GIS中折线元及面元置信水平的随机模拟

置信水平是描述GIS中线元素与面元素的位置不确定性的重要指标之一.文中应用Monte Carlo方法,利用C 中的rand()函数,经过转换,首先产生符合对点误差分布的节点坐标,再按照置信域的构建规则生成大量的随机区域,通过数值判断,算出区域覆盖要素真值的频率,以此频率作为置信水平的近似值,克服了用解析证明方法研究置信水平时结果往往过于保守这一缺陷.本模拟方法有较大的适用范围,为类似的置信水平研究提供了一般方法.     

蒙特卡洛方法及其在测绘科学中的应用

任何使用统计抽样检验技术来求解数学和物理问题的概率近似值的方法，称为蒙特卡洛法（Monte Carlo method）。有的文献把这类方法叫做统计试验法或随机模拟法；而测绘科     

类型地图的空间相关关系

针对类型地图,在介绍已有空间相关关系分析方法的基础上,给出一种计算空间相关关系的简便方法,其基本思想是用现实分布与随机分布下相对重叠度的差异来表达类型之间的相关程度;同时还讨论了位置不确定性对类型地图空间相关关系的影响.实验表明,该方法能够有效表达类型地图之间的整体空间相关和局部空间相关.     

FPAR的Monte Carlo模拟研究

FPAR (fraction of photosynthetically active radiation absorbed by the canopy)是植被冠层阻截太阳光合有效辐射的比例,是遥感估算陆地生态系统植被净第一性生产力(NPP)的重要参数.利用Monte Carlo方法模拟光子在植被冠层中的辐射传输过程,以植被冠层二向反射分布函数的模拟来验证模拟的正确性;在此基础上对400-700nm光合作用波段范围内的植被叶片吸收光子辐射比例的FPAR进行模拟.FPAR的Monte Carlo模拟结果,揭示了FPAR与太阳天顶角及植被冠层参数之间的关系.     

像元尺度上分类不确定性评价及可视化表达

传统的地物类别尺度上的遥感影像分类不确定性评价仅能实现分类精度的数学度量,无法反映分类不确定性的空间分布,难以完整、准确地描述和理解遥感影像分类信息的不确定性.文章首先在像元尺度上对遥感影像分类的不确定性进行评价,并在此基础上利用计算机技术从彩色可视化、用户交互式可视化和用户选择性可视化模式3个方面在空间域上对分类不确定性进行描述,从视觉感知角度表达遥感影像分类的不确定性信息,从而使用户更加形象直观地理解不确定性信息数据的大小及其空间分布.     

基于粗集的多尺度空间拓扑关系不确定性定量评价模型

空间拓扑关系不确定性的定量评价可为多尺度拓扑关系一致性的自动评价、空间推理与空间查询等应用的可靠性提供依据。定义了基于几何度量的拓扑距离,构建了拓扑关系不确定性的粗集表达模型;提出了不确定性粗集表达中拓扑距离的量化方法;进而提出了基于粗集的多尺度空间拓扑关系不确定性度量指标。实例研究证明了本文提出模型的科学性与合理性,该方法可用于多尺度表达过程中引起的拓扑关系不确定性的定量评价。     

基于混合熵模型的遥感分类不确定性的多尺度评价方法研究

不确定性是影响遥感图像分类质量的最主要因素,针对在遥感图像分类过程中同时存在随机不确定性和模糊不确定性的特点,提出基于混合熵模型来综合测度这两种不确定性的方法,并建立起多尺度的评价指标.在分析混合熵模型基本原理的基础之上,提出利用特征空间的和模糊分类器的统计数据来建立信息熵、模糊熵以及混合熵的方法.同时,在像元和类别尺度上,分别建立像元混合熵和类别混合熵的指标对分类不确定性进行评价.最后,应用湖北省黄石市的遥感影像对上述评价方法进行验证分析,实验结果表明,混合熵模型能有效地反映分类过程中随机不确定性和模糊不确定性的综合影响,并从不同尺度反映出遥感影像分类的质量问题.     

