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Remote Sensing, GIS, and Artificial Intelligence for Monitoring Environmental Changes

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 (19 January 2024) | Viewed by 1807

Special Issue Editors


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Guest Editor
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea
Interests: satellite remote sensing; sar; thermal infrared sensor (TIR); optical sensor; disaster monitoring; deep learning; radar image processing; environmental changes; surface displacement; detection of volcanic eruption; sea ice thickness
Special Issues, Collections and Topics in MDPI journals
Future Innovation Institute, Seoul National University, Seoul, Republic of Korea
Interests: remote sensing; GIS; artificial intelligence; environmental geography; climate change/disaster monitoring

Special Issue Information

Dear Colleagues,

Remote sensing and spatial information production continue to develop with the increasing number of available optical and non-optical sensors, the improvement of computing power and data storage capacity, and the use of emerging analytic methods such as artificial intelligence and more advanced GIS analysis. For the spatial information production, optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data have been exploited, which can be obtained through sensors mounted on various platforms. For providing usable information by analyzing data obtained with the aforementioned optical and non-optical sensors, such technologies have been applied to various areas, including environmental change, disaster, climate change, land-use/cover changes, urban changes, and ecosystem/biodiversity monitoring, in addition to other areas that can be applied. To accelerate academic discovery, technological development, and information production, it is obvious that the remote sensing domain requires further research by exploiting newly acquired and/or historical data, which can cover wider spatial and temporal coverage, with more advanced analytic methods. This will broaden our understanding of the Earth and environments and their interactions with human beings and society, which can eventually help to improve the quality of human life and prosperity.

As this Special Issue aims to introduce and disseminate academic findings, state-of-the-art technologies, and analytic methods in the field of remote sensing, GIS, and artificial intelligence for monitoring environmental changes, we welcome articles related to the research areas mentioned above and the keywords listed below. The types of articles for this Special Issue include research articles, reviews, technical notes, and other types of manuscripts described on the journal information page.

Prof. Dr. Duk-Jin Kim
Dr. Junwoo Kim
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

  • environmental changes
  • disaster monitoring
  • climate change
  • land-use/cover changes
  • ecosystem and biodiversity
  • urban environmental changes
  • synthetic aperture radar (SAR)
  • optical satellite image
  • artificial intelligence
  • GIS analysis

Published Papers (1 paper)

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Research

26 pages, 17229 KiB  
Article
Water Dynamics Analysis in Karst Flood Areas Using Sentinel-1 Time Series
by Jana Breznik, Krištof Oštir, Matjaž Ivačič and Gašper Rak
Remote Sens. 2023, 15(15), 3861; https://doi.org/10.3390/rs15153861 - 3 Aug 2023
Viewed by 1345
Abstract
Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment [...] Read more.
Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment of a water bodies dataset, automatically detected from Sentinel-1 imagery, for karst flood research. Statistical and visual analyses were conducted to assess the reliability and effectiveness of the dataset. Spearman’s correlation coefficients were employed for statistical analysis to determine the degree of correlation between the areas of water bodies dataset and official water level data. Visual analyses involved the creation of heat maps based on the identified water areas, which were then compared to official flood maps, and the preparation of an analysis of historical flood events or results of hydrological and hydraulic modelling. Additionally, vegetation maps were produced to identify areas that lacked detection and complemented the heat maps. Statistical assessment showed a strong correlation (≥0.6) between the dataset and official water level data in smaller flood-prone areas with less complex inflow. Visual analyses using heat maps and vegetation maps effectively identified frequently flooded areas but had limitations in areas with dense vegetation. Comparisons with flood maps showed an important value of the dataset as an additional source of information for karst flood studies. This assessment highlights the dataset’s potential in combination with other data sources and modelling approaches. Full article
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