DOI: 10.1016/j.jag.2017.07.017
Scopus记录号: 2-s2.0-85032486770
论文题名: Updating Landsat-based forest cover maps with MODIS images using multiscale spectral-spatial-temporal superresolution mapping
作者: Zhang Y ; , Li X ; , Ling F ; , Atkinson P ; M ; , Ge Y ; , Shi L ; , Du Y
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 63 起始页码: 129
结束页码: 142
语种: 英语
英文关键词: Forest cover mapping
; Landsat
; MODIS
; Spectral-spatial-temporal
; Super-resolution mapping
; Updating
Scopus关键词: forest cover
; image resolution
; Landsat
; map
; mapping
; MODIS
; spatiotemporal analysis
; spectral analysis
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. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79960
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China; Lancaster Environment Centre, Faculty of Science and Technology, Lancaster University, Lancaster, United Kingdom; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
Recommended Citation:
Zhang Y,, Li X,, Ling F,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,2017-01-01,63