Functions for 10 Vegetation Indices in Python, Matlab and R Languages

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The functions provide the mathematical formulas for 10 common vegetation indices, implemented in Python, Matlab and R languages for data analysis and processing in remote sensing applications.

Previous article: 10 Common Vegetation Indices and Their Applications in Remote Sensing

Here are the functions for the 10 vegetation indices in Python, Matlab, and Python:

Vegetation Indices

Python

1. Normalized Difference Vegetation Index (NDVI):

def ndvi(nir, red):
return (nir - red) / (nir + red)

2. Enhanced Vegetation Index (EVI):

def evi(nir, red, blue):
return 2.5 * (nir - red) / (nir + 6 * red - 7.5 * blue + 1)

3. Soil Adjusted Vegetation Index (SAVI):

def savi(nir, red, L=0.5):
return (nir - red) / (nir + red + L) * (1 + L)

4. Transformed Vegetation Index (TVI):

def tvi(nir, red, green):
return 0.5 * (120 * (nir - green) - 200 * (red - green))

5. Green Normalized Difference Vegetation Index (GNDVI):

def gndvi(nir, green):
return (nir - green) / (nir + green)

6. Modified Soil-Adjusted Vegetation Index (MSAVI):

def msavi(nir, red):
return 0.5 * (2 * nir + 1 - np.sqrt((2 * nir + 1) ** 2 - 8 * (nir - red)))

7. Normalized Difference Infrared Index (NDII):

def ndii(nir, swir):
return (nir - swir) / (nir + swir)

8. Chlorophyll Vegetation Index (CVI):

def cvi(nir, red):
return nir / red - 1

9. Red Edge Vegetation Index…

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