Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

Zhang, Zhiming; De Wulf, Robert R.; Van Coillie, Frieke M. B.; Verbeke, Lieven P. C.; De Clercq, Eva M.; Ou, Xiaokun

doi:doi:10.1117/1.3569124

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Journal of Applied Remote Sensing / Volume 5 / Research Papers

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Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

J. Appl. Remote Sens. 5, 053512 (Mar 24, 2011); http://dx.doi.org/10.1117/1.3569124

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Abstract

References (43)

Zhiming Zhang and Xiaokun Ou

Yunnan University, Institute of Ecology and Geobotany, 650091, China

Robert R. De Wulf, Frieke M. B. Van Coillie,

Ghent University, Laboratory of Forest Management and Spatial Information Techniques, Ghent, 9000, Belgium

Lieven P. C. Verbeke

Geo Solutions, Kontich, 2550, Belgium

Eva M. De Clercq

Agriculture and Veterinary Intelligence and Analysis, Zoersel 2980, Belgium

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.

History

Received Jun 06, 2010
Accepted Feb 24, 2011
Revised Feb 23, 2011
Published online Mar 24, 2011

Digital Object Identifier

http://dx.doi.org/10.1117/1.3569124

Citation

Zhiming Zhang, Robert R. De Wulf, Frieke M. B. Van Coillie, Lieven P. C. Verbeke, Eva M. De Clercq and Xiaokun Ou, "Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China", J. Appl. Remote Sens. 5, 053512 (Mar 24, 2011); http://dx.doi.org/10.1117/1.3569124