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Applied Sciences
Special Issues
New Technologies for Observation and Assessment of Coastal Zones
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New Technologies for Observation and Assessment of Coastal Zones

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 1085

Special Issue Editors

Dr. Lorena Parra

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Guest Editor
Research Institute for the Integrated Management of Coastal Zones (IGIC), Polytechnic University of Valencia, 46022 València, Spain
Interests: aquaculture; environmental monitoring; precision agriculture; water quality; wireless sensor networks; chemical sensors; physical sensors; pollution monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. José Miguel Jiménez Herranz

E-Mail Website
Guest Editor
Research Institute for the Integrated Management of Coastal Zones (IGIC), Polytechnic University of Valencia, 46022 València, Spain
Interests: network protocols; network algorithms; network security; Multimedia; WSN; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The coastal zone is a very important area for human beings since several economic activities are concentrated on it, and for biodiversity due to the wide range and variability of ecosystems such as coral reefs, mangroves, seagrass meadows, kelp forests, intertidal zones, coastal dunes, estuaries, wetlands, salt marches, and others. Although the coastal zone is not legally well bounded, it includes both on-shore and off-shore regions. The underwater area is a challenge to monitor given the limited usability of remote sensing and the reduced bandwidth of telecommunication technologies. Meanwhile, the considerable variability and high rate of anthropization has turned the inland region into a complex environment.

The scarce monitoring of these regions supposes a lack of data that are sorely necessary for adequate management. Several technologies can be applied, from sensors being part of wireless sensor nodes, Internet of Things, landers, and remote sensing to artificial intelligence for analyzing the generated data in databases or making predictions. Creating indicators based on sensed data might suppose an important advantage in characterizing the areas to generate early warning systems.

In this Special Issue, we aim to exchange knowledge and experiences on any aspect related to the latest technological advances applied in coastal zones, including marine and fluvial water monitoring, ecosystem monitoring, land cover challenges, and fauna tracking, among others. Both original papers and comprehensive surveys are welcomed.

Dr. Lorena Parra
Dr. José Miguel Jiménez Herranz
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

  • physical sensors
  • chemical sensors
  • acoustic sensors
  • optical sensors
  • magnetic sensors
  • remote sensing
  • land cover
  • fauna tracking
  • water monitoring
  • ecosystems monitoring
  • transmission technology
  • underwater communication
  • artificial intelligence
  • database

Published Papers (1 paper)

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Research

19 pages, 8684 KiB  
Article
Progressive Gaussian Decomposition of Airborne Bathymetric LiDAR Waveform for Improving Seafloor Point Extraction
by Hyejin Kim, Minyoung Jung, Jaebin Lee and Gwangjae Wie
Appl. Sci. 2023, 13(19), 10939; https://0-doi-org.brum.beds.ac.uk/10.3390/app131910939 - 03 Oct 2023
Viewed by 698
Abstract
Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water column backscattering, and noise, and it [...] Read more.
Airborne bathymetric LiDAR (ABL) acquires waveform data with better accuracy and resolution and greater user control over data processing than discrete returns. The ABL waveform is a mixture of reflections from the water surface and bottom, water column backscattering, and noise, and it can be separated into individual components through waveform decomposition. Because the point density and positional accuracy of the point cloud are dependent on waveform decomposition, an effective decomposition technique is required to improve ABL measurement. In this study, a new progressive waveform decomposition technique based on Gaussian mixture models was proposed for universal applicability to various types of ABL waveforms and to maximize the observation of seafloor points. The proposed progressive Gaussian decomposition (PGD) estimates potential peaks that are not detected during the initial peak detection and progressively decomposes the waveform until the Gaussian mixture model sufficiently represents the individual waveforms. Its performance is improved by utilizing a termination criterion based on the time difference between the originally detected and estimated peaks of the approximated model. The PGD can be universally applied to various waveforms regardless of water depth or underwater environment. To evaluate the proposed approach, it was applied to the waveform data acquired from the Seahawk sensor developed in Korea. In validating the PGD through comparative evaluation with the conventional Gaussian decomposition method, the root mean square error was found to decrease by approximately 70%. In terms of point cloud extractability, the PGD extracted 14–18% more seafloor points than the Seahawk’s data processing software. Full article
(This article belongs to the Special Issue New Technologies for Observation and Assessment of Coastal Zones)
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