Ecosystem analysis using fuzzy set theory

Bosserman, Robert W.; Ragade, Rammohan K.

doi:10.1016/0304-3800(82)90008-4

Cited by 53 publications

(27 citation statements)

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“…Fuzzy logic was first assumed to be useful for ecological modelling in the 1980s (BOSSERMAN and RAGADE, 1982) and especially in the 1990s (SALSKI, 1992). At that time it was also applied to habitat modelling.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, a certain amount of impreciseness and subjectivity is inherent in classifications of real ecological phenomena. A number of authors propose the use of fuzzy logic for ecological modelling (BOSSERMAN and RAGADE, 1982;SALSKI, 1992;SCHULTZ and WIELAND, 1995;WIELAND, LUTZE and HOFFMANN, 1996;WIELAND, 1997;LUTZE, WIELAND and SCHULTZ, 1999). Fuzzy logic is able to cope with the properties of the existing data.…”

Section: Introductionmentioning

confidence: 99%

“…It takes into account a degree of uncertainty in estimations and measurements, which humans can include in their decision process, but machines or computers cannot. Human decision-making is fuzzy, and fuzzy-logic tries to imitate it to produce more human-like results requiring less information (BOSSERMAN and RAGADE, 1982;SALSKI, 1992;TRAEGER, 1994;PIEGAT, 2001). In classical set theory an element either belongs to a set or it does not.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Habitat Suitability Index for the Noble Crayfish Astacus Astacus Using Fuzzy Modelling

Zuther

Schulz

Lentzen-Godding

et al. 2005

Bull. Fr. Pêche Piscic.

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A Geographic Information System (GIS) and fuzzy modelling were used to develop a habitat suitability index for the noble crayfish, Astacus astacus. The model is based on crayfish distribution data for the federal state Hesse, Germany, which had been recorded between 1988 and 1996. It includes 185 sites with noble crayfish in 126 watercourses. Official data on the morphological quality of surface waters recorded between 1996 and 1998 by order of the Ministry of Hesse for Environment, Rural Areas and Consumers Protection was used to describe the habitat features. One third of the crayfish sites was selected by chance to determine habitat properties significantly associated with crayfish occurrence by means of the Kolmogorov-Smirnoff test and frequency analysis. Meaningless associations were excluded based on expert knowledge. Five parameters related to the structure of the riverbed and the bankside were incorporated in the fuzzy model. The model was complemented by a number of parameters known to exclude the occurrence of the noble crayfish (e.g. pipes, concrete embankments). Finally, a habitat suitability index for every stretch of water in Hesse was calculated. The predictive power of the fuzzy model was tested on the remaining distribution data set for Astacus astacus. The habitat suitability index differed slightly but significantly (p < 0.001) between sites with and sites without crayfish occurrence. Fuzzy modelling proved to be useful for assessing habitat suitability with respect to crayfish, though further improvements of the model appeared to be necessary for a more reliable prediction of noble crayfish sites. Key-words:Astacus astacus, fuzzy modelling, Germany, Geographic Information System (GIS), habitat suitability, stream structure, Water Framework Directive. DÉVELOPPEMENT D'UN INDICE DE QUALITÉ D'HABITAT POUR L'ÉCREVISSE NOBLE ASTACUS ASTACUS PAR MODÉLISATION FLOUE RÉSUMÉ

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a Habitat Suitability Index for the Noble Crayfish Astacus Astacus Using Fuzzy Modelling

Zuther

Schulz

Lentzen-Godding

et al. 2005

Bull. Fr. Pêche Piscic.

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“…Edward and Abraham (1983) introduced fuzzy sets to ecosystems and studied interspecific competition. Bosserman and Ragade (1982) used fuzzy sets for analysis of ecosystems, and Cao (1995) defined niche as an a-cut set in a fuzzy set, stating that index a was a measure of competition. Wang et al (2003) presented a fuzzy model for niche and community.…”

Section: Introductionmentioning

confidence: 99%

Niche width and niche overlap: a method based on type‐2 fuzzy sets

Li¹,

Sun²,

Gong³

2006

Ecological Research

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Complicated ecosystems and the high non-linearity of evolution has made biology more adaptable to a variety of environments. The relationship between life and environment demands a dynamic definition of niche and its measurement. In this paper we propose a model of niche with dynamic character based on the ''broad band'' effect in type-2 fuzzy sets. The niche in this definition is an interval in each ecological dimension which is dynamic in character and depends on the actual environment. We also give formulas for niche width and niche overlap. We compute the niche width and overlap for plants and animals and then compare these results with previous results. The results for niche width in this paper reflect the diversity of resources used by species or communities. The results for niche overlap demonstrate overlap under different environmental conditions. The results are, moreover, intervals, which could provide more information. The model in this paper could therefore be used to describe the state of every resource comprehensively, reflecting the interaction between species and environment.

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“…Since the concept of multiple and partial class membership is fundamental to fuzzy sets techniques (Bosserman and Ragade, 1982;Hisdal, 1994) these may, however, be more appropriate for land cover representation than softened classifications. One technique which has been used widely in the classification of remotely sensed data is the fuzzy c-means algorithm.…”

Section: Introductionmentioning

confidence: 99%

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Foody

1996

International Journal of Remote Sensing

431

199

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data Foody, G. M. Sensing, 17, 1317Sensing, 17, -1340Sensing, 17, (1996 The manuscript of the above article revised after peer review and submitted to the journal for publication, follows. Please note that small changes may have been made after submission and the definitive version is that subsequently published as: Remote sensing is an attractive source of data for land cover mapping applications. International Journal of RemoteMapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional 'hard' classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for 'hard' classifications.The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the 'hard' classifications. Moreover, the outputs derived from the artificial neural network and the

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Ecosystem analysis using fuzzy set theory

Cited by 53 publications

References 3 publications

Development of a Habitat Suitability Index for the Noble Crayfish Astacus Astacus Using Fuzzy Modelling

Development of a Habitat Suitability Index for the Noble Crayfish Astacus Astacus Using Fuzzy Modelling

Niche width and niche overlap: a method based on type‐2 fuzzy sets

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

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