中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping

文献类型:期刊论文

作者Zhang, Yihang1; Li, Xiaodong1; Ling, Feng1; Atkinson, Peter M.2; Ge, Yong3; Shi, Lingfei1,4; Du, Yun1
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2017-12-01
卷号63页码:129-142
ISSN号0303-2434
关键词Forest cover mapping MODIS Landsat Updating Spectral-spatial-temporal Super-resolution mapping
DOI10.1016/j.jag.2017.07.017
通讯作者Ling, Feng(lingf@whigg.ac.cn)
英文摘要With 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.
WOS关键词REMOTELY-SENSED IMAGERY ; MARKOV RANDOM-FIELD ; ENDMEMBER SELECTION ; CLIMATE-CHANGE ; CLASSIFICATION ; ALGORITHM ; DEFORESTATION ; PREDICTION ; DEPENDENCE ; DATABASE
资助项目Youth Innovation Promotion Association CAS[2017384] ; Natural Science Foundation of China[61671425] ; State Key Laboratory of Resources and Environmental Informational System of China
WOS研究方向Remote Sensing
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000411848500013
资助机构Youth Innovation Promotion Association CAS ; Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Informational System of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/62104]  
专题中国科学院地理科学与资源研究所
通讯作者Ling, Feng
作者单位1.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
推荐引用方式
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.

入库方式: OAI收割

来源:地理科学与资源研究所

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