Heng Lv

Light scattering properties and their relation to the biogeochemical composition of turbid productive waters: a case study of Lake Taihu

Light scattering properties in such a highly turbid productive lake as Lake Taihu in China were examined through 118 samples collected during three cruises in November 2006, March 2007, and November 2007. The particulate scattering and backscattering coefficients were observed using WETLabs AC-S and ECO-BB9. A power model with a spectral exponent of -0.729 was used to simulate the particulate scattering coefficient (b(p)) spectra. It has a better performance than the linear model. Scattering parameters are more closely related to inorganic suspended matter (ISM) concentration than to other water components, such as total suspended matter (TSM), organic suspended matter (OSM), and chlorophyll a (Chla). This indicates that ISM dominates the scattering signal in the lake. Three discrepancies with oceanic/coastal waters are observed: (a) the backscattering ratio (b (bp)) decreases with an increase in the ISM concentration because of a highly strong contribution by ISM to b(p); (b) the mass-specific scattering coefficient (b(p) (m)) exhibits a wider range of variability than that reported in previous studies, which can be attributed to considerable variation in the OSM and ISM distributions; (c) the particle size distribution slope (xi) is mostly larger than 4.0 in Lake Taihu, whereas it is usually within 3.5-4.0 for marine particles. In addition, the bulk refractive index (n (p)) calculated according to the Twardowski et al. model [J. Geophys. Res. 106, 14129 (2001)JGREA20148-0227] indicates that some stations (n (p)<1.07) can be regarded as organic-particle dominant. Other stations with high ISM concentrations have a very small n (p) value mostly within 1.10-1.17. Overall, the knowledge on the scattering properties gained in this study broadens our understanding of water optics in highly turbid productive water columns.

Long-Term Satellite Observations of Microcystin Concentrations in Lake Taihu during Cyanobacterial Bloom Periods.

Microcystins (MCs) produced by cyanobacteria pose a serious threat to public health. Intelligence on MCs distributions in freshwater is therefore critical for environmental agencies, water authorities, and public health organizations. We developed and validated an empirical model to quantify MCs in Lake Taihu during cyanobacterial bloom periods using the atmospherically Rayleigh-corrected moderate resolution imaging spectroradiometer (MODIS-Aqua) (Rrc) products and in situ data by means of chlorophyll a concentrations (Chla). First, robust relationships were constructed between MCs and Chla (r = 0.91; p < 0.001; t-test) and between Chla and a spectral index derived from Rrc (r = -0.86; p < 0.05; t-test). Then, a regional algorithm to analyze MCs in Lake Taihu was constructed by combining the two relationships. The model was validated and then applied to an 11-year series of MODIS-Aqua data to investigate the spatial and temporal distributions of MCs. MCs in the lake were markedly variable both spatially and temporally. Cyanobacterial bloom scums, temperature, wind, and light conditions probably affected the temporal and spatial distribution of MCs in Lake Taihu. The findings demonstrate that remote sensing reconnaissance in conjunction with in situ monitoring can greatly aid MCs assessment in freshwater.

A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance

This study develops a novel support vector regression (SVR) model for retrieving the specific cyanobacterial pigment C-phycocyanin (C-PC) concentrations in cyanobacteria-dominated large turbid lakes of China. Lake Taihu, Lake Chaohu, and Lake Dianchi in China were our study areas. Five field cruises were carried out to collect data sets of optical and water quality parameters. To retrieve the C-PC, three types of reflectance forms, including single band, band ratio, and three-band-combination, were compared. The band ratio was the best candidate to serve for algorithm development. On this basis, two types of models, including linear models and a SVR model, were originally established. The previous typical algorithms were also examined. The obtained results showed that the best-performing model was the SVR model. By our validation data set, the proposed SVR model also presented accurate prediction results, with the lowest errors among all methods. The novelty of the SVR model compared to the previous ones lies in the inclusion of band ratios that are located outside of the main pigment absorption peaks but hold information on inflection points, curvature, etc., into empirical optimization. The implications of these findings indicates the potential applicability of the SVR models in lakes of the similar type.

