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Open AccessArticle

Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery

1
Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Hamburg, Germany
2
German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen, Germany
3
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2010, 2(6), 1416-1438; https://doi.org/10.3390/rs2061416
Received: 15 March 2010 / Revised: 12 April 2010 / Accepted: 19 May 2010 / Published: 27 May 2010
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Bush encroachment is a form of land degradation prominent worldwide, but particularly present in semi-arid areas. In this study, we mapped the spatial distribution of the two encroacher species, Acacia mellifera and Acacia reficiens,in Central Namibia, based on their different phenological behavior. We used constrained principal curves to extract a one dimensional gradient of phenological change from two hyperspectral images taken in different seasons. Field measurements of species composition and cover values were statistically related to bi-temporal differences in hyperspectral vegetation indices in a direct gradient analysis. The extracted gradient reflected the relationship between species composition and cover values, and the phenological pattern as captured by the image data. Cover values of four dominant plant species were mapped and species responses along the phenological gradient were interpreted. View Full-Text
Keywords: principal curve; constrained ordination; savanna; change detection; vegetation index differencing; bush encroachment; tree-grass ratio; imaging spectroscopy principal curve; constrained ordination; savanna; change detection; vegetation index differencing; bush encroachment; tree-grass ratio; imaging spectroscopy
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MDPI and ACS Style

Oldeland, J.; Dorigo, W.; Wesuls, D.; Jürgens, N. Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery. Remote Sens. 2010, 2, 1416-1438.

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