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Advances in Remote Sensing to Understand Hydrological and Meteorological Extreme Events

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

Deadline for manuscript submissions: 15 March 2025 | Viewed by 4327

Special Issue Editor

Prof. Dr. Hatim Sharif

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: hydrometeorology; hydrologic modeling and forecasting; environmental applications of remote sensing; natural hazards; public health; water quality modeling; transportation safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrological and meteorological extremes, encompassing floods, droughts, heatwaves, and extreme precipitation events, represent substantial global threats. These events have devastating consequences, causing loss of life, widespread infrastructure damage, and significant economic losses and having disruptive ecological impacts. Recent advancements in remote sensing technology offer the opportunity to revolutionize our understanding and management of these extreme events. Novel platforms, cutting-edge techniques, and innovative data products are significantly improving our ability to achieve the following:

  • Observe hydrological and meteorological variables across vast geographical scales with unprecedented temporal resolution.
  • Develop and improve simulation and analysis models to investigate the intricate processes leading to the formation of extreme events.
  • Enhance forecasting capabilities across diverse spatial and temporal scales, paving the way for improved early warning systems.
  • Assess the multifaceted impacts of extreme events on ecosystems, infrastructure, and human populations.

This Special Issue aims to gather pioneering research that explores the latest applications of remote sensing in understanding and managing hydrological and meteorological extremes. We invite researchers from diverse disciplines, including hydrology, meteorology, remote sensing, and disaster management, to submit original research addressing, but not limited to, the following key themes:

  • The development and application of innovative remote sensing-based observation and modeling tools specifically tailored to studying extreme events.
  • The exploration of novel methodologies for identifying and characterizing extreme events utilizing the power of advanced remote sensing data.
  • The integration of remote sensing data with existing models to improve forecasting accuracy and lead time for extreme events.
  • The assessment of the diverse impacts of extreme events on aspects including infrastructure, ecosystems, and human well-being using remote sensing techniques.
  • The exploration of how remote sensing can be utilized in disaster risk management, response planning, and educational initiatives related to extreme events.

Prof. Dr. Hatim Sharif
Guest Editor

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

  • hydrometeorology
  • remote sensing
  • natural hazards
  • extreme events
  • hydrologic modelling and forecasting
  • public health
  • risk management
  • disaster mitigation
  • satellite
  • radar

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

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Research

Jump to: Review

26 pages, 23421 KiB  
Article
Assessment of Post-Fire Impacts on Vegetation Regeneration and Hydrological Processes in a Mediterranean Peri-Urban Catchment
by Evgenia Koltsida, Nikos Mamassis, Evangelos Baltas, Vassilis Andronis and Andreas Kallioras
Remote Sens. 2024, 16(24), 4745; https://doi.org/10.3390/rs16244745 - 19 Dec 2024
Viewed by 236
Abstract
This study aimed to evaluate the impact of a wildfire on vegetation recovery and hydrological processes in a Mediterranean peri-urban system, using remote sensing and hydrological modeling. NDVI and MSAVI2 time series extracted from burned areas, control plots, and VAR-modeled plots were [...] Read more.
This study aimed to evaluate the impact of a wildfire on vegetation recovery and hydrological processes in a Mediterranean peri-urban system, using remote sensing and hydrological modeling. NDVI and MSAVI2 time series extracted from burned areas, control plots, and VAR-modeled plots were used to analyze vegetation regeneration. The SWAT model, calibrated for pre-fire conditions due to data limitations, was used to evaluate subbasin-scale hydrological impacts. Results showed limited recovery in the first post-fire year, with vegetation indices remaining lower in burned areas compared to control plots. High- and moderate-burn-severity areas presented the most significant NDVI and MSAVI2 increases. The SWAT model showed increased water yield, percolation, and surface runoff with reduced evapotranspiration in post-fire conditions. Peak discharges were notably higher during wet periods. Modified land use and soil properties affected the catchment’s hydrological balance, emphasizing the complexities of post-fire catchment dynamics. Full article
(This article belongs to the Special Issue Advances in Remote Sensing to Understand Hydrological and Meteorological Extreme Events)
►▼ Show Figures

