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Remote Sensing of Dryland River Systems
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Remote Sensing of Dryland River Systems

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 14191

Special Issue Editors

Dr. Jiaguang Li

E-Mail Website
Guest Editor
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Interests: flood mapping; channel morphology; dryland river hydrology; land cover and land use
Special Issues, Collections and Topics in MDPI journals
Dr. Qiusheng Wu

E-Mail Website1 Website2
Guest Editor
Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: remote sensing; GIS; wetland hydrology; LiDAR; google earth engine
Special Issues, Collections and Topics in MDPI journals
Dr. Laurence Hawker

E-Mail Website
Guest Editor
School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Interests: digital elevation model; TanDEM-X; river systems; hydrodynamic modelling
Prof. Stephen Tooth

E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK
Interests: geomorphology of global dryland rivers; floodplains and floodouts; wetlands in dryland; channel–vegetation interactions; bedrock-influenced rivers

Special Issue Information

Dear Colleagues,

Dryland river systems are the subject of growing research attention owing to their scientific importance and significance for environmental management. Drylands (dry subhumid through hyperarid environments) cover 40%–50% of the Earth’s land surface and host ~28% of the world’s population. Dryland rivers are fundamentally important for delivering provisioning, regulating, and supporting cultural ecosystem services in these moisture-stressed regions. Additionally, sparsely or non-vegetated dryland river systems are receiving increasing research attention, as they provide excellent modern analogues for the study of ancient (especially pre-vegetation) rock records and extra-terrestrial surface environments.

However, because of the notorious difficulties of access in remote drylands, particularly during peak floods, the direct observations or long-term monitoring of dryland rivers are commonly impractical or costly. Remote sensing techniques therefore provide valuable opportunities for investigating these rivers, not only in real-time, but also by enabling repeat observations over the long term.

Recently, the increasing availability of remote sensing datasets (e.g., passive or active, and spaceborne or airborne) have significantly enriched data pools, and powerful new analytical methods have been developed to characterize dryland river systems with increasing resolution and accuracy.

This Special Issue will demonstrate how advances in remote sensing, including different datasets from different platforms and new analytical methods, are contributing to a better understanding of dryland river systems, particularly their geomorphology, topography, and associated flood dynamics.

Dr. Jiaguang Li
Dr. Qiusheng Wu
Dr. Laurence Hawker
Prof. Stephen Tooth
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Channel change detection
  • Channel form quantification
  • Flood extent mapping
  • Topographic characterization
  • Integration of remote sensing in hydrodynamic modelling

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Published Papers (4 papers)

