Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data
As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gro...
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ftryersonuniv:oai:digital.library.ryerson.ca:RULA_4385 2023-05-15T14:38:46+02:00 Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data Atkinson, David M. (Author) Treitz, Paul (Author) 2012-12-10 https://doi.org/10.3390/rs4123948 https://digital.library.ryerson.ca/islandora/object/RULA%3A4385 eng eng Creative Commons Attribution 4.0 International Public License; https://creativecommons.org/licenses/by/4.0/ CC-BY Vegetation -- Remote sensing Vegetation classification -- Arctic regions Vegetation mapping -- Arctic regions Text article 2012 ftryersonuniv https://doi.org/10.3390/rs4123948 2020-07-17T13:01:05Z As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes. Atkinson, D., & Treitz, P. (2012). Arctic ecological classifications derived from vegetation community and satellite spectral data. Remote Sensing, 4(12), 3948-3971. doi:10.3390/rs4123948 Article in Journal/Newspaper Arctic Ryerson University: RULA Digital Repository Arctic Atkinson ENVELOPE(-85.483,-85.483,-78.650,-78.650) Remote Sensing 4 12 3948 3971 |
institution |
Open Polar |
collection |
Ryerson University: RULA Digital Repository |
op_collection_id |
ftryersonuniv |
language |
English |
topic |
Vegetation -- Remote sensing Vegetation classification -- Arctic regions Vegetation mapping -- Arctic regions |
spellingShingle |
Vegetation -- Remote sensing Vegetation classification -- Arctic regions Vegetation mapping -- Arctic regions Atkinson, David M. (Author) Treitz, Paul (Author) Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
topic_facet |
Vegetation -- Remote sensing Vegetation classification -- Arctic regions Vegetation mapping -- Arctic regions |
description |
As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes. Atkinson, D., & Treitz, P. (2012). Arctic ecological classifications derived from vegetation community and satellite spectral data. Remote Sensing, 4(12), 3948-3971. doi:10.3390/rs4123948 |
format |
Article in Journal/Newspaper |
author |
Atkinson, David M. (Author) Treitz, Paul (Author) |
author_facet |
Atkinson, David M. (Author) Treitz, Paul (Author) |
author_sort |
Atkinson, David M. (Author) |
title |
Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
title_short |
Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
title_full |
Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
title_fullStr |
Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
title_full_unstemmed |
Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data |
title_sort |
arctic ecological classifications derived from vegetation community and satellite spectral data |
publishDate |
2012 |
url |
https://doi.org/10.3390/rs4123948 https://digital.library.ryerson.ca/islandora/object/RULA%3A4385 |
long_lat |
ENVELOPE(-85.483,-85.483,-78.650,-78.650) |
geographic |
Arctic Atkinson |
geographic_facet |
Arctic Atkinson |
genre |
Arctic |
genre_facet |
Arctic |
op_rights |
Creative Commons Attribution 4.0 International Public License; https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/rs4123948 |
container_title |
Remote Sensing |
container_volume |
4 |
container_issue |
12 |
container_start_page |
3948 |
op_container_end_page |
3971 |
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1766310787769434112 |