Figure 1

Figure 1
<p>Elevation of the study area (<b>a</b>), spatial distribution of land use (<b>b</b>), burn severity (<b>c</b>), and soil types (<b>d</b>). Zoomed-out display of Greece and Athens location in red box (<b>upper left</b>) and Athens metropolitan area (<b>lower left</b>). The study area includes 25 subbasins, of which the subbasin numbers 1, 2, 3, 4, 6, 11, 17, 18, 19, and 20 indicate the subbasins inside the burn scar.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> predictions for the period August 2021 to January 2023 without the influence of fire. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>S</mi> <mi>A</mi> <mi>V</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> predictions for the period August 2021 to January 2023 without the influence of fire. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest.</p> Full article ">Figure 4
<p>Mean <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> for the burned and control plots and VAR modeling results from August 2021 to August 2022. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest.</p> Full article ">Figure 5
<p>Mean <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>S</mi> <mi>A</mi> <mi>V</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> for the burned and control plots and VAR modeling results for August 2016–August 2022. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest.</p> Full article ">Figure 6
<p>Mean <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> by burn severity class, August 2016–August 2022. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest. All classes demonstrated consistently positive post-fire <math display="inline"><semantics> <mrow> <mi>N</mi> <mi>D</mi> <mi>V</mi> <mi>I</mi> </mrow> </semantics></math> gains in the first post-fire year.</p> Full article ">Figure 7
<p>Mean <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>S</mi> <mi>A</mi> <mi>V</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> by burn severity class, August 2016–August 2022. (<b>a</b>) AGRL, agriculture. (<b>b</b>) RNGB, shrubland. (<b>c</b>) UCOM, transportation/green areas. (<b>d</b>) FRSD, deciduous forest. (<b>e</b>) FRSE, evergreen forest. (<b>f</b>) FRST, mixed forest. All classes demonstrated consistently positive post-fire <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>M</mi> <mi>S</mi> <mi>A</mi> <mi>V</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> gains in the first post-fire year.</p> Full article ">Figure 8
<p>Simulated daily discharge results (<b>a</b>) and flow duration curves (<b>b</b>) (m<sup>3</sup> s<sup>−1</sup>) during the pre-fire and post-fire scenarios.</p> Full article ">Figure 9
<p>Simulated hourly discharge results (<b>a</b>) and flow duration curves (<b>b</b>) (m<sup>3</sup> s<sup>−</sup><sup>1</sup>) during the pre-fire and post-fire scenarios.</p> Full article ">Figure 10
<p>Maximum hourly discharge (m<sup>3</sup>s<sup>−1</sup>) of the rainfall events that occurred in the pre-fire and post-fire periods compared to maximum hourly rainfall intensity (mmh<sup>−1</sup>). The size of the circles is according to the peak discharge for the pre- and post-fire conditions.</p> Full article ">Figure 11
<p>Monthly values of the major hydrological components of the daily (<b>a</b>–<b>d</b>) model during the pre-fire and post-fire scenarios. SURQ: surface runoff (mm), PERC: percolation (mm), AET: actual evapotranspiration (mm), and WYLD: water yield (mm).</p> Full article ">Figure 12
<p>Monthly values of the major hydrological components of hourly (<b>a</b>–<b>d</b>) model during the pre-fire and post-fire scenarios. SURQ: surface runoff (mm), PERC: percolation (mm), AET: actual evapotranspiration (mm), and WYLD: water yield (mm).</p> Full article ">
17 pages, 7188 KiB  
Article
Spatial and Temporal Evolution of Precipitation in the Bahr el Ghazal River Basin, Africa
by Jinyu Meng, Zengchuan Dong, Guobin Fu, Shengnan Zhu, Yiqing Shao, Shujun Wu and Zhuozheng Li
Remote Sens. 2024, 16(9), 1638; https://doi.org/10.3390/rs16091638 - 3 May 2024
Cited by 2 | Viewed by 1642
Abstract
Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives [...] Read more.
Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention. Full article
(This article belongs to the Special Issue Advances in Remote Sensing to Understand Hydrological and Meteorological Extreme Events)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Geographic location of the Bahr el Ghazal Basin; (<b>b</b>) spatial distribution of climate types in the Bahr el Ghazal Basin; (<b>c</b>) elevation map of the Bahr el Ghazal Basin.</p> Full article ">Figure 2
<p>Total monthly precipitation and fitted monthly precipitation. (<b>a</b>) WAU; (<b>b</b>) MALAKAL.</p> Full article ">Figure 3
<p>Taylor diagram of precipitation information for three reanalysis datasets.</p> Full article ">Figure 4
<p>Average precipitation in the Bahr el Ghazal River Basin from CRU TS for the years 1961–2022.</p> Full article ">Figure 5
<p>Spatial distribution of multi-year monthly average precipitation in the Bahr el Ghazal River Basin from 1961 to 2022 using CRU TS precipitation data.</p> Full article ">Figure 6
<p>The annual precipitation series for different climatic zones in the Bahr el Ghazal River Basin from 1901 to 2022.</p> Full article ">Figure 7
<p>Monthly and seasonal precipitation processes for three climate zones, 1901–2022.</p> Full article ">Figure 8
<p>Spatial distribution of decade-average precipitation every ten years.</p> Full article ">

Review

Jump to: Research

22 pages, 3135 KiB  
Review
Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States
by Abhilash Dutta Roy, Daria Agnieszka Karpowicz, Ian Hendy, Stefanie M. Rog, Michael S. Watt, Ruth Reef, Eben North Broadbent, Emma F. Asbridge, Amare Gebrie, Tarig Ali and Midhun Mohan
Remote Sens. 2024, 16(19), 3596; https://doi.org/10.3390/rs16193596 - 26 Sep 2024
Cited by 2 | Viewed by 1909
Abstract
Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm [...] Read more.
Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article
(This article belongs to the Special Issue Advances in Remote Sensing to Understand Hydrological and Meteorological Extreme Events)
►▼ Show Figures

Figure 1

Figure 1
<p>PRISMA workflow representing the systematic literature review process.</p> Full article ">Figure 2
<p>Applications of remote sensing for studying impacts of hurricanes on mangroves.</p> Full article ">Figure 3
<p>Coastal southeastern United States showing some locations where studies on hurricane impact on mangroves were carried out, that included (<b>A</b>) Everglades National Park, Florida, (<b>B</b>) Florida Keys, (<b>C</b>) Port Fourchon, Louisiana, (<b>D</b>) (Inset): Puerto Rico.</p> Full article ">Figure 4
<p>The regional frequency of remote sensing based peer-reviewed articles published on studying impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p> Full article ">Figure 5
<p>Percentage breakdown of sensors used for studying impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p> Full article ">Figure 6
<p>Data analysis methods used to study the impacts of hurricanes on mangroves in the United States from January 2010 to September 2024.</p> Full article ">
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