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Lijuan Cui; Xueyan Zuo; Zhiguo Dou; Yilan Huang; Xinsheng Zhao; Xiajie Zhai; Yinru Lei; Jing Li; Xu Pan; Wei Li. Plant identification of Beijing Hanshiqiao wetland based on hyperspectral data. Spectroscopy Letters 2021, 54, 381 -394.
AMA StyleLijuan Cui, Xueyan Zuo, Zhiguo Dou, Yilan Huang, Xinsheng Zhao, Xiajie Zhai, Yinru Lei, Jing Li, Xu Pan, Wei Li. Plant identification of Beijing Hanshiqiao wetland based on hyperspectral data. Spectroscopy Letters. 2021; 54 (5):381-394.
Chicago/Turabian StyleLijuan Cui; Xueyan Zuo; Zhiguo Dou; Yilan Huang; Xinsheng Zhao; Xiajie Zhai; Yinru Lei; Jing Li; Xu Pan; Wei Li. 2021. "Plant identification of Beijing Hanshiqiao wetland based on hyperspectral data." Spectroscopy Letters 54, no. 5: 381-394.
Carbon, nitrogen, and phosphorus—nutrient and restrictive elements for plant growth and important components of the plant body—are mainly transferred and exchanged between plants and the soil environment. Changes in the carbon, nitrogen, and phosphorus eco-stoichiometry greatly impact the growth and expansion of Spartina alterniflora, and understanding these changes can reveal the nutrient coordination mechanism among ecosystem components. To explore the relationship between leaf and soil eco-stoichiometry and determine the key soil factors that affect leaf eco-stoichiometry, we collected leaf and soil samples of S. alterniflora at different tidal levels (i.e., 1, 3, and 5 km away from the coastline) in a coastal wetland in the Yancheng Elk Nature Reserve, Jiangsu province. We measured the leaf and soil carbon, nitrogen, and phosphorus contents and ratios, as well as the soil salinity and soil organic carbon. The results revealed the following. (1) The leaf stoichiometric characteristics and soil properties of S. alterniflora differed significantly between tidal levels; for example, total carbon, nitrogen, soil organic carbon were detected at their highest levels at 3 km and lowest levels at 5 km. (2) Significant correlations were detected between the leaf stoichiometric characteristics and soil characteristics. Additionally, nitrogen limitation was evident in the study area, as indicated by the nitrogen–phosphorus ratio being less than 14 and the soil nitrogen–phosphorus ratio being less than 1. (3) Soil salinity and the soil carbon–nitrogen ratio were shown to be the key factors that affect the eco-stoichiometric characteristics of S. alterniflora. These findings furthered our understanding of the nutrient distribution mechanisms and invasion strategy of S. alterniflora and can thus be used to guide S. alterniflora control policies formulated by government management departments in China.
Xueyan Zuo; Lijuan Cui; Wei Li; Yinru Lei; Zhiguo Dou; Zhijun Liu; Yang Cai; Xiajie Zhai. Spartina alterniflora Leaf and Soil Eco-Stoichiometry in the Yancheng Coastal Wetland. Plants 2020, 10, 13 .
AMA StyleXueyan Zuo, Lijuan Cui, Wei Li, Yinru Lei, Zhiguo Dou, Zhijun Liu, Yang Cai, Xiajie Zhai. Spartina alterniflora Leaf and Soil Eco-Stoichiometry in the Yancheng Coastal Wetland. Plants. 2020; 10 (1):13.
Chicago/Turabian StyleXueyan Zuo; Lijuan Cui; Wei Li; Yinru Lei; Zhiguo Dou; Zhijun Liu; Yang Cai; Xiajie Zhai. 2020. "Spartina alterniflora Leaf and Soil Eco-Stoichiometry in the Yancheng Coastal Wetland." Plants 10, no. 1: 13.
Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.
Lijuan Cui; Zhiguo Dou; Zhijun Liu; Xueyan Zuo; Yinru Lei; Jing Li; Xinsheng Zhao; Xiajie Zhai; Xu Pan; Wei Li. Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models. Remote Sensing 2020, 12, 1998 .
