Show simple item record

dc.contributor.authorStoy, Paul C.
dc.contributor.authorWilliams, Mathew
dc.contributor.authorPrieto-Blanco, Ana
dc.contributor.authorHuntley, Brian
dc.contributor.authorBaxter, Robert
dc.contributor.authorLewis, Philip
dc.date.accessioned2019-02-25T16:04:02Z
dc.date.available2019-02-25T16:04:02Z
dc.date.issued2009-06
dc.identifier.citationStoy, Paul C., Mathew Williams, Mathias Disney, Ana Prieto-Blanco, Brian Huntley, Robert Baxter, and Philip Lewis. “Upscaling as Ecological Information Transfer: a Simple Framework with Application to Arctic Ecosystem Carbon Exchange.” Landscape Ecology 24, no. 7 (June 3, 2009): 971–986. doi:10.1007/s10980-009-9367-3.en_US
dc.identifier.issn1572-9761
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/15287
dc.description.abstractTransferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO2 sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.en_US
dc.description.sponsorshipUS National Science Foundation (Grant numbers OPP-0096523, OPP-0352897, DEB-0087046, and DEB-00895825); University of Edinburgh, and from the Natural Environment Research Council, grant number ARSF 03/17en_US
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.titleUpscaling as ecological information transfer: A simple framework with application to arctic ecosystem carbon exchangeen_US
dc.typeArticleen_US
mus.citation.extentfirstpage971en_US
mus.citation.extentlastpage986en_US
mus.citation.issue7en_US
mus.citation.journaltitleLandscape Ecologyen_US
mus.citation.volume24en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1007/s10980-009-9367-3en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage11en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record