遥感数据的模糊不确定性及其处理方法探讨

通过对遥感数据生成机理的分析,得出遥感数据存在不确定性,并进一步论证了不确定性中含有模糊不确定性,这样对遥感数据的不确定性处理更加全面和合理,从而达到提高遥感数据的精度和消除遥感数据不确定性的目的。综合国内外对遥感数据模糊不确定性的处理研究,探讨了几种处理方法,发现还没有一种方法能圆满解决遥感数据的模糊不确定性。     

GIS不确定性框架体系与数据不确定性研究方法

该文分析了GIS数据不确定性研究现状,总结了GIS中数据不确定性的研究问题,提出GIS中数据不确定的框架体系,并结合非线性复杂科学的研究方法,对GIS数据不确定性的研究方法进行了分析、归纳和阐述。     

GIS不确定性框架体系与数据不确定性研究方法

该文分析了GIS数据不确定性研究现状，总结了GIS中数据不确定性的研究问题，提出GIS中数据不确定的框架体系，并结合非线性复杂科学的研究方法，对GIS数据不确定性的研究方法进行了分析，归纳和阐述。     

利用场模型拓扑表达GIS中的地理目标

该文阐述为了实现在GIS中描述自然界里带有模糊不确定性的地理目标 ,扩充GIS的模糊查询功能 ,首先基于集合论思想探讨了确定性地理目标的代数解析表达与其拓扑表达 ,指出了确定性点、线、面之间拓扑表达的构成机理 ;然后 ,基于场模型建立了模糊地理目标的空间表达 ,分析了模糊地理目标的位置不确定性 ,进而建立了模糊地理目标的拓扑表达模型 ,即点集拓扑内部、边界和外部。分析表明 ,公认的Egenhofer模型是该文模型在地理目标不带有误差或不确定性情况下的特例。最后 ,与Clementini(1996 )提出的模型做了比较分析 ,表明了该文模型的合理性。     

利用场模型拓扑表达GIS中的地理目标

该文阐述为了实现在GIS中描述自然界里带有模糊不确定性的地理目标，扩充GIS的模糊查询功能，首先基于集合论思想探讨了确定性地理目标的代数解析表达与其拓扑表达，指出了确定性点、线、面之间拓扑表达的构成机理；然后，基于场模型建立了模糊地理目标的空间表达，分析了模糊地理目标的位置不确定性，进而建立了模糊地理目标的拓扑表达模型，即点集拓扑内部、边界和外部。分析表明，公认的Egenhofer模型是该文模型在地理目标不带有误差或不确定性情况下的特例。最后，与Clementini(1996)提出的模型做了比较分析，表明了该文模型的合理性。     

GIS中的模糊形态运算

空间数据的不确定性是当前GIS领域的研究难点之一。为了描述空间数据的模糊性,把模糊集理论引入GIS,以加强GIS对模糊现象建模的能力,因而产生了模糊数据。但现有GIS缺乏对模糊数据分析和处理的能力。该文把模糊集理论引入数学形态学,提出能处理模糊数据的模糊形态运算,并给出模糊形态运算的隶属函数,使传统的数学形态学能够处理模糊数据且容易在计算机上实现。     

气候变化风险的新型分类

风险分类是对气候变化风险进行系统风险评估和管理的前提和基础。本文首先按部门和领域识别了主要的气候变化风险,然后采用国际风险管理理事会(International Risk Governance Council, IRGC)的新型风险分类体系(简单风险、复杂风险、不确定风险和模糊风险)进行分类:提出以"不确定性"作为分类依据;把IPCC第四次评估报告中描述不确定性的主要术语——"信度"和"可能性"作为分类特征参数,分别构建了四类风险的模糊隶属函数,根据最大隶属度原则从定量角度对气候变化风险进行分类;同时利用IPCC的两个定性指标"达成一致的程度和证据量"对定量分类方法进行补充,初步建立了气候变化风险的分类方法体系。获得的气候变化风险分类的初步结果,可以为风险管理机构选择不同类别的评估和管理方法进行风险研究提供科学依据。     

土壤侵蚀模型研究中的几个问题

土壤侵蚀模型是定量研究土壤侵蚀的有效工具,探讨土壤侵蚀模型研究中的有关问题对于土壤侵蚀模型的发展及相关研究具有重要意义。从模型的过程模拟、模型时空尺度变异、模型验证和不确定性几方面对模型研究现状及局限进行了归纳和分析,认为:不同过程的模拟及其模拟方法的不同导致了各具特色的土壤侵蚀模型的出现;大多水蚀模型是对不同时空尺度上的水流及相应侵蚀过程进行模拟预测,在不同的时空尺度上,不同的侵蚀过程占主导地位;现实过程和现象的复杂性、观测方法和手段的限制以及模型的自身的局限性是导致土壤侵蚀模型预测的不确定性的主要原因;与GIS结合的强预测力、低数据要求的模型是土壤侵蚀模型发展的趋势。     

基于场所的GIS中的不确定性问题及分类框架

基于场所的GIS直接表达人类地理空间知识的管理和加工过程,而不确定性是人类智能的基本特点,因此GIS的智能化需要研究其中的不确定性问题。与传统的GIS相比,基于场所的GIS中的不确定性问题更为丰富,既包括随机性,也包括含糊性,而不确定性的主体既可以是地理要素、场所和空间关系,也包括命题和规则。该文介绍该领域相关的研究成果,基于不确定性主体、类型、表达手段及相关的活动4个视角,建立了基于场所的GIS中所涉及的不确定性框架,从而为相关的不确定性建模提供指导。     

