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Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery.


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Oldeland, J, Dorigo, W, Lieckfeld, L, Lucieer, A and Jurgens, N 2010 , 'Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery.' , Remote Sensing of Environment, vol. 114, no. 6 , pp. 1155-1166 , doi: 10.1016/j.rse.2010.01.003.

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Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensing
technology. However, combining ecological ground truth information and remote sensing datasets for
mapping approaches is complicated by the complexity of ecological datasets. In this study, we present a new
approach that uses high spatial resolution hyperspectral datasets to map vegetation units of a semiarid
rangeland in Central Namibia. Field vegetation surveys provide the input to the workflow presented in this
study. The collected data were classified by hierarchical cluster analysis into seven vegetation units that reflect
different ecological states occurring in the study area. Spectral indices covering vegetation and soil characteristics
were calculated from hyperspectral remote sensing imagery and used as environmental variables in a
constrained ordination by applying redundancy analysis (RDA). The resulting statistical relationships between
vegetation data and spectral indices were transferred into images of ordination axes, which were subsequently
used in a supervised fuzzy c-means classification approach relying on a k-NN distance metric. Membership
images for each vegetation unit as well as a confusion image of the classification result allowed a sound
ecological interpretation of the resulting hard classification map. Classification results were validated with two
independent reference datasets. For an internal and external validation dataset, overall accuracy reached 98%
and 64% with kappa values of 0.98 and 0.53, respectively. Critical steps during the mapping workflow were
highlighted and compared with similar mapping approaches.

Item Type: Article
Authors/Creators:Oldeland, J and Dorigo, W and Lieckfeld, L and Lucieer, A and Jurgens, N
Keywords: Cluster analysis Redundancy analysis Multivariate Supervised fuzzy c-means Semiarid Rangeland Namibia Imaging spectroscopy
Journal or Publication Title: Remote Sensing of Environment
ISSN: 0034-4257
DOI / ID Number: 10.1016/j.rse.2010.01.003
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