Vegetation Indices in GeoIndexR

1. NDVI (Normalized Difference Vegetation Index)

Rouse et al. (1974) formulated the normalized ratio:

\[\text{NDVI} = \frac{\text{NIR} - \text{RED}}{\text{NIR} + \text{RED}}\]

• Theoretical range: \([-1, 1]\)
• Values typically range from \(0.2\) to \(0.8\) for photosynthetically active green vegetation.

library(GeoIndexR)
img <- get_example_data()

ndvi <- geo_index(img, "NDVI")
index_summary(ndvi)
#> 
#> === GeoIndexR Spectral Summary ===
#> 
#>  index     min    max   mean median     sd     q05     q25    q75    q95 na_pct
#>   NDVI -0.5508 0.9233 0.2963 0.1161 0.4884 -0.3796 -0.0787 0.8546 0.8972      1
#>  total_cells
#>          100

2. SAVI (Soil Adjusted Vegetation Index)

In areas with sparse or intermediate canopy cover (\(< 40\%\)), exposed background soil alters the red and near-infrared reflectance. Huete (1988) introduced the soil adjustment factor \(L\):

\[\text{SAVI} = \frac{\text{NIR} - \text{RED}}{\text{NIR} + \text{RED} + L} \times (1 + L)\]

• \(L = 0.5\) is standard and optimal for intermediate vegetation density.
• \(L \to 0\) approaches NDVI (dense cover), whereas \(L = 1.0\) is suitable for very sparse vegetation.

In GeoIndexR, \(L\) is configurable directly through geo_index() or calc_savi():

savi_standard <- geo_index(img, "SAVI", L = 0.5)
savi_sparse   <- geo_index(img, "SAVI", L = 1.0)

index_summary(c(savi_standard, savi_sparse))
#> 
#> === GeoIndexR Spectral Summary ===
#> 
#>  index     min    max   mean median     sd     q05     q25    q75    q95 na_pct
#>   SAVI -0.1194 0.8802 0.3094 0.0933 0.3727 -0.0594 -0.0209 0.7361 0.8293      1
#>   SAVI -0.1091 0.8601 0.2939 0.0863 0.3518 -0.0430 -0.0148 0.6854 0.8019      1
#>  total_cells
#>          100
#>          100

3. EVI (Enhanced Vegetation Index)

Liu & Huete (1995) designed EVI to decouple the canopy background signal and reduce atmospheric influences through blue band feedback:

\[\text{EVI} = G \times \frac{\text{NIR} - \text{RED}}{\text{NIR} + C_1 \times \text{RED} - C_2 \times \text{BLUE} + L}\]

Default coefficients (standard MODIS/Sentinel-2/Landsat): - \(G = 2.5\) (Gain factor) - \(C_1 = 6.0\) (Aerosol coefficient for red) - \(C_2 = 7.5\) (Aerosol coefficient for blue) - \(L = 1.0\) (Canopy background adjustment)

evi <- geo_index(img, "EVI")
index_summary(evi)
#> 
#> === GeoIndexR Spectral Summary ===
#> 
#>  index     min    max  mean median    sd     q05     q25    q75    q95 na_pct
#>    EVI -0.1012 1.1634 0.373  0.089 0.457 -0.0821 -0.0267 0.8837 1.0352      1
#>  total_cells
#>          100

4. GNDVI (Green NDVI)

Gitelson et al. (1996) substituted the red band with the green band to enhance sensitivity to chlorophyll concentrations in dense canopies where red reflectance becomes saturated:

\[\text{GNDVI} = \frac{\text{NIR} - \text{GREEN}}{\text{NIR} + \text{GREEN}}\]

gndvi <- geo_index(img, "GNDVI")
index_summary(gndvi)
#> 
#> === GeoIndexR Spectral Summary ===
#> 
#>  index     min    max   mean median     sd     q05     q25    q75    q95 na_pct
#>  GNDVI -0.7652 0.8373 0.2001 0.2426 0.5451 -0.6222 -0.4403 0.7482 0.7976      1
#>  total_cells
#>          100