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Research

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20 pages, 14605 KiB  
Article
Modern Dryland Source-to-Sink System Segments and Coupling Relationships from Digital Elevation Model Analysis: A Case Study from the Mongolian Altai
by Zhiwei Zeng and Hongtao Zhu
Remote Sens. 2022, 14(5), 1202; https://doi.org/10.3390/rs14051202 - 28 Feb 2022
Cited by 2 | Viewed by 3796
Abstract
Source-to-sink (S2S) systems have represented a major area of research in recent years; however, few modern S2S system analyses have been applied to typical dryland uplifts/mountains. A modern lacustrine sedimentary system is widely developed in the Great Lakes Basin of western Mongolia, and [...] Read more.
Source-to-sink (S2S) systems have represented a major area of research in recent years; however, few modern S2S system analyses have been applied to typical dryland uplifts/mountains. A modern lacustrine sedimentary system is widely developed in the Great Lakes Basin of western Mongolia, and the Jargalant Nuruu in the Mongolian Altai is a suitable natural laboratory for modern dryland S2S system analysis. In this study, the multi-order S2S system of the Jargalant Nuruu was applied based on a digital elevation model (DEM) and Google Earth database analysis. The Jargalant Nuruu system is subdivided into three second-order sub-S2S systems of the eastern, western, and southern parts (S2S-E, S2S-W, and S2S-S, respectively) and 35 third-order sub-S2S systems (E1–E18, W1–W9, and S1–S8) according to the slope gradients, altitude, and hydrographic net of the Jargalant Nuruu recognized by DEM data, integrated with the quantitative recognition of the topographic drainage divide and structural patterns of the uplift margin. The three second-order S2S systems correspond to three various S2S system coupling models. The S2S-E is characterized by a steep slope gradient system (average 15.61°) with small-scale dominantly alluvial fan deposits (average 4.56 km2). S2S-W is represented by a gentle slope gradient system (average 10.24°) with large-scale dominated fan-shaped lobes (average 30.04 km2). S2S-S, in contrast, is a transformation zone system with transitional features between the two former types. Four major potential controlling factors for the difference in sub-S2S systems are summarized here, including tectonic activity, bedrock properties in the source area, morphology from source to sink, and climatic conditions. The landforms, sedimentary characteristics, and their differences in these sub-S2S systems are the result of the comprehensive influence and control of these multiple factors. This case study could serve as a useful reference for characterizing the sedimentary features of a modern or even ancient S2S system in other regions. Full article
(This article belongs to the Special Issue Remote Sensing of Dryland River Systems)
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21 pages, 7913 KiB  
Article
Variations in Channel Centerline Migration Rate and Intensity of a Braided Reach in the Lower Yellow River
by Junqiang Xia, Yingzhen Wang, Meirong Zhou, Shanshan Deng, Zhiwei Li and Zenghui Wang
Remote Sens. 2021, 13(9), 1680; https://doi.org/10.3390/rs13091680 - 27 Apr 2021
Cited by 17 | Viewed by 2942
Abstract
The Yellow River (YR) covers three climatic zones including arid region, semi-arid region and temperate monsoon region, with frequent appearance of flow intermittence in the Lower Yellow River (LYR) before 1999. Channel migration occurs frequently in braided rivers, which is a major focus [...] Read more.
The Yellow River (YR) covers three climatic zones including arid region, semi-arid region and temperate monsoon region, with frequent appearance of flow intermittence in the Lower Yellow River (LYR) before 1999. Channel migration occurs frequently in braided rivers, which is a major focus of study in geomorphology and river dynamics. The braided reach in the LYR is featured by a complexly spatio-temporal variation in channel migration parameters owing to the varying condition of flow and sediment. It is crucial to investigate the migration characteristics of channel centerline for the sake of fully understanding channel evolution. A detailed calculation procedure is proposed to quantify migration rates and intensities of channel centerline at section- and reach-scales, using the measurements of remote sensing images and cross-sectional topography. Migration rates and intensities of channel centerline at section- and reach-scales from 1986 to 2016 were calculated, with the characteristics and key factors to control the migration intensity of channel centerline being identified quantitatively. Calculated results indicate that: (i) the mean probability of centerline migrating toward the left side was approximately equal to the probability of rightward migration from a long-term sequence; (ii) the mean reach-scale migration rate of channel centerline was reduced from 410 m/yr in 1986–1999 to 185 m/yr in 1999–2016, with a reduction of 55% owing to the Xiaolangdi Reservoir operation in 1999, and the mean reach-scale migration intensity of channel centerline was decreased from 0.28 to 0.16 m/(yr·m), with a reduction of 43%; (iii) the incoming flow-sediment regime was a dominant factor affecting the degree of channel migration, although the channel boundary conditions could influence the intensity of channel migration; and (iv) the reach-scale migration intensity of channel centerline can be written as a power function of the previous two-year average incoming sediment coefficient or fluvial erosion intensity, and the reach-scale migration intensities of channel centerline calculated using the proposed relations are generally in close agreement with the measurements over the period of 30 years. Full article
