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Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps
Li, Xiaodong1; Ling, Feng1,2; Foody, Giles M.2; Ge, Yong3; Zhang, Yihang1; Du, Yun1
2017-07-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume196Pages:293-311
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractStudies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial-temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial-Temporal remotely sensed Images and land cover Maps Fusion Model ( STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre-and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial-temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single- date image such as the super-resolution approaches ( e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial-temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusionmodel (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single- date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial-temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods. (C) 2017 Elsevier Inc. All rights reserved.
KeywordSpatial temporal fusion Super-resolution mapping Endmember extraction
DOI10.1016/j.rse.2017.05.011
WOS KeywordMARKOV-RANDOM-FIELD ; SPATIOTEMPORAL REFLECTANCE FUSION ; MODIS DATA FUSION ; TIME-SERIES ; SATELLITE IMAGERY ; NEURAL-NETWORK ; CLASSIFICATION ; ALGORITHM ; DATABASE ; MODEL
Indexed BySCI
Language英语
Funding ProjectYouth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; Distinguished Young Scientist Grant of the Chinese Academy of Sciences ; State Key Laboratory of Resources and Environmental Informational System
Funding OrganizationYouth Innovation Promotion Association CAS ; Natural Science Foundation of China ; Distinguished Young Scientist Grant of the Chinese Academy of Sciences ; State Key Laboratory of Resources and Environmental Informational System
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000403443700022
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:23[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/63532
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Monitoring & Estimate Environm & Disaster, Wuhan 430077, Peoples R China
2.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Li, Xiaodong,Ling, Feng,Foody, Giles M.,et al. Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps[J]. REMOTE SENSING OF ENVIRONMENT,2017,196:293-311.
APA Li, Xiaodong,Ling, Feng,Foody, Giles M.,Ge, Yong,Zhang, Yihang,&Du, Yun.(2017).Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps.REMOTE SENSING OF ENVIRONMENT,196,293-311.
MLA Li, Xiaodong,et al."Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps".REMOTE SENSING OF ENVIRONMENT 196(2017):293-311.
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