基于湿地植物光谱的水体总氮估测

利用再生水补充城市湿地是目前湿地恢复与重建的主要方向,然而再水中高浓度的氮、磷含量极易导致水体富营养化。遥感技术已成为富营养化监测的重要手段,但对于植被覆盖水域的富营养化直接探测存在一定的局限性。以北京市典型再生水补水湿地奥林匹克公园南园湿地为研究区,利用湿地植物光谱进行水体富营养化主控因子总氮的遥感探测。测定芦苇(Phragmites australis)和香蒲(Typha angustifolia)的叶片光谱及水体总氮含量,在对数据进行预处理的基础上建立二者的关系模型,包括单变量模型(比值光谱指数(SR)模型和归一化差值光谱指数(ND)模型),与多变量模型(逐步多元线性回归(SMLR)模型和偏最小二乘回归(PLSR)模型),并利用交叉验证决定系数(R2cv)和均方根误差(RMSEcv)进行模型精度检验。结果表明,不同回归模型相比,多变量回归模型精度较高;多变量回归模型中,PLSR模型精度较高,R2cv可达0.72,RMSEcv仅为0.24,是建立湿地植物光谱与水体总氮含量关系的最优模型。不同湿地植物类型相比,利用芦苇反射光谱建立的各种预测模型的精度都高于香蒲。其他环境因子(总磷)也是影响TN含量与湿地植物反射光谱关系的重要因素。研究成果可以弥补现有水体富营养化遥感探测的不足,并为再生水利用的城市湿地水质监测与管理提供有力的科学依据。

Estimating total nitrogen content in water body based on reflectance from wetland vegetation

Supplying urban wetlands with reclaimed water is recognized as a superior way for wetland restoration and reconstruction. However, the high concentration of nitrogen and phosphorus in reclaimed water can easily lead to water eutrophication. Although remote sensing technology has become a useful tool to monitor the eutrophication of water body, it is usually employed to detect eutrophication in open water. Limited applications have been found in measuring eutrophication of wetland covered by vegetation. Utilizing plants spectral response to environment can monitor environmental changes. This study explores the possibility to use wetland vegetation reflectance spectra in estimating total nitrogen content which is one of the key indicators of water eutrophication. The South Wetland in the Olympic Park in Beijing, a typical wetland using reused water, was selected as our study area. The leaf reflectance spectra of main wetland plants, reed (Phragmites australis) and cattail (Typha angustifolia), were acquired by means of an ASD FieldSpec 3 spectrometer (350-2500nm). Water quality samples were collected at the same time and analyzed by Center for Environmental Quality Test, Tsinghua University subsequently. The research established several univariate models including 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. The accuracy of these models was tested through cross-validated coefficient of determination (R_cv²) and cross-validated root mean square error (RMSE_cv). The results have shown that 1) In comparison with univariate techniques, multivariate regressions can improve the estimation of total nitrogen concentration in water. The accuracy of PLSR model was the highest (R_cv²=0.72, RMSE_cv=0.24) among all models. PLSR provides the most useful explorative tool for unraveling the relationship between spectral reflectance of wetland plants and total nitrogen content in water at leaf scale. 2) The accuracy of prediction models built in this study using Phragmites australis reflectance spectra is higher than those using Typha angustifolia reflectance spectra. 3) Other environmental factors should also be discreetly considered in modeling exercise. Total phosphorus is found to have impact on the relationship between TN and reflectance spectra from wetland vegetation. Strong predictive power for multiple regression equations has been achieved when the range of total phosphorus was restricted. The result from this study can not only fill the gaps in the detection of eutrophication using remote sensing, but also provide a strong scientific basis for the water quality monitoring and management of urban wetlands using recycled water.