Catálogo de publicaciones - libros

Traditionally, information in geographic information systems (GIS) is represented as crisp information. While for many applications, this is a good enough approximation of reality, some models would benefit from having the inherent imprecision or uncertainty incorporated in the model. In literature, several ideas and concepts to improve on the crisp models have been considered. In the past, we have presented different models to represent and to work with the concept of regions, defined using fuzzy set theory in GIS systems. For such fuzzy regions, a number of approaches already have been described in detail. In this paper, we will elaborate on a fuzzy set approach and practical implementations of the concept. Apart from the concept, two developed techniques (one based on triangulated networks, one based on bitmap models) are presented along with some of the operators. An overview of application fields is provided to illustrate where and how the techniques can be used.

Ecotones are the zones of transition between patches of different ecological character. In landscapes where humans manage the land, they can be sharp or abrupt in space, but in semi-natural environments they more typically occupy space showing an intergrade between one ecological area and another. Semi-natural ecotones are, therefore, poorly treated by the traditional Boolean mapping of vegetation and land cover with sharp spatial boundaries which are implicit in it. Fuzzy sets provide a means by which ecotones can be represented as 2-dimensional spatial objects; fuzzy type 2 sets provide a further dimension to this characterization which is more in keeping with the higher order fuzzy nature of ecotones. This paper presents a methodology for representing ecotones as fuzzy objects with examples of a forest-savanna ecotone from Bolivia.

An information-based framework is presented for spatially explicit GIS-based ecological modeling. Within this framework some of the important issues and challenges of incorporating fuzzy sets in spatially explicit population models (SEPM) are discussed. Examples of current work are used to illustrate the main issues and challenges facing the incorporation of fuzzy sets in ecological modeling. Among the challenges to be discussed are fuzzy-based techniques for data acquisition, model control/ evaluation, heterogeneous representations of spatial data, parameterization of models, and hypothesis testing. There is special attention given to the issue of habitat modeling and presence/absence problem. Many scientific issues facing the incorporation of ecological models will be raised such as hypothesis testing, and relationship between statistical analysis and fuzzy techniques. Software availability is discussed as challenge for past and future. use of fuzzy techniques directed at handling uncertainty in GIS-based SEPM.

Descriptions of vegetation communities are often based on vague semantic terms describing species presence and dominance. For this reason, some researchers advocate the use of fuzzy sets in the statistical classification of plant species data into communities. In this study, spatially referenced vegetation abundance values collected from Greek were analysed by ordination (DECORANA), and classified on the resulting axes using fuzzy -means to yield a point data-set representing local memberships in characteristic plant communities. The fuzzy clusters matched vegetation communities noted in the field, which tended to grade into one another, rather than occupying discrete patches. The fuzzy set representation of the community exploited the strengths of detrended correspondence analysis while retaining richer information than a TWINSPAN classification of the same data. Thus, in the absence of phytosociological benchmarks, meaningful and manageable information could be derived from complex, multivariate data. We also analysed the influence of the reliability of different surveyors’ field observations by multiple sampling at a selected sample location. We show that the impact of surveyor error was more severe in the Boolean than the fuzzy classification.

The chapter describes how the Rough Set-based approximation of polygon classes with point-based elementary sets can lead to classification of point-in-polygon data patterns and consequently to knowledge in terms of classification rules, which are logical statements of the “…, …” type. The chapter also discusses properties of Rough Set-based approximation when indiscernibility relationship is substituted with dominance relationship due to preference ordered attributes in the classification table. Since the preference order attributes are common in spatial multiple criteria evaluation the presented approach has applications in spatial decision analysis.

The advent of new high resolution sensors, either airborne or spaceborne, leads to new applications and further impulses for an integration of remotely sensed and GIS data. Along with these new data sources, existing processing methods have to be adopted which is in particular also valid for the assessment of the post classification quality. In this overall context our contribution will outline general uncertainty aspects in the integration process and particular problems with the accuracy assessment based on high resolution data. These problems lead to the motivation to develop a new characteristic value, called the Fuzzy Certainty Measure (FCM), which considers indeterminate boundaries in the classification result as well as in the reference data, and can be applied in a class- and even object-specific manner.

Ontology for geographic information is assumed to contribute to the design of GIS and to improve usability. Most contributions consider an ideal world where information is complete and without error. This article investigates the effects of incompleteness, error, approximation, and uncertainty in geographic information on the design of a GIS restricted to description of physical reality. The discussion is organized around ontological commitments, first listing the standard assumptions for a realist approach to the design of an information system and then investigating the effects of the limitations in observation methods and the necessary incompleteness of information. The major contribution of the article is to replace the not-testable definition of data quality as “corresponding to reality” by an operational definition of data quality with respect to a decision. I argue that error, uncertainty, and incompleteness are necessary and important aspects of how humans organized and use their knowledge; it is recommended to take them into account when designing and using GIS.

The contribution presents a flexible approach to model spatial decision strategies in GISs when either the data, or the knowledge of the phenomenon, or both, are affected by some form of imperfection. The proposal is particularly appealing for its flexibility and usefulness in many real applications encompassing geosciences, and environmental protection systems. Strategies based on fuzzy inference, soft integration of criteria with distinct importances, and consensual fusion are proposed and modeled within fuzzy set theory.

The paper presents the characteristics of the created and introduced GIS on the level of the city, oblast, country, and the basin of the river, which flows in seven oblasts. These systems allow to solve different tasks on simulations and prognostication of changing and controlling over the ecological situations, including the application of the fuzzy sets theory.

Discussion of the logic underlying the fuzzy change matrix is extended in this chapter to incorporate a consideration of type 2 fuzzy sets. Type 2 sets are parameterized from the differences in fuzzy set membership yielded by multiple values of the fuzziness or overlap parameter of the wellknown fuzzy -means classifier. Type 2 fuzzy sets can be seen as a response to the philosophical issue of higher order vagueness. It is shown that the type 2 analysis yields a variety of answers to any query about the amount of change, reflecting the higher order vagueness in the uncertainty of the change in a vague phenomenon.