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Remote Sensing of Climate-Related Hazards
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Remote Sensing of Climate-Related Hazards

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 18897

Special Issue Editors

Dr. Raffaele Albano

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Potenza, Italy
Interests: floods; water resource management; soil moisture; hydrology; artificial intelligence; GIS; geomorphic method; DEM; geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios D. Alexakis

E-Mail Website
Guest Editor
Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: remote sensing; GIS; geomorphology; landscape ecology; landscape archaeology; soil erosion; land cover/land use change; natural hazards monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Ferentinou

E-Mail Website
Guest Editor
School of Civil Engineering and Built Environment, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: geohazard and risk assessment; landslide susceptibility assessment; slope stability; rock engineering systems; artificial intelligence and data mining techniques in geotechnics; monitoring of ground deformation
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Polykretis

E-Mail Website
Guest Editor
Lab of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch), Institute for Mediterranean Studies (IMS), Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: GIS; remote sensing; spatial analysis; (geo)statistical analysis; environmental modeling; natural hazard assessment; landslides; soil erosion; land use/land cover monitoring; social sciences; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing (RS) as well new observational modalities for local monitoring (unmanned aerial vehicles (UAVs), drones, distributed sensing), and their integration with in situ measurements where the main attention is given to the design/implementation/validation of effective integration strategies (data fusion, data correlation, data assimilation, modeling) have been proved to be powerful tools in monitoring, assessing and mapping change and rate of change in relation to hydrological hazards. This is particularly true in data-scarce environments, thanks to the great advantage of sensing extended areas at low cost and with regular revisit capability. Furthermore, they offer the opportunity to gain fresh insights into biophysical environments through the spatial, temporal, spectral and radiometric resolutions of remote sensing systems. They also allow for precursor identification, and the setting of alarms, under an early warning framework, which supports effective risk management and enhances future sustainability.

The main aim of this Special Issue is to present the recent advancements and range of applications in the fields of hazard monitoring and early warning, using remote sensing (active and passive sensors, Lidar, UAVs, thermal, etc.) for the successful assessment and management of climate-related hazards. In particular, this Special Issue intends to give the floor to novel studies and applications in the analysis of earth observation (EO) and other geospatial data and big data for the detection, monitoring, modeling and mapping of phenomena such as floods, landslides, soil erosion, droughts, etc. Water resources management, urban and cultural heritage management, and agriculture adaptation to address extreme conditions will be thoroughly discussed.

The purpose of this Special Issue is to provide an overview of the research advancements, scientific lessons learned, as well as operational issues and challenges in this rapidly evolving and expanding field. Case studies and other experiences are welcome as long as they are rigorously presented and evaluated. The contributions to this Special Issue will encompass a broad spectrum of topics in remote sensing and natural hazards including, but not limited to:

  • Innovative applications of remote sensing for rapid mapping;
  • Innovative applications of remote sensing for hazard, vulnerability, and risk monitoring and mapping;
  • Innovative applications in support of disaster risk reduction and adaptation strategies;
  • Integration of the data from satellite and airborne with ground-based measurements;
  • Field study cases and innovative operational services.

Dr. Raffaele Albano
Dr. Dimitrios D. Alexakis
Dr. Maria Ferentinou
Dr. Christos Polykretis
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

  • floods and droughts
  • natural hazards monitoring and mapping
  • soil erosion
  • landslides
  • risk management and assessment
  • adaptation and resilience strategies
  • operational services
  • UAVs and drones
  • earth observations
  • climate change
  • sustainability and sustainable development
  • early warning

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

Published Papers (7 papers)

