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| An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications | |
Guan, Hongcan1; Su, Yanjun1; Hu, Tianyu1; Chen, Jin2; Guo, Qinghua1
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| 2019 | |
| Source Publication | REMOTE SENSING
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| Volume | 11Issue:24 |
| Abstract | Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the pepper-salt effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications. |
| Keyword | spatiotemporal data fusion object-based framework similar pixel vegetation mapping |
| Subject Area | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.3390/rs11242927 |
| Indexed By | SCI |
| Language | 英语 |
| WOS Keyword | CLASSIFICATION ; MODIS ; LANDSAT ; REFLECTANCE ; MODEL ; ALGORITHM ; WETLANDS ; SERIES |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000507333400043 |
| Publisher | MDPI |
| Subtype | Article |
| Publication Place | BASEL |
| EISSN | 2072-4292 |
| Funding Organization | Strategic Priority Research Program of Chinese Academy of SciencesChinese Academy of Sciences [XDA19050401] ; National Key Research Program of China [2016YFC0500202] ; CAS Pioneer Hundred Talents Program |
| Corresponding Author Email | guanhc@ibcas.ac.cn ; ysu@ibcas.ac.cn ; tianyuhu@ibcas.ac.cn ; chenjin@bnu.edu.cn ; qguo@ibcas.ac.cn |
| OA | gold |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ibcas.ac.cn/handle/2S10CLM1/19618 |
| Collection | 植被与环境变化国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China |
| Recommended Citation GB/T 7714 | Guan, Hongcan,Su, Yanjun,Hu, Tianyu,et al. An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications[J]. REMOTE SENSING,2019,11(24). |
| APA | Guan, Hongcan,Su, Yanjun,Hu, Tianyu,Chen, Jin,&Guo, Qinghua.(2019).An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications.REMOTE SENSING,11(24). |
| MLA | Guan, Hongcan,et al."An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications".REMOTE SENSING 11.24(2019). |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Guan-2019-An Object-（10441KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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