Landscape Ecology

, Volume 24, Issue 7, pp 971–986 | Cite as

Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

  • Paul C. StoyEmail author
  • Mathew Williams
  • Mathias Disney
  • Ana Prieto-Blanco
  • Brian Huntley
  • Robert Baxter
  • Philip Lewis
Research Article


Transferring 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.


Abisko Information content Information theory Leaf area index Net ecosystem exchange Normalized difference vegetation index Skew-normal distribution Tundra Upscaling Wavelet decomposition 



We acknowledge funding from the US National Science Foundation (Grant numbers OPP-0096523, OPP-0352897, DEB-0087046, and DEB-00895825), from the University of Edinburgh, and from the Natural Environment Research Council, grant number ARSF 03/17 for the ASRF flight that carried the ATM sensor. We would like to thank Terry Callaghan for general support, Annika Kristofferson for the provision of meteorological data, and two anonymous reviewers for valuable comments. Wavelab version .850 ( was used for NDVI image compression.


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Paul C. Stoy
    • 1
    Email author
  • Mathew Williams
    • 1
  • Mathias Disney
    • 2
  • Ana Prieto-Blanco
    • 2
  • Brian Huntley
    • 3
  • Robert Baxter
    • 3
  • Philip Lewis
    • 2
  1. 1.School of GeoSciencesUniversity of EdinburghEdinburghUK
  2. 2.Department of GeographyUniversity College LondonLondonUK
  3. 3.School of Biological and Biomedical SciencesDurham UniversityDurhamUK

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