Extraction of floating-leaved vegetation information based on HyMap data
TAO Ting1, RUAN Renzong1, SUI Xiuzhen2, WANG Yuqiang3, LIN Peng1
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China; 2. Yiwu City, Zhejiang Province Survey and Design Institute, Yiwu 322000, China; 3. Shandong Province Disaster Reduction Center, Ji’nan 250000, China;
Abstract：In this paper, Sacramento, California - San Joaquin River Delta was taken as the study area, and HyMap hyperspectral data with 3 m spatial resolution acquired in June 2007 combined with ground truth data were used for pattern recognition of floating-leaved vegetation in the study area. The study was based on the spectral differences of wetland vegetations, and the “trilateral” parameters of vegetation were analyzed. Then the authors selected suitable vegetation indices combined with “trilateral” parameter features and built a decision tree model to extract the floating-leaved vegetation of the study area in comparison with the maximum likelihood classification results. The results show that the use of decision tree classification model can achieve overall accuracy of 82.68%, and that, compared with the maximum likelihood method, the total accuracy was improved by 6%, which can well identify the floating-leaved vegetation in the wetland vegetation of the study area.
陶婷, 阮仁宗, 岁秀珍, 王玉强, 林鹏. 基于HyMap数据的浮水植被信息提取[J]. 国土资源遥感, 2017, 29(2): 187-192.
TAO Ting, RUAN Renzong, SUI Xiuzhen, WANG Yuqiang, LIN Peng. Extraction of floating-leaved vegetation information based on HyMap data. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 187-192.
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2017, Vol. 29 
