ISWC OpenIR  > 水保所知识产出(1956---)
基于像元分解的区域地表覆盖信息提取 ——以延河流域为例
陆广勇
Subtype硕士
2011-05
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword延河流域 线性混合光谱模型 植被盖度 植被覆盖与管理因子
Abstract

延河流域地处陕北黄土高原中部,流域内沟壑纵横,地形复杂、地表破碎,土壤侵蚀严重。本文运用遥感特征指标将研究区域分割成两个子图像区,基于线性光谱混合模型(Linear Spectral Mixture Model,LSMM),利用IDL编写的程序,对延河流域3景TM影像分区进行混合像元分解,并基于像元内所有类型的面积比例生成土地利用类型图,估算植被盖度,最后基于本文建立的亚像元C因子估算模型,结合研究区的经验C因子值,计算流域的植被覆盖与管理因子。经过对分解的亚像元各类型丰度及其估算的植被盖度、植被覆盖与管理因子值进行定性、定量评价,分析像元异质性对估算结果的影响,得出以下主要结论:
(1)基于结合SMACC和MNF变换,确定影像端元(endmember)的最佳数量为5类;参考4m的IKONOS影像、运用MNF和PPI法选择“纯净”像元作为各种典型覆盖类型的端元,分别确定各子图像区的端元组合及其光谱反射率。在自主编写的IDL程序下,对两个子图像区进行独立像元分解。通过对分解结果的误差图分析、各endmember的分量值与IKONOS影像、TM自身相应特征指数的相关性分析以及土地利用分类精度评价,分析均表明LSMM分解精度总体较好,提取的丰度信息可靠,该方法能应用于本研究区域中,是提取亚像元信息可行、有效的方法。
(2)基于LSMM的林地和草地丰度综合估算像元的植被盖度,其估算结果与NDVI具有明显的线性相关关系,且在高植被覆盖区域,随着NDVI值的缓慢增加,前者增加的梯度明显大于中等植被覆盖区域。本实验结果与基于NDVI最大、最小值经验估算的植被盖度具有良好的相关性,相关系数高达0.9025。但研究区域内,混合像元分解法比NDVI指数法的结果整体偏低,偏低的均值为0.0617,与实验中草地端元的选择有较大关系。实验表明,基于LSMM估算延河流域的植被盖度是一种有效的方法,其结果在提取高覆盖的植被信息方面比NDVI指数法更具优势。
(3)结合延河流域各个土地利用类型经验C因子值,本实验基于混合像元分解的所有端元类型的丰度,运用本实验提出的亚像元C因子值估算模型,估算研究区域的植被覆盖与管理因子。利用NDVI估算植被盖度、结合土地利用类型图、经验进行估算,以此作为对比研究。本实验估算C因子值是连续变化的,较后者更符合实地C因子变化情况。两种方法计算结果的偏差约为0.0249,但两者的相关系数高达0.9314,说明两者整体上具有较好的一致性。两种结果之间的差异来源主要包括:(1)植被盖度估算方法;(2)是否利用像元内所有端元的丰度。植被盖度估算方法造成混合像元分解法的C因子整体偏大于NDVI法,约0.0706。植被盖度估算方法的不同,造成了林地和草地的C因子值估算结果中混合像元分解法大于NDVI植被指数法。运用像元所有端元的丰度进行计算,使得前者小于后者,差值的均值约为-0.0457。各种土地利用类型对差异的响应有所不同。对于土地利用类型的丰度未占绝对优势的情况下,像元异质性越强,两种估算方法差异就越大。
关键词:延河流域;线性混合光谱模型;植被盖度;植被覆盖与管理因子

Other Abstract

The Yanhe Basin, as the experimental area of this study, has a complicated terrain surface and is located in the central of Loess Plateau that is well known all over the world for its deep loess deposits and serious soil erosion. To extract the information of land cover in sub-pixel level from Remote Sensing Data, firstly the paper used decision tree model to build two masks, and to every mask, selected different endmember models for the spectral unmixing of three Landsat Thematic Mapper imageries by using our IDL program. Based on the result of Linear Spectral Mixture Model (LSMM), sub-pixel classification of land cover was established. Then the fractional vegetation cover, the vegetation cover and the management factor were estimated. Finally, we assessed the accuracy of LSMM, the impact of heterogeneous surface and different methodologies. The main results are presented below.
(1) Comprehensive application of SMACC and MNF rotation in our work suggested that the reasonable number of endmembers is five. The MNF transform was applied in this study, and the pixel purity index (PPI) was used to find the most spectrally pure in the image. Final endmembers were then selected with a reference from IKONOS images. Seven distinct endmembers were identified, i.e. forest, grass, farmland, bare soil, residence and road, dark lake/shadow and river. The average root mean squared error (RMSE) was 0.0129. The total accuracy of the sub-pixel classification was 75.56%, and the KAPPA Coefficient was 0.6354. Meanwhile, there is good linear relationship between the abundance of LSMM and the land cover of IKONOS. The correlation coefficient between the abundance of forest and TC2, NDVI respectively were 0.8529 and 0.9028, while the coefficient between the abundance of farmland and TC1, NDSI respectively were 0.7625 and 0.7441. Furthermore, the coefficient between the fraction of water and NDWI, NDMI respectively were 0.6192 and 0.6885. It is suggested that extracting the sub-pixel  information by applying LSMM is feasible and promising.
(2) This paper calculated vegetation fractions by the abundance of forest directly. This result had a significant linear relationship with NDVI. In the dense vegetation area especially, the rise of the vegetation fractions along with the increase of NDVI was faster than that in the moderate vegetation cover district. There was a strong linear relationship between the results of this study and the one that was estimated via the minimum and maximum of NDVI. It correlation coefficient reached 0.9025. In Yanhe Basin, the fraction of vegetation of LSMM was slightly smaller than that from NDVI, and the mean difference between them was 0.1484. This was mainly impacted by its significant relation with the choice of grass endmember. Experiment results indicated that the vegetation fraction can be derived by using LSMM with a promising accuracy and the LSMM can be able to achieve an improvement in the dense forest area.
(3) This study estimated vegetation cover and management factor (C factor) based on the fractional abundance of all endmembers (LSMM-C). And the results (NDVI-C), calculated by using the vegetation fraction of NDVI and the experiential value of relevant type of land cover, were applied to comparisons. The experiment results showed that there was significant linear relationship between the two sides, whose correlation coefficient was as high as 0.9314. It is suggested that the end of LSMM was little higher than the other. The difference came from the methods that were applied for vegetation fraction and application of abundances of sub-pixel types of land cover. LSMM-C was greater than NDVI-C due to the difference of Vegetation factors and the difference were 0.0706. While, applications of fraction of sub-pixel leaded that LSMM-C were 0.0457 smaller than NDVI-C. All of the results show that the more heterogeneous at one pixel, the more differences between the two sides. Each type of land use has its own response to heterogeneity in the pixel.
Key Words:Yanhe Basin, Linear Spectral Mixture Model, Vegetation Fraction, Vegetation Cover and Management Factor

Language中文
Document Type学位论文
Identifierhttp://ir.iswc.ac.cn/handle/361005/8887
Collection水保所知识产出(1956---)
Recommended Citation
GB/T 7714
陆广勇. 基于像元分解的区域地表覆盖信息提取 ——以延河流域为例[D]. 北京. 中国科学院研究生院,2011.
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