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Journal of Applied Remote Sensing

Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China
Author(s): Zhiming Zhang; Robert R. De Wulf; Frieke M. B. Van Coillie; Lieven P. C. Verbeke; Eva M. De Clercq; Xiaokun Ou
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Paper Abstract

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Paper Details

Date Published: 1 January 2011
PDF: 22 pages
J. Appl. Rem. Sens. 5(1) 053512 doi: 10.1117/1.3569124
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Zhiming Zhang, Yunnan Univ. (China)
Robert R. De Wulf, Univ. Gent (Belgium)
Frieke M. B. Van Coillie, Univ. Gent (Belgium)
Lieven P. C. Verbeke, Geo Solutions (Belgium)
Eva M. De Clercq, Agriculture and Veterinary Intelligence and Analysis (Belgium)
Xiaokun Ou, Yunnan Univ. (China)

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