Effect of source-varying input data on erosion potential model performance

This paper examines the performance of the Erosion Potential Method (EPM or Gavrilovi?) and the model response to input data variations caused by choosing different sources of information for the same parameter. The research presented addresses the input data uncertainty via an analysis of the two model input parameters (the soil protection coefficient and the soil erodibility coefficient). The parameter uncertainty analysis is performed following two different approaches: uncertainty analyses of both the selected sample size and the entire population are conducted for an erosion assessment case study of the Dubra?ina River catchment, Croatia. The analysis indicated that, when changing the data source, significant changes in the model outcome values can occur (up to 47% for this case study). Future method modifications should consider the mitigation of these two parameters by potentially making structural changes in the model and therefore moderating the effect.     

基于FIR滤波的磁静期电离层-磁层磁场信号分离

Swarm是欧空局第4个"地球探测者"任务,其主要目的是确定地球磁场及其时空演化特征。利用Swarm构建地球各圈层磁场模型的关键问题之一是对不同场源的地磁信号实施准确分离。以Swarm磁静期观测数据为例,采用FIR沿轨高通滤波对外源场信号进行处理,分析并给出了滤波器窗口长度的确定方法,利用欧空局2016年发布的MIO_SHA_2D和MMA_SHA_2C电离层-磁层磁场模型验证了该方法的可行性。结果表明:FIR对外源场长波信号的滤波结果与模型改正结果的一致性吻合较好,在给定通带波长1 200 km、阻带波长3 000 km的情况下,将滤波器长度设置为87~129阶,可同时顾及水平方向和径向方向的滤波精度。     

A Conceptual Framework for Uncertainty Investigation in Map‐based Land Cover Change Modelling

Uncertainty research represents a research stream of high interest within the community of geographical information science. Its elements, terminology and typology are still under strong discussion and adopted methods for analysis are currently under intensive development. This paper presents a conceptual framework for systematic investigation of uncertainty which occurs in applications of land cover change modelling in Geographical Information Systems (GIS) based on historical map data. Historical, in this context, means the map is old enough to allow identification of changes in landscape elements of interest, such as vegetation. To date such analyses are rarely conducted or not satisfactorily carried out, despite the fact that historical map data represent a potentially rich information source. The general validity and practicability of the framework for related applications is demonstrated with reference to one example in which forest cover change in Switzerland is investigated. The conceptual model consists of three domains in which main potential sources of uncertainty are systematically exposed. Existing links between data quality research and uncertainty are investigated to access the complex nature of uncertainty and to characterise the most suitable concepts for analysis. In accordance with these concepts appropriate methods and procedures are suggested to assess uncertainty in each domain. One domain is the production‐oriented amount of uncertainty which is inherent in the historical map. Vagueness and ambiguity represent suitable concepts for analysis. Transformation‐oriented uncertainty as the second domain occurs owing to editing and processing of digital data. Thereby, the suitable concept of uncertainty is error. The third domain is the application‐oriented uncertainty which occurs in comparing semantically different data. This domain relates to multi‐temporal discord which assumes the assessment of ‘equi‐temporal’ ambiguity and is thus connected to the production‐oriented domain. The framework provides an estimation of the overall amount of uncertainty. This can be linked to subsequent assessment of ‘fitness for use’. Thus the model provides a practicable and systematic approach to access the complex nature of uncertainty in the scope of land cover change modelling.     

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.     

融合多尺度影像数据的杞麓湖流域景观格局分析

景观格局是景观斑块秩序和规律的具体体现,分析景观格局特征能够在空间结构上推进流域生态保护红线的划定和管控工作,维持可持续发展的底线。基于不同遥感数据源测算的景观格局可能存在计算结果差异,以及对景观格局描述不一致的问题。本文以杞麓湖流域为研究区,选取最新的Sentinel-2、GF-1和Landsat 8卫星遥感影像,基于斑块尺度、类型尺度和景观尺度的7个景观指数,从景观类型分布和整体格局两个方面探讨了杞麓湖流域景观格局现状,并分析不同数据源对景观格局评价结果的异同。结果表明：①杞麓湖流域的景观格局以林地和耕地为基底,零碎分散着建设用地和其他用地;②不同卫星遥感影像对流域景观格局的描述存在差异,影像分辨率与斑块数目、最大斑块指数、形状指数、分离度指数存在显著的正相关关系,而对于测度整体的香农多样性指数、香农均匀度指数、优势度指数的影响相对较小。     

Sensing Sprawl: Towards the Monitoring of Urban Expansion Using Dempster‐Shafer Theory

