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Remote Sensing for Multifaceted Disaster and Cascading Disasters

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 1754

Special Issue Editors

Dr. Sérgio Cruz de Oliveira
E-Mail Website
Guest Editor
1. Centre of Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276 Lisbon, Portugal
2. Associated Laboratory TERRA, 1349-017 Lisbon, Portugal
Interests: slope instability; early warning systems; natural hazards; vulnerability and risk assessment; applied geomorphology and spatial planning; coastal erosion
Special Issues, Collections and Topics in MDPI journals
Associated Laboratory Terra, Centre for Geographical Studies, Institute of Geography and Spatial Planning, University of Lisbon, 1600-276 Lisbon, Portugal
Interests: slope instability; natural hazards and risks assessment; applied geomorphology; spatial planning; cartography and GIS
Institute of Geography and Spatial Planning, Universidade de Lisboa, 1649-004 Lisbon, Portugal
Interests: geosimulation; geocomputation; artificial neural networks; graphs theory; cellular automata; multi-agent systems; urban morphology; remote sensing; epidemiology; health geography; geomarketing; tourism; smart cities; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The challenges posed by multifaceted disasters and the inherent cascading disasters have been changing in recent years. The interconnection of global and socioeconomic risks determines these changes and leads to greater and more complex disasters such as hurricane Katrina and to cascading disasters (earthquakes, tsunamis, and nuclear disturbances) such as occurred at Fukushima.

The impacts of cascading disasters result frequently from unforeseen and/or unacknowledged risks. These impacts test the strategy and competencies of governance and of official organizations for operative disaster management. Recently, the growing number of extreme events, some of them possibly related to climate changes, and the opportunity to explore timely information have led to the operational use of remote sensing as a tool for emergency management.

Remote sensing is among many tools available for disaster management nowadays, making the planning process and emergency management more effective and accurate. Due to its intrinsic characteristics, e.g., spatial continuity, uniform accuracy, multi-temporality and geographical coverage, remotely sensed data could be helpful for disaster prevention and preparedness (before the disaster), emergency mapping/monitoring (during the disaster) and disaster relief, rehabilitation and reconstruction (after the disaster).

Different sensors and/or different methods can deliver distinctive information about earth’s surface or shallow layers. Thanks to the increasing accessibility to data/products of high accuracy and to the development of advanced analysis/classification algorithms, it is now possible to access multifaceted disasters and cascading disasters.

In this Special Issue, one invites submission of original works regarding the application of state-of-the-art sensors/data, algorithms, and schemes for multifaceted disasters and cascading disaster management. We specially encourage, but do not limit the scope to, submissions regarding multifaceted disasters and cascading disasters detection and monitoring, case studies regarding the use of different remote sensing data and/or algorithms, multitemporal analysis and the development of early warning systems, etc.

Dr. Sérgio Oliveira
Dr. Ricardo Garcia
Prof. Dr. Jorge Rocha
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

  • risks
  • disasters
  • time-series
  • mapping
  • monitoring
  • classification algorithms
  • ancillary data
  • remote sensing
  • early warning

Published Papers (1 paper)

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12 pages, 4532 KiB  
Technical Note
Orthogonal Msplit Estimation for Consequence Disaster Analysis
Remote Sens. 2023, 15(2), 421; https://doi.org/10.3390/rs15020421 - 10 Jan 2023
Viewed by 832
Abstract
Nowadays, the data processing used for analyzing multifaceted disasters is based on technologies of mass observation acquisition. Terrestrial laser scanning is one of those technologies and enables the quick, non-invasive acquisition of information about an object after a disaster. This manuscript presents an [...] Read more.
Nowadays, the data processing used for analyzing multifaceted disasters is based on technologies of mass observation acquisition. Terrestrial laser scanning is one of those technologies and enables the quick, non-invasive acquisition of information about an object after a disaster. This manuscript presents an improvement in the approach to the reconstruction and modeling of objects, based on data obtained by terrestrial laser scanning presented by the authors in previous work, as a method for the detection and dimensioning of the displacement of adjacent planes. The original Msplit estimation implemented in previous research papers has a specific limitation: the functional model must be selected very carefully in terms of the mathematical description of the estimated model and its data structure. As a result, using Msplit estimation on data from laser scanners is not a universal approach. The solution to this problem is the orthogonal Msplit estimation method proposed by the authors. The authors propose a new solution: the orthogonal Msplit estimation (OMsplit). The authors propose a modification of the existing method using orthogonal regression and the Nelder–Mead function as the minimization function. The implementation of orthogonal regression facilitates the avoidance of misfitting in cases of unfavorable data acquisition because the corrections are calculated perpendicularly to the estimated plane. The Nelder–Mead method was introduced to the orthogonal Msplit estimation due to it being more robust to the local minimum of the objective function than the LS method. To present the results, the authors simulated the data measurement of a retaining wall that was damaged after a disaster (violent storm) using a terrestrial laser scanner and their own software. The conducted research confirmed that the OMsplit estimation can be successfully used in the two-plane detection of terrestrial laser scanning data. It allows one to conduct the correct separation of the data set into two sets and to match the planes to the appropriate data set. Full article
(This article belongs to the Special Issue Remote Sensing for Multifaceted Disaster and Cascading Disasters)
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