矿产资源潜力评价成果图空间数据库中属性数据质量检查探讨

属性数据是矿产资源潜力评价成果的重要组成部分,从某种意义上说,属性数据质量直接影响矿产资源潜力评价的效果。基于此认识,通过分析属性数据填写的原则、技术要求,提出了采取软件和人工两种方式检查属性数据质量的方法。     

三组火山强迫资料差异及其对模式模拟结果的影响

Gao2008、Crowley2013和Sigl2015火山强迫资料,均基于极地冰芯重建.由于每组重建使用的冰芯数据和分析方法等不同,因此结果存在差异,从而影响气候模式应用.文中详细梳理三组火山强迫资料在原始冰芯数据、信号识别提取和沉积通量计算等方面的差异;介绍重建中涉及的对未知火山事件发生季节、纬度及从极地硫酸盐沉积...     

Adaptation of the vertical resolution in the mixed layer for HYCOM

This paper focuses on the dynamics of the mixed layer. When the mixed layer depth increases, the vertical discretisation eventually becomes too sparse at the bottom of this layer to accurately resolve its evolution and strong numerical errors can appear. This is linked to the fact that the vertical resolution is concentrated in the upper part of the ocean and does not adapt to the deepening of the mixed layer.Knowing that the HYbrid Coordinate Ocean Model (HYCOM) is able to modify the distribution of the vertical levels, we suggest in this paper a method to adapt the resolution to the mixed layer extension. This method is tested in 1-D configurations for two academic atmospheric forcing conditions (strong convection and wind-mixing) and a realistic forcing extending over one year, with seasonal restratification following strong winter convection. The new method improves the results in all cases, and in particular when the mixed layer reaches deep layers.     

.Net环境下空间数据缓存方案的设计与实现

空间数据查询效率的优劣决定着三层体系结构空间信息系统的应用效果。空间数据缓存技术是提高空间数据查询效率的有效方法之一。在数据缓存技术逐渐成熟的今天,如何利用数据缓存技术来构建空间数据缓存是目前研究的热点。基于Microsoft新的数据缓存技术,借助常用的格网索引,在客户端和应用服务器端开辟空间数据缓存区,以降低数据的传输量,减少用户的请求响应时间,从而提高空间数据查询效率。在利用版本控制技术实现空间数据缓存的同时,解决客户端、应用服务器和数据库服务器的数据一致性问题,确保空间数据的有效性和可用性。     

面状矢量空间数据匹配技术研究

数据匹配技术是空间数据集成与融合中的关键技术。对面积相似度进行了描述,对面积相似度进行了几何度量上的定义,提出了基于面积相似度的数据匹配思路。最后,以面状目标为研究对象,以城市居民地的大比例尺数据为例,对基于面积相似度的面状要素数据匹配算法进行了试验与分析。     

海洋水声环境调查数据处理系统的设计与实现

针对目前海洋环境调查现状,结合国内现有的技术条件和现有的处理方法、原理,详细论述了海洋水声环境调查处理系统软件主要功能的设计及关键技术,该系统软件满足海洋调查的需求,极大提高了海洋环境数据处理的自动化、数字化和信息化程度。     

基于要素规则分类的城镇地籍数据建库方法研究

城镇地籍数据的有效分类与正确组织是地籍数据库规范建立和正常运行的前提条件。本文提出了基于地籍要素规则的分类方法,按照地籍数据的表达类型,描述了宗地背景图形数据、宗地图形数据、测量控制点数据、属性数据和档案数据的各个要素定义和编码规则。以成都市地籍数据库的建设为实例,根据各种来源的地籍数据分别开展了不同类型地籍数据的快速分类与入库工作。     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.