AMA StyleLijuan Cui, Zhiguo Dou, Zhijun Liu, Xueyan Zuo, Yinru Lei, Jing Li, Xinsheng Zhao, Xiajie Zhai, Xu Pan, Wei Li. Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models. Remote Sensing. 2020; 12 (12):1998.
Chicago/Turabian StyleLijuan Cui; Zhiguo Dou; Zhijun Liu; Xueyan Zuo; Yinru Lei; Jing Li; Xinsheng Zhao; Xiajie Zhai; Xu Pan; Wei Li. 2020. "Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and Phosphorus Stoichiometry Using Three Models." Remote Sensing 12, no. 12: 1998.
The nitrogen and phosphorus content in water and sediment is an important index for evaluating the nutritional status of wetland ecosystems. This study used an inversion model to assess the total nitrogen (TN) and total phosphorus (TP) content of constructed wetland using the canopy spectral reflectance data of four wetland plants. And then determine their relative suitability as a remotely sensed environmental monitoring tool. For water, the coefficient of determination (R2) of floating plants (up to 0.92) was higher than that of emergent plants (up to 0.82). For sediment, the R2 of TN inversion for S. natans was 0.59 and that of TP inversion for L. minor was 0.52, suggesting that floating plant canopy spectral reflectance data are more useful for assessing water directly, while indicators for the sedimentary environment can be assessed using emergent plants. Overall, the results clearly show that it is feasible to estimate water and sediment TN and TP content using plant canopy spectral reflectance data, providing the basis for widespread, rapid, and reliable monitoring of wetland ecosystem health via hyperspectral remote sensing. This study provides a reference for the timely development of further wetland restoration and protection measures.
Wei Li; Zhiguo Dou; Lijuan Cui; Rumiao Wang; Zhijiang Zhao; Shifeng Cui; Yinru Lei; Jing Li; Xinsheng Zhao; Xiajie Zhai. Suitability of hyperspectral data for monitoring nitrogen and phosphorus content in constructed wetlands. Remote Sensing Letters 2020, 11, 495 -504.
AMA StyleWei Li, Zhiguo Dou, Lijuan Cui, Rumiao Wang, Zhijiang Zhao, Shifeng Cui, Yinru Lei, Jing Li, Xinsheng Zhao, Xiajie Zhai. Suitability of hyperspectral data for monitoring nitrogen and phosphorus content in constructed wetlands. Remote Sensing Letters. 2020; 11 (5):495-504.
Chicago/Turabian StyleWei Li; Zhiguo Dou; Lijuan Cui; Rumiao Wang; Zhijiang Zhao; Shifeng Cui; Yinru Lei; Jing Li; Xinsheng Zhao; Xiajie Zhai. 2020. "Suitability of hyperspectral data for monitoring nitrogen and phosphorus content in constructed wetlands." Remote Sensing Letters 11, no. 5: 495-504.
Wetland soil fauna support material cycling and restoration processes in wetland ecosystems. In our study, we observed variations in wetland soil fauna on the shores of Lake Taihu, China. We examined the relationships between fauna and major environmental factors, and looked at the short-and long-term changes in reed wetlands under restoration and in the natural reed lakeshore. We identified 93 groups of soil fauna in different wetlands and found significant differences in the lakeshore wetlands’ soil fauna assemblages, depending on the length of the restoration period. By analyzing the soil fauna community evenness, dominance, number of taxa, and diversity, we found minimal seasonal variation in the soil fauna community diversity and abundance. The abundance of soil fauna in the sites under restoration decreased with depth below the soil surface. The reed restoration was obvious in the succession of the soil fauna groups in the long-term site. Although the restoration had an overall positive long-term effect on the soil fauna communities, there were no obvious short-term changes in the number of individuals. The study explored various potential measures to restore soil fauna in the Lake Taihu wetland and developed a theoretical basis for restoring the lakeshore wetland ecosystem.
