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 |
DOI | 10.1016/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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