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.
terra::SpatRaster objects or direct file paths
("image.tif").geo_index_custom()): Define arbitrary
mathematical expressions ("(nir - red) / (nir + red)") with
custom parameters and secure AST execution.c(red = 3, nir = 4)), layer name
(c(red = "B4", nir = "B8")), or sensor preset
(sentinel2, landsat8,
landsat9).scale_factor = 10000 to convert
integer Digital Numbers (DN) into physical surface reflectance \([0, 1]\).NA without arbitrary clamping.index_registry()): Comprehensive catalog of
indices with descriptions, purposes, interpretations, limitations, and
literature citations.index_summary()) and thematic
color ramps (plot_index()).| 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) |
Install the latest version from GitHub:
# install.packages("devtools")
devtools::install_github("sowsalim01/GeoIndexR")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)MIT © Mamadou Sow