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IJGI
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Advances in Satellite-Based Mapping and Monitoring of Natural Disasters
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Advances in Satellite-Based Mapping and Monitoring of Natural Disasters

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 17382

Special Issue Editor

Dr. Simon Plank

E-Mail Website
Guest Editor
German Remote Sensing Data Center (DFD) - Geo-Risks and Civil Security, German Aerospace Center (DLR), 82234 Weßling, Germany
Interests: remote sensing of natural hazards (volcanoes, earthquakes, landslides, floods, fires); SAR polarimetry; SAR interferometry; thermal remote sensing; satellite-based monitoring of volcanoes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural disasters cause significant damages and losses of human lives and property. These physical phenomena are caused either by rapid or slowly occurring events that can be hydrological (such as avalanches and floods), meteorological (cyclones and storms), geophysical (earthquakes, tsunamis, landslides and volcanic eruptions), or climatological (such as extreme temperatures, drought and wildfires). With the launch of the Copernicus Sentinel satellite missions, we are now in the „golden age of remote sensing“. Supported by a series of national satellites, the Sentinel satellites enable the mapping and monitoring of natural disasters at a hitherto unknown high spatial resolution and temporal frequency.

This Special Issue focuses on recent advances in satellite-based mapping and monitoring of natural disasters. You are encouraged to contribute to this Special Issue by submitting your latest research and developments in the areas of, but not limited to:

  • Rapid mapping of natural disasters by means of Earth Observation data
  • Remote-sensing-based monitoring of natural disasters
  • Satellite-based long-term monitoring of slowly evolving natural disasters
  • Combination of in situ measurements with Earth Observation data for monitoring natural disasters
  • Monitoring of disaster recovery

Dr. Simon Plank
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1900 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.

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

Published Papers (3 papers)

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Research

23 pages, 17141 KiB  
Article
Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey
by Xiaoxuan Li, Anthony R. Cummings, Ali Rashed Alruzuq, Corene J. Matyas and Amobichukwu Chukwudi Amanambu
ISPRS Int. J. Geo-Inf. 2019, 8(5), 231; https://doi.org/10.3390/ijgi8050231 - 18 May 2019
Cited by 5 | Viewed by 4475
Abstract
Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster [...] Read more.
Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster rescue and emergency response. In this study, a combined Digital Elevation Model (DEM) based water fraction (DWF) method was implemented to simulate coastal floods during Hurricane Harvey on the South Texas coast. Water fraction values were calculated to create a 15 km flood map from multiple channels of the Advanced Technology Microwave Sound dataset. Based on hydrological inundation mechanism and topographic information, the coarse-resolution flood map derived from water fraction values was then downscaled to a high spatial resolution of 10 m. To evaluate the DWF result, Storm Surge Hindcast product and flood-reported high-water-mark observations were used. The results indicated a high overlapping area between the DWF map and buffered flood-reported high-water-marks (HWMs), with a percentage of more than 85%. Furthermore, the correlation coefficient between the DWF map and CERA SSH product was 0.91, which demonstrates a strong linear relationship between these two maps. The DWF model has a promising capacity to create high-resolution flood maps over large areas that can aid in emergency response. The result generated here can also be useful for flood risk management, especially through risk communication. Full article
(This article belongs to the Special Issue Advances in Satellite-Based Mapping and Monitoring of Natural Disasters)
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27 pages, 10909 KiB  
Article
Regional Landslide Identification Based on Susceptibility Analysis and Change Detection
by Alu Si, Jiquan Zhang, Siqin Tong, Quan Lai, Rui Wang, Na Li and Yongbin Bao
ISPRS Int. J. Geo-Inf. 2018, 7(10), 394; https://doi.org/10.3390/ijgi7100394 - 29 Sep 2018
Cited by 15 | Viewed by 3954
Abstract
Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. [...] Read more.
Landslide identification is an increasingly important research topic in remote sensing and the study of natural hazards. It is essential for hazard prevention, mitigation, and vulnerability assessments. Despite great efforts over the past few years, its accuracy and efficiency can be further improved. Thus, this study combines the two most popular approaches: susceptibility analysis and change detection thresholding, to derive a landslide identification method employing novel identification criteria. Through a quantitative evaluation of the proposed method and masked change detection thresholding method, the proposed method exhibits improved accuracy to some extent. Our susceptibility-based change detection thresholding method has the following benefits: (1) it is a semi-automatic landslide identification method that effectively integrates a pixel-based approach with an object-oriented image analysis approach to achieve more precise landslide identification; (2) integration of the change detection result with the susceptibility analysis result represents a novel approach in the landslide identification research field. Full article
(This article belongs to the Special Issue Advances in Satellite-Based Mapping and Monitoring of Natural Disasters)
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Graphical abstract

25 pages, 9531 KiB  
Article
Digital Image Correlation (DIC) Analysis of the 3 December 2013 Montescaglioso Landslide (Basilicata, Southern Italy): Results from a Multi-Dataset Investigation
by Paolo Caporossi, Paolo Mazzanti and Francesca Bozzano
ISPRS Int. J. Geo-Inf. 2018, 7(9), 372; https://doi.org/10.3390/ijgi7090372 - 8 Sep 2018
Cited by 44 | Viewed by 8243
Abstract
Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and [...] Read more.
Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset. Full article
(This article belongs to the Special Issue Advances in Satellite-Based Mapping and Monitoring of Natural Disasters)
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