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A Bayesian Space-Time Hierarchical Model for Remotely Sensed Lattice Data Based on Multiscale Homogeneous Statistical Units
Li, Junming1; Wang, Jinfeng2; Wang, Nannan3; Li, Honglin4
2018-07-01
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume11Issue:7Pages:2151-2161
Corresponding AuthorWang, Jinfeng(Wangjf@lreis.ac.cn)
AbstractThe Bayesian hierarchical model has the outstanding capacity to combine prior knowledge with observations, as well as to generate rich spatiotemporal patterns. However, this advanced method has limited use in remotely sensed lattice data applications due to the large computational burden and unreasonable statistical inferences based on statistical units with a fixed scale. This paper presents a multiscale spatial homogeneous subdivision method and develops a Bayesian space-time hierarchical model (BSTHM) for remotely sensed lattice data. This can solve the above-mentioned limitations by constructing multiscale spatial homogeneous units with good statistical properties. The quantitative criteria for subdivision are provided. The outcome of the BSTHM is not only more reasonable in theory but also much easier to interpret; meanwhile, the computational efficiency is also considerably improved. This novel approach and its merits are illustrated by a case study that examines the spatiotemporal variation of PM2.5 pollution in Asia from 2000 to 2014 using remotely sensed data describing PM2.5 annual mean concentrations. Overall spatial pattern, common time trend, and local variation trend were decomposed and quantificationally estimated from an intricate spatiotemporal process.
KeywordBayesian statistics multiscale subdivision PM2.5 pollution
DOI10.1109/JSTARS.2018.2818286
WOS KeywordFINE PARTICULATE MATTER ; MOUTH-DISEASE ; CHINA ; TEMPERATURE ; PATTERN ; PM2.5 ; RISK
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41531179] ; National Natural Science Foundation of China[41421001] ; National ST Major Program[2016YFC1302504]
Funding OrganizationNational Natural Science Foundation of China ; National ST Major Program
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000440035600001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54454
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Jinfeng
Affiliation1.Shanxi Univ Finance & Econ, Sch Stat, Taiyuan 030006, Shanxi, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 10010, Peoples R China
3.Henan Univ, Sch Environm & Planning, Kaifeng 475004, Peoples R China
4.Prov Ctr Remote Sensing Shanxi, Taiyuan 030001, Shanxi, Peoples R China
Recommended Citation
GB/T 7714
Li, Junming,Wang, Jinfeng,Wang, Nannan,et al. A Bayesian Space-Time Hierarchical Model for Remotely Sensed Lattice Data Based on Multiscale Homogeneous Statistical Units[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(7):2151-2161.
APA Li, Junming,Wang, Jinfeng,Wang, Nannan,&Li, Honglin.(2018).A Bayesian Space-Time Hierarchical Model for Remotely Sensed Lattice Data Based on Multiscale Homogeneous Statistical Units.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(7),2151-2161.
MLA Li, Junming,et al."A Bayesian Space-Time Hierarchical Model for Remotely Sensed Lattice Data Based on Multiscale Homogeneous Statistical Units".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.7(2018):2151-2161.
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