[Research on remote sensing inversion biomass method based on the Suaeda salsa's measured spectrum].

Suaeda salsa is one of characteristic vegetation of wetlands in Northern China. By measuring the spectral data and leaf area index (LAI) of the Suaeda salsa in the ShuangTai Estuary of Liaodong Bay by the use of portable spectrometer and vegetation canopy analyzer, collecting the biomass of the Suaeda salsa samples, setting up the spectral reflectance curve of the Suaeda salsa, probing into the relationship between the vegetation index and the leaf area index of the Suaeda salsa, carrying out regression analysis of LAI and biomass and constructing the function equation, some conclusion were drawn: (1) By the end of September the spectral characteristics of Suaeda salsa show that at the red band 630 nm there is a clear reflection of the peak with a reflection rate of 12%-15%; there is a clear "red valley" configuration between 680 and 700 nm, and there is a clear "red edge" reflection rate of 25%-30% about 760 nm. (2) It was found that there is best correlation between vegetation index (SAVI and MSAVI) and LAI compared to other vegetation index in the regression analysis of the LAI and vegetation index. The correlation coefficient R2 is 0.711. By comparison of vegetation index linear regression equations, the correlation coefficient (SAVI and LADI) R2 is 0.696; the value of R2 (LAI and MSAV) is 0.695; the value of R2 (RVI) is 0.664; the value of R2(NDVI) is 0.649 and the value of R2 (PVI) is 0.466. (3) The value of correlation coefficient is low between the biomass and the vegetation indexes (RVI and NDVI) and the value of linear regression equation's R2 is 0.342 and 0.316, and the Logarithmic regression equation's R2 is 0.319 and 0.21, and the quadratic equation's R2 is 0.589 and 0.568, the value of correlation coefficient is high between the biomass and the vegetation indexes (PVI, SAVI and MSAVI), the value of linear regression equation is 0.626, 0.698 and 0.679, that of logarithmic regression equation is 0.592, 0.706 and 0.683 and that of the quadratic equation is 0.688, 0.711 and 0.683.

[Analysis on heavy metal concentrations in agricultural soils of Baoshan, Shanghai].

Based on the acquisition of heavy metal data from 216 topsoil samples of the agricultural land in Baoshan District, a typical region of Shanghai City, the content, distribution characteristics and sources of the heavy metals in agricultural soil of Shanghai Baoshan District were analyzed with the methods of combining multivariate statistics and geo-statistical. The results show that the average concentrations of the Cd, Hg, As, Cr, Pb, Cu and Zn in agricultural soil of Baoshan District are 0.195, 0.148, 7.44, 82.5, 29.1, 33.2 and 124.5 mg x kg(-1), they are lower than the secondary standards of the national soil environment; but the average concentrations of the Cd, Hg, Cr, Pb, Cu and Zn are more than background values of soil in Shanghai. Especially the Cd, Hg, Zn, they are 1.50, 1.48, 1.45 times higher than the background values, showing a net cumulative trend. The results of correlation analysis and factor analysis show that the sources of these elements can be divided into three categories; the Zn, Cd, Hg and Pb as one class; the Cr and Cu as another class; the As in a separate category. The concentrations of the former two classes are much higher than the background value, implying mainly from various human activities; the content of the As is almost the same as the background value of soil, it has the lowest degree of variation. Spatial structure analysis shows that the distribution of the As is influenced by the soil structural variations, such as soil parent material, topography, and other elements are mainly affected by random factors of human activities. Through the probability distribution of contour lines of the critical value, it can be found that the Cd, Zn, Cr, Cu, Hg come mainly from the point sources of pollution, but the source of Pb is relatively dispersed.