(This article belongs to the Special Issue Remote Sensing of Dryland River Systems)
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15 pages, 6460 KiB  
Article
New Understanding of Bar Top Hollows in Dryland Sandy Braided Rivers from Outcrops with Unmanned Aerial Vehicle and Ground Penetrating Radar Surveys
by Xianguo Zhang, Chengyan Lin, Tao Zhang, Daowu Huang, Derong Huang and Shanwei Liu
Remote Sens. 2021, 13(4), 560; https://doi.org/10.3390/rs13040560 - 4 Feb 2021
Cited by 6 | Viewed by 3041
Abstract
Bar top hollows (BTHs) are morphological elements recognized in modern braided rivers; however, information regarding their depositional features and types of filling units in ancient strata is unclear. This is an important reason behind why it is difficult to identify BTH units in [...] Read more.
Bar top hollows (BTHs) are morphological elements recognized in modern braided rivers; however, information regarding their depositional features and types of filling units in ancient strata is unclear. This is an important reason behind why it is difficult to identify BTH units in subsurface reservoirs. A Middle Jurassic dryland sandy braided river outcrop in northwestern China is characterized in this study through the application of an unmanned aerial vehicle (UAV) surveying and mapping, and ground penetrating radar (GPR). A workflow of UAV data processing has been established, and a 3D digital outcrop model has been built. By observation and measurement of the outcrop model and GPR profiles, two types of BTH filled units were found: (a) sandstone-filled, and (b) mudstone-filled hollows. Both of these units were located between two adjacent bar units in an area that is limited to a compound bar, and were developed in the upper part of a braided bar depositional sequence. The ellipse-shaped, sandstone-filled unit measures 10 m × 27 m in map view and reaches a maximum thickness of 5 m. The transversal cross-section across the BTHs displays a concave upward basal surface, while the angle of the inclined structures infilling the BTHs decreases up-section. The GPR data show that, in the longitudinal profile, the basal surface is relatively flat, and low-angle, inclined layers can be observed in the lower- and middle part of the sandstone-filled BTHs. In contrast, no obvious depositional structures were observed in the mudstone-filled BTH in outcrop. The new understanding of BTH has a wide application, including the optimization of CO2 storage sites, fresh water aquifers exploration, and oil and gas reservoir characterization. Full article
(This article belongs to the Special Issue Remote Sensing of Dryland River Systems)
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16 pages, 59920 KiB  
Technical Note
Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica
by Mary C. Barlow, Xinxiang Zhu and Craig L. Glennie
Remote Sens. 2022, 14(1), 234; https://doi.org/10.3390/rs14010234 - 5 Jan 2022
Cited by 2 | Viewed by 2800
Abstract
Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to [...] Read more.
Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to locate because they have little hydraulic flow throughout the short summer months. This paper utilizes a U-Net CNN to map stream boundaries from lidar derived rasters in Taylor Valley located within the MDVs, covering ∼770 km2. The training dataset consists of 217 (300 × 300 m2) well-distributed tiles of manually classified stream boundaries with diverse geometries (straight, sinuous, meandering, and braided) throughout the valley. The U-Net CNN is trained on elevation, slope, lidar intensity returns, and flow accumulation rasters. These features were used for detection of stream boundaries by providing potential topographic cues such as inflection points at stream boundaries and reflective properties of streams such as linear patterns of wetted soil, water, or ice. Various combinations of these features were analyzed based on performance. The test set performance revealed that elevation and slope had the highest performance of the feature combinations. The test set performance analysis revealed that the CNN model trained with elevation independently received a precision, recall, and F1 score of 0.94±0.05, 0.95±0.04, and 0.94±0.04 respectively, while slope received 0.96±0.03, 0.93±0.04, and 0.94±0.04, respectively. The performance of the test set revealed higher stream boundary prediction accuracies along the coast, while inland performance varied. Meandering streams had the highest stream boundary prediction performance on the test set compared to the other stream geometries tested here because meandering streams are further evolved and have more distinguishable breaks in slope, indicating stream boundaries. These methods provide a novel approach for mapping stream boundaries semi-automatically in complex regions such as hyper-arid environments over larger scales than is possible for current methods. Full article
(This article belongs to the Special Issue Remote Sensing of Dryland River Systems)
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