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Applied Sciences
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Advanced Remote Sensing Technologies for Disaster Monitoring, Volume II
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Advanced Remote Sensing Technologies for Disaster Monitoring, Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 4704

Special Issue Editors

Dr. Seonyoung Park

E-Mail Website
Guest Editor
Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: drought; evapotranspiration; disaster; environmental impact assessment; soil and water conservation; remote sensing and GIS; agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youkyung Han

E-Mail Website
Guest Editor
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, Korea
Interests: image processing; classification; image fusion; image registration; high-resolution imaging

Special Issue Information

Dear Colleagues,

For the last decade or so, there has been intense research activity regarding the exploitation of remote sensing technologies in disasters such as drought, extreme temperatures, earthquakes, cyclones, flooding, landslides, wildfires, etc. Climate change is affecting the occurrence of disasters, resulting in the higher vulnerability of regions to severe events. It is important to prevent, mitigate, and recover from disasters by monitoring these disasters using enhanced technologies. Remote sensing is one of such technologies that is suitable for effectively collecting data on a large scale with varied spatial, spectral, and temporal resolutions. A mass of satellite data has been employed to monitor disasters, identify the damage due to disasters, and assess the recovery from disaster.

This Special Issue invites state-of-the-art research on disaster monitoring using satellite remote sensing data. In this Special Issue, we expect to introduce various studies covering remote sensing technologies that can be applied in disaster monitoring.

Prof. Dr. Seonyoung Park
Prof. Dr. Youkyung Han
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. Applied Sciences 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 2400 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

  • monitoring natural hazards
  • landslides and land degradation
  • climate change
  • land use and land cover change
  • typhoon
  • droughts
  • floods, and floodplains
  • earthquakes
  • tsunamis
  • hazard and vulnerability assessments
  • risk mapping
  • early warning systems

Published Papers (2 papers)

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Research

20 pages, 4643 KiB  
Article
Machine Learning-Based Forest Burned Area Detection with Various Input Variables: A Case Study of South Korea
by Changhui Lee, Seonyoung Park, Taeheon Kim, Sicong Liu, Mohd Nadzri Md Reba, Jaehong Oh and Youkyung Han
Appl. Sci. 2022, 12(19), 10077; https://doi.org/10.3390/app121910077 - 7 Oct 2022
Cited by 5 | Viewed by 2465
Abstract
Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the [...] Read more.
Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring, Volume II)
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18 pages, 60342 KiB  
Article
Using the Local Drought Data and GRACE/GRACE-FO Data to Characterize the Drought Events in Mainland China from 2002 to 2020
by Lilu Cui, Cheng Zhang, Zhicai Luo, Xiaolong Wang, Qiong Li and Lulu Liu
Appl. Sci. 2021, 11(20), 9594; https://doi.org/10.3390/app11209594 - 14 Oct 2021
Cited by 10 | Viewed by 1490
Abstract
Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery [...] Read more.
Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to quantify drought characteristics. In order to improve drought detection capability, we used the local drought data as calibration criteria to improve the accuracy of the drought characterization approach to determine the onset of drought. Additionally, the local precipitation data was used to test drought severity determined by the calibrated drought characterization approach. Results show that the drought event probability of detection (POD) of this approach in the four study regions increased by 61.29%, 25%, 94.29%, and 66.86%, respectively, after calibration. We used the calibrated approach to detect the drought events in Mainland China (MC) during 2016 and 2019. The results show that CAR of the four study regions is 100.00%, 92.31%, 100.00%, and 100.00%. Additionally, the precipitation anomaly index (PAI) data was used to evaluate the severity of drought from 2002 to 2020 determined by the calibrated approach. The results indicate that both have a strong similar spatial distribution. Our analysis demonstrates that the proposed approach can serve a useful tool for drought monitoring and characterization. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technologies for Disaster Monitoring, Volume II)
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