Migration of barchan dunes in the western Quruq Desert, northwestern China

Yang, Junhuai; Dong, Zhibao; Liu, Zhengyao; Shi, Weikang; Chen, Guoxiang; Shao, Tianjie; Zeng, Hanmin

doi:10.1002/esp.4629

Cited by 28 publications

(16 citation statements)

References 43 publications

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“…Among the virtual globe tools described above, GE has proved to be the most popular and has advantages in terms of visualization, ease of access to a wide range of geospatial data, and a unified coordinate system; however, it still lacks extendibility [2]. In recent years, GE was used for research on geomorphology [5][6][7][8], ecology [9][10][11][12], geology [13][14][15][16], the atmosphere [17,18], disasters [19][20][21][22][23][24][25], social science [26], and urban studies [27][28][29][30][31][32], and has served as an essential tool in studies of global environmental change. In addition, GE was also widely used in education, especially in the teaching of geography, because of its great ability to provide virtual visualizations of the Earth [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

145

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Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

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Section: Introductionmentioning

confidence: 99%

Progress and Trends in the Application of Google Earth and Google Earth Engine

Zhao

et al. 2021

Remote Sensing

145

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“…The migration rates of bedforms are considered first. Although dune migration rates can be influenced by different factors (including wind intensity, the number of dunes per unit surface area, dune shape, topography, grain-size, vegetative cover, and precipitation; Bogle et al, 2015;Boulghobra, 2016;Hamdan et al, 2016;Yang et al, 2019), they are notably markedly governed by the size of the original bedform. Overall, smaller bedforms migrate more quickly than larger bedforms (Hersen et al, 2002;Groh et al, 2008).…”

Section: Angle-of-climbmentioning

confidence: 99%

The role of subsidence and accommodation generation in controlling the nature of the aeolian stratigraphic record

Cosgrove¹,

Colombera²,

Mountney³

2021

JGS

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Despite a well-documented record of preserved aeolian successions from sedimentary basins characterised by widely variable subsidence rates, the relationship between aeolian architecture and subsidence-driven accommodation generation remains poorly constrained and largely unquantified. Basin subsidence as a control on aeolian sedimentary architecture is examined through analysis of 55 ancient case-studies categorised into settings of ‘slow’ (1–10 m/Myr), ‘moderate’ (10–100 m/Myr) and ‘rapid’ (>100 m/Myr) time-averaged subsidence rates. In rapidly subsiding basins, aeolian successions are thicker and associated with: (1) thicker and more laterally extensive dune-sets with increased foreset preservation; (2) greater proportions of wet-type interdunes and surface stabilization features; (3) more extensive interdune migration surfaces, bounding sets that climb more steeply. In slowly subsiding basins, aeolian successions are thinner, and associated with a greater proportion of (1) aeolian sandsheets; (2) supersurfaces indicative of deflation and bypass. Rapid subsidence promotes: (1) steeper bedform climb, resulting in increased preservation of the original dune foreset deposits; (2) relatively elevated water-tables, leading to sequestration of deposits beneath the erosional-baseline and encouraging development of stabilizing agents; both factors promote long-term preservation. Slow subsidence results in (1) lower angles-of-climb, associated with increased truncation of the original dune forms; (2) greater post-depositional reworking, where sediment is exposed above the erosional-baseline for protracted time. Quantitative analysis of sedimentary stratal architecture in relation to rates of basin subsidence helps demonstrate the mechanisms by which sedimentary successions are accumulated and preserved into the long-term stratigraphic record.Supplementary material:https://doi.org/10.6084/m9.figshare.c.5515695

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“…Dune migration can be evaluated from multi-temporal data such as aerial photographs (Finkel, 1961;Stafford and Langfelder, 1971;Baughman et al, 2018), satellite images (Hoover et al, 2018;Dong, 2015;Yang et al, 2019) or LiDAR DEMs (Mitasova et al, 2004(Mitasova et al, , 2005aBaughman et al, 2018). Dune migration between the 2010 (ALS) and 2019 (SfM-MVS) surveys was determined as the displacement of dune crest lines.…”

Section: Dune Migration and Sand Volumementioning

confidence: 99%

Aeolian dune modelling from airborne LiDAR, terrestrial LiDAR and Structure from Motion-Multi View Stereo

Grohmann,

Garcia,

Affonso

et al. 2019

Preprint

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In this work, we present an analysis of dune migration and volume change of the Garopaba dune field, southern Brazil, based on data from Airborne LiDAR (ALS -surveyed in 2010) and Structure from Motion-Multi View Stereo (SfM-MVS -surveyed in 2019). The accuracy of the SfM-MVS reconstruction was validated by a comparison between Terrestrial LiDAR (TLS) and SfM-MVS Digital Elevation Models (DEMs). Elevation differences of 2 000 random points resulted in RMSE of 0.16 m and MAE of 0.13 m, with SfM-MVS elevations slightly higher than the TLS ones. Although sand dunes are commonly regarded as a challenge to traditional photogrammetry due their homogeneous texture and spectral response, in this research image matching was successful in all areas of the survey due the presence of superficial features (footprints and sandboard tracks) and visibility of the sedimentary stratification, highlighted by heavy minerals. Displacement of dune crest lines from the ALS and SfM-MVS DEMs resulted in a migration rate of ≈5 m/year between 2010 and 2019, in good agreement with rates derived from satellite images and historical aerial photographs of the same area. Sand volume change in the same period showed an increase of only 0.5%, which can be related to the installation of sand fences to promote dune stabilization and sand removal from the front of the dune field to keep a road open to vehicles. ALS can cover large areas in little time but its high cost still remains a barrier to wider usage, especially by researchers in developing countries. TLS has an intermediate cost but demands more fieldwork and more processing time. In our case we needed three days for the TLS survey and around three weeks to produce a DEM of ≈80 400m 2 . On the other hand, we were able to cover ≈740 900m 2 with six flight missions in under three hours, with ≈13 hours processing time in a medium-range workstation. This makes SfM-MVS a low-cost solution with fast and reliable results for 3D modelling and continuous monitoring of coastal dunes.

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Migration of barchan dunes in the western Quruq Desert, northwestern China

Cited by 28 publications

References 43 publications

Progress and Trends in the Application of Google Earth and Google Earth Engine

Progress and Trends in the Application of Google Earth and Google Earth Engine

The role of subsidence and accommodation generation in controlling the nature of the aeolian stratigraphic record

Aeolian dune modelling from airborne LiDAR, terrestrial LiDAR and Structure from Motion-Multi View Stereo

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