Modified Nonlinear Index

A modified version of the Nonlinear vegetation index that uses SWIR bands. This index is designed to improve vegetation monitoring in areas with high biomass by incorporating SWIR reflectance. Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. MNLI is particularly suited for forest crown closure mapping, lai estimation, high biomass vegetation monitoring. The general formula is ((swir² - nir) * 1.5) / (swir² + nir + 0.5), which requires nir and swir spectral bands.

MNLI is supported by 2 satellite sensors in our database, including SuperView-2, WorldView 3. Each sensor uses different band designations — for example, SuperView-2 uses the formula ((SWIR² - NIR1) * 1.5) / (SWIR² + NIR1 + 0.5), while WorldView 3 uses ((SWIR4² - NIR1) * 1.5) / (SWIR4² + NIR1 + 0.5). Select a sensor above to see its specific band mapping.

MNLI requires nir (824), swir (1760). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

While NDVI is the most common vegetation index, MNLI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.