Normalized Difference Lignin Index

Vegetation index for detecting lignin content in plant tissues. Uses logarithmic transformation of shortwave infrared reflectance to quantify structural components of vegetation. 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. NDLI is particularly suited for lignin content analysis, forest health assessment, plant biochemistry. The general formula is (log(1/1754nm) - log(1/1680nm)) / (log(1/1754nm) + log(1/1680nm)), which requires SWIR1 and SWIR2 spectral bands.

NDLI is supported by 1 satellite sensor in our database, including WorldView 3. Each sensor uses different band designations — for example, WorldView 3 uses the formula (log(1/SWIR4) - log(1/SWIR3)) / (log(1/SWIR4) + log(1/SWIR3)). Select a sensor above to see its specific band mapping.

NDLI requires SWIR1 (1680 nm), SWIR2 (1754 nm). 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, NDLI provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.