Remmel20131587

Référence

Remmel, T.K., Fortin, M.-J. (2013) Categorical, class-focused map patterns: Characterization and comparison. Landscape Ecology, 28(8):1587-1599. (Scopus )

Résumé

We present a rigorous and simple approach for the comparison of binary landscapes by class-focused metric values that complements the ease of computing these metrics for landscape ecology research. First, we assess whether a class-focused pattern metric value could have emerged due to random chance. Second, we compare two landscapes and assess whether class-focused pattern metrics computed for each landscape are significantly different or not. Our frameworks are based on conditional autoregressive simulations to derive empirical distributions for each metric where composition and configuration parameters are controlled. Our method permits the computation of probabilities that an observed metric value is either greater than or less than a given level of expectation. We also provide means for situating any landscape on a selected pattern metric-surface defined by parameters of composition and configuration. These surfaces illustrate which parameter would be most easily adjusted to effect a desired change in a selected class-focused pattern metric's value. Implementation is fully within the R statistical computing environment. © 2013 Springer Science+Business Media Dordrecht.

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@ARTICLE { Remmel20131587,
    AUTHOR = { Remmel, T.K. and Fortin, M.-J. },
    TITLE = { Categorical, class-focused map patterns: Characterization and comparison },
    JOURNAL = { Landscape Ecology },
    YEAR = { 2013 },
    VOLUME = { 28 },
    NUMBER = { 8 },
    PAGES = { 1587-1599 },
    NOTE = { cited By 8 },
    ABSTRACT = { We present a rigorous and simple approach for the comparison of binary landscapes by class-focused metric values that complements the ease of computing these metrics for landscape ecology research. First, we assess whether a class-focused pattern metric value could have emerged due to random chance. Second, we compare two landscapes and assess whether class-focused pattern metrics computed for each landscape are significantly different or not. Our frameworks are based on conditional autoregressive simulations to derive empirical distributions for each metric where composition and configuration parameters are controlled. Our method permits the computation of probabilities that an observed metric value is either greater than or less than a given level of expectation. We also provide means for situating any landscape on a selected pattern metric-surface defined by parameters of composition and configuration. These surfaces illustrate which parameter would be most easily adjusted to effect a desired change in a selected class-focused pattern metric's value. Implementation is fully within the R statistical computing environment. © 2013 Springer Science+Business Media Dordrecht. },
    AFFILIATION = { Department of Geography, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada; Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, ON, M5S 3G5, Canada },
    AUTHOR_KEYWORDS = { Binary maps; Comparison; Expectation; Fragmentation; Random chance; Spatial autocorrelation },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1007/s10980-013-9905-x },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884816101&doi=10.1007%2fs10980-013-9905-x&partnerID=40&md5=b4ecbfe274d356ab2ff08f9695c79164 },
}

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