Relationships between heavy metals and iron oxides,fulvic acids,particle size fractions in urban roadside soils

Urban roadside soils are the “recipients” of large amounts of heavy metals from a variety of sources including vehicle emissions, coal burning waste and other activities. The behavior of heavy metals in urban roadside soils depends on the occurrence as well as the total amount. Accordingly, knowledge of the interactions between heavy metals and other constituents in the soil is required to judge their environmental impact. In this study, correlations of heavy metal concentrations (Pb, Zn, Cu, Ag, Se, Ni, Cr and Ba) to iron extracted using dithionite–citrate–bicarbonate (DCB) buffer (Fe_DCB), fulvic acids and particle size fractions were examined from the Xuzhou urban roadside soils. Heavy metals except for Cr and fulvic acids had a positive significant correlation with Fe_DCB, indicating these metals and fulvic acids are principally associated with the surfaces of iron oxides of the soils. Significant positive correlations were also found between the contents of fulvic acids and heavy metals, showing these heavy metals (especially for Cu, Ni and Cr) form stable complexes with fulvic acids. Such finding is of importance with regard to the increased mobilization of heavy metals, e.g., into freshwater ecosystems. Ag, Se and Cr are independent of particle size fractions because of their low concentrations of Ag and Se in the studied soils. Pb, Zn, Cu, Ba and Ag are mainly enriched in the finer soil particles (especially <16 μm).     

基于偏最小二乘与神经网络耦合的储层参数预测

将偏最小二乘回归（PLS）与神经网络（NN）耦合，建立了储层参数预报模型。利用偏最小二乘对影响储层参数的诸多因素进行分析，提取对因变量影响强的成分，从而克服了变量间的多重相关性问题，降低了神经网络的输入维数；同时，利用神经网络建模可以较好地解决非线性的储层参数预测问题。计算实例表明，本耦合模型的拟合和预报精度优于独立使用神经网络模型的精度。     

基于人工免疫算法的数据压缩技术在滑坡异常提取中的应用研究

滑坡异常信息提取对滑坡预测预报至关重要。以四川丹巴滑坡为例，应用人工免疫算法对滑坡镜2、镜6和镜9的位移监测数据进行压缩，分别获得最优压缩率为0．821，0．819，0．829，保真度为0．95。并在置信水平为0．95的条件下，对压缩后数据进行异常提取，分别获得了3个监测点的4个异常信息。这些异常信息所反映的滑坡变形发展演化规律与滑坡声发射等其他监测信息所反映的规律基本保持一致，说明应用人工免疫算法对滑坡位移监测数据进行压缩处理可以在保持原有数据的特性与规律的同时，有效除去监测数据中的冗余与不相关信息，增加有效数据密度，有利于准确提取滑坡异常信息。     

基于偏微分方程的去噪方法

利用偏微分方程抑制图像噪声已经被认为是具有显著效果的图像去噪技术。在阐述图像噪声模型的基础上研究了图像去噪的偏微分方程模型,并对偏微分方程中的参数选择进行了讨论。为了保持图像的边缘信息,对经典模型参数进行了改进。实验结果表明,偏微分方程适用于图像噪声的抑制,并且可以在抑制图像噪声的同时,保持较好的边缘信息。     

利用土地利用现状数据提取1：10000地形图植被信息研究

1：10000地形图植被要素的采集和更新较为复杂，考虑国土部门现有的现势性较强的土地利用现状数据可以用来补充1：10000地形图中的植被信息，积极开展试验。本文作者利用广东省现有的土地利用现状数据，结合航空（卫星）正射影像图、1：10000矢量数据进行了充分试验，走出了一条植被更新的新路子，为省级基础地理信息数据库的完善和更新提供了一种新方法。     

Road detection from high-resolution satellite images using artificial neural networks

This article treats the possibility of using artificial neural networks for road detection from high-resolution satellite images on a part of RGB Ikonos and Quick-Bird images from Kish Island and Bushehr Harbor, respectively. Attempts are also made to verify the impacts of different input parameters on network's ability to find out optimum input vector for the problem. A variety of network structures with different iteration times are used to determine the best network structure and termination condition in training stage.It was found that when the input parameters are made up of spectral information and distances of pixels to road mean vector in a 3 × 3 window, the network's ability in both road and background detection can be improved in comparison with simple networks that simply use spectral information of a single pixel in their input vector.     

基于ENVI的高分辨率遥感图像应用试验研究

介绍了高分辨率遥感图像用于城市土地利用信息提取的主要过程,应用ENVI软件作为试验研究平台对试验区进行了遥感图像分类处理。试验表明,遥感技术是进行城市土地利用信息提取的有效手段之一,针对遥感图像中出现的水体和建筑混杂的现象,提出了应用监督分类与矢量操作相结合的处理方法,可得到较好的分类结果。     

基于MODIS的鄱阳湖区水体水灾遥感影像图制作

 提出了利用空间分辨率为250 m的MODIS影像第1、2波段数据制作水体遥感影像图、水体变化遥感影像图和水灾遥感影像图的方法。对鄱阳湖区的遥感影像图制作实例表明，该方法不仅具有快速、简便和易于操作等优点，还能有效保留MODIS的空间分辨率，较清晰地识别水体和洪水，可用于水灾的遥感动态监测与制图。     

EVALUATION OF GEOCHEMICAL QUALITY CONTROL IN DETERMINATION OF Mn IN SOILS USING A SEQUENTIAL CHEMICAL EXTRACTION

Sequential chemical extraction procedure has been widely used to partition particulate trace metals into vari-ous fractions and to describe the distribution and the statue of trace metals in geo-environment.One sequential chemical extraction procedure was employed here to partition various fractions of Mn in soils.The experiment was designed with quality controlling concept in order to show sampling and analytical error.Experimental results obtained on duplicate analy-sis of all soil samples demonstrated that the precision was less than 10%(at 95% confidence level).The accuracy was estimated by comparing the accepted total concentration of Mn in standard reference materials (SRMs) with the measured sum of the individual fractions.The recovery of Mn from SRM1 and SRM2 was 94.1% and 98.4%,respectively.The detection limit,accuracy and precision of the sequential chemical extraction procedure were discussed in detailed.All the results suggest that the trueness of the analytical method is satisfactory.