Quantifying spatial classification uncertainties of the historical Wisconsin landscape (USA)

Landscape feature can be classified by creating categories based on aggregation of spatially explicit information. However, many landscape features appear continuous rather than discrete. The aggregation process likely involves loss of information and introduces a variety of uncertainties whose degree and extent may differ spatially. Since landscape classifications have found wide application in e.g. natural resource policies or ecological research, assessments of spatial classification uncertainties are required.
We present a quantitative framework to identify the degree of landscape continuity (fuzziness) and structure (categorization) based on fuzzy classification and offer measures to quantify uncertainties originating from aggregating features into categories. Fuzzy classification is a non-hierarchical, quantitative method of assessing class definitions using degrees of association between features and class. This results in classes which are well defined and compositionally distinct, as well as classes which are less clearly defined but which, to various degrees, share characteristics with some or all classes. The spatial variation in the degree of class definition on the landscape is used to assess classification uncertainties. The two aspects of uncertainty investigated are the degree of association of a feature with the overall class definitions (membership diffusion), and the class-specific degree of association of each pixel on the landscape with each class (membership saturation).
Three classification scenarios, one fuzzy and one discrete, of the historical landscape of Wisconsin (USA) were compared for spatial classification uncertainties. Membership diffusion is highest in topographically heterogeneous environments, or areas characterized by many species occupying similar ecological niches. Classification uncertainties for individual classes show that differentiated species distributions can be identified, not only distribution centers.     

Mapping historical forest types in Baraga County Michigan,USA as fuzzy sets

Data on tree location and species in a portion of Northern Michigan were gathered from General Land Office (GLO) survey notes (ca. 1850), digitized, and generalized to represent forest types. Fuzzy membership values describing the degree of membership of each species in each forest type were derived from (a) semantic information in the forestry literature and (b) a fuzzy clustering routine applied to data from randomly placed circular plots. The fuzzy membership values assigned to each tree point for each forest type were interpolated to form continuous surfaces using kriging and co-kriging. Advantages of this method over traditional discrete mapping methods include: (a) multiple options are available for the display and analysis; (b) classification uncertainty and the continuity of natural vegetation can be represented; and (c) the classification scheme is applied systematically across the entire map area and can be altered to produce alternative maps. The subset of available display and analytical products presented include: discrete forest type maps; a surface representing the confusion between forest types; fuzzy logical overlays of forest types; and discrete class maps with color value altered within each class to indicate degree of confusion at each location.     

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.     

基于CVM的海涂湿地生态服务价值的模糊评估模型

正确评估海涂湿地生态服务价值有助于加强人们保护海涂湿地的意识,为海涂湿地围垦生态补偿标准的制定提供依据。视价格"便宜"、"适中"和"昂贵"为模糊集,基于CVM法区间型数据建立了传统模糊统计模型、赋权模糊统计模型和三相划分模型,并以此评价了杭州湾国家湿地公园单位面积年生态系统服务价值。结果显示:3种方法得到的单价分别为10.28、10.38、9.76元m~(-2)a~(-1),三者一致程度较高,与国内沿海湿地价值比较接近。分析了"生态价值"概念客观上的"模糊性"引致模糊数学模型的合理性;采用"橄榄球"式赋权建立赋权模糊统计模型以克服传统模糊统计模型中对区间数据均匀赋权的不合理性,结果在隶属度函数的光滑性和拟合度上好于后者;第三,三相划分模型拟合出3个模糊集的隶属度函数,可以比较相同价格在不同模糊集中隶属度差异。建立的模型对于基于CVM法的生态资源价值评估具有借鉴意义。     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

Fusing multidimensional hierarchical information into finer spatial landscape metrics

