西部测图移动应急安全自救保障系统研究

移动应急安全自救保障系统是整个"西部测图工程"安全保障体系建设中的最重要组成部分。本文从"西部测图"移动应急安全自救保障建设实际需求出发,结合当前PDA硬件资源的特点,在分析测区作业特点的基础上探讨了系统建设的整体架构以及系统建设的需求特征,并详细阐述了系统的数据组织结构模型,最后给出了系统的相关功能实现和应用。     

基于Google Earth的遥感影像数据资料归档系统

将Google Earth应用于遥感影像数据资料归档系统中,是一种新的遥感资料归档方法,能将归档的成果文件整合,克服了传统归档方式不能快速查看数据效果、位置、属性信息的缺点,很好地适应了信息化工作程序对图像数据库的快速响应的要求.ARCGIS补充了Google Earth薄弱的空间分析功能.系统再联合EXCLE表件,方...     

Word2003文档中插入AutoCAD2004图形的三种方法

对于测绘技术人员来说,需要编写设计书、技术总结、监测方案等测绘类文档。利用Word2003自带的绘图工具,远远不能满足要求。介绍了“利用复制＋粘贴工具”、“转换图像格式”、“使用插入对象”三种方式在Word2003文档中插入AutoCAD2004图形的三种方法,     

Assimilating data into open ocean tidal models

The problem of deriving tidal fields from observations by reason of incompleteness and imperfectness of every data set practically available has an infinitely large number of allowable solutions fitting the data within measurement errors and hence can be treated as ill-posed. Therefore, interpolating the data always relies on some a priori assumptions concerning the tides, which provide a rule of sampling or, in other words, a regularization of the ill-posed problem. Data assimilation procedures used in large scale tide modeling are viewed in a common mathematical framework as such regularizations. It is shown that they all (basis functions expansion, parameter estimation, nudging, objective analysis, general inversion, and extended general inversion), including those (objective analysis and general inversion) originally formulated in stochastic terms, may be considered as utilizations of one of the three general methods suggested by the theory of ill-posed problems. The problem of grid refinement critical for inverse methods and nudging is discussed.     

GIS中多幅地图的缩放漫游及其数据组织

本文在分析现存GIS软件中处理多幅不同比例尺或相同比例尺地图的放缩漫游时的不足及其原因的基础上,提出了一种使用地理逻辑窗口动态裁剪快速而安全的多幅地图无级缩放和全区域自由漫游的方法,并对其数据组织和管理作了探讨。     

Comparative assessment of the relationship of satellite data with the above ground biomass of Sal trees (<Emphasis Type="Italic">Shorea robusta</Emphasis>) determined from phenologically different time periods

The Above Ground Biomass(AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground.For mature trees,the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height(DBH) and height(H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period.In the present study,we have determined the AGB of Sal trees(Shorea robusta) in two contrasting seasons:the peak green period in October being devoid of lit-ter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground.Estimation of AGB for the month of February included the litter biomass.In contrast,the AGB for October represented only the bole biomass.AGB was estimated for ten different plots selected in the study area.The AGB estimated from ten sampling plots for each time period was re-gressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots.The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February.Furthermore,the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February.This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the re-gression relationship between AGB and DBH and also between the remotely sensed data and AGB.Therefore,these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.     

3维地理信息系统技术综述

文中对3维地理信息系统技术进行综述性介绍,扼要地讨论了国内外有关3维地球空间信息的获取、管理、分析与可视化等技术的进展与动态,并分别探讨了它们的优势和局限。     

一种基于信息网格的空间数据分解算法

针对空间数据库数据海量且分布的特点,为了能从这些空间数据中更加有效的收集信息和发现知识,介绍了基于信息网格的空间数据挖掘计算模型,在分析空间数据划分一般原则和方法的基础上,针对该计算模型给出了一个数据划分的初步算法,并通过模拟实验对算法的时间性能进行了评价与分析。     

欧氏聚类算法支持下的点云数据分割

欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为：①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。