Wei Li; Zhiguo Dou; Lijuan Cui; Xinsheng Zhao; Manyin Zhang; Yan Zhang; Changjun Gao; Zheng Yang; Yinru Lei; Xu Pan. Soil fauna diversity at different stages of reed restoration in a lakeshore wetland at Lake Taihu, China. Ecosystem Health and Sustainability 2020, 6, 1 .
AMA StyleWei Li, Zhiguo Dou, Lijuan Cui, Xinsheng Zhao, Manyin Zhang, Yan Zhang, Changjun Gao, Zheng Yang, Yinru Lei, Xu Pan. Soil fauna diversity at different stages of reed restoration in a lakeshore wetland at Lake Taihu, China. Ecosystem Health and Sustainability. 2020; 6 (1):1.
Chicago/Turabian StyleWei Li; Zhiguo Dou; Lijuan Cui; Xinsheng Zhao; Manyin Zhang; Yan Zhang; Changjun Gao; Zheng Yang; Yinru Lei; Xu Pan. 2020. "Soil fauna diversity at different stages of reed restoration in a lakeshore wetland at Lake Taihu, China." Ecosystem Health and Sustainability 6, no. 1: 1.
Human activities alter the growth of coastal wetland vegetation. In the present study, we used a spectrometer and hyperspectral data to determine and compare the biomass of Suaeda salsa in a coastal wetland under protective and destructive activities. Using typical discriminants, the hyperspectral data of Suaeda salsa were distinguished under the influence of two kinds of human activity, and the accuracy of the inversion model of biomass was established following improved differentiation of the data under the influence of human activities. The original spectral reflectance and vegetation index were selected, and the biomass-inversion model was established by linear regression and partial least-squares regression. The model established by partial least-squares regression had a good precision (R2>0.85, RMSE%<5.6%). Hyperspectral technology can accurately show plant biomass and the indirect effects of interference by human activities of different intensity on coastal wetlands. The accuracy of the models can be improved by distinguishing the vegetation patterns under the influence of different types of human activity, and then constructing the biomass models. This study provides technical support for the use of quantitative remote sensing-based methods to monitor the fragile ecology of coastal wetlands under the influence of human activities.
Zhiguo Dou; Youzhi Li; Lijuan Cui; Xu Pan; Qiongfang Ma; Yilan Huang; Yinru Lei; Jing Li; Xinsheng Zhao; Wei Li. Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China. Marine and Freshwater Research 2020, 71, 482 .
AMA StyleZhiguo Dou, Youzhi Li, Lijuan Cui, Xu Pan, Qiongfang Ma, Yilan Huang, Yinru Lei, Jing Li, Xinsheng Zhao, Wei Li. Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China. Marine and Freshwater Research. 2020; 71 (4):482.
Chicago/Turabian StyleZhiguo Dou; Youzhi Li; Lijuan Cui; Xu Pan; Qiongfang Ma; Yilan Huang; Yinru Lei; Jing Li; Xinsheng Zhao; Wei Li. 2020. "Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China." Marine and Freshwater Research 71, no. 4: 482.
Accurate estimates of reed (Phragmites communis) biomass are critical for efficient reed swamp monitoring and management. This study compared the accuracy of commonly used empirical models in estimating above-ground biomass in dense swamp reeds in the Beijing Hanshiqiao Wetland Nature Reserve, northern China. Two-thirds of the samples were used for model construction, and one-third for model validation. Models for estimating reed above-ground biomass, based on original spectral reflectance, first-order differential spectrum, trilateral parameters and partial least squares (PLS), were constructed using univariate linear regression and the PLS method. Results showed that the biomass estimation model based on the first-order differential spectrum was relatively inefficient. Model accuracy was highest in the PLS model, followed by the original spectral reflectance model and was lowest in the trilateral parameters model. The model validation results were consistent with the accuracy of the established estimation model, so the model has good stability. We conclude that above-ground biomass can be successfully estimated using canopy hyperspectral information on wetland plants, based on the empirical model. The PLS method not only was more accurate in estimating fresh biomass but also represented a significant improvement in estimating dry biomass.