One of the core issues of ecology is to understand the effects of landscape patterns on ecological processes. For this, we need to accurately capture changes in the fine landscape structures to avoid losing information about spatial heterogeneity. The landscape pattern indicators (LPIs) can characterize the spatial structures and give some information about landscape patterns. However, researches on LPIs had mainly focused on the horizontal structure of landscape patterns, while few studies addressed vertical relationships between the levels of hierarchical landscape structures. Thus, the ignorance of the vertical hierarchical relationships may cause serious biases and reduce LPIs'' representational ability and accuracy. The hierarchy theory about the landscape pattern structures could notably reduce the loss of hierarchical information, and the information entropy could quantitatively describe the vertical status of landscape units. Therefore, we established a new multidimensional fusion method of LPIs based on hierarchy theory and information entropy. Here, we created a general fusion formula for commonly used simple LPIs based on two‐grade land use data (whose land use classification system contains two grades/levels) and derived 3 fusion landscape pattern indicators (FLIs) with a case study. The results show that the information about fine spatial structure is captured by the fusion method. The regions with the most differences between the FLIs and the traditional LPIs are those with the largest vertical structure such as the ecological ecotones, where vertical structure was ignored before. The FLIs have a finer spatial representational ability and accuracy, not only retaining the main trend information of first‐grade land use data, but also containing the internal detail information of second‐grade land use data. Capturing finer spatial information of landscape patterns should encourage the application of fusion method, which should be suitable for more LPIs or more dimensional data. And the increased accuracy of FLIs will improve ecological models that rely on finer spatial information.     

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process

Abstract

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model’s adaptive abilities in various operation conditions and can improve its generalization ability.     

Gene clustering based on clusterwide mutual information.

Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.     

From Umwelt to Soundtope: An Epistemological Essay on Cognitive Ecology

Capturing information means for every organism acquiring knowledge about the living and not living objects that exist in its surroundings. In this way, the “historical” concept of Umwelt, as a subjective surrounding has been recently integrated in the theory of landscape ecology where a landscape is not only a geographical entity but also a cognitive medium. The landscape may be considered a semiotic context used by the organisms to locate resources heterogeneously distributed in space and time. In particular, inside a landscape there are different eco-fields defined as spatial arrangements of objects carrier of meaning that organisms utilize to track resources. Along this epistemic path the sonic component of the landscape is an important carrier of information commonly used by the majority of animal species to managing many vital functions. In particular birds, which are animals with a complex system of acoustic communication, seem to organize acoustic centers for public information. These sonic patterns (soundtopes) are characterized by a great variability in space and time and function like a special eco-field that allows species to share information about the status of resources and the dynamics of populations. The availability of such public information avoids a deeper and more expensive exploration of the environment to assess its quality.     

A fuzzy anomaly indicator for environmental monitoring at continental scale

Environmental status assessment and monitoring can be performed by the integration of multi-source datasets at continental and global scales. We propose a methodology for the development of a new anomaly indicator (AI) which can highlight the occurrence of anomalous conditions in a synthetic fashion by analysis of a set of spatial input data. Anomalous conditions are defined relative to long-term average assumed as normal or reference status of the vegetated land surface. The indicator is defined according to fuzzy set theory which is a powerful means of handling uncertain and imprecise knowledge of environmental systems. The indicator integrates, in an innovative way, the anomaly scores of a set of contributing factors extracted from the analysis of historical time series, mainly of Earth observations data. These time series are used to automatically derive the fuzzy membership functions that quantify the contribution of each factor to the final indicator. No reference data and expert knowledge are strictly required for the implementation of the AI although the methodology allows customization where this type of information is available. The method was tested over the African continent for the period 1996–2002; monthly AI values were derived with input datasets of vegetation phenology and rainfall estimates. The output AI continental maps bring new information by integrating multiple factors and they highlight patterns of anomalous conditions of the status of the environment. The analysis of the correlation with the El Niño Southern Oscillation (ENSO) shows that the AI is able to identify the effects of this phenomenon and its spatio-temporal dynamics. The 1997–1998 and 2000–2001 ENSO events are clearly highlighted by the highest AI values in specific regions of the continent. The indicator proposed is a valuable tool which can help guide in depth and detailed investigations of environmental conditions at local scale.     

