Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation

Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images.     

Modelling a discrete spatial response using generalized linear mixed models: application to Lyme disease vectors

Predicting disease risk by identifying environmental factors responsible for the geographical distribution of disease vectors can help target control strategies and optimize preventive measures. In this study we present a hierarchical approach to model the distribution of Lyme disease ticks as a function of environmental factors. We use the Poisson framework natural for count data while allowing for spatial correlations. To help identify environmental factors that best explain tick abundance, we develop an intuitive procedure for covariate selection in the spatial context. These methods could be useful in analysing effects of environmental and climatological changes on the distribution of disease vectors, and the spatial extrapolation of vector abundance under such scenarios.     

Terrain complexity and uncertainties in grid‐based digital terrain analysis

The objective of this research is to study the relationship between terrain complexity and terrain analysis results from grid‐based digital elevation models (DEMs). The impact of terrain complexity represented by terrain steepness and orientation on derived parameters such as slope and aspect has been analysed. Experiments have been conducted to quantify the uncertainties created by digital terrain analysis algorithms. The test results show that (a) the RMSE of derived slope and aspect is negatively correlated with slope steepness; (b) the RMSE of derived aspect is more sensitive to terrain complexity than that of derived slope; and (c) the uncertainties in derived slope and aspect tend to be found in flatter areas, and decrease with increasing terrain complexity. The study shows that although primary surface parameters can be well defined mathematically, the implementation of those mathematical models in a GIS environment may generate considerable uncertainties related to terrain complexity. In general, when terrain is rugged with steep slopes, the uncertainty of derived parameters is quite minimal. While in flatter areas, the DEM‐based derivatives, particularly the aspect, may contain a great amount of uncertainty, causing significant limitation in applying the analytical results.     

Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios

In accounting for uncertainties in future simulations of hydrological response of a catchment, two approaches have come to the fore: deterministic scenario‐based approaches and stochastic probabilistic approaches. As scenario‐based approaches result in a wide range of outcomes, the role of probabilistic‐based estimates of climate change impacts for policy formulation has been increasingly advocated by researchers and policy makers. This study evaluates the impact of climate change on seasonal river flows by propagating daily climate time series, derived from probabilistic‐based climate scenarios using a weather generator (WGEN), through a set of conceptual hydrological models. Probabilistic scenarios are generated using two different techniques. The first technique used probabilistic climate scenarios developed from statistically downscaled scenarios for Ireland, hereafter called SDprob. The second technique used output from 17 global climate models (GCMs), all of which participated in CMIP3, to generate change factors (hereafter called CF). Outputs from both the SDprob and the CF approach were then used in combination with WGEN to generate daily climate scenarios for use in the hydrological models. The range of simulated flow derived with the CF method is in general larger than those estimated with the SDprob method in winter and vice versa because of the strong seasonality in the precipitation signal for the 17 GCMs. Despite this, the simulated probability density function of seasonal mean streamflow estimated with both methods is similar. This indicates the usefulness of the SDprob or probabilistic approach derived from regional scenarios compared with the CF method that relies on sampling a diversity of response from the GCMs. Irrespective of technique used, the probability density functions of seasonal mean flow produced for four selected basins is wide indicating considerable modelling uncertainties. Such a finding has important implications for developing adaptation strategies at the catchment level in Ireland. Copyright © 2011 John Wiley & Sons, Ltd.     

A Monte-Carlo game theoretic approach for Multi-Criteria Decision Making under uncertainty

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California’s Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes.     

短时交通流量模式提取及时变特征分析

提出了一种基于三步策略的方法来提取交通流模式并对其时变特征进行分析。首先,根据交通流量内部变化将其分割成非等长的一系列具有明显物理意义的子序列;然后,利用定量递归分析（recurrence quantification analysis,RQA）提取各子序列的统计参数;最后,通过聚类获得交通流典型模式。实验结果表明,此方法能有效提取交通流中隐含的4种模式及它们在全天中的时间分布和时变特征,以及它们在工作日和非工作日时态分布上的差异。     

影响热带气旋路径的敏感性试验与研究

针对热带气旋观测中的敏感区域问题,用数值模拟的方法,以GRAPES-TCM为试验模式,对登陆中国的2个热带气旋“派比安”与“圣帕”进行了敏感性试验,并通过模拟其他4个热带气旋路径对试验结果进行了验证.结果显示,初始场的不确定性导致了热带气旋路径的不确定性,但初始场中涡旋场的变化对路径几乎没有影响,环境场中位于涡旋外、距...     

Uncertain climate: An investigation into public scepticism about anthropogenic climate change

This study presents a detailed investigation of public scepticism about climate change in Britain using the trend, attribution, and impact scepticism framework of Rahmstorf (2004). The study found that climate scepticism is currently not widespread in Britain. Although uncertainty and scepticism about the potential impacts of climate change were fairly common, both trend and attribution scepticism were far less prevalent. It further showed that the different types of scepticism are strongly interrelated. Although this may suggest that the general public does not clearly distinguish between the different aspects of the climate debate, there is a clear gradation in prevalence along the Rahmstorf typology. Climate scepticism appeared particularly common among older individuals from lower socio-economic backgrounds who are politically conservative and hold traditional values; while it is less common among younger individuals from higher socio-economic backgrounds who hold self-transcendence and environmental values. The finding that climate scepticism is rooted in people's core values and worldviews may imply a coherent and encompassing sceptical outlook on climate change. However, attitudinal certainty appeared mainly concentrated in non-sceptical groups, suggesting that climate sceptical views are not held very firmly. Implications of the findings for climate change communication and engagement are discussed.     

离子色谱法测定冰川雪冰样品中阴离子含量不确定度评定

雪冰内化学成分的记录为全球变化的各个方面,如气候变化、生物地球化学循环、人类活动、地质和宇宙事件等诸多科学命题的研究提供了直接或间接依据,这些化学成分记录主要包括雪冰内的稳定氢氧同位素、重金属、微粒、气溶胶、黑炭、离子等.雪冰内的离子化学成分的研究在冰川雪冰化学研究中具有重要的地位.为了更为准确地反映冰川雪冰内的记录信...