Detection of Suspended-Matter Concentrations in the Shallow Subtropical Lake Taihu, China, Using the SVR Model Based on DSFs

Accurate detection of suspended-matter concentrations in water columns is an important task in remotely sensing water color. This letter aims to identify an optimal model for estimating suspended-matter concentration in the optically complex Lake Taihu of China. Remote sensing reflectance Rrs(λ), inherent optical properties, and constituent concentrations of the Lake water were synchronously measured in November of 2007. After the effects of water constituents on Rrs(λ) were analyzed, the definitive spectral factors were determined, which are indicative primarily of total suspended matter (TSM). Several methods were compared in modeling the relationship between Rrs(λ) and TSM. Results show that the support vector regression (SVR) model performs best with a root-mean-square error of 4.7 mg · l-1 (R2 = 0.968). Its predictive errors in four seasons were also assessed with the mean absolute percentage errors varying in the range of 22.0%-60.0%. Thus, the SVR model can be used to reliably retrieve TSM concentrations in Lake Taihu. This finding offers new insights into the optical signals of in-water constituents in optically complex lakes.

Classification of Inland Waters Based on Bio-Optical Properties

Multiple bio-optical measurements of optically active substances were conducted in Taihu Lake, Chaohu Lake, Three Gorges Reservoir, and Dianchi Lake (China). A hierarchical cluster analysis was applied to remote sensing spectra (Rrs(λ)), by which those waters were clustered into three optically distinct types (Type I, Type II, and Type III). Absorption coefficients of phytoplankton were simulated using the linear function and the coefficients of the functions varied in different water types. The slope of colored dissolved organic matter (CDOM) absorption could be clustered into two classes, one class of high slope for Types I and II, whereas the other class of low slope for Type III. A uniform model was used for parameterization of the nonalgal particles absorption coefficients spectral between different water types. The power-law function was used to parameterize the scattering coefficient and the slope of scattering spectra could be classified into two groups of high slope for Types I and II, low slope for Type III. The composition of particles, particulate scattering, and nonalgal particulate absorption are the factors controlling the variability in Rrs(λ) for corresponding type waters. Those results support such a fact that it is indispensable to classify waters before developing a model to estimate water quality parameters for multi-type optical waters.

Quantificational effect of reforestation to soil erosion in subtropical monsoon regions with acid red soil by sediment fingerprinting

Reforestation and afforestation have been implemented over large regions in China for decades, and this has significantly reduced dust storms, desertification and soil/water erosion in China. However, the quantificational evaluation of the reforestation program in China is still incomplete, especially in the southwest China, due to the limitation of survey data. In order to assess the effects of reforestation on soil erosion in southwest China, a subtropical monsoon region with acid red soil, a representative watershed (Dianchi, Yunnan, in southwest China), was selected to study the effect of reforestation on soil erosion over nearly five decades. The land surface soil samples and sedimentary core in this watershed were examined in 2012 to evaluate the soil erosion and sediment deposition during the period of deforestation and reforestation. The results from the multi-source indexes (nutrients, radionuclides, metallic and isotope elements) measurement from soil samples and analyses (composite fingerprinting, remote sensing and geographic information system) indicate that reforestation significantly reduced the presence of sand from surrounding basin via the sedimentary records. Natural forests had the greatest soil conservation capacity, followed by economic forests and farmland, according to a composite fingerprinting. The distribution of total phosphorus, total nitrogen (TN), total organic carbon (TOC) and isotope carbon (δ13CSOC) presents a high consistency to the results of the composite fingerprinting. The loss rate of TN and TOC in farmland is much greater than that in forest. Reforestation will thus increase the storage of TN and TOC in the long term, although this storage is lower than that in farmland during a short time due to the application of fertilizers. The logging of economic forests will significantly increase soil erosion in this region because of low grass coverage. Recommendations include limiting the period of logging in economic forests to periods outside the rainy season and intercropping other kinds of indigenous trees or grasses to reduce soil erosion.

