Zhaofu Li

Impacts on Wetlands of Large-scale Land-use Changes by Agricultural Development: The Small Sanjiang Plain, China

Abstract The Small Sanjiang Plain (SSP), was formerly the largest wetland complex in China, located in the Northeastern part of Heilongjiang Province, China. Home to vast numbers of waterfowls, fish, and plants, the SSP is globally significant for biodiversity conservation. The loss and fragmentation of wetlands as a result agricultural development over 50 years has impacted wetland communities and their biodiversity. We used GIS to inventory large-scale land-use changes from 1950 to 2000, together with other statistical data. We found that 73.6% of the wetlands were lost due to agricultural development. Consequences of these land-use changes included: i) a rapid decline in waterfowl and plant species with the loss and fragmentation of natural wetlands and wetland ecosystem degradation; ii) greater variation in wetland water levels as the result of land-use changes over the years; iii) disruption of the dynamic river-floodplain connection by construction of drainage ditches and levees; and iv) a decrease in floodplain area that caused increased flooding peak flows and runoff. Here we show how these changes affect wetland biodiversity and impact important wetland species.

Inferring land use and land cover impact on stream water quality using a Bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China.

Lake eutrophication has become a very serious environmental problem in China. If water pollution is to be controlled and ultimately eliminated, it is essential to understand how human activities affect surface water quality. A recently developed technique using the Bayesian hierarchical linear regression model revealed the effects of land use and land cover (LULC) on stream water quality at a watershed scale. Six LULC categories combined with watershed characteristics, including size, slope, and permeability were the variables that were studied. The pollutants of concern were nutrient concentrations of total nitrogen (TN) and total phosphorus (TP), common pollutants found in eutrophication. The monthly monitoring data at 41 sites in the Xitiaoxi Watershed, China during 2009-2010 were used for model demonstration. The results showed that the relationships between LULC and stream water quality are so complicated that the effects are varied over large areas. The models suggested that urban and agricultural land are important sources of TN and TP concentrations, while rural residential land is one of the major sources of TN. Certain agricultural practices (excessive fertilizer application) result in greater concentrations of nutrients in paddy fields, artificial grasslands, and artificial woodlands. This study suggests that Bayesian hierarchical modeling is a powerful tool for examining the complicated relationships between land use and water quality on different scales, and for developing land use and water management policies.

Methane and Nitrous Oxide Emissions Reduced Following Conversion of Rice Paddies to Inland Crab–Fish Aquaculture in Southeast China

Aquaculture is an important source of atmospheric methane (CH4) and nitrous oxide (N2O), while few direct flux measurements are available for their regional and global source strength estimates. A parallel field experiment was performed to measure annual CH4 and N2O fluxes from rice paddies and rice paddy-converted inland crab-fish aquaculture wetlands in southeast China. Besides N2O fluxes dependent on water/sediment mineral N and CH4 fluxes related to water chemical oxygen demand, both CH4 and N2O fluxes from aquaculture were related to water/sediment temperature, sediment dissolved organic carbon, and water dissolved oxygen concentration. Annual CH4 and N2O fluxes from inland aquaculture averaged 0.37 mg m(-2) h(-1) and 48.1 μg m(-2) h(-1), yielding 32.57 kg ha(-1) and 2.69 kg N2O-N ha(-1), respectively. The conversion of rice paddies to aquaculture significantly reduced CH4 and N2O emissions by 48% and 56%, respectively. The emission factor for N2O was estimated to be 0.66% of total N input in the feed or 1.64 g N2O-N kg(-1) aquaculture production in aquaculture. The conversion of rice paddies to inland aquaculture would benefit for reconciling greenhouse gas mitigation and agricultural income increase as far as global warming potentials and net ecosystem economic profits are of concomitant concern. Some agricultural practices such as better aeration and feeding, and fallow season dredging would help to lower CH4 and N2O emissions from inland aquaculture. More field measurements from inland aquaculture are highly needed to gain an insight into national and global accounting of CH4 and N2O emissions.

Simulation of runoff and nutrient export from a typical small watershed in China using the Hydrological Simulation Program–Fortran