云邻近效应对气溶胶光学厚度遥感反演的影响及其消减方法研究

全球大气中云的平均覆盖率约为60%。研究三维大气中云对气溶胶光学厚度(aerosol optical depth,AOD)遥感反演的影响,发展和改进消减云影响的气溶胶光学厚度遥感反演方法,对于减小基于卫星遥感研究气溶胶间接气候效应的不确定性、全面掌握区域气溶胶污染分布情况及其变化规律具有重要的科学意义和应用价值。论文通过分析MODIS产品资料,获得对云影响下气溶胶光学厚度反演不确定性的初步认识;开展Monte Carlo理论模拟试验,定量分析云三维邻近效应对气溶胶光学厚度遥感反演的影响;在此基础上,基于GOCI和Landsat影像提出消减云影响的气溶胶光学厚度反演模型,以期为多云天气下高时空分辨率的气溶胶遥感提供理论依据和方法参考。     

分析利用地图一例

地图是周围环境的图形表达，它以独特的形式科学而形象地显示了空间客体和现象分布的位置，特征及规律，表达了它们的数量、质量特征，揭示了它们的动态变化及发展规律。地图是地理的第二语言，它蕴藏着十分丰富的地理信息。但是长期以来，我国分析利用地图     

保持空间分布特征的群点化简方法

群点目标隐含的空间结构化信息是空间分布分析、地图综合感兴趣的内容。对群点目标分布的信息内容区分为存在性、度量结构与拓扑结构，在Delaunay三角网及其对偶Voronoi图模型上对工量结构定义4个在量；分布范围、分布密度、分布中心及分布轴线，顾及视觉识别Gestalt邻近原则，运用三角形“剥皮”法，确立了非凸多边形所表达的群点分布范围，运用图像灰度表达群点分布密度并通过图像处理方法提取分布中心。建立了Voronoi图动态重建进行群点化简的方法，该方法通过边界点和内部点的分开处理，较好地保持了4个空间分布特征。     

MapGIS与MapStar在城市地图更新多边形拓扑重建中的应用研究

多边形拓扑重建是城市地图更新建库工作中关键性的一步,针对MapStar中拓扑重建的存在的问题,笔者采用MapGIS建拓朴,MapStar赋属性两者相结合的一种方法,能快速完成的多边形拓扑关系的重建,提高建库速度,实验证明,采用此方法,在城市地图更新建库工作过程中可提高30%-40%的工作效率。     

The Visualisation of Uncertainty for Spatially Referenced Census Data Using Hierarchical Tessellations

This paper explains why it is vital to account for uncertainty when utilising socioeco‐nomic data in a GIS, focusing on a novel and intuitive method to visually represent the uncertainty. In common with other data, it is not possible to know exactly how far from the truth socioeconomic data are. Therefore, when such data are used in a decision‐making environment an approximate measure given for correctness of data is an essential component. This is illustrated, using choropleth mapping techniques on census data as an example. Both attribute and spatial uncertainty are considered, with Monte Carlo statistical simulations being used to model attribute uncertainty. An appropriate visualisation technique to manage certain choropleth issues and uncer‐tainty in census type data is introduced, catering for attribute and spatial uncertainty simultaneously. This is done using the output from hierarchical spatial data structures, in particular the region quadtree and the HoR (Hexagon or Rhombus) quadtree. The variable cell size of these structures expresses uncertainty, with larger cell size indicating large uncertainty, and vice versa. This technique is illustrated using the New Zealand 2001 census data, and the TRUST (The Representation of Uncertainty using Scale‐unspecific Tessellations) software suite, designed to show spatial and attribute uncertainty whilst simultaneously displaying the original data.     

Geo‐questionnaire: A Method and Tool for Public Preference Elicitation in Land Use Planning

Geo‐questionnaire involves an integration of sketchable maps with questions, aimed at eliciting public preferences and attitudes about land allocation and services. Respondents can link their answers with corresponding locations on a map by marking points or sketching polygon features. Geo‐questionnaires have been used to learn about perceptions and preferences of city residents for specific types of land use, place‐based services, and development projects. This article reports on results of an empirical study, in which an online geo‐questionnaire was designed and implemented to elicit preferences of residents in guiding an urban development plan. Preferences collected in the form of polygon sketches were processed using GIS operations and mapped for visual interpretation. The article focuses on aggregation and analysis of respondent preferences including the analysis of positional and attribute uncertainty. Results of the study show that geo‐questionnaire is a scalable method for eliciting public preferences with a potential for meaningfully informing land use planning.     