Abstract

The outward expansion of cities in the United States has been a source of concern and policy debate for well over forty years. This sprawling urban landscape has been cited as a contributing factor behind the loss of open space, environmental damage and increased congestion. To better understand urban expansion, monitoring programs are required to facilitate the systematic observation of urban expansion, and to provide critical information in order to adjust urban development policies. Monitoring the urban landscape has been a major application focus of satellite remote sensing technologies. Yet, research has shown that the complexity of the urban landscape frustrates simple characterization of cumulative land cover processes such as sprawl. In this paper an approach to the remote detection and characterization of sprawl is introduced based on the use of Dempster‐Shafer Theory of Evidence. Functioning as a soft‐classification algorithm, Demptster‐Shafer Theory offers a unique solution to the mapping problem when evidence of class structure in underscored by uncertainty. Through the use of this technique it was possible to model uncertainty based on the concept of belief. This conceptualization was instrumental in deciphering the complexities of urban land cover arrangements and offered an alternative logic which enhanced delineation of subtle changes in land cover indicative of sprawl.     

基于高维云模型和RBF神经网络的遥感影像不确定性分类方法

云模型是用自然语言值表示的某个定性概念与其定量表示之间的不确定性转换模型,RBF神经网络已经广泛应用于遥感影像分类。考虑到传统的RBF神经网络分类技术不能有效表达影像分类过程中存在的不确定性、难以自适应地确定隐含层神经元,本文提出了一个基于高维云模型和改进RBF神经网络的不确定性分类技术。利用高维正态云创建隐含层神经元,使RBF神经网络能充分表达影像分类过程中存在的不确定性。通过峰值法云变换和高维云算法自适应地确定最优隐含层神经元。通过基于概率的权值确定和频率阈值调整,进一步优化RBF神经网络的结构。实验表明,本文提出的方法有较高的分类精度,分类结果基本上与人眼目视解译一致。     

Simulation Modelling Applied to Runoff Modelling Using MapScript

Dynamic spatial analysis addresses computational aspects of space–time processing. This paper describes the development of a spatial analysis tool and modelling framework that together offer a solution for simulating landscape processes. A better approach to integrating landscape spatial analysis with Geographical Information Systems is advocated in this paper. Enhancements include special spatial operators and map algebra language constructs to handle dispersal and advective flows over landscape surfaces. These functional components to landscape modelling are developed in a modular way and are linked together in a modelling framework that performs dynamic simulation. The concepts and modelling framework are demonstrated using a hydrological modelling example. The approach provides a modelling environment for scientists and land resource managers to write and to visualize spatial process models with ease.     

基于最小累计阻力模型的景观通达性分析

基于最小累积阻力模型(minimum cumulative resistance,简称MCR),运用地理信息系统空间分析模块,以文化景观为源(source),土地利用、道路分布为模型参考阻力面,建立了文化景观可达性模型。根据可达性分级结果,结合野外调查,得出影响文化景观保护现状的主要因素,从而为管理部门制定保护措施提供有效的依据。     

顾及不确定度的数字水深模型内插方法

提出一种顾及不确定度的数字水深模型(DDM)内插方法.计算数据来源不同的水深不确定度,构建水深数据权重配赋中加入不确定度的数据内插模型,实现水深内插点的不确定度估计.试验证明,所提方法提高了DDM的构建质量,并可评估内插水深的不确定度.     

A VLBI Delay Model for Radio Sources at a Finite Distance

A relativistic delay model for Earth-based very long baseline interferometry (VLBI) observation of sources at finite distances is derived. The model directly provides the VLBI delay in the scale of terrestrial time. The effect of the curved wave front is represented by using a pseudo source vector K = (R ₁ + R ₂)/(R ₁ + R ₂), and the variation of the baseline vector due to the difference of arrival time is taken into account up to the second-order by using Halley’s method. The precision of the new VLBI delay model is 1 ps for all radio sources above 100 km altitude from the Earth’s surface in Earth-based VLBI observation. Simple correction terms (parallax effect) are obtained, which can also adopt the consensus model (e.g. International Earth Rotation and Reference Frames Service conventions) to finite-distance radio source at R > 10 pc with the same precision. The new model may enable estimation of distance to the radio source directly with VLBI delay data.     

基于VC、Opengl和Directx的三维景观浏览

以VC、Opengl和Directx为基础,创建三维景观浏览场景;结合3DS MAX 6.0和Photoshop,三维建模、纹理映射,处理模型纹理,建立三维景观模型;并将构建的精细三维景观以3DS模型形式读入到创建的场景中,并最终实现在场景中任意漫游、碰撞检测、雾的探测、阴影、阳光光源位置的控制、播放音乐等诸多功能。     

Accounting for image uncertainty in SAR-based flood mapping

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required.This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required.Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.     

