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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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.