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Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping
Zhang, Yihang1; Li, Xiaodong1; Ling, Feng1; Atkinson, Peter M.2; Ge, Yong3; Shi, Lingfei1,4; Du, Yun1
2017-12-01
Source PublicationINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN0303-2434
Volume63Pages:129-142
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractWith the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images.
KeywordForest cover mapping MODIS Landsat Updating Spectral-spatial-temporal Super-resolution mapping
DOI10.1016/j.jag.2017.07.017
WOS KeywordREMOTELY-SENSED IMAGERY ; MARKOV RANDOM-FIELD ; ENDMEMBER SELECTION ; CLIMATE-CHANGE ; CLASSIFICATION ; ALGORITHM ; DEFORESTATION ; PREDICTION ; DEPENDENCE ; DATABASE
Indexed BySCI
Language英语
Funding ProjectYouth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; State Key Laboratory of Resources and Environmental Informational System of China
Funding OrganizationYouth Innovation Promotion Association CAS ; Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Informational System of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000411848500013
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62105
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, Hubei, Peoples R China
2.Univ Lancaster, Fac Sci & Technol, Lancaster Environm Ctr, Lancaster LA1 4YQ, England
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yihang,Li, Xiaodong,Ling, Feng,et al. Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2017,63:129-142.
APA Zhang, Yihang.,Li, Xiaodong.,Ling, Feng.,Atkinson, Peter M..,Ge, Yong.,...&Du, Yun.(2017).Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,63,129-142.
MLA Zhang, Yihang,et al."Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 63(2017):129-142.
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