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Recent Advances in Water and Wetland Studies with Remote Sensing...
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Recent Advances in Water and Wetland Studies with Remote Sensing Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 4657

Special Issue Editors

Dr. Zhenhua Zou

E-Mail Website
Guest Editor
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: water mapping and change analysis; wetland mapping and change analysis; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Zhang

E-Mail Website
Guest Editor
School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK
Interests: cloud computing; machine learning; remote sensing

Special Issue Information

Dear Colleagues,

Water and wetland ecosystems not only provide valuable water resources that all lives depend on but also serve as home to various precious animal and plant species. However, water and wetlands security is threatened by natural drivers and anthropogenic factors. Climate change and global warming have altered the patterns of precipitation and evaporation, changing water availability and causing severe droughts and floods. The increase in the global population has expedited the exploitation of wetlands for crop cultivation and urban expansion. Multisource remote sensing data (e.g., optical, SAR, and Lidar) and various approaches (e.g., machine learning, deep learning, and cloud computing) were used to map the extent of water and wetlands, analyze their changes, and evaluate water and wetlands security.

This Special Issue aims at studies covering different uses of remote sensing data and techniques in water and wetlands sciences. The potential topics include but are not limited to:

  • Novel uses of various remote sensing data (e.g., UAV, Sentinel, Landsat, SAR, Lidar, GRACE) in water and wetland studies;
  • Novel applications of advanced approaches (e.g., big data, machine learning, deep learning, and cloud computing) in water and wetland studies;
  • Water and wetland mapping, change analysis, and security evaluation
  • Droughts, floods, ground water depletion, wetland reclamation, etc.

Dr. Zhenhua Zou
Dr. Xin Zhang
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

  • wetland security
  • wetland mapping and change analysis
  • water security
  • water mapping and change analysis
  • droughts and floods
  • land cover/change
  • multisource remote sensing
  • synthetic aperture radar
  • machine learning and deep learning
  • google earth engine

Published Papers (3 papers)

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Research

20 pages, 8518 KiB  
Article
The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes
by Zhenru Ma, Weizhe Chen, Anguo Xiao and Rui Zhang
Remote Sens. 2023, 15(18), 4534; https://doi.org/10.3390/rs15184534 - 14 Sep 2023
Cited by 2 | Viewed by 903
Abstract
Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted in wetland loss in the middle and lower reaches of the Yangtze River Basin (MLYRB), while the wetland distribution in this area shows great discrepancy in [...] Read more.
Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted in wetland loss in the middle and lower reaches of the Yangtze River Basin (MLYRB), while the wetland distribution in this area shows great discrepancy in previous estimates. It is, therefore, imperative to estimate the distribution of potential wetlands at present and project their variation under future climate change scenarios. In this study, we simulate the wetland distribution in the MLYRB at 15″ resolution using 5 machine learning methods with 19 predicting factors of topographic index, vegetation index, climate data, hydrological data, and soil type data. A 5-fold cross-validation with observed permanent wetlands shows that the reconstructions from Adaptive Boosting tree (AdaBoost) algorithm have the highest accuracy of 97.5%. The potential wetland area in the MLYRB is approximately ~1.25 × 105 km2, accounting for 15.66% of the study region. Direct human activities have led to the loss of nearly half of the potential wetlands. Furthermore, sensitivity experiments with the well-trained models are performed to quantify the response of the total wetland area to each influencing factor. Results indicate vulnerability of wetland areas to increases in leaf area index (LAI), coldest season temperature, warmest season temperature, and solar radiation. By the 2100s, the potential wetland area is expected to decrease by 40.5% and 50.6% under the intermediate and very high emissions scenarios, respectively. The changes in LAI and the coldest season temperature will contribute to 50% and 40% of this loss of potential wetlands, respectively. Wetland loss may further undermine biodiversity, such as waterfowl, and fail to provide functions such as flood protection, and water supply. This work reveals the spatial pattern of potential wetland areas and their sensitivity to climate changes, stressing the need for effective strategies to mitigate wetland loss at specific regions in the MLYRB. Full article
(This article belongs to the Special Issue Recent Advances in Water and Wetland Studies with Remote Sensing Techniques)
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18 pages, 6849 KiB  
Article
Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region
by Zhenhua Zou, Chengquan Huang, Megan W. Lang, Ling Du, Greg McCarty, Jeffrey C. Ingebritsen, Nate Herold, Rusty Griffin, Weishu Gong and Jiaming Lu
Remote Sens. 2023, 15(16), 4075; https://doi.org/10.3390/rs15164075 - 18 Aug 2023
Viewed by 880
Abstract
The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and [...] Read more.
The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance. Full article
(This article belongs to the Special Issue Recent Advances in Water and Wetland Studies with Remote Sensing Techniques)
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22 pages, 8171 KiB  
Article
A Remote Sensing View of the 2020 Extreme Lake-Expansion Flood Event into the Peace–Athabasca Delta Floodplain—Implications for the Future SWOT Mission
by Nicolas M. Desrochers, Daniel L. Peters, Gabriela Siles, Elizabeth Cauvier Charest, Mélanie Trudel and Robert Leconte
Remote Sens. 2023, 15(5), 1278; https://doi.org/10.3390/rs15051278 - 25 Feb 2023
Cited by 4 | Viewed by 2069
Abstract
The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of [...] Read more.
The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors. Full article
(This article belongs to the Special Issue Recent Advances in Water and Wetland Studies with Remote Sensing Techniques)
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