Kaishan Song

Wetlands shrinkage, fragmentation and their links to agriculture in the Muleng-Xingkai Plain, China.

In the past five decades, the wetlands in the Muleng-Xingkai Plain, Northeast China, have experienced rapid shrinkage and fragmentation. In this study, wetlands cover change and agricultural cultivation were investigated through a time series of thematic maps from 1954, and Landsat satellite images representing the last five decades (1976, 1986, 1995, 2000, and 2005). Wetlands shrinkage and fragmentation were studied based on landscape metrics and the land use changes transition matrix. Furthermore, the driving forces were explored according to socioeconomic development and major natural environmental factors. The results indicate a significant decrease in the wetlands area in the past five decades, with an average annual decrease rate of 9004 ha/yr. Of the 625,268 ha of native wetlands in 1954, approximately 64% has been converted to other land use types by 2005, of which conversion to cropland accounts for the largest share (83%). The number of patches decreased from 1272 (1954) to 197 (1986) and subsequently increased to 326 (2005). The mean patch size changed from 480 ha (1954) to 1521 ha (1976), and then steadily decreased to 574 ha (2005). The largest patch index (total core area index) indicates wetlands shrinkage with decreased values from 31.73 (177,935 ha) to 3.45 (39,421 ha) respectively. Climatic changes occurred over the study period, providing a potentially favorable environment for agricultural development. At the same time population, groundwater harvesting, and fertilizer application increased significantly, resulting in wetlands degradation. According to the results, the shrinkage and fragmentation of wetlands could be explained by socioeconomic development and secondarily aided by changing climatic conditions.

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification.

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (R(rs)) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every R(rs) spectrum into one of the three water types by comparing the values from band 7 (central band: 665 nm), band 8 (central band: 681.25 nm), and band 9 (central band: 708.75 nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R2 = 0.94, 0.91) than their performance on Chl-a concentration (R2 = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R2 = 0.91, 0.82) and TSM (R2 = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R2 = 0.75 and 0.90 for Chl-a; R2 = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.

Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China

Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data

Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs

The connection between nutrient input and algal blooms for inland water productivity is well known but not the spatial pattern of water nutrient loading and algae concentration. Remote sensing provides an effective tool to monitor nutrient abundances via the association with algae concentration. Twenty-one field campaigns have been conducted with samples collected under a diverse range of algal bloom conditions for three central Indiana drinking water bodies, e.g., Eagle Creek Reservoir (ECR), Geist Reservoir (GR), and Morse Reservoir (MR) in 2005, 2006, and 2008, which are strongly influenced anthropogenic activities. Total phosphorus (TP) was estimated through hyperspectral remote sensing due to its close association with chlorophyll a (Chl-a), total suspended matter, Secchi disk transparency (SDT), and turbidity. Correlation analysis was performed to determine sensitive spectral variables for TP, Chl-a, and SDT. A hybrid model combining genetic algorithms and partial least square (GA-PLS) was established for remote estimation of TP, Chl-a, and SDT with selected sensitive spectral variables. The result indicates that TP has close association with diagnostic spectral variables with R2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R2 of 0.87 for aggregated dataset. GA-PLS was applied to the airborne imaging data (AISA) to map spatial distribution of TP, Chl-a, and SDT for MR and GR. The eutrophic status was evaluated with Carlson trophic state index using TP, Chl-a, and SDT maps derived from AISA images. Mapping results indicated that most MR belongs to mesotrophic (48.6%) and eutrophic (32.7%), while the situation was more severe for GR with 57.8% belongs to eutrophic class, and more than 40% to hypereutrophic class due to the high turbidity resulting from dredging practices.

Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling

Morse Reservoir (MR), a major source of the water supply for the Indianapolis metropolitan region, is now experiencing nuisance cyanobacterial blooms. These blooms cause water quality degradation, as well as reducing the aesthetic quality of water by producing toxins, scums, and foul odors. Hyperspectral remote sensing data from both in situ and airborne AISA measurements were applied to GA-PLS by relating the spectral signal with measured water eutrophication parameters, e.g., chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), and Secchi disk depth (SDD). Our results indicate that GA-PLS relating field sensor acquired spectral reflectance to the above-mentioned four parameters yielded low root mean square error between measured and estimated Chl-a (RMSE=10.4; Range (R): 1.8-215.8 μg/L), PC (RMSE=18.6; R: 1.4-371.0 μg/L), TSM (RMSE=3.8; R: 3.6-81.4 mg/L), SDD (RMSE=5.8; R: 25-135 cm) for MR. The GA-PLS model also yielded high performance with AISA image spectra, and the RMSEs were 12.1 μg/L, 25.3 μg/L, 5.9 mg/L and 5.7 cm, respectively for Chl-a, PC, TSM, and SDD. Four water quality parameters were mapped with GA-PLS using AISA hyperspectral image. Based on these results, in situ and airborne hyperspectral remote sensors can provide both quantitative and qualitative information on the distribution and concentration of cyanobacteria, suspended matter, and transparency in MR.

