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Study on Hydrological Hazards Based on Multi-source Remote Sensing
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Study on Hydrological Hazards Based on Multi-source Remote Sensing

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2130

Special Issue Editors

Dr. Jun Zhang

E-Mail Website
Guest Editor
Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Interests: hydrological modeling; land surface modeling; remote sensing; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Shaonan Zhu

E-Mail Website
Guest Editor
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: spatio-temporal data modelling; social computing; urban hydrology; flood exposure and vulnerability assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Dai

E-Mail Website
Guest Editor
Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Interests: urban hydrology; radar hydrology; precipitation remote sensing; multi-hazards; weather forecasting; geographical information science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Amidst intensifying global concern over the growing prevalence of hydrological hazards, our Special Issue aims to offer a cutting-edge exploration of this dynamic field. Drawing upon the prowess of multi-source remote sensing techniques, we aim to provide a comprehensive overview of the current research status and prospects in this domain. The current research landscape in this area is rich and diverse, leveraging advancements in satellite imagery, unmanned aerial vehicles (UAVs), and ground-based sensors. These multi-source remote sensing platforms offer unprecedented spatial and temporal resolutions, enabling us to monitor, assess, and predict hydrological hazards with unprecedented accuracy.

This Special Issue aims to gather leading researchers from the fields of remote sensing, hydrology, geospatial analysis, and disaster management to share their expertise and insights. By promoting interdisciplinary collaborations, we aim to further advance the scientific understanding of hydrological hazards and develop more effective management strategies. We believe that this Special Issue will serve as a valuable resource for researchers seeking to address the pressing challenge of hydrological hazards in today's changing climate. We invite you to contribute original research, reviews, and case studies to this endeavour.

Dr. Jun Zhang
Dr. Shaonan Zhu
Dr. Qiang Dai
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

  • multi-source remote sensing
  • hydrological hazards
  • flood monitoring
  • drought assessment
  • landslide risk
  • remote sensing data fusion
  • machine learning
  • climate change

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Published Papers (2 papers)

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Research

19 pages, 19608 KiB  
Article
Identifying the Characteristics and Implications of Post-Earthquake Debris Flow Events Based on Multi-Source Remote Sensing Images
by Wen Jin, Guotao Zhang, Yi Ding, Nanjiang Liu and Xiaowei Huo
Remote Sens. 2024, 16(17), 3336; https://doi.org/10.3390/rs16173336 - 8 Sep 2024
Viewed by 878
Abstract
Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris [...] Read more.
Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris flow events, as well as to explore their long-term development and impact on internal and external channels. Using multi-source remote sensing images from four perspectives, hillslope, channel, accumulation fan, and their relationship with the mainstream, we reconstructed a debris flow event dataset from 2008 to 2020, explored a method for identifying these events, and analyzed their impacts on channels and accumulation fans in Mozi Gully affected by the Wenchuan earthquake. Loose matter was predominantly found in areas proximate to the channel and at lower elevations during debris flow events. Alterations in channel width, accumulation fans downstream, and their potential to obstruct rivers proved to be vital for identifying the large scale of debris flow event. Finally, we encapsulated the evolution patterns and constraints of post-earthquake debris flows. Determination in frequency and scale could offer valuable supplementary data for scenario hypothesis parameters in post-earthquake disaster engineering prevention and control. Full article
(This article belongs to the Special Issue Study on Hydrological Hazards Based on Multi-source Remote Sensing)
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18 pages, 2209 KiB  
Article
Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing
by Tongxiao Zeng, Jun Zhang, Yulin Chen and Shaonan Zhu
Remote Sens. 2024, 16(16), 3089; https://doi.org/10.3390/rs16163089 - 22 Aug 2024
Viewed by 794
Abstract
With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for [...] Read more.
With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for regional analysis. In this study, spatiotemporal information on landslides was extracted using multiple remote sensing data from Emilia, Italy. An automated algorithm for extracting spatial information of landslides was developed with NDVI datasets. Then, we established a landslide prediction model based on a hydrometeorological threshold of three-day soil moisture and three-day accumulated rainfall. Based on this model, the locations and dates of rainfall-induced landslides were identified. Then, we further matched these identified locations with the extracted landslides from remote sensing data and finally determined the occurrence time. This approach was validated with recorded landslides events in Emilia. Despite some temporal clustering, the overall trend matched historical records, accurately reflecting the dynamic impacts of rainfall and soil moisture on landslides. The temporal bias for 87.3% of identified landslides was within seven days. Furthermore, higher rainfall magnitude was associated with better temporal accuracy, validating the effectiveness of the model and the reliability of rainfall as a landslide predictor. Full article
(This article belongs to the Special Issue Study on Hydrological Hazards Based on Multi-source Remote Sensing)
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