Research Papers

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

[+] Author Affiliations
Zhiming Zhang

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

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

Robert R. De Wulf

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

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

Xiaokun Ou

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

J. Appl. Remote Sens. 5(1), 053512 (March 24, 2011). doi:10.1117/1.3569124
History: Received June 06, 2010; Revised February 23, 2011; Accepted February 24, 2011; Published March 24, 2011; Online March 24, 2011
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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.

© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Zhiming Zhang ; Robert R. De Wulf ; Frieke M. B. Van Coillie ; Lieven P. C. Verbeke ; Eva M. De Clercq, et al.
"Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China", J. Appl. Remote Sens. 5(1), 053512 (March 24, 2011). ; http://dx.doi.org/10.1117/1.3569124


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