Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

被引:27
|
作者
Zheng, Zhijia [1 ,4 ]
Du, Shihong [1 ]
Taubenboeck, Hannes [2 ,3 ]
Zhang, Xiuyuan [1 ]
机构
[1] Peking Univ, Inst Remote Sensing & GIS, Beijing 100871, Peoples R China
[2] German Aerosp Ctr DLR, German Remote Sensing Data Ctr DFD, D-82234 Wessling, Germany
[3] Julius Maximilians Univ Wurzburg, Inst Geog & Geol, D-97074 Wurzburg, Germany
[4] Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Natl & Local Joint Engn Res Ctr Satellite Geospat, Fuzhou 350108, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Sand dune; Remote sensing; Mapping; Dune pattern; Dune dynamics; Geomorphology; FIELD SELF-ORGANIZATION; TOPOGRAPHY MISSION SRTM; GOOGLE EARTH ENGINE; RUB AL-KHALI; WHITE SANDS; PATTERN-ANALYSIS; MORPHOLOGICAL VARIABILITY; SPATIALLY DIVERSE; NORTHERN MARGIN; WESTERN DESERT;
D O I
10.1016/j.rse.2022.112913
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sand dunes are one of the most abundant aeolian landforms and play an important role in understanding how aeolian environments evolve. Since the 1970s, remote sensing has enabled large-scale investigations of dunes at comparatively low costs and with temporally continuous observations, which greatly advances our knowledge of aeolian systems. In this context, we provide a review of recent progress in three research topics that have been greatly facilitated by remote sensing techniques. These topics are 1) mapping sand extent and dune types, 2) dune pattern quantification, and 3) monitoring dune dynamics. Sand dune mapping was the early focus of aeolian geomorphologists, and continued progress has been made in refining classification schemes and developing advanced classification techniques. Dune pattern quantification can be resolved in two geomorphometric approaches, and a careful design that takes into consideration the image resolution, the data quality, and the uncertainty in dune discretization is necessary. Dune dynamics typically exhibit as dune migration, dune interactions, and dune fine-scale morphodynamics. The wide application of change detection algorithms, especially COSI-Corr, provides great insights into dune migration, while the exploration of dune interactions is still in its infancy. Future directions are highlighted in four key areas: unifying classification schemes regarding dune morphology, developing methods that are capable of recognizing diverse dune forms at large spatial extents, designing modularized workflows and more complex matching rules to quantify dune migration, and improving quantitative analysis of dune interactions.
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页数:23
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