Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.


Durham Research Online
You are in:

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

Stoy, P. C. and Williams, M. and Disney, M. and Prieto-Blanco, A. and Huntley, B. and Baxter, R. and Lewis, P. (2009) 'Upscaling as ecological information transfer : a simple framework with application to Arctic ecosystem carbon exchange.', Landscape ecology., 24 (7). pp. 971-986.

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.

Item Type:Article
Keywords:Abisko, Information content, Information theory, Leaf area index, Net ecosystem exchange, normalized difference vegetation index, Skew-normal distribution, Tundra, Upscaling, Wavelet decomposition.
Full text:Full text not available from this repository.
Publisher Web site:http://dx.doi.org/10.1007/s10980-009-9367-3
Record Created:16 Sep 2009 12:35
Last Modified:22 May 2014 14:16

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitterExport: EndNote, Zotero | BibTex
Usage statisticsLook up in GoogleScholar | Find in a UK Library