{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T07:06:05Z","timestamp":1784963165232,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2012,12,10]],"date-time":"2012-12-10T00:00:00Z","timestamp":1355097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u00b0 latitudinal gradient (approximately 70\u00b0 to 75\u00b0N). 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%\u201379% and Kappa values of 0.54\u20130.69. 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.<\/jats:p>","DOI":"10.3390\/rs4123948","type":"journal-article","created":{"date-parts":[[2012,12,10]],"date-time":"2012-12-10T11:03:00Z","timestamp":1355137380000},"page":"3948-3971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data"],"prefix":"10.3390","volume":"4","author":[{"given":"David","family":"Atkinson","sequence":"first","affiliation":[{"name":"Department of Geography, Ryerson University, Toronto, ON M5B 2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Treitz","sequence":"additional","affiliation":[{"name":"Department of Geography, Queen's University, Kingston, ON K7L 3N6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2012,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1080\/01431169308954008","article-title":"The relationship between tussock tundra spectral reflectance properties and biomass and vegetation composition","volume":"14","author":"Hope","year":"1993","journal-title":"Int. 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