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Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets
Xue Cunjin1,2; Liao Xiaohan3
2017-05-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume6Issue:5Pages:15
Corresponding AuthorLiao Xiaohan(liaoxh@igsnrr.ac.cn)
AbstractThe ENSO (El Nino Southern Oscillation) is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern (EOMSAP) mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Nina events, neutral conditions, or El Nino events using an ENSO index; then, the EOMSAP designs a linking-pruning-generating recursive loop to generate (m + 1)-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the user-specified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean.
Keyworddata mining marine spatial association pattern marine remote sensing products ENSO Pacific Ocean
DOI10.3390/ijgi6050139
WOS KeywordSEA-SURFACE TEMPERATURE ; NORTH PACIFIC ; EARTH-SCIENCE ; VARIABILITY ; RULES ; CLIMATE ; LEVEL ; OCEAN
Indexed BySCI
Language英语
Funding ProjectState Key Laboratory of Resources and Environmental Information System ; National key research and development program of China[2016YFA0600304] ; National Natural Science Foundation of China[41671401] ; National Natural Science Foundation of China[41401439] ; National Natural Science Foundation of China[41371385]
Funding OrganizationState Key Laboratory of Resources and Environmental Information System ; National key research and development program of China ; National Natural Science Foundation of China
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000404525500014
PublisherMDPI AG
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/63206
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiao Xiaohan
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
2.Key Lab Earth Observat, Beijing 100094, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Xue Cunjin,Liao Xiaohan. Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(5):15.
APA Xue Cunjin,&Liao Xiaohan.(2017).Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(5),15.
MLA Xue Cunjin,et al."Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.5(2017):15.
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