correction for spatial scaling bias of bivariate lai with a general spatialization method

ISCAS OpenIR > 2009年期刊/会议论文

	correction for spatial scaling bias of bivariate lai with a general spatialization method
	Ma Ling-Ling; Li Chuan-Rong; Tang Ling-Li; Bao Kai; Wang Xin-Hong
	2009
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
会议录名称	Proceedings of SPIE - The International Society for Optical Engineering
页码	-
会议日期	40846
会议地点	Yichang, China
收录类别	ei
出版地	United States
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
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.
关键词	Computational Methods Data Processing Gas Burners Geographic Information Systems Image Processing Mathematical Transformations Pattern Recognition Pixels Remote Sensing Satellites
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
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8508
专题	2009年期刊/会议论文
推荐引用方式 GB/T 7714	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]. United States,2009:-.