Lijuan Cui

Absorption and backscattering coefficients and their relations to water constituents of Poyang Lake, China

The measurement and analysis of inherent optical properties (IOPs) of the main water constituents are necessary for remote-sensing-based water quality estimation and other ecological studies of lakes. This study aimed to measure and analyze the absorption and backscattering coefficients of the main water constituents and, further, to analyze their relations to the water constituent concentrations in Poyang Lake, China. The concentrations and the absorption and backscattering coefficients of the main water constituents at 47 sampling sites were measured and analyzed as follows. (1) The concentrations of chlorophyll a (C(CHL)), dissolved organic carbon (C(DOC)), suspended particulate matter (C(SPM)), including suspended particulate inorganic matter (C(SPIM)) and suspended particulate organic matter (C(SPOM)), and the absorption coefficients of total particulate (a(p)), phytoplankton (a(ph)), nonpigment particulate (a(d)), and colored/chromophoric dissolved organic matter (a(g)) were measured in the laboratory. (2) The total backscattering coefficients, including the contribution of pure water at six wavelengths of 420, 442, 470, 510, 590, and 700 nm, were measured in the field with a HydroScat-6 backscattering sensor. (3) The backscattering coefficients without the contribution of pure water (b(b)) were then derived by subtracting the backscattering coefficients of pure water from the total backscattering coefficients. (4) The C(CHL), C(SPM), C(SPIM), C(SPOM), and C(DOC) of the 41 remaining water samples were statistically described and their correlations were analyzed. (5) The a(ph), a(d), a(p), a(g), and b(b) were visualized and analyzed, and their relations to C(CHL), C(SPM), C(SPIM), C(SPOM), or C(DOC) were studied. Results showed the following. (1) Poyang Lake was a suspended particulate inorganic matter dominant lake with low phytoplankton concentration. (2) One salient a(ph) absorption peak was found at 678 nm, and it explained 72% of the variation of C(CHL). (3) The a(d) and a(p) exponentially decreased with increasing wavelength, and they explained 74% of the variation of C(SPIM) and 71% variation of C(SPM), respectively, at a wavelength of 440 nm. (4) The a(g) also exponentially decreased with increasing wavelength, and it had no significant correlation to C(DOC) at a significance level of 0.05. (5) The b(b) decreased with increasing wavelength, and it had strong and positive correlations to C(SPM), C(SPIM) and C(SPOM), a strong and negative correlation to C(CHL), and no correlation to C(DOC) at a significance level of 0.05. Such results will be helpful for the understanding of the IOPs of Poyang Lake. They, however, only represented the IOPs during the sampling time period, and more measurements and analyses in different seasons need to be carried out in the future to ensure a comprehensive understanding of the IOPs of Poyang Lake.

Using remotely sensed suspended sediment concentration variation to improve management of Poyang Lake, China

Abstract This study applied Moderate-Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2010 to obtain and analyze the spatiotemporal variation of suspended sediment concentration (SSC) and discussed factors affecting it in Poyang Lake, China. Results showed that (1) the mean SSC was lower in the south, higher in the north, and moderate in the central lake region; (2) the mean SSC in the south was lower than or close to 20 mg/L, with no clear annual trend; (3) the mean SSC in the north was slightly higher than 20 mg/L in 2000 and increased from 2001, with the highest value >60 mg/L in 2006; (4) the mean SSC in the central lake region, except for 2009, ranged from 20 to 40 mg/L and had an annual pattern similar to that in the southern lake region; (5) for the entire lake, the mean SSC declined from January to March, increased from September to December, and fluctuated from April to August; and (6) several higher SSC values were found in the central or southern lake regions. The spatiotemporal variation of SSC was controlled by natural and human factors, in which dredging was dominant. Limiting the area of dredging and reducing dredging intensity would decrease SSC and maintain sustainable development of Poyang Lake. Remote sensing can obtain the spatiotemporal information of some water quality parameters, which will help managers understand the lake dynamics and mechanisms to make better decisions for lake management.

Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China

Suspended particulate matter (SPM) is a dominant water constituent of case-II waters, and SPM concentration (CSPM) is a key parameter describing water quality. This study, using Landsat 8 Operational Land Imager (OLI) images, aimed to develop the CSPM retrieval models and further to estimate the CSPM values of Dongting Lake. One Landsat 8 OLI image and 53 CSPM measurements were employed to calibrate Landsat 8-based CSPM retrieval models. The CSPM values derived from coincident Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) images were compared to validate calibrated Landsat 8-based CSPM models. After the best stable Landsat 8-based CSPM retrieval model was further validated using an independent Landsat 8 OLI image and its coincident CSPM measurements, it was applied to four Landsat 8 OLI images to retrieve the CSPM values in the South and East Dongting Lake. Model calibration results showed that two exponential models of the red band explained 61% (estimated standard error (SE) = 7.96 mg l–1) and 67% (SE = 6.79 mg l–1) of the variation of CSPM; two exponential models of the red:panchromatic band ratio obtained 81% (SE = 5.48 mg l–1) and 77% (SE = 4.96 mg l–1) fitting accuracy; and four exponential and quadratic models of the infrared band explained 72–83% of the variation of CSPM (SE = 5.18–5.52 mg l–1). By comparing the MODIS- and Landsat 8-based CSPM values, an exponential model of the Landsat 8 OLI red band (CSPM = 1.1034 × exp(23.61 × R)) obtained the best consistent CSPM estimations with the MODIS-based model (r = 0.98, p < 0.01), and its further validation result using an independent Landsat 8 OLI image showed a significantly strong correlation between the measured and estimated CSPM values at a significance level of 0.05 (r = 0.91, p < 0.05). The CSPM spatiotemporal distribution derived from four Landsat 8 images revealed a clear spatial distribution pattern of CSPM in the South and East Dongting Lake, which was caused by natural and anthropogenic factors together. This study confirmed the potential of Landsat 8 OLI images in retrieving CSPM and provided a foundation for retrieving the spatial distribution of CSPM accurately from this new data source in Dongting Lake.

Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem

Quantifying the variability and changes in phenology and gross primary production (GPP) of alpine wetlands in the Qinghai–Tibetan Plateau under climate change is essential for assessing carbon (C) balance dynamics at regional and global scales. In this study, in situ eddy covariance (EC) flux tower observations and remote sensing data were integrated with a modified, satellite-based vegetation photosynthesis model (VPM) to investigate the variability in climate change, phenology, and GPP of an alpine wetland ecosystem, located in Zoige, southwestern China. Two-year EC data and remote sensing vegetation indices showed that warmer temperatures corresponded to an earlier start date of the growing season, increased GPP, and ecosystem respiration, and hence increased the C sink strength of the alpine wetlands. Twelve-year long-term simulations (2000–2011) showed that: (1) there were significantly increasing trends for the mean annual enhanced vegetation index (EVI), land surface water index (LSWI), and growing season GPP (R2 ≥ 0.59, p < 0.01) at rates of 0.002, 0.11 year−1 and 16.32 g·C·m−2·year−1, respectively, which was in line with the observed warming trend (R2 = 0.54, p = 0.006); (2) the start and end of the vegetation growing season (SOS and EOS) experienced a continuous advancing trend at a rate of 1.61 days·year−1 and a delaying trend at a rate of 1.57 days·year−1 from 2000 to 2011 (p ≤ 0.04), respectively; and (3) with increasing temperature, the advanced SOS and delayed EOS prolonged the wetland’s phenological and photosynthetically active period and, thereby, increased wetland productivity by about 3.7–4.2 g·C·m−2·year−1 per day. Furthermore, our results indicated that warming and the extension of the growing season had positive effects on carbon uptake in this alpine wetland ecosystem.

Estimating Soil Organic Carbon Content with Visible–Near-Infrared (Vis-NIR) Spectroscopy:

