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Classification of Sweden’s forest and alpine vegetation using optical satellite and inventory data

Reese, Heather (2011). Classification of Sweden’s forest and alpine vegetation using optical satellite and inventory data. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae, 1652-6880 ; 2011:86
ISBN 978-91-576-7630-6
[Doctoral thesis]

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Creation of accurate vegetation maps from optical satellite data requires use of reference data to aid in interpretation or to verify map results. Reference data may be taken, for example, from field visits, aerial photo-interpretation, or ground-based inventories. National inventories are a potential source of reference data useful in land cover mapping projects.

This thesis addresses aspects of mapping forest and alpine vegetation in Sweden through combined use of optical satellite data and inventory data. Issues such as reference and satellite data pre-processing, spatial scale, quantity and quality of reference data, and classification methods have been examined. Optical satellite data with pixel sizes ranging from 10 to 300 m have been used together with reference data from the Swedish National Forest Inventory (NFI), National Inventory of Landscapes in Sweden (NILS), a point sample based on the Terrestrial Habitat Monitoring program (THUF), and a forest stand database.

Results include modifications to common remote sensing methods, such as introducing iterative adjustment of prior probabilities in Maximum Likelihood classification, and improved topographic normalization (C-correction) of satellite data. Probability-based samples such as NFI, NILS and THUF provide data necessary for assignment of prior probabilities, estimation of continuous values, and are useful as training and validation data. For managed boreal forest stands, coarser pixel (60 m) AWiFS data were nearly as effective for stem volume estimation as SPOT 5 data (10 m). On the other hand, the most accurate classification of detailed alpine vegetation types (72.9% overall accuracy) was from SPOT 5 data combined with elevation derivatives, while classifications of Landsat TM (25 m), AWiFS, and MERIS (300 m) were less accurate. Non-parametric methods (e.g., random forests, decision/regression trees) produced higher classification accuracies than traditional parametric methods for alpine vegetation. The quantity of reference data affected classification accuracy, as more reference data produced higher map accuracy, although other factors such as distribution and quality of the reference data should be considered. As seen in this thesis, the characteristics of the landscape exert an influence on satellite and training data requirements, classification methods and resulting map accuracy.

Authors/Creators:Reese, Heather
Title:Classification of Sweden’s forest and alpine vegetation using optical satellite and inventory data
Series Name/Journal:Acta Universitatis Agriculturae Sueciae
Year of publishing :2011
Number of Pages:76
IHagner, O., and Reese, H. (2007). A method for calibrated maximum likelihood classification of forest types. Remote Sensing of Environment 110(4), 438-444.
IIReese, H., Nilsson, M., and Olsson, H. (2007). Using MERIS for mountain vegetation mapping and monitoring in Sweden. In European Space Agency (Special Publication), Proceedings of the ENVISAT Symposium, Montreux, Switzerland, 23-27 April.
IIIReese, H., Nilsson, M., and Olsson, H. (2009). Comparison of Resource sat-1 AWiFS and SPOT-5 data over managed boreal forest stands. International Journal of Remote Sensing 30(19), 4957-4978.
IVReese, H. and Olsson, H. (2011). C-correction of optical satellite data over alpine vegetation areas: A comparison of sampling strategies for determining the empirical c-parameter. Remote Sensing of Environment 115(6), 1387-1400.
VReese, H., Allard, A., Nilsson, M., and Olsson, H. Varying training data set size for supervised classification of alpine vegetation (manuscript).
Place of Publication:Umeå
Publisher:Institutionen för skoglig resurshushållning, Sveriges lantbruksuniversitet
ISBN for printed version:978-91-576-7630-6
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:U Auxiliary disciplines > U40 Surveying methods
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Other Earth and Related Environmental Sciences
(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Agrovoc terms:remote sensing, monitoring, forest inventories, satellites, highlands, vegetation, land cover mapping, sweden
Keywords:satellite data, classification, land cover, alpine vegetation, forest
Permanent URL:
ID Code:8349
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
External funders:Swedish Environmental Protection Agency and Swedish National Space Board
Deposited By: Heather Reese
Deposited On:06 Oct 2011 13:59
Metadata Last Modified:02 Dec 2014 10:47

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