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

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

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…