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...
Published in: | Ecosystems |
---|---|
Main Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2009
|
Subjects: | |
Online Access: | https://research.wur.nl/en/publications/using-information-theory-to-determine-optimum-pixel-size-and-shap https://doi.org/10.1007/s10021-009-9243-7 |
id |
ftunivwagenin:oai:library.wur.nl:wurpubs/380055 |
---|---|
record_format |
openpolar |
spelling |
ftunivwagenin:oai:library.wur.nl:wurpubs/380055 2024-02-04T09:52:10+01:00 Using information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystems Stoy, P.C. Williams, M. Spadavecchia, L. Bell, R.A. Prieto-Blanco, A. Evans, J.G. van Wijk, M.T. 2009 application/pdf https://research.wur.nl/en/publications/using-information-theory-to-determine-optimum-pixel-size-and-shap https://doi.org/10.1007/s10021-009-9243-7 en eng https://edepot.wur.nl/7434 https://research.wur.nl/en/publications/using-information-theory-to-determine-optimum-pixel-size-and-shap doi:10.1007/s10021-009-9243-7 info:eu-repo/semantics/restrictedAccess Wageningen University & Research Ecosystems 12 (2009) 4 ISSN: 1432-9840 agriculture co2 flux heterogeneity leaf-area index model photosynthesis productivity temperature transpiration vegetation indexes info:eu-repo/semantics/article Article/Letter to editor info:eu-repo/semantics/publishedVersion 2009 ftunivwagenin https://doi.org/10.1007/s10021-009-9243-7 2024-01-10T23:23:33Z 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 m2 estimates on a 600 × 600-m2 grid) and small (0.04 m2 measurements on a 40 × 40-m2 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 Tundra Wageningen UR (University & Research Centre): Digital Library Abisko ENVELOPE(18.829,18.829,68.349,68.349) Arctic Ecosystems 12 4 574 589 |
institution |
Open Polar |
collection |
Wageningen UR (University & Research Centre): Digital Library |
op_collection_id |
ftunivwagenin |
language |
English |
topic |
agriculture co2 flux heterogeneity leaf-area index model photosynthesis productivity temperature transpiration vegetation indexes |
spellingShingle |
agriculture co2 flux heterogeneity leaf-area index model photosynthesis productivity temperature transpiration vegetation indexes Stoy, P.C. Williams, M. Spadavecchia, L. Bell, R.A. Prieto-Blanco, A. Evans, J.G. van Wijk, M.T. Using information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystems |
topic_facet |
agriculture co2 flux heterogeneity leaf-area index model photosynthesis productivity temperature transpiration vegetation indexes |
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 m2 estimates on a 600 × 600-m2 grid) and small (0.04 m2 measurements on a 40 × 40-m2 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.C. Williams, M. Spadavecchia, L. Bell, R.A. Prieto-Blanco, A. Evans, J.G. van Wijk, M.T. |
author_facet |
Stoy, P.C. Williams, M. Spadavecchia, L. Bell, R.A. Prieto-Blanco, A. Evans, J.G. van Wijk, M.T. |
author_sort |
Stoy, P.C. |
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 |
publishDate |
2009 |
url |
https://research.wur.nl/en/publications/using-information-theory-to-determine-optimum-pixel-size-and-shap 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 Tundra |
genre_facet |
Abisko Arctic Tundra |
op_source |
Ecosystems 12 (2009) 4 ISSN: 1432-9840 |
op_relation |
https://edepot.wur.nl/7434 https://research.wur.nl/en/publications/using-information-theory-to-determine-optimum-pixel-size-and-shap doi:10.1007/s10021-009-9243-7 |
op_rights |
info:eu-repo/semantics/restrictedAccess Wageningen University & Research |
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 |
_version_ |
1789971955490750464 |