Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange |
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Authors: | Paul C Stoy Mathew Williams Mathias Disney Ana Prieto-Blanco Brian Huntley Robert Baxter Philip Lewis |
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Institution: | (1) School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UK;(2) Department of Geography, University College London, 26 Bedford Way, London, WC1H 0AP, UK;(3) School of Biological and Biomedical Sciences, Durham University, South Road, Durham, DH1 3LE, UK |
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Abstract: | 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. |
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Keywords: | Abisko Information content Information theory Leaf area index Net ecosystem exchange Normalized difference vegetation index Skew-normal distribution Tundra Upscaling Wavelet decomposition |
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