Wei Li; Zhiguo Dou; Yan Wang; Gaojie Wu; Manyin Zhang; Yinru Lei; Yunmei Ping; Jiachen Wang; Lijuan Cui; Wu Ma. Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China. Wetlands Ecology and Management 2018, 27, 87 -102.
AMA StyleWei Li, Zhiguo Dou, Yan Wang, Gaojie Wu, Manyin Zhang, Yinru Lei, Yunmei Ping, Jiachen Wang, Lijuan Cui, Wu Ma. Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China. Wetlands Ecology and Management. 2018; 27 (1):87-102.
Chicago/Turabian StyleWei Li; Zhiguo Dou; Yan Wang; Gaojie Wu; Manyin Zhang; Yinru Lei; Yunmei Ping; Jiachen Wang; Lijuan Cui; Wu Ma. 2018. "Estimation of above-ground biomass of reed (Phragmites communis) based on in situ hyperspectral data in Beijing Hanshiqiao Wetland, China." Wetlands Ecology and Management 27, no. 1: 87-102.
The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value). We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves.
Zhiguo Dou; Lijuan Cui; Jing Li; Yinuo Zhu; Changjun Gao; Xu Pan; Yinru Lei; Manyin Zhang; Xinsheng Zhao; Wei Li. Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China. Sustainability 2018, 10, 1127 .
AMA StyleZhiguo Dou, Lijuan Cui, Jing Li, Yinuo Zhu, Changjun Gao, Xu Pan, Yinru Lei, Manyin Zhang, Xinsheng Zhao, Wei Li. Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China. Sustainability. 2018; 10 (4):1127.
Chicago/Turabian StyleZhiguo Dou; Lijuan Cui; Jing Li; Yinuo Zhu; Changjun Gao; Xu Pan; Yinru Lei; Manyin Zhang; Xinsheng Zhao; Wei Li. 2018. "Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China." Sustainability 10, no. 4: 1127.
Nutrient removal in tidal flow constructed wetlands (TF-CW) is a complex series of nonlinear multi-parameter interactions. We simulated three tidal flow systems and a continuous vertical flow system filled with synthetic wastewater and compared the influent and effluent concentrations to examine (1) nutrient removal in artificial TF-CWs, and (2) the ability of a backpropagation (BP) artificial neural network to predict nutrient removal. The nutrient removal rates were higher under tidal flow when the idle/reaction time was two, and reached 90 ± 3%, 99 ± 1%, and 58 ± 13% for total nitrogen (TN), ammonium nitrogen (NH4+-N), and total phosphorus (TP), respectively. The main influences on nutrient removal for each scenario were identified by redundancy analysis and were input into the model to train and verify the pollutant effluent concentrations. Comparison of the actual and model-predicted effluent concentrations showed that the model predictions were good. The predicted and actual values were correlated and the margin of error was small. The BP neural network fitted best to TP, with an R2 of 0.90. The R2 values of TN, NH4+-N, and nitrate nitrogen (NO3−-N) were 0.67, 0.73, and 0.69, respectively.
Wei Li; Lijuan Cui; YaQiong Zhang; Zhangjie Cai; Manyin Zhang; Weigang Xu; Xinsheng Zhao; Yinru Lei; Xu Pan; Jing Li; Zhiguo Dou. Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands. Water 2018, 10, 83 .
AMA StyleWei Li, Lijuan Cui, YaQiong Zhang, Zhangjie Cai, Manyin Zhang, Weigang Xu, Xinsheng Zhao, Yinru Lei, Xu Pan, Jing Li, Zhiguo Dou. Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands. Water. 2018; 10 (1):83.
Chicago/Turabian StyleWei Li; Lijuan Cui; YaQiong Zhang; Zhangjie Cai; Manyin Zhang; Weigang Xu; Xinsheng Zhao; Yinru Lei; Xu Pan; Jing Li; Zhiguo Dou. 2018. "Using a Backpropagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands." Water 10, no. 1: 83.