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Remote Sensing and GIS for Natural Hazards Mapping
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Remote Sensing and GIS for Natural Hazards Mapping

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (10 April 2024) | Viewed by 1132

Special Issue Editor

Dr. Ronghua Ma

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing of inland lakes; water quality; water environment; aquatic ecology; machine learning; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural risk assessment is one of the disciplines that has seen the greatest advances in the field of GIS and remote sensing in recent years. The implementation of more sophisticated analysis methodologies, more accurate remote sensing systems, more innovative damage assessment protocols, etc., are some of the various tools that have improved the management of these phenomena.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating RS technology, ML methods and GIS so as to map, monitor, evaluate and assess natural hazards.

Potential topics of interest include (but are not limited to) regional or global case studies concerning natural risk phenomena prediction and assessment, software development and the implementation of machine learning, optimization, deep learning techniques, meta-heuristic algorithms and risk mapping methodology. Specifically, this Special Issue aims to cover, but is not limited to, the following areas:

  • Monitoring, mapping and assessing earthquakes, landslides, floods, wildfires, soil erosion and water ecology;
  • Evaluating the loss and damages after earthquakes, floods, landslides, wildfires, soil erosion and water ecology.

Dr. Ronghua Ma
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. Sensors 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 2600 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

  • remote sensing
  • GIS
  • machine learning
  • deep learning
  • risk mapping methodology
  • hazardous and risk mapping
  • damage assessment
  • natural hazards

Published Papers (1 paper)

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Research

16 pages, 17045 KiB  
Article
Vector Angular Continuity in the Fusion of Coseismic Deformations at Multiple Optical Correlation Scales
by Rui Guo, Qiming Zeng and Shangzong Lu
Sensors 2023, 23(15), 6677; https://doi.org/10.3390/s23156677 - 26 Jul 2023
Viewed by 664
Abstract
As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar [...] Read more.
As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results; the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the correlation window size. This study regards coseismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the correlation window. This paper takes the coseismic deformations in both the east–west and north–south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different correlation window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity. Full article
(This article belongs to the Special Issue Remote Sensing and GIS for Natural Hazards Mapping)
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