Vegetation Indices in GeoIndexR

In GeoIndexR: Computation of Spectral and Geospatial Indices from Multispectral Raster Data

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Introduction

Vegetation indices exploit the distinct spectral reflectance curve of healthy green canopies: strong absorption in the red chlorophyll band (0.64 - 0.67 um) and high scattering/reflectance in the near-infrared (NIR) plateau (0.85 - 0.88 um).

GeoIndexR provides four primary vegetation indices, each tailored to different canopy densities and atmospheric or soil conditions:

1. NDVI: Normalized Difference Vegetation Index
2. SAVI: Soil Adjusted Vegetation Index
3. EVI: Enhanced Vegetation Index
4. GNDVI: Green Normalized Difference Vegetation Index

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Formulations and Parameters

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)

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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))

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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)

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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)

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Comparing Vegetation Indices

You can stack all four indices together:

veg_stack <- geo_indices(img, c("NDVI", "SAVI", "EVI", "GNDVI"))
index_summary(veg_stack)

GeoIndexR documentation built on Oct. 10, 2026, 5:08 p.m.