摘要
植被覆盖度是表征陆地表面植被状况的一个重要地表微气候参数,也是监测评价荒漠化和农作物长势最有效的指数。本文比较了两种基于遥感数据的植被覆盖度估算方法。一个由混合光谱构成的遥感影像像元可近似地被分解为植被与土壤两个部分或者更精确地分解为多种组份。分解为两种组分的想法可用来构建像元二分模型,而分解为多种组份的思路则可构建基于光谱混合分析的植被覆盖度估算模型。本研究首先从概念上比较分析了像元二分模型与多端元分解模型在植被覆盖度估算中各自的优缺点。然后,利用搭载在中国自主发射的环境一号小卫星A星上的高光谱成像仪数据,设计并实现了一个经验性的比较研究实验。研究结果表明,两种像元分解模型都能得到满意的植被覆盖度估算结果,而多端元分解模型则能在高植被覆盖区域和低植被覆盖区域产生更好的估算结果。
Fractional vegetation cover（FVC） is an important surface microclimate parameter for characterizing land surface vegetation condition as well as the most effective indicator for assessing desertification and crop growth condition. This paper compares two methods for the retrieval of FVC from remotely sensed data. An image pixel with spectral mixture can be decomposed into two types： vegetation and soil, or into multiple components. The former idea can be used to create a dimidiate pixel model（DPM） and the latter can be used in the extraction of FVC information from remotely sensed data based on a spectral mixture analysis method. The advantages and disadvantages of the DPM model and the multiple endmember spectral mixture analysis（MESMA） based approach were conceptually examined and compared in this study. After that, an empirically comparative study of these two pixel decomposition models was designed and implemented using the hyperspectral imager onboard Chinese HJ-1-A small satellite. The results show that the two types of pixel decomposition methods can achieve satisfactory FVC estimation, and the MESMA based approach performed even better in the low- and high-density vegetation areas.
出处
《石河子大学学报（自然科学版）》
CAS
2016年第2期141-147,共7页
Journal of Shihezi University(Natural Science)
基金
financially supported by the Chinese Ministry of Science and Technology(MOST)through a research grant (No.2012BAH27 B03)
关键词
像元分解
植被覆盖度
HJ-1/HSI
多端元
光谱混合分析
pixel decomposition
fractional vegetation cover
HJ-1/HSI
multiple endmembers
spectral mixture analysis