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Research

15 pages, 2158 KiB  
Article
How Can Seasonality Influence the Performance of Recent Microwave Satellite Soil Moisture Products?
by Raffaele Albano, Teodosio Lacava, Arianna Mazzariello, Salvatore Manfreda, Jan Adamowski and Aurelia Sole
Remote Sens. 2024, 16(16), 3044; https://doi.org/10.3390/rs16163044 - 19 Aug 2024
Viewed by 723
Abstract
In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and [...] Read more.
In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient RALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (RALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10−16) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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20 pages, 18014 KiB  
Article
Outbreak Mechanism of Locust Plagues under Dynamic Drought and Flood Environments Based on Time Series Remote Sensing Data: Implication for Identifying Potential High-Risk Locust Areas
by Longlong Zhao, Hongzhong Li, Wenjiang Huang, Yingying Dong, Yun Geng, Huiqin Ma and Jinsong Chen
Remote Sens. 2023, 15(21), 5206; https://doi.org/10.3390/rs15215206 - 2 Nov 2023
Cited by 2 | Viewed by 2142
Abstract
Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is [...] Read more.
Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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23 pages, 22321 KiB  
Article
Land Subsidence Phenomena vs. Coastal Flood Hazard—The Cases of Messolonghi and Aitolikon (Greece)
by Nikolaos Antoniadis, Stavroula Alatza, Constantinos Loupasakis and Charalampos (Haris) Kontoes
Remote Sens. 2023, 15(8), 2112; https://doi.org/10.3390/rs15082112 - 17 Apr 2023
Cited by 7 | Viewed by 2728
Abstract
Land subsidence in coastal and delta cities often results in infrastructure and residential building damages, while also increasing the area’s flooding vulnerability. The coastal cities of Messolonghi and Aitolikon are typical examples, as they are built on top of old stream deposits near [...] Read more.
Land subsidence in coastal and delta cities often results in infrastructure and residential building damages, while also increasing the area’s flooding vulnerability. The coastal cities of Messolonghi and Aitolikon are typical examples, as they are built on top of old stream deposits near the coast. In the last several years, the gradual subsidence of the sites, combined with the impact of climate change, resulted in multiple floods. The rush of seawater over the lowlands has also been reported. Persistent scatterer interferometry (PSI) is a remote-sensing technique that can provide a reliable and cost-effective solution, as it can be used to identify and monitor soil displacements. In this study, a novel parallelized PSI (P-PSI) processing chain, developed by the Operational Unit Center for Earth Observation Research and Satellite Remote Sensing (BEYOND) of the National Observatory of Athens, as well as the Copernicus EGMS product were used to identify these displacements. The results were examined in correlation with other potential factors such as the overexploitation of the underground water, the natural compaction of the clay soil layers, the primary and secondary consolidation due to the external construction loading, the oxidation of the organic soils, tidal gauge data, precipitation data, and ground truth data. In Messolonghi, various deformation rates were recorded, with maximum mean values of −5 mm/year in the eastern part, whereas in Aitolikon, the maximum values were around −4.5 mm/year. The displacements were mostly attributed to the primary consolidation due to the building loads. Deformation patterns and their correlation with precipitation could also be witnessed. It was evident that the increased precipitation rates and sea level rise played a leading role in the constant flooding. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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21 pages, 31152 KiB  
Article
An Intercomparison of Sentinel-1 Based Change Detection Algorithms for Flood Mapping
by Mark Edwin Tupas, Florian Roth, Bernhard Bauer-Marschallinger and Wolfgang Wagner
Remote Sens. 2023, 15(5), 1200; https://doi.org/10.3390/rs15051200 - 22 Feb 2023
Cited by 15 | Viewed by 5123
Abstract
With its unrivaled and global land monitoring capability, the Sentinel-1 mission has been established as a prime provider in SAR-based flood mapping. Compared to suitable single-image flood algorithms, change-detection methods offer better robustness, retrieving flood extent from a classification of observed changes. This [...] Read more.
With its unrivaled and global land monitoring capability, the Sentinel-1 mission has been established as a prime provider in SAR-based flood mapping. Compared to suitable single-image flood algorithms, change-detection methods offer better robustness, retrieving flood extent from a classification of observed changes. This requires data-based parametrization. Moreover, in the scope of global and automatic flood services, the employed algorithms should not rely on locally optimized parameters, which cannot be automatically estimated and have spatially varying quality, impacting much on the mapping accuracy. Within the recently launched Global Flood Monitoring (GFM) service, we implemented a Bayes-Inference (BI)-based algorithm designed to meet these ends. However, whether other change detection algorithms perform similarly or better is unknown. This study examines four Sentinel-1 change detection models: The Normalized Difference Scattering Index (NDSI), Shannon’s entropy of NDSI (SNDSI), Standardized Residuals (SR), and Bayes Inference over Luzon in the Philippines, which was flood-hit by a typhoon in November 2020. After parametrization assessment against an expert-created Sentinel-1 flood map, the four models are inter-compared against an independent Sentinel-2 classification. The obtained findings indicate that the Bayes change detection profits from its scalable classification rules and shows the least sensitivity to parametrization choices while also performing best in terms of mapping accuracy. For all change detection models, a backscatter seasonality model for the no-flood reference delivered best results. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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16 pages, 7937 KiB  