An Artificial-Neural-Network-based,Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.     

Revealing Uncertainties in Land Change Modeling Using Probabilities

Land change models are frequently used to analyze current land change processes and possible future developments. However, the outcome of such models is accompanied by uncertainties that have to be taken into account in order to address their reliability for science and decision‐making. While a range of approaches exist that quantify the disagreement of land change maps, the quantification of uncertainty remains a major challenge. The aim of this article is therefore to reveal uncertainties in land change modeling by developing two measures: quantity uncertainty and allocation uncertainty. We choose a Bayesian Belief Network modeling approach for deforestation in Brazil to develop and apply the two measures to the resulting probability surface. Quantity uncertainty describes the uncertainty about the correct number of cells in a land change map assigned to different land change categories and allocation uncertainty expresses the uncertainty about the correct spatial placement of a cell in the land change map. Thus, uncertainty can be quantified even in those cases where no reference data exist. Informing about uncertainty in probabilistic outcomes may be an important asset when land change projections are being used in science and decision‐making and moreover, they may also be further evaluated for other spatial applications.     

多时相遥感数据在四川省茂县景观生态安全格局评价中的应用

以Landsat系列遥感数据为基础数据源,基于"压力-状态-响应"模型和景观生态学理论,采用层次分析法构建茂县景观生态安全评价指标体系,利用综合指数法分别获取研究区2000年、2007年、2015年的景观生态安全综合指数值;并结合景观生态安全系统分级标准用ArcGIS软件将四川省茂县的景观生态安全状况,定量分析出危险级、风险级、敏感级、良好级、安全级5个等级;再利用转移矩阵剖析2000—2015年期间四川省茂县景观生态安全动态变化过程。研究表明:茂县生态安全状况之间的转换平稳,且转换面积和比重保持在一定的范围。该研究思路与研究结果可为四川省茂县区域宏观规划及山区地貌景观有效评估提供参考与借鉴。     

融入空间关系的二型模糊模型高分辨率遥感影像分割

为解决高分辨率遥感影像分割中,由光谱测度的空间复杂性、相同类型地物目标异质性增大带来的类属不确定性以及分割决策不确定性等引起的分割精度下降问题,提出一种融入空间关系的区间二型模糊模型高分辨率遥感影像监督分割方法。(1)建立高斯函数模型作为一型模糊模型,用来刻画像素类属的不确定性;(2)模糊化一型模糊模型中的均值或标准差,建立区间二型模糊模型,以强化类属的不确定表达和增加分割决策信息;(3)综合一型模糊模型及区间二型模糊模型的上、下隶属函数建模模糊决策模型;(4)融入邻域像素关系,使用待分像素及其邻域像素在模糊决策模型中的隶属度共同决定像素的类属。采用本文算法分别对真实高分辨遥感影像及合成影像进行分割,并对测试结果进行定性和定量分析。结果表明,本文算法可以得到更高的分割精度。     

On strategies and automation in upgrading 2D to 3D landscape representations

3D scenes within all media indicate a societal preference shift toward 3D presentations. In spite of wide data availability and successful standardization efforts in 3D modeling, it is not a standard practice to offer large-scale topographic references to the end user in the form of 3D models. This motivates to propose automated strategies for the generation of closed 3D representations of a complete urban landscape, which at the same time account for capabilities of consumer-class devices. The campus of Dresden University of Technology served as a test case. The final appearance of the 3D model will be steered directly by the original geographic information system (GIS) data source. Such a “schematic model” displays source classes and attributes by nonphotorealistic rendering. A tested generic workflow can be presented, which programmatically integrates attributed 2D GIS entities and digital elevation model data, checks for compliancy with consistency rules and generates a slim geometric model. Only detailed GIS references can be considered to allow close-range visualization as needed in virtual walks. In delegating the geometric processing to automated workflows, playing room is gained for as well innovative as expressive texturing and, thus, design of the final 3D model.     

基于C5.0决策树算法的元胞自动机的洪河湿地演化模拟

以洪河自然保护区1992年、2001年、2010年三期TM遥感影像为数据源,利用C5.0决策树算法从已有的数据及其影响因子数据中挖掘出洪河湿地的演变规则,并将获得的转换规则应用到元胞自动机模型中进行洪河湿地演变的动态模拟与预测,分析和探讨了元胞自动机模型在湿地景观模拟和预测中的重要作用。结果表明,在现有的空间变量和条件不变的情况下,在未来的洪河自然保护区湿地面积将减小,洪河自然保护区干旱化将加重。通过对湿地景观的动态变化模拟和预测研究,能够较好地反映湿地景观的动态变化情况。