GeoIndexR
Computation of Spectral and Geospatial Indices from Multispectral Raster Data
A unified, fast, and extensible framework for calculating spectral
and geospatial indices from multispectral raster datasets (e.g., NDVI - Normalized
Difference Vegetation Index, NDWI - Normalized Difference Water Index, MNDWI -
Modified Normalized Difference Water Index, NDBI - Normalized Difference Built-up
Index, SAVI - Soil Adjusted Vegetation Index, EVI - Enhanced Vegetation Index,
GNDVI - Green Normalized Difference Vegetation Index, NDMI - Normalized Difference
Moisture Index, BSI - Bare Soil Index). Designed around 'terra' 'SpatRaster'
objects, it supports multi-band rasters, automatic band resolution, sensor
presets (Sentinel-2, Landsat-8/9), customizable index parameters, vectorized
computations, and comprehensive validation. Methods based on Rouse et al. (1973)
, McFeeters (1996)
GeoIndexR 
GeoIndexR: A flexible, fast, and extensible R framework for computing spectral and geospatial indices from raster data, combining ready-to-use standard indices with a secure custom formula engine.
Built natively on terra, GeoIndexR enables researchers, remote sensing scientists, and GIS professionals to calculate standard indices or define their own custom formulas seamlessly.
Key Features
- 🛰️ Unified SpatRaster & File Path API: Pass
terra::SpatRasterobjects or direct file paths ("image.tif"). - 🧩 Custom Formula Engine (
geo_index_custom()): Define arbitrary mathematical expressions ("(nir - red) / (nir + red)") with custom parameters and secure AST execution. - 🎯 Intelligent Band Resolution: Map bands by index (
c(red = 3, nir = 4)), layer name (c(red = "B4", nir = "B8")), or sensor preset (sentinel2,landsat8,landsat9). - ⚖️ Reflectance & Scale Factor Management: Built-in support for
scale_factor = 10000to convert integer Digital Numbers (DN) into physical surface reflectance $[0, 1]$. - 🛡️ Zero-Division & Singularity Safety: Automatic near-zero denominator tolerance and conversion of non-finite values to
NAwithout arbitrary clamping. - 📚 Rich Metadata Registry (
index_registry()): Comprehensive catalog of indices with descriptions, purposes, interpretations, limitations, and literature citations. - 📊 Descriptive Statistics & Plotting: Detailed summaries with percentiles (
index_summary()) and thematic color ramps (plot_index()).
Supported Standard Indices
| Index | Category | Required Bands | Formula | Reference |
|---|---|---|---|---|
| NDVI | Vegetation | nir, red |
$(NIR - RED) / (NIR + RED)$ | Rouse et al. (1974) |
| SAVI | Vegetation | nir, red |
$((NIR - RED) / (NIR + RED + L)) \times (1 + L)$ | Huete (1988) |
| EVI | Vegetation | nir, red, blue |
$G \times (NIR - RED) / (NIR + C_1 RED - C_2 BLUE + L)$ | Liu & Huete (1995) |
| MSAVI | Vegetation | nir, red |
$(2 NIR + 1 - \sqrt{(2 NIR + 1)^2 - 8(NIR - RED)}) / 2$ | Qi et al. (1994) |
| OSAVI | Vegetation | nir, red |
$((NIR - RED) / (NIR + RED + \theta)) \times (1 + \theta)$ | Rondeaux et al. (1996) |
| ARVI | Vegetation | nir, red, blue |
$(NIR - RB) / (NIR + RB)$ | Kaufman & Tanre (1992) |
| GNDVI | Vegetation | nir, green |
$(NIR - GREEN) / (NIR + GREEN)$ | Gitelson et al. (1996) |
| NDWI | Water | green, nir |
$(GREEN - NIR) / (GREEN + NIR)$ | McFeeters (1996) |
| MNDWI | Water | green, swir1 |
$(GREEN - SWIR1) / (GREEN + SWIR1)$ | Xu (2006) |
| AWEI | Water | green, nir, swir1, swir2 |
$4(GREEN - SWIR1) - (0.25 NIR + 2.75 SWIR2)$ | Feyisa et al. (2014) |
| NDBI | Urban | swir1, nir |
$(SWIR1 - NIR) / (SWIR1 + NIR)$ | Zha et al. (2003) |
| IBI | Urban | swir1, nir, red, green |
$(NDBI - (SAVI + MNDWI)/2) / (NDBI + (SAVI + MNDWI)/2)$ | Xu (2007) |
| NDMI | Moisture | nir, swir1 |
$(NIR - SWIR1) / (NIR + SWIR1)$ | Gao (1996) |
| MSI | Moisture | swir1, nir |
$SWIR1 / NIR$ | Rock et al. (1986) |
| BSI | Soil | swir1, red, nir, blue |
$((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))$ | Rikimaru et al. (2002) |
| NDSI | Snow | green, swir1 |
$(GREEN - SWIR1) / (GREEN + SWIR1)$ | Hall et al. (1995) |
Installation
Install the latest version from GitHub:
# install.packages("devtools")
devtools::install_github("sowsalim01/GeoIndexR")
Quick Start
library(GeoIndexR)
library(terra)
# 1. Load raster image (or provide filepath: "image.tif")
img <- get_example_data()
# 2. Compute a standard index
ndvi <- geo_index(img, "NDVI", bands = c(red = "red", nir = "nir"))
# 3. Compute a scale-sensitive index with scale_factor (e.g. for Sentinel-2 DN)
evi <- geo_index(
img,
"EVI",
bands = c(blue = "blue", red = "red", nir = "nir"),
scale_factor = 1 # or 10000 for raw integer DN
)
# 4. Compute a custom user formula
custom <- geo_index_custom(
img,
formula = "(nir - swir1) / (nir + swir1)",
bands = c(nir = "nir", swir1 = "swir1"),
name = "CustomMoistureIndex"
)
# 5. Summarize statistics with percentiles
index_summary(ndvi)
# 6. Plot the index
plot_index(ndvi, "NDVI")
# 7. Save output to GeoTIFF
writeRaster(ndvi, "NDVI_result.tif", overwrite = TRUE)
License
MIT © Mamadou Sow