基于时序栅格的海洋异常事件提取方法

doi:10.3724/SP.J.1047.2016.00453

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (4): 453-460.doi: 10.3724/SP.J.1047.2016.00453

基于时序栅格的海洋异常事件提取方法

李晓红^1,²(), 闫金凤¹, 李溢龙², 薛存金^2,^3,*^*()

1. 山东科技大学测绘科学与工程学院,青岛 266590
2. 中国科学院遥感与数字地球研究所数字地球重点实验室,北京 100094
3. 海南省地球观测重点实验室,三亚 572029

收稿日期:2015-10-13 修回日期:2015-12-04 出版日期:2016-04-20 发布日期:2016-04-19
通讯作者: 薛存金 E-mail:li_xiaohong2011@163.com;xuecj@radi.ac.cn
作者简介:
作者简介：李晓红（1988-）,女,硕士生,研究方向地图学与地理信息系统。E-mail: li_xiaohong2011@163.com
基金资助:
国家自然科学基金项目（41371385）;中国科学院青年促进会项目（2013113）

Time Series of Raster-oriented Method for Marine Abnormal Events Extraction

LI Xiaohong^1,²(), YAN Jinfeng¹, LI Yilong², XUE Cunjin^2,^3,^*()

1. Shandong University of Science and Technology, Qingdao 266590, China
2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3. Key Laboratory of Earth Observation, Sanya 572029, China

Received:2015-10-13 Revised:2015-12-04 Online:2016-04-20 Published:2016-04-19
Contact: XUE Cunjin E-mail:li_xiaohong2011@163.com;xuecj@radi.ac.cn

摘要/Abstract

摘要：

海洋异常事件（Marine Abnormal Event,MAE）可为区域海气相互作用和全球气候变化研究提供重要的时空特征参考,具有重要的科学意义。鉴此,本文基于长时间序列的栅格数据集,提出了一种海洋异常事件时空提取算法（Marine Abnormal Event Spatio-Temporal Extraction Method,MAESTEM）。MAESTEM的核心步骤包括事件的时间维度提取、事件的空间维度提取和事件追踪。在时间维度提取方面,将每一个栅格像元作为一维时间序列,计算其平均值和标准差作为判断每个时刻是否异常的标准,并根据异常持续发生的时间长短来提取时间维度的海洋异常事件（Temporal MAE,TMAE）。在空间维度提取方面,利用空间邻域统计方法,统计栅格像元的空间邻域中属于TMAE的个数,并通过空间维度异常判断规则获取空间维度的海洋异常事件（Spatial MAE,SMAE）。利用时刻状态的SMAE的空间拓扑关系,根据事件前后时刻覆盖的空间区域是否重叠以及事件持续的时间长短,实现异常事件的追踪。最后,通过提取太平洋海域1993年1月至2012年12月的月均海面高度异常（Sea Level Anomaly,SLA）事件,验证了该算法的有效性和实用性。

关键词: 海洋异常事件（MAE）, 提取算法, 栅格数据集, 太平洋

Abstract:

Marine Abnormal Event (MAE) is an abnormal decrease or increase of marine environmental parameters, which covers a specified spatial domain and lasts for a specified temporal duration. Marine abnormal event can provide the temporal and spatial characteristics of regional sea-air interactions and global climate change, which has an important scientific significance. Based on the above information, we propose a novel algorithm to extract the MAE from the long-term raster datasets, named as MAESTEM (Marine Abnormal Event Spatio-Temporal Extraction Method). The MAESTEM has three key steps: the extraction of MAE at a temporal dimension, the extraction of MAE at a spatial dimension, and the tracking of MAE. At the temporal dimension, each grid pixel within an image is taken to be the one-dimensional time series, and its mean and standard deviation are taken as the criteria to define its abnormal snapshot status. If and only if the abnormal snapshot and its subsequent ones are not smaller than the specified threshold, i.e. T, the abnormal snapshots are defined as a temporal MAE, denoted by TMAE. We utilize the spatial neighborhood statistics method to count the number of spatial neighborhoods of a raster pixel which belongs to TMAE and to obtain the marine abnormal events at the spatial dimension, denoted as SAME, by using the spatial dimension abnormal extracting method. In the final step, we use the spatial topological relationship of SMAE to identify whether the SAMEs at the previous and post snapshots belongs to the same event. If they overlap, they are considered being in the same event. If the temporal duration of the event exceeds the temporal threshold, save the event, otherwise, delete it. Finally, the Pacific Ocean is taken as a research area, and its monthly averaged sea level anomaly (SLA), which is obtained from the remote sensing imagery during a period from January 1993 to December 2012, is used to test the feasibility and efficiency of MAESTEM.

