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

Stoy, P.; Williams, M.; Spadavecchia, L.; Bell, R.; Prieto-Blanco, A.; Evans, J. ORCID: https://orcid.org/0000-0003-4194-1416; van Wijk, M.. 2009 Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems. Ecosystems, 12 (4). 574-589. https://doi.org/10.1007/s10021-009-9243-7

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Abstract/Summary

Quantifying 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 m(2) estimates on a 600 x 600-m(2) grid) and small (0.04 m(2) measurements on a 40 x 40-m(2) 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s10021-009-9243-7
Programmes:	CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 1 - Monitoring and Interpretation of Biogeochemical and Climate Changes > BGC - 1.1 - Monitor concentrations, fluxes, physico-chemical forms of current and emerging pollutants ... CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 1 - Monitoring and Interpretation of Biogeochemical and Climate Changes > BGC - 1.3 - Quantify & attribute changes in biogeochemiical cycles ... CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 2 - Biogeochemistry and Climate System Processes > BGC - 2.2 - Measure and model surface atmosphere exchanges of energy ...
UKCEH and CEH Sections/Science Areas:	Harding (to July 2011)
ISSN:	1432-9840
Additional Keywords:	information content, Kullback-Liebler divergence, leaf area index, Shannon entropy, spatial averaging, triangulated irregular network, tundra, upscaling
NORA Subject Terms:	Earth Sciences Ecology and Environment Biology and Microbiology
Date made live:	30 Jan 2013 15:34 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/21127

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