Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems
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 meas...
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ftnerc:oai:nora.nerc.ac.uk:21127 2023-05-15T12:59:48+02:00 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. van Wijk, M. 2009 http://nora.nerc.ac.uk/id/eprint/21127/ https://doi.org/10.1007/s10021-009-9243-7 unknown Springer Stoy, P.; Williams, M.; Spadavecchia, L.; Bell, R.; Prieto-Blanco, A.; Evans, J. orcid: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 <https://doi.org/10.1007/s10021-009-9243-7> Earth Sciences Ecology and Environment Biology and Microbiology Publication - Article PeerReviewed 2009 ftnerc https://doi.org/10.1007/s10021-009-9243-7 2023-02-04T19:33:17Z 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. Article in Journal/Newspaper Abisko Arctic Arctic Tundra Natural Environment Research Council: NERC Open Research Archive Abisko ENVELOPE(18.829,18.829,68.349,68.349) Arctic Ecosystems 12 4 574 589 |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
unknown |
topic |
Earth Sciences Ecology and Environment Biology and Microbiology |
spellingShingle |
Earth Sciences Ecology and Environment Biology and Microbiology Stoy, P. Williams, M. Spadavecchia, L. Bell, R. Prieto-Blanco, A. Evans, J. van Wijk, M. Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
topic_facet |
Earth Sciences Ecology and Environment Biology and Microbiology |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Stoy, P. Williams, M. Spadavecchia, L. Bell, R. Prieto-Blanco, A. Evans, J. van Wijk, M. |
author_facet |
Stoy, P. Williams, M. Spadavecchia, L. Bell, R. Prieto-Blanco, A. Evans, J. van Wijk, M. |
author_sort |
Stoy, P. |
title |
Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
title_short |
Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
title_full |
Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
title_fullStr |
Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
title_full_unstemmed |
Using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in Arctic ecosystems |
title_sort |
using information theory to determine optimum pixel size and shape for ecological studies: aggregating land surface characteristics in arctic ecosystems |
publisher |
Springer |
publishDate |
2009 |
url |
http://nora.nerc.ac.uk/id/eprint/21127/ https://doi.org/10.1007/s10021-009-9243-7 |
long_lat |
ENVELOPE(18.829,18.829,68.349,68.349) |
geographic |
Abisko Arctic |
geographic_facet |
Abisko Arctic |
genre |
Abisko Arctic Arctic Tundra |
genre_facet |
Abisko Arctic Arctic Tundra |
op_relation |
Stoy, P.; Williams, M.; Spadavecchia, L.; Bell, R.; Prieto-Blanco, A.; Evans, J. orcid: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 <https://doi.org/10.1007/s10021-009-9243-7> |
op_doi |
https://doi.org/10.1007/s10021-009-9243-7 |
container_title |
Ecosystems |
container_volume |
12 |
container_issue |
4 |
container_start_page |
574 |
op_container_end_page |
589 |
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1766106288310190080 |