An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

doi:10.3390/rs11242927

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications
	Guan, Hongcan 2; Su, Yanjun 2; Hu, Tianyu 2; Chen, Jin 3; Guo, Qinghua 2
刊名	REMOTE SENSING
	2019
卷号	11 期号:24
关键词	spatiotemporal data fusion object-based framework similar pixel vegetation mapping
DOI	10.3390/rs11242927
文献子类	Article
英文摘要	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.
学科主题	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号	2072-4292
出版地	BASEL
WOS关键词	CLASSIFICATION ; MODIS ; LANDSAT ; REFLECTANCE ; MODEL ; ALGORITHM ; WETLANDS ; SERIES
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000507333400043
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/19618]
专题	植被与环境变化国家重点实验室
作者单位	1.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Inst Remote Sensing Sci & Engn, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式 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).