Special Issue "Remote Sensing for Mapping and Monitoring Anthropogenic Debris"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Assist. Prof. Dr. Gil Rito Gonçalves
E-Mail Website
Guest Editor
University of Coimbra, INESC Coimbra, Department of Mathematics, Faculty of Sciences and Technology, 3001-454 Coimbra, Portugal
Interests: unmanned aerial systems; satellite image processing; satellite image analysis; geoinformation; mapping; spatial analysis; geospatial science; digital mapping; remote sensing; geographical information systems; environment
Special Issues and Collections in MDPI journals
Dr. Umberto Andriolo
E-Mail Website
Guest Editor
University of Coimbra; Institute for Systems Engineering and Computers (INESC), Portugal
Interests: Signal processing; data management; programming in MATLAB; hydraulic engineering; coastal processes; marine geology; environmental hydraulics; coastal engineering; sediments

Special Issue Information

Dear Colleagues,

Anthropogenic debris abundance has become a global issue for marine, coastal, and terrestrial environments, as it represents a threat for species, ecosystems, and, potentially, human health. Innovative and robust remote sensing tools, methods, and techniques are beneficial for improving the current anthropogenic debris monitoring programs. For instance, remote sensing provides a reliable source of data collection to widen observations, which are usually limited in traditional surveys, and to monitor inaccessible areas. These improvements are essential in finding the appropriate mitigation measures and to optimize the removal of anthropogenic debris.

This Special Issue proposes to include research on anthropogenic debris detection, mapping, and monitoring in the environment using different remote sensing techniques. We welcome original contributions on all possible types of remote sensing platforms, such as satellite, airborne, unmanned aerial systems, and terrestrial and underwater robotic systems, such as remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs). Research on all environmental domains is welcome, with emphasis on marine and ocean litter; coastal litter, including beaches and dunes; and riverine litter.

We look forward to receiving your submissions for this Special Issue.

 

Dr. Gil Rito Gonçalves
Dr. Umberto Andriolo
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 papers will be 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 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

  • anthropogenic debris monitoring
  • coastal litter
  • riverine litter
  • urban litter
  • terrestrial and underwater robotic systems
  • coastal and terrestrial environments
  • plastic
  • marine litter
  • floating litter
  • marine pollution
  • urban pollution
  • beach litter
  • river pollution
  • ocean pollution
  • micro-plastic
  • macro-plastic
  • machine learning
  • marine litter detection

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Remote Sens. 2020, 12(16), 2599; https://doi.org/10.3390/rs12162599 - 12 Aug 2020
Cited by 1 | Viewed by 1051
Abstract
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS [...] Read more.
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping and Monitoring Anthropogenic Debris)
Show Figures

Graphical abstract

Back to TopTop