Issues and Challenges of Incorporating Fuzzy Sets in Ecological Modeling

Robinson, Vincent Β.

doi:10.1007/978-1-4020-6438-8_3

Cited by 4 publications

(3 citation statements)

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“…This study found that discrete classification schemes were the dominant models used to represent land cover in landscape ecology, with 25% of the articles reviewed using a binary classification scheme and 68% using a multi-class classification scheme (Table 1). However, many alternative methods are available to represent landscapes, such as point based and continuous field data and fuzzy sets (Gustafson 1998;Robinson 2007). This review found that these alternative methods were rarely used.…”

Section: Classification Scheme Uncertaintymentioning

confidence: 99%

“…Already existing techniques for addressing spatial uncertainty are found more widely in the remote sensing and spatial science community. An example of this is the use of fuzzy classification schemes that are not patch based (Robinson 2007). In most cases though, these solutions tend to address a single form of spatial uncertainty and are often tailored to a given ecological model.…”

Section: Solutions To Spatial Uncertaintymentioning

confidence: 99%

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Are landscape ecologists addressing uncertainty in their remote sensing data?

et al. 2012

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In this quantitative review, we investigate the degree to which landscape ecology studies that use spatial data address spatial uncertainty when conducting analyses. We identify three broad categories of spatial uncertainty that are important in determining the characterisation of landscape pattern and affect the outcome of analysis in landscape ecology: i) classification scheme uncertainty, ii) spatial scale and iii) classification error. The second category, spatial scale, was further subdivided into five scale dependent factors i) pixel size ii) minimum mappable unit, iii) smoothing, iv) thematic resolution and v) extent. We reviewed all articles published in the journal Landscape ecology in 2007 and recorded how spatial data was used and whether spatial uncertainty was addressed or reported in ecological analyses. This review found that spatial uncertainty was rarely addressed and/or reported. Most articles used the default pixel size of the sensor. Only 23% of articles addressed one or more scale dependent factors and 47% reported one or more as issues. A single study of the 59 investigated the effect of classification accuracy on ecological analyses. We demonstrate that spatial uncertainty is not being addressed as standard practice in analyses in landscape ecology, and then describe methods to test for spatial uncertainty and potential solutions that can be developed in the future.

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Section: Classification Scheme Uncertaintymentioning

confidence: 99%

Section: Solutions To Spatial Uncertaintymentioning

confidence: 99%

Are landscape ecologists addressing uncertainty in their remote sensing data?

et al. 2012

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“…The methods have found applications in other geosimulation domains too, in particular in hydrological modelling (Hemer 2006, Leyk et al 2005, Leyk et al 2004, Pappenberger et al 2006, Wealands et al 2005, Zappa et al 2008 and meteorological modelling (Ebert 2008, Cloke & Pappenberger 2008) and integrated assessment / ecological modelling (Nasiri & Huang 2008, van Delden et al 2007. Loonen et al 2006, Robinson 2007, Metzger et al 2005a. Many ecological models concern the spatial distribution of species.…”

Section: Current Applications and Further Developmentsmentioning

confidence: 99%

Measuring the performance of geosimulation models by map comparison

Hagen‐Zanker¹

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