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30 October 2009 Integration of vegetation inventory data and ALOS image for vegetation classification in Yancheng coastal wetlands
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Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74983H (2009) https://doi.org/10.1117/12.832514
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
Systematic mapping and monitoring of wetland landscape are of fundamental importance for wetland development and management. To accurately classify wetland in Yancheng coastal wetland, ground investigation was conducted in 2006. Integrated with ground investigation, the wetland was classified into 8 categories such as Spartina alterniflora Loisel, Farm land, Phragmites Australis, Artemisia halodendron Turcz, Bare beach, Salt field, Fish & shrimp pond, and Sea water. A total of three decision trees were successfully produced. The first represented broad divisions of vegetation (in fact, at this stage, it just can be called vegetated cover like) and non-vegetation, and the second two represented more detailed vegetation classes and non-vegetation classes. To construct the decision trees, NDVI and principal component analysis were used as the evaluation factors. The thresholds were built combining with ground investigation and spectral property. Firstly, almost all kinds of vegetable were divided out of non-vegetation by NDVI. Secondly, the different species of vegetation were distinguished and some vegetated cover like was eliminated out of vegetation. Phragmites Australis belt, Artemisia halodendron Turcz belt, Spartina alterniflora Loisel belt and bare beach belt were distributed regularly from land to sea.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunmei Li, Lan Wu, Yu Yang, Rui Xia, Yanfei Wang, and Xing Jin "Integration of vegetation inventory data and ALOS image for vegetation classification in Yancheng coastal wetlands", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74983H (30 October 2009); https://doi.org/10.1117/12.832514
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