城市空间信息规则网格与不规则网格的数据转换

对从不规则网格向规则网格进行社会经济信息转换的方法进行了比较和研究，并采用蒙特卡罗和GIS相结合的方法，以人口数据为例，利用土地利用分类信息将以行政区为基础采集的人口数据转换到规则网格中。实验证明，转换后的人口分布数据较好地体现了人口在空间的分布情况，并可根据不同的需要，聚合成不同大小的网格，能较好地满足城市微观建模和宏观社会经济信息统计分析的需要。     

A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation

Uncertainty quantification is not often performed in spatial modeling applications, especially when there is a mixture of probabilistic and non‐probabilistic uncertainties. Furthermore, the effect of positional uncertainty is often not assessed, despite its relevance to geographical applications. Although there has been much work in investigating the aforementioned types of uncertainty in isolation, combined approaches have not been much researched. This has resulted in a lack of tools for conducting mixed uncertainty analyses that include positional uncertainty. This research addresses the issue by first presenting a new, flexible, simulation‐oriented conceptualization of positional uncertainty in geographic objects called F‐Objects. F‐Objects accommodates various representations of uncertainty, while remaining conceptually simple. Second, a new Python‐based framework is introduced, termed Wiggly and capable of conducting mixed uncertainty propagation using fuzzy Monte Carlo simulation (FMCS). FMCS combines both traditional Monte Carlo with fuzzy analysis in a so‐called hybrid approach. F‐Objects is implemented within the Wiggly framework, resulting in a tool capable of considering any combination of: (1) probabilistic variables; (2) fuzzy variables; and (3) positional uncertainty of objects (probabilistic/fuzzy). Finally, a realistic GIS‐based groundwater contamination problem demonstrates how F‐Objects and Wiggly can be used to assess the effect of positional uncertainty.     

Rendering 2D Lines on 3D Terrain Model with Optimization in Visual Quality and Running Performance

With the gradual shift from 2D maps to a 3D virtual environment, various visual artifacts were generated by overlaying 2D map symbols on 3D terrain models. This work proposes a novel screen‐based method for rendering 2D vector lines with the accuracy of more than one pixel on the screen in real time. First, screen pixels are inversely projected onto a 3D terrain surface, and then onto the 2D vector plane. Next, these pixels are classified into three categories in terms of their intersection situation with the 2D lines. After that, a multiple sampling process is applied to the pixels that intersect with the 2D lines in order to eliminate visual artifacts, such as intermittence and aliasing (in pixel scale). Finally, a suitable point‐in‐polygon judgment is implemented to color each sample point quickly. The algorithm is realized in a heterogeneously parallel model so that the performance is improved and becomes acceptable.     

一种快速制作地形图图幅结合表的方法

以1:1万比例尺地形图图幅结合表制作为例,详细介绍了如何应用ArcGIS Create Fishnet工具制作地形图图幅结合表的方法,通过对图幅多边形要素属性表的操作,完成图幅号的计算、标注以及图幅级的分析操作,方便广大测绘工作者对测绘项目或工程的管理。     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

非线性平差精度评定的自适应蒙特卡罗法

针对现有的非线性平差精度评定理论中,蒙特卡罗法模拟次数的选择不具有客观性,无法对结果进行直接控制,以及没有同时考虑到平差参数估值、随机量改正数和单位权方差估值的有偏性等问题,把自适应蒙特卡罗法融入到非线性平差精度评定理论中。通过基于自适应蒙特卡罗法的估值偏差计算和参数估值协方差阵计算,设计了非线性平差精度评定一套理论完整的算法流程。基于对偶变量的思想,提出了参数估值偏差计算的对偶自适应蒙特卡罗法。直线拟合模型和椭圆拟合模型两个算例结果表明,非线性平差精度评定的自适应蒙特卡罗法能获得稳定且合理的精度评定结果,具有更强的适用性;对偶自适应蒙特卡罗法计算估值偏差的收敛速度更快,效率更高。     

Uncertainty Modeling in Buffer Operations Applied to Connectivity Analysis

In this paper we will study the potential connectivity of red squirrels in a fragmented landscape, using a buffer operation that takes into account the difficulty of moving through the landscape. The outcome of such an analysis is greatly influenced by the various sources of uncertainty that are introduced in the model. Two main sources of uncertainty can be identified: source layer uncertainty and model uncertainty. In this paper the propagation of source layer uncertainty resulting from a multivariate statistical classification of remotely sensed data is studied using Monte Carlo simulation, taking the spatial structure of uncertainty into account. Model uncertainty results from the adoption of deterministic model parameters regarding the dispersal capacity and the landscape effect, and is examined using fuzzy set theory. Comparing the outcome of error sensitized models to the observed dispersal activity of squirrels, demonstrates how modeling of uncertainty can help to explain the dispersal activity of red squirrels.