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题名:
correction for spatial scaling bias of bivariate lai with a general spatialization method
作者: Ma Ling-Ling ; Li Chuan-Rong ; Tang Ling-Li ; Bao Kai ; Wang Xin-Hong
会议文集: Proceedings of SPIE - The International Society for Optical Engineering
会议名称: MIPPR 2009 - Remote Sensing and GIS Data Processing and Other Applications: 6th International Symposium on Multispectral Image Processing and Pattern Recognition
会议日期: 40846
出版日期: 2009
会议地点: Yichang, China
关键词: Computational methods ; Data processing ; Gas burners ; Geographic information systems ; Image processing ; Mathematical transformations ; Pattern recognition ; Pixels ; Remote sensing ; Satellites
出版地: United States
收录类别: ei
ISSN: 0277786X
ISBN: 9780819478092
部门归属: (1) Academy of Opto-Electronics, Chinese Academy of Sciences; (2) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
主办者: Natl. Lab. Multi-spectral Inf. Process. Technol.; Huazhong University of Science and Technology; National Natural Science Foundation of China; China Three Gorges University
英文摘要: As a key input parameter in many climate and land-atmosphere models, the validation of retrieved leaf area index (LAI) on regional scale from remote sensing data makes great senses. The problem of scale between the field experiments and the ground parameters retrieved from satellites is still one of the most difficult problems in the validation of satellite remote sensing data. The difficulty is twofold: First, the field measurements are not exhaustive; Secondly, the model is not linear and surface on satellite pixels is not homogenous. Therefore the objective of the scaling transform study is to estimate a non-linear function describing spatial distribution information of pixels from information on sub-pixels. The Computational Geometry Model is a general spatialization method which can realize the scaling of non-linear and discontinuous function. However it needs a large amount of computing time and a special algorithm to retrieve convex hull when facing a large number of input arguments. In this paper QuickHull algorithm is introduced to resolve the scaling problem of the bivariate LAI retrieval function. The scaling effect is analyzed through aggregating the high-resolution LAI (pixel size of 30 meters) retrieved from TM images by means of CGM method and directly aggregated method respectively. The CGM method is proved to have the capability of improving the scaling effect of LAI at larger aggregated scales. It is a prospect method to resolve the scaling problem and will take effect for the validation with limited field experiments. © 2009 Copyright SPIE - The International Society for Optical Engineering.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/8508
Appears in Collections:中科院软件所图书馆_2009年期刊/会议论文

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Recommended Citation:
Ma Ling-Ling,Li Chuan-Rong,Tang Ling-Li,et al. correction for spatial scaling bias of bivariate lai with a general spatialization method[C]. 见:MIPPR 2009 - Remote Sensing and GIS Data Processing and Other Applications: 6th International Symposium on Multispectral Image Processing and Pattern Recognition. Yichang, China. 40846.
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