The selection of a calibration method is one of the main factors influencing measurement accuracy with visible-near-infrared (Vis-NIR, 350–2500 nm) spectroscopy. This study, based on both air-dried unground (DU) and air-dried ground (DG) soil samples, used nine spectral preprocessing methods and their combinations, with the aim to compare the commonly used partial least squares regression (PLSR) method with the new machine learning method of support vector machine regression (SVMR) to find a robust method for soil organic carbon (SOC) content estimation, and to further explore an effective Vis-NIR spectral preprocessing strategy. In total, 100 heterogeneous soil samples collected from Southeast China were used as the dataset for the model calibration and independent validation. The determination coefficient (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile range were used for the model evaluation. The results of this study show that both the PLSR and SVMR models were significantly improved by the absorbance transformation (LOG), standard normal variate with wavelet detrending (SW), first derivative (FD), and mean centering (MC) spectral preprocessing methods and their combinations. SVMR obtained optimal models for both the DU and DG soil, with R2, RMSE, and RPD values of 0.72, 2.48 g/kg, and 1.83 for DU soil and 0.86, 1.84 g/kg, and 2.60 for DG soil, respectively. Among all the PLSR and SVMR models, SVMR showed amore stable performance than PLSR, and it also outperformed PLSR, with a smaller mean RMSE of 0.69 g/kg for DU soil and 0.50 g/kg for DG soil. This study concludes that PLSR is an effective linear algorithm, but it might not be sufficient when dealing with a nonlinear relationship, and SVMR turned out to be a more suitable nonlinear regression method for SOC estimation. Effective SOC estimation was obtained based on the DG soil samples, but the accurate estimation of SOC with DU soil samples needs to be further explored. In addition, LOG, SW, FD, and MC are valuable spectral preprocessing methods for Vis-NIR optimization, and choosing two of them (except for FD + SW and LOG + FD) in a simple combination is a good way to get acceptable results.

Quantitative analysis of the driving forces causing declines in marsh wetland landscapes in the Honghe region, northeast China, from 1975 to 2006

The Sanjiang Plain has the most representative and largest concentration of inland freshwater wetlands in China, most of which have been degraded or have disappeared as a result of agricultural development and climatic change since the 1950s. To better understand the spatial and temporal variation and driving forces of marsh reduction, this study investigated variations of marsh reduction in the Honghe region of the Sanjiang Plain, Northeast China over a 30-year period, and analyzed the role of the different driving forces separately and their combined effect on marsh reduction and identified what driving forces have played key roles on the reduction in different periods. Nine natural and anthropogenic variables from remote sensing, GIS data and field surveys, such as precipitation, temperature, precipitation anomaly, population density, agricultural population density, per capita GDP, distance to road, distance to canal and distance to settlement, were evaluated on their impact on observed variations of marsh reduction between 1975 and 2006. The results show that all of these driving forces have significant influences on the decline of the marsh area, and the combination of driving forces that has crucial impacts on marsh reduction varied largely from 1975 to 2006. During 1975–1989, it was the construction of canal and road networks in farms and changes in average annual precipitation that led to marsh reduction. After 1989, the reduction was mainly related to increases in agricultural population, per capita GDP and settlements. These findings may help understand the declines or degradation of marsh areas and provide an empirical and theoretical base for managers, who design and implement wetland management and planning.

Effect of mangrove restoration on crab burrow density in Luoyangjiang Estuary, China

BackgroundMangrove restoration seeks to restore or rebuild degraded mangrove systems. The methods of mangrove restoration include ecological projects and restoration-oriented technologies, the latter of which are designed to restore the structure, processes as well as related physical, chemical and biological characteristics of wetlands and to ensure the provision of ecosystem services. As important components of mangrove ecosystem, benthic organisms and crabs play a key role in nutrient cycling. In addition, mangrove restoration, such as vegetation restoration measures, can lead to changes in the benthic faunal communities. This study investigates whether the presence of different mangrove species, age and canopy cover of mangrove communities affect the density of crab burrows.MethodsThe Luoyangjiang Estuary, in the southeast of Fujian Province, was selected as our research area. A survey, covering 14 sites, was conducted to investigate the impacts of mangrove restoration on the density of crab burrows in four rehabilitated forests with different stand ages and canopy.ResultsIt was found that differences in vegetation types had a large impact on crab density and that the density of crab burrows was lower on exposed beaches (non-mangrove) than under mature Kandelia candel, Aegiceras corniculatum and Avicennia marina communities. In general, the amount of leaf litter and debris on mangrove mudflats was greater than on the beaches as food sources for crabs. Two-factor analysis of variance (ANOVA) shows that changes in mangrove species and age since restoration had different effects on crab burrow density. The effect of canopy cover was highly significant on crab burrow density.ConclusionsThe results suggest that in the process of mangrove restoration the combined effects of mangrove stand age, canopy cover and other factors should be taken into account. This study further supports the findings of the future scientific research and practice on mangrove restoration and management measures.

