Dingtian Yang

Detection of Seagrass Distribution Changes from 1991 to 2006 in Xincun Bay, Hainan, with Satellite Remote Sensing

Seagrass distribution is a very important index for costal management and protection. Seagrass distribution changes can be used as indexes to analyze the reasons for the changes. In this paper, in situ hyperspectral observation and satellite images of QuickBird, CBERS (China Brazil Earth Resources Satellite data) and Landsat data were used to retrieve bio-optical models and seagrass (Enhalus acoroides, Thalassia hemperichii) distribution in Xincun Bay, Hainan province, and seagrass distribution changes from 1991 to 2006 were analyzed. Hyperspectral results showed that the spectral bands at 555, 635, 650 and 675 nm are sensitive to leaf area index (LAI). Seagrass detection with QuickBird was more accurate than that with Landsat TM and CBERS; five classes could be classified clearly and used as correction for seagrass remote sensing data from Landsat TM and CBERS. In order to better describe seagrass distribution changes, the seagrass distribution area was divided as three regions: region A connected with region B in 1991, however it separated in 1999 and was wholly separated in 2001; seagrass in region C shrank gradually and could not be detected in 2006. Analysis of the reasons for seagrass reduction indicated it was mainly affected by aquaculture and typhoons and in recent years, by land use changes.

Analysis of seagrass reflectivity by using a water column correction algorithm

Seagrass in optically shallow water can generate optical signals that can be tracked remotely. Unfortunately the signals from the bottom are relatively weak and can be affected by the water column when concentrations of suspended particles, chlorophyll and coloured dissolved organic matter are high. An optical model simulating the propagation of light for retrieving the bottom reflectance was developed. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps, and essential for deriving empirical relationships between remotely sensed data and interesting features in the marine environment. The appropriate wavebands for seagrass mapping, which generally lay between 500 and 630 nm and 680 and 710 nm, were obtained by means of full visual inspection and analysis of the correct spectra. Additionally, a strong relationship between the reflectance value at 715 nm and Leaf Area Index was found, with a correlation coefficient of 0.99.

Spectral absorption coefficient of phytoplankton and its relation to chlorophyll a and remote sensing reflectance in coastal waters of southern China

The spectral absorption coefficient of phytoplankton in coastal waters of southern China is investigated. Large variations in the absorption coefficient of phytoplankton are found. The absorption coefficient of phytoplankton at 443 urn ranged from 0.006 m(-1) to 0.484 m(-1), with an average value of 0.067 m(-1). The chlorophyll-specific absorption coefficient of phytoplankton is also a bio-optical variable, with a spectrally averaged value of 0.025 m(2) (.) mg(-1). The variations of chlorophyll-specific absorption coefficient are mainly attributed to pigment composition of phytoplankton and package effect. The chlorophyll-specific absorption coefficient of phytoplankton decreases with the increasing of chlorophyll a concentration. This relationship can be described by a power law function, with the parameters and the coefficient of determination r(2) as functions of wavelength, but the parameters describing the relationships in present study differed from that in Case 1 waters, thus the regional adjustment of model parameters was of particular significance for improving the accuracy of bio-optical algorithms for estimation of Chl-a concentration and primary production from remotely sensed data. Regression analysis of reflectance (R-D) ratio and absorption coefficient of phytoplankton (a(ph)) indicates a close correlation between them, which means that it is possible to retrieve absorption coefficient of phytoplankton using ocean color remote sensing data in optically complex coastal waters.

Detection of seagrass in optical shallow water with Quickbird in Xincun Bay of Hainan province, China

Seagrass, mainly distributed in the coastal water along South China Sea, is very important for coastal ecosystem. In order to obtain precise data about seagrass distribution in optical shallow water in Xincun Bay of Hainan province, Quickbird data was used in the paper. Radiance transfer model in optical shallow water was used to retrieve the information of bottom reflectivity, and supervise classification was used to retrieve the distribution and density of submerged seagrass; Results showed that seagrass distributed in the northeast coast of Xincun Bay with the pattern of stripe can be clearly detected. Density gradient was very clear, coverage under 20% was mainly distributed at the out range of seagrass bed and coverage greater than 80% distributed in the centre of the seagrass bed. The accuracy of seagrass retrieval was more than 80% and the density of seagrass can also be distinguished evidently.

An Improved Empirical Model for Retrieving Bottom Reflectance in Optically Shallow Water

Satellite remote sensing has become an essential observing system to obtain comprehensive information on the status of coastal habitats. However, a significant challenge in remote sensing of optically shallow water is to correct the effects of the water column. This challenge becomes particularly difficult due to the spatial and temporal variability of water optical properties. In order to model the light distribution for optically shallow water and retrieve the bottom reflectance, a parameterized model was proposed by introducing an important adjusted factor g. The synthetic data sets generated by HYDROLIGHT were utilized to train a neural network (NN) and then to derive the adjustable parameter values. The parameter g was found to vary with water depth, water optical properties, and bottom reflectance. Specifically, it revealed two obvious patterns among the different benthic habitat types. In coral reef, seagrass, and macrophyte habitats, g exhibited a remarkable peak at about 550 nm. The peak has a value of about 2.47-2.49. In white sand or hardpan habitats, g spectra are relatively flat. The semi-empirical model was applied to calculate the bottom reflectance from the new weighting factor, the downward diffuse attenuation coefficient, and the irradiance reflectance just below the sea surface collected in Sanya Bay in 2008 and 2009. Good agreement between the predicted and measured values demonstrated that the weighting factor g is an effective tool to modify the model for interpreting and predicting bottom reflectance without the need for any localized input (R2 > 0.79).

Bio-optical model of chromatic dissolved organic matter in lake TaiHu, China

In this paper, the light attenuation, fluorescence and absorbance of DOM in Lake TaiHu was mainly studied from July 2001 to December 2002. Results showed that DOM mainly contributed to the attenuation at the ultraviolet, and the best correlation coefficient was at 355 nm. The absorption of DOM could be well described by the exponential decay model. The slope value S, retrieved from absorbance of 350 nm and 440 nm, was 0.0046 nm^-1(SD=0.0015,n=284), far less than the published 0.014 nm^-1. The absorbance at 350 nm (a(350)) well correlated with DOM concentration (R^2=0.673, n=284), and also linearly correlated with fluorescence at 430 nm (R^2=0.497, n=214). The relationship between DOM concentration and fluorescence intensity was also regressed, and showed that it was polynomial.

Retrieval of water quality parameters by hyperspectral remote sensing in Lake TaiHu, China

Monitoring and restoration the water quality of lake need proper water quality parameters. Traditional measurement of water quality requiring laborious laboratory work is expensive and time consuming. Hyperspectral measurement can offer fast and easy way for estimating trophic status. Hyperspectral data on 7-8 March 2004 and water chemical data from 1997 to 2003 was used for retrieval of water quality parameters. The quantification of spectra with water quality parameters: chlorophyll a suspended solids total nitrogen(TN) total phosphorus(TP) chemical oxygen demand(COD) secchi depth(SD) were regressed. Results showed that the reflectance ratio of R702/R685 R6201R53 1 and R554/R675 had high correlations with the concentration of chlorophyll a suspended solids and total phosphorus respectively TN COD can be calculated from TP or Chi a for good relations between them SD is negatively correlated with suspended solids concentration total phosphorus (TP) (less than 0. 25 mg/L) linearly correlated with logarithm chlorophyll a concentration trophic status index (TSI) exponentially correlated with COD concentration.