Ecological classifications derived from spectral and vegetation data for Cape

• Main Authors: David M. Atkinson, Paul Treitz
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Arctic; Vegetation Mapping; Ordination; Clustering; Remote Sensing; Cape Bounty; Nunavut; Melville Island
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.489.3831; http://www.geog.queensu.ca/larsees/pdfs/AtkinsonD_North2007_P.pdf

Vegetation is both an integrator and indicator of climate and ecosystem properties. Discerning the pattern of vegetation can provide a connection to the patterns of carbon flux. It may be possible to measure ecosystem processes in common vegetation communities, at the plot level, and extrapolate them over a larger area using spatially-continuous remote sensing data. In the arctic environment where vegetation is highly spatially variable, the use of high resolution imagery can help in discerning the patterns of vegetation and ecosystem processes. The primary objective of this research is to explore a link between the theories and practices of classification of vegetation data by ecologists and image classification for mapping vegetation by remote sensing scientists. This study looks to develop a methodology of relating ecological ordination and classifications techniques, derived using species and cover abundance data, along with measured environmental variables, from Cape Bounty, Melville Island, Nunavut, with remotely-sensed data. Ordination techniques are used to determine the natural arrangement of sample sites followed by cluster analysis to create ecological classes. Multi-response permutation procedure (MRPP) is applied to compare clusters. The derived cluster classes are then used to classify high spatial resolution IKONOS imagery. Ordination, clustering, and classification results showed moderate levels of success. Correspondence analysis (CA) cluster classifications performed slightly better (overall accuracy = 70.9%) than CCA classifications (overall accuracy = 66.2%). The results of this study illustrate that combination of ecological and remote sensing techniques can produce classifications that are ecologically meaningful and spectrally significant in the arctic environment.