Modeling total phosphorus removal in an aquatic environment restoring horizontal subsurface flow constructed wetland based on artificial neural networks

A horizontal subsurface flow constructed wetland (HSSF-CW) was designed to improve the water quality of an artificial lake in Beijing Wildlife Rescue and Rehabilitation Center, Beijing, China. Artificial neural networks (ANNs), including multilayer perceptron (MLP) and radial basis function (RBF), were used to model the removal of total phosphorus (TP). Four variables were selected as the input parameters based on the principal component analysis: the influent TP concentration, water temperature, flow rate, and porosity. In order to improve model accuracy, alternative ANNs were developed by incorporating meteorological variables, including precipitation, air humidity, evapotranspiration, solar heat flux, and barometric pressure. A genetic algorithm and cross-validation were used to find the optimal network architectures for the ANNs. Comparison of the observed data and the model predictions indicated that, with careful variable selection, ANNs appeared to be an efficient and robust tool for predicting TP removal in the HSSF-CW. Comparison of the accuracy and efficiency of MLP and RBF for predicting TP removal showed that the RBF with additional meteorological variables produced the most accurate results, indicating a high potentiality for modeling TP removal in the HSSF-CW.

Identification and modelling the HRT distribution in subsurface constructed wetland

This study focused on the identification of the hydrodynamics of a horizontal subsurface constructed wetland (HSSF-CW) located in Beijing wildlife rescue and rehabilitation center, Beijing. The effects of plant growth of iris tectorum on the hydrodynamic behaviours were studied and the distribution of the hydraulic residence time was simulated by several mathematical models in order to understand the fluctuations and mixing processes of pollutants in the HSSF-CW. Treatment performance of the HSSF-CW was evaluated by comparing the area-based removal rates of different pollutants. According to the results, water depth has a negative effect on the plant growth and a larger hydraulic loading rate is not conducive to the growth of wetland plants. Modelling the probability density of the residence time distribution indicated that the shorter hydraulic residence time of 10.16 hours compared with a theoretical hydraulic residence time of 12.81 hours was responsible for the lower removal efficiency of pollutants (T-P: 0.17 ± 0.04 g m(-2) day(-1), T-N: 1.10 ± 0.05 g m(-2) day(-1), PO(4)-P: 0.08 ± 0.04 g m(-2) day(-1), NH(4)-N: 0.19 ± 0.02 g m(-2) day(-1), NO(3)-N: 0.52 ± 0.03 g m(-2) day(-1), Chl_a: 18.26 ± 0.09 g m(-2) day(-1)). The results of a superposition simulation of residence time distribution indicated that the asymmetric double sigmoidal (asym2sig) model is competent at providing a reasonable match between the measured and the predicted values to some extent. Based on the good fit of the experimental datasets by the asym2sig probability density function, the mathematical expectation approximated to the actual hydraulic residence time (10.16 hours) of the HSSF-CW.

Estimating Carex quality with laboratory-based hyperspectral measurements

The quality of certain plants is considered to be a key factor affecting the food habitat or migration of some herbivorous species, and, thus, to estimate the spatial and temporal variation of plant quality is crucial for understanding the grazing and migrating behaviours of these herbivores. This study aimed to explore the possibilities of estimating plant protein and phosphorus contents, with the laboratory-based hyperspectral measurements of fresh Carex leaves, which are the main food source of many wintering bird species in Poyang Lake, China. Fifty-four Carex leaf samples were collected, and their hyperspectral reflectance (at 350–2500 nm) and crude protein and phosphorus contents were measured in the laboratory. The successive projections algorithm (SPA) was applied for spectral dimension reduction, and a multiple linear regression model was calibrated to estimate the crude protein and phosphorus contents from the wavelengths selected with the SPA. The model validation results showed that the root mean square errors (RMSEs) of estimation were 2.51% for crude protein and 0.06% for phosphorus. Compared with a multiple linear model with randomly selected inputs and full-spectrum partial least-square regression (PLSR), the multiple linear regression model combined with the SPA method exhibited a significant advantage in terms of accuracy in estimating the crude protein and phosphorus contents of Carex leaves.