Mapping and analysing arctic vegetation: Evaluating a method coupling numerical classification of vegetation data with SPOT satellite data in a probability model

Abstract

Vegetation and environmental data were collected at 266 sampling points distributed in a regular manner along transects covering the Broggerhalvoya peninsula, on the north-western coast of Spitsbergen. Transects with sampling points were drawn in advance on aerial photographs. The analysis of releves and collection of ground data along transects represent an efficient, representative and precise way of sampling. The vegetation data were classified and 19 plant communities distinguished. The plant communities were subjected to detrended correspondence analysis (DCA). Among the recorded variables, moisture is the one with the highest correlation along axes one and two, and reflects a coincidental moisture and vegetation cover gradient. The vegetation component responsible for this positive correlation is the bryophytes. Likewise, the TWINSPAN classification confirms this gradient in a dendrogram reflecting the hierarchical structure of the plant communities. Plant communities constitute the base of a statistical model that links the communities and the SPOT satellite data. The model then classifies and maps plant communities by means of satellite data, covering the entire Broggerhalvoya peninsula. Satellite data and environmental data were analysed regarding their ability to distinguish the plant communities in a discriminant function analysis (DFA). The results of the DFA indicate that it may be reasonable to include all the information from the different satellite channels when using satellite data for vegetation classification purposes. Among the satellite data the panchromatic channel is the one adding the most unique information to the power of the model in separating plant communities. The classification of satellite data using the probability model indicates that plant communities with less than 30% vegetation cover could be classified with the same degree of confidence or better, as compared with plant communities with more than 30% vegetation cover. The overall percentage of correctly classified releves increased by 13% when using probability level two instead of level one (57.8 to 71.1%). The probability classification model makes it possible to experiment with different probability levels to improve the fit between the vegetation and satellite data classification.