一种新的景观扩张指数的定义与实现

景观格局动态信息的定量表达始终是景观生态学研究的一个重要科学问题,景观格局指数是其中的一种重要方法,但其多是静态指数,难以有效定量表达景观格局的动态信息.因此,针对景观扩张过程以斑块扩张面积为基础提出了一种新的景观扩张指数,来表达景观格局的动态信息.并以妫水河流域1998-2009年的景观农田化过程为例,验证该指数的适用性,结果表明:该指数不仅能够定量表达斑块的空间扩张规模,而且可以准确识别斑块的空间扩张模式.根据扩张斑块与原斑块的空间位置关系,将景观的空间扩张模式划分为邻接扩张式和外部扩张式两种.提出的景观扩张指数在技术方法上计算简便,易于实现,完善了景观格局动态的量化表征科学方法.     

黄土沟壑区小流域景观格局演变及生态服务价值响应

以黄土沟壑区典型小流域泥河沟为研究区,基于1986年的彩红外航空相片、2002年SPOT影像、2016年GF-1卫星影像的解译结果和社会经济统计数据,利用景观指数、土地利用程度、信息熵等方法分析了该流域近30年的景观格局演变规律,并借鉴生态服务价值当量估算法定量探讨了生态系统服务价值的变化特征。结果表明:(1)近30年来研究区土地利用景观格局发生了显著变化,除了林地和建设用地景观面积增加,耕地、园地、未利用地均有不同比例的减少。(2)流域内景观整体破碎度减少,优势斑块的连通性呈增加趋势。(3)土地利用程度整体呈上升趋势且高于全国平均水平231,土地利用信息熵先减少后增加,流域景观经历了"无序-有序-无序"的变化。(4)研究期内流域的总生态系统服务价值呈持续上升趋势,单项生态服务功能主要为土壤形成与保护、废物处理、水源涵养和生物多样性保护。高分辨率卫星影像为流域景观格局演变和生态服务价值分析提供了较为详细的数据支撑。     

基于点云技术的乡村景观遗产空间信息记录与可视化方法研究

点云技术为提升乡村景观遗产保护方法带来了重要机遇。从乡村景观的遗产价值保护出发,以识别乡村景观空间模式为目标,探索如何运用点云技术定量化记录和表现乡村景观的空间信息。以贵州安顺鲍家屯古村落为研究案例,全面集成数字近景摄影测量技术、激光雷达技术和点云可视化技术,构建了一套多尺度的空间信息数字化采集、处理和分析方法,能够快速、精确、全面地记录和表现乡村景观的空间特征,为识别其空间模式提供数据基础,也为遗产保护和发展提供新的视角和工具。     

基于无人机航测的漯河市土地利用景观格局尺度效应

景观格局的尺度效应一直是景观生态学研究的核心内容,对于揭示景观空间格局变化规律及其生态过程具有重要意义。以漯河市中心城区为研究对象,基于景观生态学原理,采用无人机航测技术获取空间分辨率为0.09 m的无人机影像,结合GIS空间分析法,量化分析了漯河市土地利用景观格局的尺度效应。结果表明:(1)漯河市中心城区土地利用景观格局具有明显的粒度和幅度效应。(2)粒度越小,景观格局指数随空间粒度的变化趋势越稳定,其表达的生态过程越真实;景观水平上景观格局的粒度效应是由建筑、道路和绿地景观在景观优势度、破碎度和聚集度等方面的变化导致的;35 m和3 m分别为研究粒度效应的临界阈值和最佳粒度。(3)景观优势度、破碎度和蔓延度随空间幅度的增加而降低,景观复杂程度和聚集度随空间幅度的增加而增加;景观格局具有明显的空间梯度分布特征——从市中心往外由不透水地面向透水地面过渡;建筑和道路在城市中心区聚集度较高,而绿地景观在城市内部破碎度较高,进而主导了整体景观格局的梯度变化;景观组分的稳定与城市规模有关。(4)无人机航测技术可以更快速、准确地获取城市尺度上的景观生态信息,揭示景观格局对尺度效应的响应特征,可为景观格局优化和城乡景观规划提供科学依据。     

