X = an adjustment factor that is set to minimize soil noise
OSAVI
Information:
OSAVI (Optimized Soil Adjusted Vegetation Index) is a spectral vegetation index used in remote sensing to estimate the amount of green biomass and vegetation coverage. It is particularly useful in areas with high soil background values, as it is designed to be less sensitive to soil conditions than other vegetation indices. OSAVI is often used in agricultural monitoring and for assessing the health of vegetation in semi-arid regions.
Formula:
OSAVI = (NIR – RED) / (NIR + RED + 0.16)
- NIR: represents reflectance in the near-infrared (NIR) band.
- RED: represents reflectance in the red band.
- 0.16: is a fixed soil adjustment factor.
Why is the 0.16 factor important?
The 0.16 value was selected through optimization to minimize the variation in OSAVI due to soil background, making it a more reliable indicator of vegetation vigor or biomass, especially when soil brightness varies.
How it works?
1. Data Input:
OSAVI utilizes reflectance data from the near-infrared (NIR) and red spectral bands of satellite or aerial imagery.
2. Formula:
The index is calculated using the following formula: OSAVI = (NIR – RED) / (NIR + RED + 0.16).
3. Soil Adjustment:
The inclusion of the constant value (0.16) in the denominator helps to reduce the influence of soil background reflectance, making it more suitable for areas with variable soil conditions and low vegetation density.
4. Interpretation:
The OSAVI value is a measure of vegetation health and density. Higher OSAVI values generally indicate greater green biomass and vegetative cover.
5. Applications:
OSAVI is used to monitor crop growth, assess rangeland health, and detect changes in vegetation due to factors like drought or deforestation.
GVMI
Information:
GVMI refers to the Global Vegetation Moisture Index, a spectral index used in remote sensing to estimate vegetation water content. It’s designed to maximize sensitivity to vegetation water content while minimizing sensitivity to other factors like atmospheric effects and angular variations.
Global Vegetation Moisture Index (GVMI):
- The GVMI is a spectral index that provides information on vegetation water content from local to global scales.
- It was developed using radiative transfer models at leaf, canopy, and atmospheric levels.
- The GVMI is designed to maximize sensitivity to vegetation water content while minimizing sensitivity to other factors like atmospheric perturbations and angular effects.
- It aims to provide quick and robust information over different ecosystems.
- The GVMI is particularly suitable for retrieving vegetation water content when the Leaf Area Index (LAI) is greater than or equal to 2.
- For sparsely vegetated areas (LAI less than 2), further research is needed to understand the impact of soil effects on reflectance.
Formula:
- GVMI is calculated using a formula that involves near-infrared (NIR) and short-wave infrared (SWIR) reflectance bands. For example, one common formula is:
((NIR + 0.1) – (SWIR + 0.02)) / ((NIR + 0.1) + (SWIR + 0.02)).
How it works?
1. Development and Principles:
- Radiative Transfer Models:
The GVMI is based on analytical methods using radiative transfer models at the leaf, canopy, and atmospheric levels.
- Sensitivity and Robustness:
It’s designed to maximize sensitivity to vegetation water content while minimizing sensitivity to other factors like atmospheric conditions and angular effects.
- EWTcanopy:
The GVMI aims to provide direct information on the EWTcanopy, which is the equivalent water depth in the vegetation canopy.
- Comparison to NDVI:
While both GVMI and NDVI are used to assess vegetation health, GVMI focuses specifically on water content, whereas NDVI provides information about greenness, says a study.
2. Data Acquisition and Processing:
- Satellite Data: GVMI is calculated using data from satellites like SPOT-VEGETATION.
- Spectral Bands: Specific spectral bands from the satellite data are used in the GVMI calculation.
- Calculation: The GVMI value is derived through a specific formula that combines the reflectance or radiance from these spectral bands.
3. Validation and Applications:
- Field Measurements:
The GVMI has been validated against field measurements of vegetation water content.
- Ecosystem Comparison:
Studies have compared GVMI-derived water content with field measurements across different ecosystems.
- Fire Risk Management:
GVMI can be used for fire risk management, as it provides information about vegetation water content, which is a key factor in fire behavior.
Research Contributors:
- Soundarya Dayanand Kamble
- (Shivaji University Students)
- Linkedin:
- https://www.linkedin.com/in/soundarya-kamble-2b0b7a34b/
