Hyperspectral Vegetation Indices for Estimation of Foliar Nitrogen in a Mixed Forest

Abstract:

Foliar nitrogen is a critical factor for plant growth, and plays an important role in terrestrial ecosystem processes. Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of empirical techniques. Vegetation indices (VIs) are the most popular and simplest means of retrieving nitrogen, however, limited test has been performed over a mixed forest including both broadleaf and needle leaf species. This study tested the performance of 26 commonly used vegetation indices for estimating foliar nitrogen using airborne hyperspectral data. The vegetation indices were divided into four categories: pigment-related, physiological, structural and nitrogen-related indices. Broadleaf, coniferous and mixed stands were chosen for representing different species and canopy structure. Results showed that the Boochs2 index gave the most accurate estimation of foliar nitrogen (R_CV² = 0.78, RMSE_CV = 0.22). Comparable estimation accuracy was obtained by a nitrogen-related index NDNI (R_CV² = 0.76, RMSE_CV = 0.23). The best performing VIs provided consistent results for the sub plots of LAI between 3 and 4, which minimized the variation of canopy structure. Our findings indicated the feasibility of hyperspectral vegetation indices of foliar nitrogen in a mixed forest regardless of the canopy structure effects.