Article
Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach
by Matías García, Hernán Alcayaga and Alonso Pizarro
Remote Sens. 2023, 15(5), 1170; https://doi.org/10.3390/rs15051170 - 21 Feb 2023
Cited by 4 | Viewed by 2774
Abstract
A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high [...] Read more.
A novel method is proposed to automatically segment water extent using optical data. The key features of this approach are (i) the development of a simple physically based model that utilises only RGB data for water extent segmentation; (ii) the achievement of high accuracy in the results, particularly in the estimation of water surface area and perimeter; (iii) the avoidance of any data training process; (iv) the requirement of minimal computational resources; and (v) the release of an open-source software package that provides both command-line codes and a user-friendly graphical interface, making it accessible for various applications, research, and educational purposes. The physically based model integrates reflectance of the water surface with spectral and quantum interpretation of light. The algorithm was tested on 27 rivers and compared to manually-based delimitation, with a resulting robust segmentation procedure. Quantified errors were RMSE = 11.91 (m2) for surface area, RMSE = 12.25 (m) for perimeter, and RMSE in x: 52 (px), RMSE in y: 93 (px) for centroid location. Processing time was faster for automatic segmentation than manual delimitation, with a time reduction of 40% (case-by-case analysis) and 65% (using all case studies together in one run). Shadows, light spots, and natural and non-natural elements in the field of view may affect the accuracy of results. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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19 pages, 15064 KiB  
Article
A Procedure for the Quantitative Comparison of Rainfall and DInSAR-Based Surface Displacement Time Series in Slow-Moving Landslides: A Case Study in Southern Italy
by Francesca Ardizzone, Stefano Luigi Gariano, Evelina Volpe, Loredana Antronico, Roberto Coscarelli, Michele Manunta and Alessandro Cesare Mondini
Remote Sens. 2023, 15(2), 320; https://doi.org/10.3390/rs15020320 - 5 Jan 2023
Cited by 3 | Viewed by 2012
Abstract
Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, [...] Read more.
Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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18 pages, 11544 KiB  
Article
Assessing the Optimal Stage-Cam Target for Continuous Water Level Monitoring in Ephemeral Streams: Experimental Evidence
by Flavia Tauro, Simone Noto, Gianluca Botter and Salvatore Grimaldi
Remote Sens. 2022, 14(23), 6064; https://doi.org/10.3390/rs14236064 - 30 Nov 2022
Cited by 5 | Viewed by 1594
Abstract
Recently, increased attention has been devoted to intermittent and ephemeral streams (IRES) due to the recognition of their importance for ecology, hydrology, and biogeochemistry. However, IRES dynamics still demand further research, and traditional monitoring approaches present several limitations in continuously and accurately capturing [...] Read more.
Recently, increased attention has been devoted to intermittent and ephemeral streams (IRES) due to the recognition of their importance for ecology, hydrology, and biogeochemistry. However, IRES dynamics still demand further research, and traditional monitoring approaches present several limitations in continuously and accurately capturing river network expansion/contraction. Optical-based approaches have shown promise in noninvasively estimating the water level in intermittent streams: a simple setup made up of a wildlife camera and a reference white pole led to estimations within 2cm of accuracy in severe hydrometeorological conditions. In this work, we investigate whether the shortcomings imposed by adverse illumination can be partially mitigated by modifying this simple stage-cam setup. Namely, we estimate the image-based water level by using both the pole and a larger white bar. Further, we compare such results to those obtained with larger bars painted in the red, green, and blue primary colors. Our findings show that using larger white bars also increases reflections and, therefore, the accuracy in the estimation of the water level is not necessarily enhanced. Likewise, experimenting with colored bars does not significantly improve image-based estimations of the stage. Therefore, this work confirms that a simple stage-cam setup may be sufficient to monitor IRES dynamics, suggesting that future efforts may be rather focused on including filters and polarizers in the camera as well as on improving the performance of the image processing algorithm. Full article
(This article belongs to the Special Issue Remote Sensing of Climate-Related Hazards)
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