The Hydrological Simulation Program-Fortran (HSPF), which is a hydrological and water-quality computer model that was developed by the United States Environmental Protection Agency, was employed to simulate runoff and nutrient export from a typical small watershed in a hilly eastern monsoon region of China. First, a parameter sensitivity analysis was performed to assess how changes in the model parameters affect runoff and nutrient export. Next, the model was calibrated and validated using measured runoff and nutrient concentration data. The Nash–Sutcliffe efficiency (ENS) values of the yearly runoff were 0.87 and 0.69 for the calibration and validation periods, respectively. For storms runoff events, the ENS values were 0.93 for the calibration period and 0.47 for the validation period. Antecedent precipitation and soil moisture conditions can affect the simulation accuracy of storm event flow. The ENS values for the total nitrogen (TN) export were 0.58 for the calibration period and 0.51 for the validation period. In addition, the correlation coefficients between the observed and simulated TN concentrations were 0.84 for the calibration period and 0.74 for the validation period. For phosphorus export, the ENS values were 0.89 for the calibration period and 0.88 for the validation period. In addition, the correlation coefficients between the observed and simulated orthophosphate concentrations were 0.96 and 0.94 for the calibration and validation periods, respectively. The nutrient simulation results are generally satisfactory even though the parameter-lumped HSPF model cannot represent the effects of the spatial pattern of land cover on nutrient export. The model parameters obtained in this study could serve as reference values for applying the model to similar regions. In addition, HSPF can properly describe the characteristics of water quantity and quality processes in this area. After adjustment, calibration, and validation of the parameters, the HSPF model is suitable for hydrological and water-quality simulations in watershed planning and management and for designing best management practices.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.

Comprehensive Performance Evaluation for Hydrological and Nutrients Simulation Using the Hydrological Simulation Program–Fortran in a Mesoscale Monsoon Watershed, China

The Hydrological Simulation Program–Fortran (HSPF) is a hydrological and water quality computer model that was developed by the United States Environmental Protection Agency. Comprehensive performance evaluations were carried out for hydrological and nutrient simulation using the HSPF model in the Xitiaoxi watershed in China. Streamflow simulation was calibrated from 1 January 2002 to 31 December 2007 and then validated from 1 January 2008 to 31 December 2010 using daily observed data, and nutrient simulation was calibrated and validated using monthly observed data during the period from July 2009 to July 2010. These results of model performance evaluation showed that the streamflows were well simulated over the study period. The determination coefficient (R2) was 0.87, 0.77 and 0.63, and the Nash-Sutcliffe coefficient of efficiency (Ens) was 0.82, 0.76 and 0.65 for the streamflow simulation in annual, monthly and daily time-steps, respectively. Although limited to monthly observed data, satisfactory performance was still achieved during the quantitative evaluation for nutrients. The R2 was 0.73, 0.82 and 0.92, and the Ens was 0.67, 0.74 and 0.86 for nitrate, ammonium and orthophosphate simulation, respectively. Some issues may affect the application of HSPF were also discussed, such as input data quality, parameter values, etc. Overall, the HSPF model can be successfully used to describe streamflow and nutrients transport in the mesoscale watershed located in the East Asian monsoon climate area. This study is expected to serve as a comprehensive and systematic documentation of understanding the HSPF model for wide application and avoiding possible misuses.

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake

The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS) model to predict runoff, total nitrogen (TN) and total phosphorus (TP) loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds.

Industrial Wastewater Discharge Retrieval Based on Stable Nighttime Light Imagery in China from 1992 to 2010

Industrial wastewater (IW) discharge, which is a known point source of pollution, is a major water pollution source. Increasing IW discharge has imposed considerable pressure on regional or global water environments. It is important to estimate the IW distribution in grid units to improve basin-scale hydrological processes and water quality modeling. For the first time, we use the nighttime light imagery produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) to estimate the spatial and temporal variations in the IW distribution from 1992 to 2010 in China. The digital number values per unit area (DNP) of each stable light image were calculated using nighttime light imagery and were regressed against the IW per unit area (IWP) to estimate the total industrial wastewater (TIW) for each province. The results indicated strong positive correlations between the DNP and the IWP for each province during different years. The fitted linear regression models were used to estimate IW discharge in China with reliable accuracy. The IW estimation using the satellite data was consistent with the statistical results. The results also revealed that the IW discharge coverage expanded, whereas the IW discharge intensity decreased from 1992 to 2010 in China.

Hyperspectral remote sensing of chlorophyll-a in the Xuanwu Lake

The concentration of chlorophyll-a can reflect water quality to some extent. An approach for the determination of chlorophyll-a from field reflectance spectra was presented in the Xuanwu Lake. The reflectance spectra was measured from April to May in 2009 with FieldSpec 3 portable spectrometer, and water samples were collected directly from the lake to be analyzed in lab. Using correlations between the ground-truth data and combinations of spectral bands from the field spectral data, spectral indices including single band, band ratios, and the first-derivative models were developed which could be used to estimate chlorophyll-a. The results show that single band, band ratios and the first-derivative models have a better correlation with chlorophyll-a. But the model validation results show that coefficient of determination of the band ratios and the first-derivative models are higher (R2=0.7835 and R2=0.5913 with significance level P<0.01), and the single band model is the lowest (R2=0.4748). Therefore, band ratios and the first-derivative models can be used to indicate chlorophyll-a concentration of Xuanwu Lake, and the band ratios model is better than first-derivative model.