以美国Sacramento-San Joaquin三角洲为研究区,利用高光谱和高空间分辨率遥感影像HyMap数据,在光谱特征分析和实测数据的基础上,构造特征指数,建立决策树分类模型对湿地植被进行分类。研究结果表明,湿地植被在近红外波段(0.75～1.3 μm)上有明显的光谱特征差异,根据这些差异,可以构造合适的特征指数,实现湿地植被在物种水平上的识别。
Abstract
In this paper, we mapped wetland vegetation with 3 m spatial resolution,126-band HyMap image data in California’s Sacramento-San Joaquin delta. Specific vegetation indices were constructed and a decision tree model was used to identify wetland vegetation based on spectral analysis and field investigation. The result showed that we could construct suitable specific vegetation indices, according to the spectral difference obvious at near infrared bands(0.75-1.3 μm), which could be effective in distinguishing wetland vegetation and allowing for species-level detection necessary to map invasive species.
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基金
收稿日期:2013-11-13 修回日期:2014-04-13
基金项目:中国科学院战略性先导科技专项(XDA05050106)
第一作者:柴颖,硕士生。*通信作者:阮仁宗,副教授。E-mail: ruanrenzong@163.com
引文格式:柴颖,阮仁宗,傅巧妮. 高光谱数据湿地植被类型信息提取[J]. 南京林业大学学报:自然科学版,2015,39(1):181-184.
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