A comparison of Bayes', Dempster–Shafer and Endorsement theories for managing knowledge uncertainty in the context of land cover monitoring

Comber, Alexis; Law, A. N. R.; Lishman, Joyce

doi:10.1016/s0198-9715(03)00013-9

Cited by 33 publications

(22 citation statements)

References 19 publications

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“…Several studies define land cover estimation and land cover monitoring techniques carried out through the use of DS theory (see e.g. Cayuela et al, 2006;Comber et al, 2004). This theory was also introduced in climate change uncertainty evaluation (Raje and Mujumdar, 2010;Lou and Caselton, 1997).…”

Section: Introductionmentioning

confidence: 99%

The evaluation of forest crop damages due to climate change. An application of Dempster–Shafer method

Bernetti¹,

Ciampi²,

Fagarazzi³

et al. 2011

JFE

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a b s t r a c tIn order to assess damage risk caused by climate change in forest areas, Dempster-Shafer theory of evidence and fuzzy measures were applied to develop a framework for the estimation of economic forest damage. According to the definition of risk supported by the Intergovernmental Panel on Climate Change, a function of hazard and resilience lines of evidence was defined. The results of the hazard and resilience assessment were used to develop an economic framework based on Faustmann studies. The evaluation model, implemented through a spatial analysis procedure, was carried out linking Faustmann formula with hazard and resilience raster maps. The model permitted to estimate in monetary terms two possible costs to be supported: the first one is expressed as the expected damage to the forest crop on the basis of the current obtainable woody assortments and the second one referred to the potential expenses to pay in order to mitigate the risk. Finally, the framework was tested on an area of central Italy (Tuscany region).

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Section: Introductionmentioning

confidence: 99%

The evaluation of forest crop damages due to climate change. An application of Dempster–Shafer method

Bernetti¹,

Ciampi²,

Fagarazzi³

et al. 2011

JFE

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“…This is a generic issue with probability-based approaches where the selection of the prior probabilities influence profoundly the results of the analysis. This is a fundamental problem with statistical and probabilistic approaches, one that Comber et al (2004d) discuss at length in relation to modelling land cover and expert knowledge. For instance discriminant analyses makes assumptions that the variables are not highly correlated with each other, that the correlation amongst predictors is constant across groups and that the values of each predictor have a normal distribution.…”

Section: Resultsmentioning

confidence: 99%

Comparing statistical and semantic approaches for identifying change from land cover datasets

Comber

Fisher

Wadsworth

2005

Journal of Environmental Management

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In this paper we examine methods for integrating spatial data which apparently should be comparable because they are of the same data type or theme, but which are incompatible or discordant because the classes of that theme are different. For a variety of reasons including changes in methods, in understanding of the resource, and in policy initiatives in the commissioning of the survey, this problem is widespread in the results of natural resources surveys. We present two generic methods: one method is grounded in a statistical approach using discriminant analysis, and the other exploits the knowledge of experts. We use the context of land cover mapping of Great Britain to explore these approaches for integrating discordant data. We demonstrate that the expert-based approach gives very good levels of identification of locations with incompatible classifications at the different times, and gives a much better rate of recognition of change. Some conclusions are made about the need to expand current metadata and data quality reporting to include descriptions of: -data conceptualisations, semantic and ontologies; -who decided and defined what the features of interest in a dataset are, and why;If the benefits of spatial data initiatives such as GRID, E-science and INSPIRE are to be fully realised then some method needs to be found to communicate that information most effectively to the potential user of the data.

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“…Belief and Plausibility (Rocha, 1999;Comber et al, 2004). Techniques tying Beliefs and Behaviour together would avoid many of the problems of understanding the individual drivers behind these two issues.…”

Section: Supervaluation Semanticsmentioning

confidence: 99%

Mapping vernacular geography: web-based GIS tools for capturing 'fuzzy' or 'vague' entities

Evans

Waters

2007

IJTPM

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Most people don't use a formal geographical vocabulary, however they do use a wide variety of geographical terms on a daily basis.Identifiers such as "Downtown" are components of a vernacular geography which is vastly more used than the coordinates and scientifically defined variables beloved of most professional analysts. Terms like these build into the jointly-defined world-views within which we all act. Despite its importance for policy making and quality of life, attention is rarely paid to this vernacular geography because it is hard to capture and use. This paper presents tools for capturing this geography, an example of the tools' use to define "High Crime" areas, and an initial discussion of the issues surrounding vernacular data. While the problems involved in analyzing such data are not to be underestimated, such a system aims to pull together professional and popular geographical understanding, to the advantage of both.

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A comparison of Bayes', Dempster–Shafer and Endorsement theories for managing knowledge uncertainty in the context of land cover monitoring

Cited by 33 publications

References 19 publications

The evaluation of forest crop damages due to climate change. An application of Dempster–Shafer method

The evaluation of forest crop damages due to climate change. An application of Dempster–Shafer method

Comparing statistical and semantic approaches for identifying change from land cover datasets

Mapping vernacular geography: web-based GIS tools for capturing 'fuzzy' or 'vague' entities

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