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Remote Sensing for Hydrological Monitoring and Disaster Risk Assessment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (9 November 2023) | Viewed by 1560

Special Issue Editors


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Guest Editor
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: hydrological modelling; water environment modelling; remote sensing; water resources satellite

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research(IWHR), Beijing 100038, China
Interests: water resources remote sensing; disaster remote sensing; remote sensing data processing

Special Issue Information

Dear Colleagues:

It is challenging to manage water resources and related disasters, particularly in ungauged areas. Remote sensing has the advantages of large area coverage and relatively inexpensive and easy collection, helping to compensate for the deficiencies of the traditional monitoring system. Therefore, promoting the theoretical basis and practical application of remote sensing in hydrological monitoring and disaster risk assessment is urgent and necessary.

This Special Issue focuses on the identification, evaluation, and prediction of hydrological variables and water-related disasters, with a special focus on (i) the acquisition and decoding of high-resolution remote sensing data for a variety of hydrologic variables (e.g., water level, flow rate, and velocity); (ii) the correction technology of remote sensing data, the identification of flood disasters (e.g., inundated area, disaster-bearing body distribution, and disaster-resistant ability) for risk assessment; and (iii) new satellites or satellites constellation plans for studying water resources and disasters.

In this Special Issue, we aim to leverage the modernization of hydrological monitoring and the ability to acquire data based on remote sensing, facilitating the evaluation and prediction of hydrological processes and related disasters.

We welcome original research articles and reviews including (but not limited to) the following topics:

  1. Production of high-resolution water-related remote sensing data.
  2. Inversion method of hydrological variables based on remote sensing.
  3. Method of identifying hydrological variables and water-related disasters.
  4. Correction technology of hydrological remote sensing.
  5. Real-time prediction of hydrological variables and disasters based on remote sensing.
  6. Assessment of water resources and disaster risk based on remote sensing.
  7. New satellites or satellite constellation plans for water resources and disasters.

We look forward to receiving your contributions.

Dr. Yanjun Zhang
Prof. Dr. Zhiguo Pang
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. Sustainability 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 2400 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
  • hydrological remote sensing
  • disaster remote sensing
  • water resources satellite
  • real-time monitoring
  • disaster risk assessment
  • water resources assessment
  • satellites constellation plans

Published Papers (1 paper)

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Research

15 pages, 5572 KiB  
Article
Comparison of Ratioing and RCNA Methods in the Detection of Flooded Areas Using Sentinel 2 Imagery (Case Study: Tulun, Russia)
by Helena Maria Fernandez, Fernando Granja-Martins, Olga Dziuba, David A. B. Pereira and Jorge M. G. P. Isidoro
Sustainability 2023, 15(13), 10233; https://doi.org/10.3390/su151310233 - 28 Jun 2023
Viewed by 1089
Abstract
Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. [...] Read more.
Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes. Full article
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