The rapid growth of impervious land covers within urbanizing regions holds many negative implications for
environmental quality. The study region is the drinking water conservation areas of Shanghai, which is very important to
the megalopolis. Mapping of imperviousness has shown important potentials to acquire such information in great spatial
detail but the actual mapping process has been challenged by the heterogeneity of urban and suburb environment and the
spatial and spectra capabilities of the sensor. This study focused on mapping the imperviousness fraction using linear
spectral unmixing in the area from Landsat satellite remote sensing data. Development of high-quality fraction images
depends greatly on the selection of suitable endmembers. A multi-endmemer linear spectral unmixing were evaculated.
In the approach, each of the class hold multi-image-endmember representing the heterogeneity of them. The best
fraction images were chosen to determine the imperviousness. An unconstrained least-squares solution was used to unmix
the MNF components into fraction images. The multi-endmember linear spectral unmixing is then used to map
imperviousness fraction for the years of 1987, 1997 and 2006 in upper region of Huangpu River, respectively. In the
water resources reservation of Shanghai, the impervious surface area increases approximately 3 times from 1987 to 2002.
The aim of the study was to explore a fast way of monitoring chlorophyll a concentration in Dianshan Lake of Shanghai, China. Reflection spectra in spring of 2006 were measured and the simultaneous sampling and analysis were performed. The correlations between chlorophyll a concentration and reflection spectra were studied based on which different chlorophyll a algorithms were established. The results indicate that the accuracy of the linear algorithm using normalized reflectance is not high (R<sup>2</sup><0.6). The relationship between chlorophyll a concentration and the reflectance ratios R<sub>708</sub>/R<sub>667</sub> can improve accuracy (R<sup>2</sup>=0.68). The exponential algorithm between chlorophyll a concentration and the first order differential of the reflectance at 695.5nm is good (R<sup>2</sup>=0.76). Multivariate regression models with the normalized reflectance at 708nm and 607nm and their logarithm models were also established and the accuracy is higher than the above, in which the best is the model with the logarithm of 708nm and 607nm(R<sup>2</sup>=0.8). The study demonstrates the potential of monitoring chlorophyll a concentration using hyper-spectral remote sensing technology in Dianshan Lake.
The future world is a world of city. Spectral characterization of urban reflectance is important. The overall reflectance
of the urban mosaic is determined by the spectral reflectance of surface materials and shadows and their spatial
distribution. Building materials dominate net reflectance in most cities but in many cases vegetation also has a very
strong influence on urban reflectance. In the study, the spectral characterization of urban reflectance properties is
analyzed using Landsat TM and ETM+ imagery of a collection of the province capital city in China. The result shows
these urban areas have similar mixing space topologies and can be represented by three-component linear mixture
models The reflectance of these cities can be described as linear combinations of High Albedo, Dark and Vegetation
spectral endmembers within a three dimensional mixing space containing over 80% of the variance in the observed
reflectance. The relative proportions of these endmembers vary considerably among different cities but in all cases the
reflectance of the urban core lies near the dark end and the new build-up areas near the light end of a mixing line
between the High Albedo and Dark endmembers. In spite of the spectral heterogeneity, built-up areas do occupy distinct
regions of the spectral mixing space. Based on the above analyzation, the urban spatial extent of 34 cities of China, representing the physical manifestation of a range of social, economic, cultural, and political dimensions associated with
urban dynamics, was mapped using Landsat imagery collected of 1990 and 2000.
The method by the integration of multi-source data including remote sensing data, ground spectral measurement data and other in situ monitoring data, is presented in this paper to construct a quantitative water quality inversion model. The upper region of Whangpoo River in Shanghai is selected as a study area and the dissolved oxygen is chose as a typical water quality indicator. We first process the remote sensing imagery and field spectrometer data to obtain the accurate water reflectance. An inversion model of dissolved oxygen is then derived from the modeling analysis between the water quality data and the reflectance. The accuracy of the model is further confirmed, and this model is applied to invert the dissolved oxygen distribution in Whangpoo River upper region. The inverted water quality distribution has a high consistency with the practical case. This proves that the method by integration of multi-source data is an effective way to monitor the water quality.