气候变化领域集成服务门户(CCPortal): Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations

CCPortal

DOI	10.1016/j.rse.2019.111498
	Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations
	Ding C.; Zhang L.; Liao M.; Feng G.; Dong J.; Ao M.; Yu Y.
发表日期	2020
ISSN	00344257
卷号	236
英文摘要	Investigation of the spatio-temporal patterns of dune migration in a large-scale area with optical remote sensing techniques can help us to better understand aeolian phenomena and mitigate sand-dust disasters. With the rapid growth in data volume, extracting more accurate dune displacement time series and rates from optical observations has become possible; however, the method is not yet fully fledged. To address this issue, we propose an extended algorithm for the mature optical imagery cross-correlation (OICC) technique based on Landsat-8 (L8) and Sentinel-2 (S2) acquisitions. The main innovative points of this algorithm are: 1) the proposed pairing strategy for the OICC processing; 2) the modularized post-processing procedures for noise removal; and 3) the introduction of singular value decomposition (SVD) time-series inversion of the redundant optical observations to quantify the dune migration. To test the effectiveness of this algorithm, it was applied in the study of dune migration near Minqin Oasis in northwestern China, using enriched L8 and S2 images collected between April 2013 and April 2018. Compared with the original OICC results in stable areas, the post-processing and inversion of the proposed algorithm reduce the uncertainty by around 22–35% and 3–5% for L8, 29–48% and 5–12% for S2, respectively. The cross-comparison between the L8- and S2-derived displacement time series shows high consistency, and presents a lower uncertainty than the result of the traditional no-inversion method. Furthermore, the derived displacement rates show spatial patterns that are similar to those of the manually digitized results obtained with historical Google™ Earth (GE) images. These comparisons show the advantage of the proposed algorithm in automatically and accurately quantifying dune migration. Taking into account these measurements, the spatio-temporal evolution patterns of dune migration in the study area were analyzed. From the spatial perspective, the sand dunes move along a northwest-southeast axis with four detected transport pathways. Our research also shows that around 1087.7 km2 of dune fields present an active status. The active sand dunes are currently encroaching on around 155.5 km2 and 4.4 km2 of land each year outside and inside the oasis, respectively, representing a problem of rapid desertification. Temporally, the displacement time series along the dominant migration direction appears as seasonal variations that are seemly consistent with the changes in local atmospheric conditions. The proposed algorithm provides a new perspective to investigate the spatio-temporal evolution of dune migration with medium-resolution L8 and S2 optical datasets. © 2019 Elsevier Inc.
英文关键词	Dune migration; Landsat-8; Minqin oasis; Optical imagery cross-correlation; Sentinel-2; Spatio-temporal evolution; Time-series inversion
语种	英语
scopus关键词	Remote sensing; Singular value decomposition; Time series; Cross correlations; Dune migration; LANDSAT; Minqin oasis; Sentinel-2; Spatiotemporal evolution; Optical correlation; algorithm; correlation; dune; image processing; Landsat; observational method; quantitative analysis; remote sensing; satellite data; satellite imagery; Sentinel; spatiotemporal analysis; China; Gansu; Minqin Oasis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179566
作者单位	State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China; School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China; School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China
推荐引用方式 GB/T 7714	Ding C.,Zhang L.,Liao M.,et al. Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations[J],2020,236.
APA	Ding C..,Zhang L..,Liao M..,Feng G..,Dong J..,...&Yu Y..(2020).Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations.Remote Sensing of Environment,236.
MLA	Ding C.,et al."Quantifying the spatio-temporal patterns of dune migration near Minqin Oasis in northwestern China with time series of Landsat-8 and Sentinel-2 observations".Remote Sensing of Environment 236(2020).