Key words: Marine Abnormal Event (MAE), extraction methods, raster datasets, Pacific Ocean

李晓红, 闫金凤, 李溢龙, 薛存金. 基于时序栅格的海洋异常事件提取方法[J]. 地球信息科学学报, 2016, 18(4): 453-460.DOI:10.3724/SP.J.1047.2016.00453

LI Xiaohong,YAN Jinfeng,LI Yilong,XUE Cunjin. Time Series of Raster-oriented Method for Marine Abnormal Events Extraction[J]. Journal of Geo-information Science, 2016, 18(4): 453-460.DOI:10.3724/SP.J.1047.2016.00453

图/表 4

参考文献 22

[1]	Wu T S, Song G J, Ma X J, et al.Mining geographic episode association patterns of abnormal events in global earth science data[J]. Science in China Series E: Technological Sciences, 2008,51(1):155-164. doi: 10.1007/s11431-008-5008-3
[2]	Xue C J, Dong Q, Fan X.Spatiotemporal association patterns of multiple parameters in the northwestern Pacific Ocean and their relationships with ENSO[J]. International Journal of Remote Sensing, 2014,35(11-12):4467-4483. doi: 10.1080/01431161.2014.916436
[3]	Yang J, Gong P, Fu R, et al.The role of satellite remote sensing in climate change studies[J]. Nature Climate Change, 2013,3(10):875-883. doi: 10.1038/NCLIMATE1908
[4]	Cheng T, Li Z.A multiscale approach for spatio-temporal outlier detection[J]. Transactions in GIS, 2006,10(2):253-263. doi: 10.1111/j.1467-9671.2006.00256.x
[5]	Lu C T, Kou Y, Zhao J, et al.Detecting and tracking regional outliers in meteorological data[J]. Information Sciences, 2007,177(7):1609-1632. doi: 10.1016/j.ins.2006.09.013
[6]	Kut A, Birant D.Spatio-temporal outlier detection in large databases[J]. Journal of Computing and Information Technology, 2006,14(4):291-297. doi: 10.1126/science.134.3484.1002
[7]	Das M, Parthasarathy S.Anomaly detection and spatio-temporal analysis of global climate system[C]. Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, ACM, 2009:142-150.
[8]	Benezeth Y, Jodoin P M, Saligrama V, et al.Abnormal events detection based on spatio-temporal co-occurences[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2009:2458-2465.
[9]	Coles S, Bawa J, Trenner L, et al.An introduction to statistical modeling of extreme values[M]. London: Springer, 2001.
[10]	Yu H, Zhang L, Dauwels J.Spatio-temporal graphical models for extreme events[C]. 2014 IEEE International Symposium on Information Theory (ISIT), 2014:2032-2036.
[11]	Huang Y, Zhang L, Zhang P.A framework for mining sequential patterns from spatio-temporal event data sets[J]. IEEE Transactions on Knowledge and Data Engineering, 2008,20(4):433-448. doi: 10.1109/TKDE.2007.190712
[12]	Tan P, Steinbach M, Kumar V, et al.Finding spatio-temporal patterns in earth science data[C]. KDD 2001 Workshop on Temporal Data Mining, 2001:19.
[13]	Xue C, Dong Q, Xie J.Marine spatio-temporal process semantics and its applications-taking the El Niño Southern Oscilation process and Chinese rainfall anomaly as an example[J]. Acta Oceanologica Sinica, 2012,31(2):16-24. doi: 10.1007/s13131-012-0188-x
[14]	Zhang P, Steinbach M, Kumar V, et al.Discovery of patterns of earth science data using data mining[J]. New Generation of Data Mining Applications, 2005.
[15]	Xue C, Song W, Qin L, et al.A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,38:105-114. doi: 10.1016/j.jag.2014.12.009
[16]	McGuire M P, Janeja V P, Gangopadhyay A. Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets[J]. Data Mining and Knowledge Discovery, 2014,28(4):961-1003. doi: 10.1007/s10618-013-0324-z
[17]	薛存金,周成虎,苏奋振,等.面向过程的时空数据模型研究[J].测绘学报,2010,39(1):95-101.
	[ Xue C J, Zhou C H, Su F Z, et al.Research on process-oriented spatio-temporal data model[J]. Acta Geodaetica et Cartographica Sinica, 2010,39(1):95-101. ]
[18]	薛存金,董庆.海洋时空过程数据模型及其原型系统构建研究[J].海洋通报,2012,31(6):667-674. doi: 10.11840/j.issn. 1001-6392.2012.6.011
	[ Xue C J, Dong Q.Research on the marine spatio-temporal process data model and its prototype system construction[J]. Marine Science Bulletin, 2012,31(6):667-674. ] doi: 10.11840/j.issn. 1001-6392.2012.6.011
[19]	McPhaden M J, Zebiak S E, Glantz M H. ENSO as an integrating concept in earth science[J]. Science, 2006,314(5806):1740-1745. doi: 10.1126/science.1132588 pmid: 17170296
[20]	王绍武,龚道溢.近百年来的 ENSO 事件及其强度[J].气象,1999,25(1):9-13. doi: 10.3969/j.issn.1000-0526.1999.01.002
	[ Wang S W, Gong D Y.ENSO events and their Intensity during the past century[J]. Meteorological Monthly, 1999,25(1):9-13. ] doi: 10.3969/j.issn.1000-0526.1999.01.002
[21]	Wolter K, Timlin M S.El Niño/southern oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI. ext)[J]. International Journal of Climatology, 2011,31(7):1074-1087. doi: 10.1002/joc.2336
[22]	Casey K S, Adamec D.Sea surface temperature and sea surface height variability in the North Pacific Ocean from 1993 to 1999[J]. Journal of Geophysical Research: Oceans (1978-2012), 2002,107(C8):1-12. doi: 10.1029/2001JC001060