土壤模糊隶属度不同数据转换方法及其对空间插值结果的影响

应用模糊c-均值算法对土壤进行连续分类时,其输出的土壤模糊隶属度值具有成分数据的结构特点.直接基于土壤隶属度数据实施普通克里格插值,其空间预测结果缺乏可信度.因此,在进行插值预测之前,必须对土壤模糊隶属度值进行必要的数据转换.研究采用对数正态变换方法、对称对数比转换方法和非对称对数比转换方法对土壤模糊隶属度值进行数据转换,分析了各种数据转换形式对插值结果及其精度的影响.结果表明,对样点土壤模糊隶属度进行简单对数正态转换,其插值结果空间上任意点的土壤对于不同类别的隶属度之和均不为1,因此这样的插值结果理论上缺乏可行性.数据经非对称对数比转换和对称对数比转换后,插值结果均满足各个位置组分之和为1和非负限制,二者相比,后者对区域总体趋势的反映较前者好,且精度较高.因此,在应用对称对数比方法对样点土壤模糊隶属度值进行数据转换的基础上,应用克里格技术实施空间插值可以获得最佳预测结果.     

Evaluating an Explicit Functional Relationship by Fuzzifying the Statement of its Satisfying

Combining the concepts of interval arithmetic and fuzzy theory the observations to identify some explicit functional relationship are assumed to be fuzzy sets given by the threshold sets of their membership function, which have contours suitable for the application of interval arithmetic procedures. The result is a fuzzy set on the parameter region, which offers possibilities for reasoning on the validity of the relationship. The proceeding is illustrated by an example.     

A simulation framework for modeling anthropogenic disturbances in habitat networks

Anthropogenic disturbances, like roads, increase the landscape fragmentation and affect wildlife migration and biodiversity. Such disturbances often prevent migration of wildlife due to increased barriers and mortality effects.

The aim of our simulation based approach is to assess the landscape permeability considering anthropogenic disturbances. The developed framework SimapD imposes an abstract view of a habitat network, based on an undirected graph. The simulation is done by an individual-oriented approach, where individuals explore the idealized network. Based on the information gained during the simulation, an overall network permeability index is calculated, which can be used to compare different scenarios of landscape development. Disturbances are represented by sub-models, from which appropriate resistance and mortality rates can be deduced. In this paper this is demonstrated by the construction of a fuzzy road kill model for the federal state of Baden-Wuerttemberg, Germany. The utilization of the network permeability index and a comparison to other fragmentation measures is shown by an exemplary application.     

Genetic algorithm based fuzzy logic control of a fed-batch fermentor

In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.     

Classification of antituberculosis herbs for remedial purposes by using fuzzy sets

Using fuzzy set theory, we created a system, that assesses a herb's usefulness for the treatment of tuberculosis, based on ethnobotanical data. We analysed two systems which contain different amount of inputs. The first system contains four inputs, the second one contains six inputs. We used the Takagi–Sugeno–Kanga model. Mamdani model is poor at representation as it needs more fuzzy rules than that of TSK to model a real world system where accuracy is demanded.It has been employed a fuzzy controller, and a fuzzy model, in successfully solving difficult control and modelling problems in practice. It is implemented in the Fuzzy Logic Toolbox in Matlab.The data for inputs are gathered in the database named SOPAT (selection of plants against tuberculosis), which is part of a project coordinated by the Oxford International Biomedical Centre. In this database there could be up to one millon plant species. It would be cumbersome to select a remedy from one (or some) of these species looking at the data base one-by-one. By means of the fuzzy set theory this remedy can be chosen very quickly.