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  • Title: [Estimating total nitrogen content in wetland vegetation based on measured reflectance spectra].
    Author: Liu K, Zhao WJ, Guo XY, Wang YH, Sun YH, Miao Q.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2012 Feb; 32(2):465-71. PubMed ID: 22512191.
    Abstract:
    More and more urban wetlands have been supplied with reclaimed water. And monitoring the growth condition of large-area wetland vegetation is playing a very important role in wetland restoration and reconstruction. Recently, remote sensing technology has become an important tool for vegetation growth monitoring. The South Wetland in the Olympic Park, a typical wetland using reused water, was selected as the research area. The leaf reflectance spectra and were acquired for the main wetland plants reed (Phragmites australis) and cattail (Typha angustifolia) with an ASD FieldSpec 3 spectrometer (350 2 500 nm). The total nitrogen (TN) content of leaf samples was determined by Kjeldahl method subsequently. The research established univariate models involving simple ratio spectral index (SR) model and normalized difference spectral index (ND) model, as well as multivariate models including stepwise multiple linear regression (SMLR) model and partial least squares regression (PLSR) model. Moreover, the accuracy of all the models was tested through cross-validated coefficient of determination (R2(CV)) and cross-validated root mean square error (RMSE(CV)). The results showed that (1) comparing different types of wetland plants, the accuracy of all established prediction models using Phragmites australis reflectance spectra was higher than that using Typha angustifolia reflectance spectra. (2) compared with univariate techniques, multivariate regressions improved the estimation of TN concentration in leaves. (3) among the various investigated models, the accuracy of PLSR model was the highest (R2(CV) = 0.80, RMSE(CV) = 0.24). PLSR provided the most useful explorative tool for unraveling the relationship between spectral reflectance and TN consistence of leaves. The result would not only provide a scientific basis for remote sensing retrieval of biochemical variables of wetland vegetation, but also provide a strong scientific basis for the monitoring and management of urban wetlands using recycled water.
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