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Remote Sensing
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Remote Sensing in Water Engineering and Management
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Remote Sensing in Water Engineering and Management

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

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 6493

Special Issue Editors

Prof. Dr. Armanda Rodrigues

E-Mail Website
Guest Editor
Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Quinta da torre, 2829-516 Caparica, Portugal
Interests: Web-GIS; Spatial Context; Geo-collaboration; Digital Heritage and Emergency Management.
Dr. Elsa Alves

E-Mail Website
Guest Editor
Laboratório Nacional de Engenharia Civil; Departamento de Hidráulica e Ambiente, LNEC, Av. Do Brasil, 101, 1700-066 Lisboa, Portugal
Interests: water resources; hydraulic structures; hydrologic extreme events; climate change impacts on water resources
Prof. Dr. Giorgio Baiamonte

E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, Viale delle Scienze, Bldg. 4, 90128 Palermo, Italy
Interests: water resources management; hydrological modeling; hydrology; soil science; rainfall; irrigation
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Minacapilli

E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo,viale delle Scienze, Bldg 4, 90128 Palermo, Italy
Interests: remote sensing; hydrology; agriculture; geographical information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The impact of climate change on the availability of water is a current and pressing issue in in certain areas of the Globe, affecting water engineering and resource management.  At the same time, Remote Sensing technology is currently widely used to monitor natural resources, and particularly water, with an exponential growth on the generation and availability of satellite-based information over the past decade. Additionally, software and hardware solutions for storage, interaction and access to remote sensing data are constantly evolving, with a particular emphasis on improving access through interoperability, modularity and standardization. Satellite data streams thus generate huge amounts of data, constantly growing, creating serious challenges in data series management and analysis. This can now be handled through Big Data Technology and with Artificial Intelligence tools and integrated in Geographic Information Systems, unleashed by GPUs, powerful graphical units capable of parallel processing of large amounts of high-resolution images.

This is the current technological setting, which must be taken advantage of by Water Engineering and Management researchers and practitioners, when addressing problems that require the use of remote sensing products.

The purpose of the proposed special issue is thus to present high impact novel research in Remote Sensing for Water Engineering and Management that takes advantage of this current technological setting, while addressing problems such as water-related hazards in urban, fluvial and coastal environments, coastal and watershed management, including erosion assessment, drought monitoring and ecosystem impacts, irrigation management, water leak detections, soil water content monitoring among others.

Prof. Dr. Armanda Rodrigues
Dr. Elsa Alves
Prof. Dr. Giorgio Baiamonte
Dr. Mario Minacapilli
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

  • Remote Sensing
  • Water Engineering
  • Water Management
  • Big Data
  • Climate Chanage

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 11675 KiB  
Article
Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning
by Chih-Hung Lee, Li-Wei Liu, Yu-Min Wang, Jan-Mou Leu and Chung-Ling Chen
Remote Sens. 2022, 14(14), 3343; https://doi.org/10.3390/rs14143343 - 11 Jul 2022
Cited by 13 | Viewed by 3311
Abstract
The river cross-section elevation data are an essential parameter for river engineering. However, due to the difficulty of mountainous river cross-section surveys, the existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed to establish [...] Read more.
The river cross-section elevation data are an essential parameter for river engineering. However, due to the difficulty of mountainous river cross-section surveys, the existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed to establish a model suitable for mountainous river areas utilizing an unmanned aerial vehicle (UAV) equipped with a multispectral camera and machine learning-based gene-expression programming (GEP) algorithm. The obtained images were combined with a total of 171 water depth measurements (0.01–1.53 m) for bathymetry modeling. The results show that the coefficient of determination (R2) of GEP is 0.801, the mean absolute error (MAE) is 0.154 m, and root mean square error (RMSE) is 0.195 m. The model performance of GEP model has increased by 16.3% in MAE, compared to conventional simple linear regression (REG) algorithm, and also has a lower bathymetry retrieval error both in shallow (<0.4 m) and deep waters (>0.8 m). The GEP bathymetry retrieval model has a considerable degree of accuracy and could be applied to shallow rivers or near-shore areas under similar conditions of this study. Full article
(This article belongs to the Special Issue Remote Sensing in Water Engineering and Management)
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16 pages, 4839 KiB  
Article
Thermographic Monitoring of Scum Accumulation beneath Floating Covers
by Yue Ma, Francis Rose, Leslie Wong, Benjamin Steven Vien, Thomas Kuen, Nik Rajic, Jayantha Kodikara and Wing Kong Chiu
Remote Sens. 2021, 13(23), 4857; https://doi.org/10.3390/rs13234857 - 30 Nov 2021
Cited by 4 | Viewed by 2337
Abstract
Large sheets of high-density polyethene geomembrane are used as floating covers on some of the wastewater treatment lagoons at the Melbourne Water Corporation’s Western Treatment Plant. These covers provide an airtight seal for the anaerobic digestion of sewage and allow for harvesting the [...] Read more.
Large sheets of high-density polyethene geomembrane are used as floating covers on some of the wastewater treatment lagoons at the Melbourne Water Corporation’s Western Treatment Plant. These covers provide an airtight seal for the anaerobic digestion of sewage and allow for harvesting the methane-rich biogas, which is then used to generate electricity. There is a potential for scum to develop under the covers during the anaerobic digestion of the raw sewage by microorganisms. Due to the nature of the operating environment of the lagoons and the vast size (450 m × 170 m) of these covers, a safe non-contact method to monitor the development and movement of the scum is preferred. This paper explores the potential of using a new thermographic approach to identify and monitor the scum under the covers. The approach exploits naturally occurring variations in solar intensity as a trigger for generating a transient thermal response that is then fitted to an exponential decay law to determine a cooling constant. This approach is investigated experimentally using a laboratory-scale test rig. A finite element (FE) model is constructed and shown to reliably predict the experimentally observed thermal transients and cooling constants. This FE model is then set up to simulate progressive scum accumulation with time, using a specified scumberg geometry and a stepwise change in thermal properties. The results indicate a detectable change in the cooling constant at different locations on the cover, thereby providing a quantitative basis for characterising the scum accumulation beneath the cover. The practical application and limitations of these results are briefly discussed. Full article
(This article belongs to the Special Issue Remote Sensing in Water Engineering and Management)
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