Using information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystems

dc.contributor.authorStoy, Paul C.
dc.contributor.authorWilliams, Mathew
dc.contributor.authorBell, Robert A.
dc.contributor.authorSpadavecchia, Luke
dc.contributor.authorPrieto-Blanco, Ana
dc.contributor.authorEvans, J. G.
dc.contributor.authorvan Wijk, Mark T.
dc.date.accessioned2019-02-25T15:57:24Z
dc.date.available2019-02-25T15:57:24Z
dc.date.issued2009-03
dc.description.abstractQuantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85 m2 estimates on a 600 × 600-m2 grid) and small (0.04 m2 measurements on a 40 × 40-m2 grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy (E S,n) and Kullback–Leibler divergence (D KL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function.en_US
dc.identifier.citationStoy, P. C., M. Williams, L. Spadavecchia, R. A. Bell, A. Prieto-Blanco, J. G. Evans, and M. T. van Wijk. “Using Information Theory to Determine Optimum Pixel Size and Shape for Ecological Studies: Aggregating Land Surface Characteristics in Arctic Ecosystems.” Ecosystems 12, no. 4 (March 10, 2009): 574–589. doi:10.1007/s10021-009-9243-7.en_US
dc.identifier.issn1435-0629
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15282
dc.language.isoenen_US
dc.rightsThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.titleUsing information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystemsen_US
dc.typeArticleen_US
mus.citation.extentfirstpage574en_US
mus.citation.extentlastpage589en_US
mus.citation.issue4en_US
mus.citation.journaltitleEcosystemsen_US
mus.citation.volume12en_US
mus.data.thumbpage5en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1007/s10021-009-9243-7en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US

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