Special Issue "Remote Sensing for Disaster Risk Management"

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 2020.

Special Issue Editors

Dr. Andrea Ciampalini
Website
Guest Editor
Department of Earth Sciences, University of Pisa, Via Santa Maria 53, Pisa, Italy
Interests: remote sensing data interpretation; geohazard monitoring; landslide mapping; building monitoring; land subsidence
Special Issues and Collections in MDPI journals
Dr. William Frodella
Website
Guest Editor
UNESCO Chair on Prevention and Sustainable Management of Geo-Hydrological Hazards, University of Firenze, Via G. La Pira 4, Italy
Interests: landslide remote sensing; infrared thermography; natural hazards; geomorphological mapping; radar interferometric data interpretation; cultural heritage protection
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

There is a growing need for the assessment and reduction of disaster risk. Disasters can be generated both by natural and human activities and represent extreme environmental events that can negatively impact both human and natural systems.

Space-borne images for civil applications have been routinely acquired since the 1980s (Landsat and SPOT), while more recently, the European Union’s Copernicus project has been launched. With its seven satellite missions, the Copernicus Sentinels acquire radar and spectral images for Earth, ocean, and atmosphere observation purposes. In particular, the Copernicus project can provide remotely sensed information regarding floods, forest fires, and droughts. In general, remote sensing data from space, but also from airborne or drone platforms, can be profitably used to manage other kinds of risks, from geo-hydrological to volcanic, from seismic to anthropogenic. Remote sensing can play a key role in managing risks, leading to a new level of understanding of the complex solid Earth and ocean processes which often lead to natural or man-made disasters. In recent decades, satellite-based observations and the derived geospatial products have been successfully demonstrated to be highly valuable tools in each different phase of the risk management (forecasting, planning, emergency, and post-emergency). For example, synthetic aperture radar (SAR) can facilitate risk management since they are also acquired through dense cloud cover and in both night and day conditions. This ability can help during the emergency phase. Stacks of SAR data can be used to detect subtle ground deformation induced by slow movement phenomena (e.g., slow landslides, subsidence) that can dangerously evolve, involving elements of risk. On the other hand, optical images are fundamental products to monitor land cover changes induced by several hazards (e.g., fast landslides, volcanic eruptions). These data are routinely used to map and evaluate the element at risk scattered over wide areas.

This Special Issue will collect manuscripts focused on new methodologies or applications of well-known remote sensing techniques in the field of risk management. Examples of areas covered within this research topic include, but are not restricted to, the following:

  • Use of RS data to forecast, map, and monitor geo-hydrological hazards with particular regards to landslides and floods
  • Use of RS data to manage the risks in areas affected by forest-fire, drought, coastal erosion, eruptive events, or earthquakes/tsunamis
  • Use of RS data to manage anthropogenic risks (pollution, oil spillage, etc.)
  • Use of RS data to promote the development of innovative technologies for the prevention and mitigation of geo-hydrological hazards
  • Use of RS data to develop tools and procedures for supporting risk reduction policies and emergency management for the safety of human life
  • Use of RS data to enhance the resilience (disaster response preparedness, building the resilience of megacities and rural communities)
  • Use of RS data to promote best practices of risk mitigation in cultural heritage sites
Dr. Andrea Ciampalini
Dr. William Frodella
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 papers will be 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 2200 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

  • Risk management 
  • Natural and anthropogenic hazards 
  • Optical images 
  • SAR data 
  • Remote sensing techniques 
  • Interferometry
  • Landslides
  • Volcanoes
  • Earthquake 
  • Floods 
  • Monitoring

Published Papers (4 papers)

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Research

Open AccessArticle
Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping
Remote Sens. 2020, 12(9), 1486; https://doi.org/10.3390/rs12091486 - 07 May 2020
Cited by 1
Abstract
Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. [...] Read more.
Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures. Full article
(This article belongs to the Special Issue Remote Sensing for Disaster Risk Management)
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Open AccessFeature PaperArticle
Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry
Remote Sens. 2020, 12(9), 1462; https://doi.org/10.3390/rs12091462 - 05 May 2020
Abstract
Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as [...] Read more.
Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method—here named the vector inclination method (VIM)—to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model. Full article
(This article belongs to the Special Issue Remote Sensing for Disaster Risk Management)
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Open AccessArticle
Environmental Aftermath of the 2019 Stromboli Eruption
Remote Sens. 2020, 12(6), 994; https://doi.org/10.3390/rs12060994 - 19 Mar 2020
Cited by 1
Abstract
This study focuses on the July-August 2019 eruption-induced wildfires at the Stromboli island (Italy). The analysis of land cover (LC) and land use (LU) changes has been crucial to describe the environmental impacts concerning endemic vegetation loss, damages to agricultural heritage, and transformations [...] Read more.
This study focuses on the July-August 2019 eruption-induced wildfires at the Stromboli island (Italy). The analysis of land cover (LC) and land use (LU) changes has been crucial to describe the environmental impacts concerning endemic vegetation loss, damages to agricultural heritage, and transformations to landscape patterns. Moreover, a survey was useful to collect eyewitness accounts aimed to define the LU and to obtain detailed information about eruption-induced damages. Detection of burnt areas was based on PLÉIADES-1 and Sentinel-2 satellite imagery, and field surveys. Normalized Burn Ratio (NBR) and Relativized Burn Ratio (RBR) allowed mapping areas impacted by fires. LC and LU classification involved the detection of new classes, following the environmental units of landscape, being the result of the intersection between CORINE Land Cover project (CLC) and local landscape patterns. The results of multi-temporal comparison show that fire-damaged areas amount to 39% of the total area of the island, mainly affecting agricultural and semi-natural vegetated areas, being composed by endemic Aeolian species and abandoned olive trees that were cultivated by exploiting terraces up to high altitudes. LC and LU analysis has shown the strong correlation between land use management, wildfire severity, and eruption-induced damages on the island. Full article
(This article belongs to the Special Issue Remote Sensing for Disaster Risk Management)
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Open AccessFeature PaperArticle
Catching Geomorphological Response to Volcanic Activity on Steep Slope Volcanoes Using Multi-Platform Remote Sensing
Remote Sens. 2020, 12(3), 438; https://doi.org/10.3390/rs12030438 - 30 Jan 2020
Cited by 3
Abstract
The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed [...] Read more.
The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco. Full article
(This article belongs to the Special Issue Remote Sensing for Disaster Risk Management)
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