Canopy Nitrogen VIs provide a measure of nitrogen concentration of remotely sensed foliage. Nitrogen is an important component of chlorophyll and is generally present in high concentration in vegetation that is growing quickly. This VI uses reflectance measurements in the shortwave infrared range to measure relative amounts of nitrogen contained in vegetation canopies.
This index is designed to estimate the relative amounts of nitrogen contained in vegetation canopies. Reflectance at 1510 nm is largely determined by nitrogen concentration of leaves, as well as the overall foliage biomass of the canopy. Together, leaf nitrogen concentration and canopy foliar biomass are combined in the 1510 nm range to predict total canopy nitrogen content. This is compared to a reference reflectance at 1680 nm, which should contain a similar signal due to foliar biomass, but without the influence of nitrogen absorption. The NDNI is experimental, but it does show strong sensitivity to changing nitrogen status when the canopy is green (not senescent) and closed in architecture. Applications include precision agriculture, ecosystem analysis, and forest management.
See Narrowband Definitions for the allowable range of wavelengths.
If you used FLAASH or QUAC to create a surface reflectance image, they automatically scale the resulting data values by 10,000 to produce integer data that consumes less disk space. Before calculating NDNI, import the FLAASH or QUAC image into the Apply Gain and Offset tool (or the ENVIApplyGainOffsetTask routine in the ENVI API). Set the Gain Values for all bands to 0.0001. Keep the default value of 0 for Offset Values for all bands. Save this as a new raster. The result will have reflectance values that range from 0 to 1. Because NDNI is a logarithmic function, it is especially sensitive to scale factors. Ensuring that data values range from 0 to 1 will yield the most accurate result.
Serrano, L., J. Penuelas, and S. Ustin. "Remote Sensing of Nitrogen and Lignin in Mediterranean Vegetation from AVIRIS Data: Decomposing Biochemical from Structural Signals." Remote Sensing of Environment 81 (2002):355-364.
Fourty, T., et al. "Leaf Optical Properties with Explicit Description of Its Biochemical Composition: Direct and Inverse Problems." Remote Sensing of Environment 56 (1996):104-117.
Spectral Indices, Vegetation Indices, Vegetation Analysis Tools, Agricultural Stress Tool, Fire Fuel Tool, Forest Health Tool, Vegetation and Its Reflectance Properties, EO-1 Hyperion Vegetation Indices Tutorial