An Inversion-Based Fusion Method for Inland Water Remote Monitoring

Although remote sensing technology has been widely used to monitor inland water bodies, the lack of suitable data with high spatial and spectral resolutions has seriously obstructed the practical development of inland water color remote sensing. An inversion-based fusion (IBF) algorithm is proposed to fuse water color and high-spatial resolution images. The algorithm was applied to two datasets: The Hyperion simulated dataset (dataset #1) and a pair of Environmental Satellite 1 (HJ1, launched by China in 2009) and medium resolution imaging spectrometer images (dataset #2). The fusions are quantitatively and qualitatively compared with three widely used algorithms. The results show that the IBF algorithm performs better using both evaluation indexes and visual comparisons. A discussion of the free parameter window size n and the contribution of the low-resolution image (LRI) to the spatial distribution (WLRI) shows that a largern will result in both greater model errors and better control of geometric errors, whereas WLRI helps stabilize the algorithm. Finally, chlorophyll-a concentration maps are developed from the fusions. The significant advantage of the IBF-derived chlorophyll-a concentration map indicates that the IBF algorithm has the potential to advance the monitoring of optical complex inland water.

Spatial-temporal analysis of land use and coverage change in Nanjing based on GIS/RS

Land Use and Coverage Change (LUCC) is an important aspect of global change. It is also very meaningful to regional development and land use management towards sustainable development. In this research, Nanjing, as one representative city in Yangtze River Delta, one of the regions which have the densest population and the quickest economic growth, was chosen to be the research area. Remote Sensing technology is very popular for monitoring land use change and detecting land use change areas. In this research, three Landsat TM/ETM+ images in 1988, 1994 and 2000 are chosed as the data, and classified by supervised classification method based on maximum likelihood method so as to monitor the land use change in Nanjing. And the spatial-temporal analysis models including land use structure, transition matrix, gravity centers, have been used to analyze the dynamic change of land use and land cover. The result shows that the land use and land cover had changed a lot during 1988–2000, especially for the building land and plough land. All these achievement are very meaningful and helpful to the land policy making towards sustainability.

A novel trophic status index of Lake Taihu based on in situ measured hyperspectral data

Using the hyperspectral data to evaluate the trophic state of inland waters is a more convenient approach for monitoring water quality than traditional methods. In the present study, field experiments were conducted for collecting water quality parameters and reflectance spectra in April, 2009 (48 samples) and May, 2010 (30 samples) in Lake Taihu, respectively. It has been proved that TLI(Chl-a) could replace TLI to evaluate the eutrophic state of Lake Taihu and the three-band model can be used to reflect the eutrophic state of the lake. Thereafter, a new trophic status index, called TLI(Rrs) which was deduced by remote sensing reflectance, was developed. And then the trophic state of Lake Taihu was evaluated by the new trophic status index. The results show that: the new trophic status index has high accuracies with relative error of 20% in evaluating the trophic state of Lake Taihu. In addition, the main area of Lake Taihu was suffuring from mesotrophic status, and only 23.21% and 19.63% areas of the lake belonged to light eutrophic status in April, 2009 and May, 2010, respectively. The most serious eutrophication occurred in Gonghu bay.

Parametric models of backscattering coefficient for Taihu Lake based on spectral classification of MERIS image

Parameters of backscattering coefficient models have significant variability in different areas and different periods. In order to break the limitation, a classification algorithm based on spectra dominant factors for Taihu Lake is established. Quasi-analytical algorithm and optical closure are used in this paper to simulate the backscattering coefficient of three types of water in Taihu Lake with the field measurement data respectively. The properties of backscattering coefficient are analyzed synchronously. On this basis, parametric models of backscattering coefficient for three types of water in Taihu Lake are established respectively. Consequently, the differences of the backscattering properties in different time and space are converted into the differences of bio-optical properties of dominant factors in water. So the parametric models of backscattering coefficient are suitable for different parts and different seasons of Taihu Lake.