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Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining
Ding, Zhi1,2,3; Liao, Xiaohan1; Su, Fenzhen1; Fu, Dongjie1
2017-02-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:2Pages:18
Corresponding AuthorLiao, Xiaohan(liaoxh@igsnrr.ac.cn)
AbstractResearch on the land use of the coastal zone in the sealand direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sealand direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sealand direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sealand direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sealand direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sealand direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sealand direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean.
Keywordcoastal zone land use spatial sequence pattern from the land to the sea association rules the anisotropy
DOI10.3390/rs9020116
WOS KeywordDRIVING FORCES ; CHINA ; IMPACT ; ZONE ; SEA ; LANDSCAPE ; BAY
Indexed BySCI
Language英语
Funding ProjectNational natural Science Foundation of China[41271409] ; Youth Scinence Fund of LREIS[O8R8A080YA] ; CAS[41501473] ; National Science Foundation of China
Funding OrganizationNational natural Science Foundation of China ; Youth Scinence Fund of LREIS ; CAS ; National Science Foundation of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000397013700018
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64750
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiao, Xiaohan
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Dept Resources & Environm, Beijing 100049, Peoples R China
3.Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Ding, Zhi,Liao, Xiaohan,Su, Fenzhen,et al. Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining[J]. REMOTE SENSING,2017,9(2):18.
APA Ding, Zhi,Liao, Xiaohan,Su, Fenzhen,&Fu, Dongjie.(2017).Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining.REMOTE SENSING,9(2),18.
MLA Ding, Zhi,et al."Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining".REMOTE SENSING 9.2(2017):18.
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