Application of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation: Analysis of time-series remote sensing images using spatial statistics method

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与综合管理实验室

	Application of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation: Analysis of time-series remote sensing images using spatial statistics method
	Hou,Xi-Yong ; Han,Lei ; Gao,Meng ; Bi,Xiao-Li ; Zhu,Ming-Ming
	2010
会议日期	2010-08-10
关键词	Coastal Zones Data Mining Degradation Ecosystems Fuzzy Sets Image Reconstruction Linear Regression Remote Sensing Research Time Series Analysis
页码	2124 - 2128
通讯作者	Hou,X.-Y.
英文摘要	Increasing time-series remote sensing images provide the information about the evolution processes of ecosystems on multi-spatial scales. Vegetation plays an important role in sustaining the natural environment and supporting human being with goods and ecosystem services. Detection of vegetation degradation has become a hot spot of multi-disciplinary researches recently. In this paper, a case study of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation has been conducted. The special issues focused on the quantitative determination of historical evolutionary trend and furthermore, the sustainability of different trends in the future. Taking the Circum-Bohai-Sea region as the case study area, the Unary Linear Regression Model (ULRM) has been established based on the time-series SPOT-VGT images from 1998 to 2008, and then the Hurst index has been calculated by R/S method on the spatial scales of cell (1km²) and the whole study area. It turned out that, the combined analysis between Slope of ULRM and Hurst index could effectively reveal the characteristics of vegetation changes, which included the degraded areas in the past as well as the risk level of degradation in the future. Overall, the areas of vegetation degradation in the future amount to 38.87 thousand square kilometers, which accounts for 7.55% of the whole study area. In addition, these degraded areas mainly distributed around the metropolitan regions, coastal zone, and so on. The findings will help us with more intelligent strategies of degradation prevention. ©2010 IEEE.
产权排序	(1) Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China; (2) Graduate School of the Chinese Academy of Sciences, Beijing 100049, China
会议录	Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
会议录出版者	IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
学科主题	地理学
语种	英语
URL标识	查看原文
ISBN号	ISBN-13:9781424459346
内容类型	会议论文
源URL	[http://ir.yic.ac.cn/handle/133337/4738]
专题	烟台海岸带研究所_海岸带信息集成与综合管理实验室
推荐引用方式 GB/T 7714	Hou,Xi-Yong,Han,Lei,Gao,Meng,et al. Application of spatio-temporal data mining and knowledge discovery for detection of vegetation degradation: Analysis of time-series remote sensing images using spatial statistics method[C]. 见:. 2010-08-10.