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


Oldeland, J and Dorigo, W and Lieckfeld, L and Lucieer, A and Jurgens, N, Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery, Remote Sensing of Environment, 114, (6) pp. 1155-1166. ISSN 0034-4257 (2010) [Refereed Article]

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DOI: doi:10.1016/j.rse.2010.01.003


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 Details

Item Type:Refereed Article
Keywords:Cluster analysis, redundancy analysis, multivariate, supervised fuzzy c-means, semi-arid, rangeland, Namibia, imaging spectroscopy
Research Division:Earth Sciences
Research Group:Physical geography and environmental geoscience
Research Field:Physical geography and environmental geoscience not elsewhere classified
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Assessment and management of terrestrial ecosystems
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:60350
Year Published:2010
Web of Science® Times Cited:70
Deposited By:Geography and Environmental Studies
Deposited On:2010-02-01
Last Modified:2015-02-08
Downloads:3 View Download Statistics

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