Spatial variability of soil organic carbon under maize monoculture in the Song-Nen Plain, Northeast China.

Abstract Soil organic carbon (SOC) and its relationship with landscape attributes are important for evaluating current regional, continental, and global carbon stores. Data of SOC in surface soils (0–20 cm) of four main soils, Cambisol, Arenosol, Phaeozem, and Chernozem, were collected at 451 locations in Nongan County under maize monoculture in the Song-Nen Plain, Northeast China. The spatial characteristics of soil organic carbon were studied, using geographic information systems (GIS) and geostatistics. Effects of other soil physical and chemical properties, elevation, slope, and soil type on SOC were explored. SOC concentrations followed a normal distribution, with an arithmetic mean of 14.91 g kg −1 . The experimental variogram of SOC was fitted with a spherical model. There were significant correlations between soil organic carbon and bulk density ( r = −0.374**), pH ( r = 0.549**), total nitrogen ( r = 0.781**), extractable phosphorus ( r = −0.109*), exchangeable potassium ( r = 0.565**), and cation exchange capacity ( r = 0.313**). Generally, lower SOC concentrations were significantly associated with high elevation ( r = −0.429**). Soil organic carbon was significantly negatively correlated with slope gradient ( r = −0.195**). Samples of the Cambisol statistically had the highest SOC concentrations, and samples of the Arenosol had the lowest SOC value.

Landsat-Based Estimation of Mangrove Forest Loss and Restoration in Guangxi Province, China, Influenced by Human and Natural Factors

Mangrove forest dynamics are undergoing constant changes because of both natural and anthropogenic factors. However, the rates and causes of loss and restoration remain largely unknown. This study aims to respond to this concern by analyzing the dynamics of mangrove forests and surrounding land covers in Guangxi Province, China. We analyzed Landsat images on a decadal scale between 1973 and 2010 using an object-oriented classification method. Temporal analysis results indicated that the areal extent of mangrove forests showed the following changes: a sharp decrease from 5305 ha in 1973 to 2306 ha in 1981; a remarkable increase from 2306 ha in 1981 to 5937 ha in 2000; and a slight degradation from 5937 ha in 2000 to 5759 ha in 2010. Reclamation and natural factors resulted in the loss of mangrove forests. By comparison, protection and reforestation efforts contributed to mangrove forest restoration. During the past 40 years, mangrove forests in Guangxi were fragmented. In contrast to the movement of other mangrove forests in the world, mangroves in the coasts of Guangxi were moved seaward because the rates of change in relative sea level were exceeded by the rates of change in the elevation of sedimentation in mangrove mudflats. Simultaneously, man-made land cover prevented landward migration. These results provide valuable information for better understanding mangrove forest dynamics in developing countries. These results can also be used as guidelines in the creation and implementation of reasonable mangrove forest management policies.

A semi-analytical algorithm for remote estimation of phycocyanin in inland waters

Phycocyanin (PC) is the unique and important accessory pigment for monitoring toxic cyanobacteria in inland waters. In this study, a semi-analytical algorithm combining both three band indices and a baseline algorithm (TBBA) was developed to estimate PC concentrations and then tested in three eutrophic and turbid reservoirs. TBBA does not need to optimize wavelengths as either the traditional baseline algorithm or three-band algorithms does when it is used across different study sites. TBBA evidently corrects some effects of absorptions due to colored detritus matter and other pigments and backscattering of water column. TBBA accurately estimated PC concentrations with R(2)=0.8573 and rRMSE=31.4% for water samples with the PC range from 1.4 mgm(-3) to 146.1 mgm(-3). Particularly, TBBA outperformed three-band algorithms and a previously proposed semi-empirical algorithm for the prediction of low PC (PC ≤ 50 mgm(-3)) concentration. Further analysis reveals that both the variations of PC:Chl-a and PC:TSM are important factors influencing the performance of all PC algorithms examined in this study and more efforts are required to improve the performance of TBBA on water samples with low PC concentration.

Using CropSyst to Simulate Spring Wheat Growth in Black Soil Zone of Northeast China

ABSTRACT Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, ‘Longmai 19’, in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons (1992–1994) were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of ‘Longmai 19’ spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration (ET), grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.