Assessing Nitrogen Treatment Efficiency in Schima Superba Seedlings Detected Using Hyperspectral Reflectance

  • Author(s): Miaomiao Cheng, Hong Jiang, Zheng Guo, and Xiuying Zhang
  • DOI: 10.3319/TAO.2013.11.27.01(A)
  • Keywords: Nitrogen deposition, Stress detection, Hyperspectral reflectance, Chlorophyll content, Subtropical species
Abstract

The sharp change in nitrate (N) deposition fluxes due to anthropogenic influences has major consequences for terrestrial plant productivity. Early detection of plants under nitrate stress is important for forest management in the subtropical region. This study used leaf-scale hyperspectral reflectance measurements to detect the seedling growth response of Schima superba (S. superba) under simulated N deposition during a period of two years. Two-year-old S. superba seedlings were planted under natural field conditions and treated with four N treatments at CK, LN-6, MN-10, and HN-24g N m-2 year-1. The chlorophyll content and leaf reflectance were examined to detect the N addition temporal effects. Results indicated that S. superba responded significantly with differences in chlorophyll content and leaf reflectance to N additional treatment. Compared with the N deficiency (CK) plots, plots with higher N addition rate (HN) reduced the chlorophyll concentration of S. superba seedlings. However, the long-term observed impact of LN and MN treatments increased the S. superba chlorophyll during the two years. Nitrogen additional treatments can be distinguished using the hyperspectral indices (R700/R720, R695/R420, and R695/R760) retrieved from the differences in leaf reflectance at the green spectrum and the red spectrum. The derivative shift to longer wavelength peaks with increasing N supply, accompanied by the increase in chlorophyll content. Leaf reflectance at 559 nm was negatively correlated with leaf chlorophyll content (R = -0.77). The identified N specific spectral ratios may be used for image interpretation and plant N status diagnosis for site-specific N management.

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