基于时序栅格的海洋异常事件提取方法

Time Series of Raster-oriented Method for Marine Abnormal Events Extraction

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 22

相关文章 7

Metrics

本文评价

推荐阅读 0

[1]	徐新良, 申志成, 李嘉豪, 王世宽. “海上丝绸之路”主要海域热带气旋时空分布特征及其危险性[J]. 地球信息科学学报, 2020, 22(12): 2383-2392.
[2]	洪娅岚,薛存金,刘敬一,刘星,孙强,伍程斌. 全球海洋初级生产力时空异常变化对ENSO事件的响应[J]. 地球信息科学学报, 2019, 21(10): 1538-1549.
[3]	刘敬一, 薛存金, 樊彦国, 孔凡萍, 何亚文. 一种面向栅格的空间-属性双重约束聚类方法[J]. 地球信息科学学报, 2017, 19(4): 447-456.
[4]	杨彩虹, 薛存金*, 季民. 近30年西北太平洋热带气旋时空特征分析[J]. 地球信息科学学报, 2012, 14(5): 611-617.
[5]	张葳, 路紫, 王然. 西太平洋地区国家间电信流影响因子及其作用分析[J]. 地球信息科学学报, 2006, 8(1): 79-83,96.
[6]	马巍巍, 邵全琴, 陈卓奇, 戎恺, 贾晓峰. 西北太平洋柔鱼中心渔场环境分析系统的设计与开发[J]. 地球信息科学学报, 2005, 7(3): 39-44,96.
[7]	王文宇, 邵全琴, 薛允传, 仉天宇. 西北太平洋柔鱼资源与海洋环境的GIS空间分析[J]. 地球信息科学学报, 2003, 5(1): 39-44.