Mapping ground lichens using forest inventory and optical satellite data
2011 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 32, no 2, 455-472 p.Article in journal (Refereed) Published
Lichen is a major forage resource for reindeer and may constitute up to 80% of areindeer’s winter diet. The reindeer grazing area in Sweden covers almost half of thecountry, with reindeer using mountainous areas in the summer and forested areas inthe winter. Knowledge about the spatial distribution of ground lichens is importantfor both practical and decision-making purposes. Since the early 1980s, remotesensing research of lichen cover in northern environments has focused on reindeergrazing issues. The objective of this study was to use lichen information collected inthe Swedish National Forest Inventory (NFI) as training data to classify opticalsatellite images into ground lichen cover classes. The study site was located within thereindeer husbandry area in northern Sweden and consisted of the common areabetween two contiguous Satellite Pour l’Observation de la Terre (SPOT)-5 scenesand one Landsat-7 Enhanced Thematic Mapper Plus (ETMþ) scene. Three classificationmethods were tested: Mahalanobis distance, maximum likelihood andspectral mixture analysis. Post-classification calibration was applied using a membershipprobability threshold in order to match the NFI-measured proportions oflichen coverage classes. The classification results were assessed using an independentlycollected field dataset (229 validation areas). The results demonstrated highclassification accuracy of SPOT imagery for the classification of lichen-abundantand lichen-poor areas when using theMahalanobis distance classifier (overall accuracy84.3%, kappa ¼ 0.68). The highest classification accuracy for Landsat wasachieved using a maximum likelihood classification (overall accuracy 76.8%, kappa¼ 0.53). These results provided an initial indication of the utility of NFI data astraining data in the process of mapping lichen classes over large areas.
Place, publisher, year, edition, pages
2011. Vol. 32, no 2, 455-472 p.
spectral mixture analysis; maximum-likelihood classification; reindeer herding area; caribou habitat; reflectance spectra; northern quebec; landsat data; cover; boreal; moss
Other Earth and Related Environmental Sciences
IdentifiersURN: urn:nbn:se:umu:diva-40582DOI: 10.1080/01431160903474962OAI: oai:DiVA.org:umu-40582DiVA: diva2:401106