开放式GIS在大数据时代的机遇与障碍

GIS 领域的发展在21 纪初走到了十字路口。本文旨在讨论下一个阶段GIS 发展的多个愿景,而新兴的开放式GIS应作为一个指导思想。开放式GIS包含8 个维度：数据、软件、硬件、规范、研究、出版、资金和教育。对于GIS 研究团体,开放式GIS 提供了4 个令人振奋的机会：①技术发展。技术的发展与突破可用于解决空间上的海量数据所带来的问题;②应用开发。主要是用于集体或个人决策的应用程序的开发;③增加全民参与的机会。地理科学可以发展成一种开放性的全民科学,人们将对日益变化的地球增进了解;④教育的机遇。通过开放式GIS能更好地实现全民地理科学教育。虽然开放式GIS 的实现存在着学术、法律、社会、政治和环境等障碍,但可以肯定的是,开放式GIS在未来的科学研究和教育方面将会变得越来越重要。     

Fuzzy information representation and processing in conventional GIS software: database design and application

Currently used methods for representing geographical information are inadequate because they do not tolerate imprecision. This leads to information loss and inaccuracy in analysis. Such expressive inadequacy is largely due to the underlying membership concept of classical set theory. To improve information processing in GIS research and application, an alternative membership concept is required. In this paper, we explore the inadequacy imposed upon geographical information representation by classical set theory and address the problems of information loss. A fuzzy relational data model is defined which is more representative for geographical information. A GIS database for agricultural land resource management is created by using the data model and a new technique for assessing land suitability is developed. The fuzzy representation largely facilitates data analysis in this GIS. The methods are tested with data from North Java, Indonesia using a vector-based GIS software package, Arc Info, and the analysis results are presented.     

Incorporating fuzzy set methodologies in a DBMS repository for the application domain of GIS

It has been recently recognized that fuzzy set theory provides useful concepts and tools for both the representation and analysis of the uncertainty related to geographical data. Hence the incorporation of fuzzy set methodologies into a DBMS repository for the application domain of GIS should be beneficial and will improve its level of intelligence. Focusing in this area the paper addresses both a representation and a reasoning issue. Specifically, it extends a general spatial data model to deal with the uncertainty of geographical entities, and shows how the standard data interpretation operations available in GIS packages may be extended to support the fuzzy spatial reasoning. Representative geographical operations, suchas the fuzzy overlay, fuzzy distance and fuzzy select, are examined, while several real world examples are given.     

Performance evaluation and work-load estimation for geographic information systems

Abstract

Agencies acquiring GIS hardware and software are faced with uncertainty at two levels: over the degree to which the proposed system will perform the functions required, and over the degree to which it is capable of doing so within proposed production schedules. As the field matures the second concern is becoming more significant. A formal model of the process of acquiring a GIS is presented, based on the conceptual level of defining GIS sub-tasks. The appropriateness of the approach is illustrated using performance data from the Canada Land Data System. It is possible to construct reasonably accurate models of system resource utilization using simple predictors and least squares techniques, and a combination of inductive and deductive reasoning. The model has been implemented in an interactive package for MS-DOS systems.     

Propagation of positional error in 3D GIS: estimation of the solar irradiation of building roofs

While error propagation in GIS is a topic that has received a lot of attention, it has not been researched with 3D GIS data. We extend error propagation to 3D city models using a Monte Carlo simulation on a use case of annual solar irradiation estimation of building rooftops for assessing the efficiency of installing solar panels. Besides investigating the extension of the theory of error propagation in GIS from 2D to 3D, this paper presents the following contributions. We (1) introduce varying XY/Z accuracy levels of the geometry to reflect actual acquisition outcomes; (2) run experiments on multiple accuracy classes (121 in total); (3) implement an uncertainty engine for simulating acquisition positional errors to procedurally modelled (synthetic) buildings; (4) perform the uncertainty propagation analysis on multiple levels of detail (LODs); and (5) implement Solar3Dcity – a CityGML-compliant software for estimating the solar irradiation of roofs, which we use in our experiments. The results show that in the case of the city of Delft in the Netherlands, a 0.3/0.6 m positional uncertainty yields an error of 68 kWh/m²/year (10%) in solar irradiation estimation. Furthermore, the results indicate that the planar and vertical uncertainties have a different influence on the estimations, and that the results are comparable between LODs. In the experiments we use procedural models, implying that analyses are carried out in a controlled environment where results can be validated. Our uncertainty propagation method and the framework are applicable to other 3D GIS operations and/or use cases. We released Solar3Dcity as open-source software to support related research efforts in the future.     

Land cover classification by an artificial neural network with ancillary information

Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.     

基于.NET反射技术的插件式GIS软件设计原理与实现

当前主流的GIS软件开发方式--组件式开发已难以适应日益复杂多变的GIS应用需求,尤其表现在GIS应用软件的扩展和维护方面.该文在分析当前流行的插件技术的基础上,给出在.NET 平台上开发插件式GIS应用软件的一种有效解决方案,运用该技术设计了一款插件式GIS软件:DotMap Desktop,通过整合一些开源GIS组件能够完成常见的GIS任务且具有良好的扩展性.采用该方案设计的插件式GIS软件能够方便地运行在各种主流的操作系统上.