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Sensors and Artificial Intelligence for Wildlife Conservation: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2281

Special Issue Editors


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Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; object detection; conservation; e-health
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Interests: artificial intelligence; machine learning; deep learning; computer vision; technology in conservation and e-health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue on the use of Sensors and Artificial Intelligence in Wildlife Conservation. We are soliciting submissions for both review and original research articles related to the novel use of data obtained from sensors (camera traps, cameras, microphones, unoccupied vehicles (aerial, terrestrial, and aquatic)) and any other sensor platforms you think would support wildlife conservation) combined with AI, to manage and protect wildlife globally. The Special Issue is open to contributions ranging from systems that combat poaching and protect wildlife, support wildlife management and conservation, enable animal counting and tracking, monitor and protect the environment, support biodiversity assessments, monitor forest health and quality, as well as novel approaches to sensor fusion for remote sensing. Original contributions that look at integrated sensor-based technologies and wide-area communications across remote sensing platforms (land, sea, airborne and spaceborne) are also encouraged.

For your reference, you may refer to publications in Volume I of this Special Issue series.

Prof. Dr. Paul Fergus
Dr. Carl Chalmers
Prof. Dr. Serge Wich
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. Sensors 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 2600 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

  • multispectral remote sensing
  • hyperspectral remote sensing
  • LiDAR
  • sensor fusion
  • land-, sea-, air-, and space-based monitoring
  • machine learning
  • convolutional neural networks (1D, 2D, 3D, etc.)
  • high-performance inference
  • time-series analysis
  • poaching
  • wildlife conservation
  • animal counting
  • forest monitoring
  • land-use/cover change
  • environment monitoring
  • robotics

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Published Papers (1 paper)

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Research

19 pages, 2275 KiB  
Article
Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests
by Eduardo José Pinel-Ramos, Filippo Aureli, Serge Wich, Steven Longmore and Denise Spaan
Sensors 2024, 24(17), 5659; https://doi.org/10.3390/s24175659 - 30 Aug 2024
Cited by 4 | Viewed by 1571
Abstract
Geoffroy’s spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission–fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider [...] Read more.
Geoffroy’s spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission–fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (−45°, −90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was “substantial” (Fleiss’ kappa coefficient = 0.61–0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a −90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model. Full article
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