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Drones
Special Issues
Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition
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Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3493

Special Issue Editors

Dr. Humberto Perotto-Baldivieso

E-Mail Website
Guest Editor
Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, 313 Horticulture/Forest Science Building (HFSB), College Station, TX 77843-2138, USA
Interests: rangelands, landscape ecology, drones, remote sensing, geographic information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Aaron M. Foley

E-Mail Website
Guest Editor
Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, 700 University Blvd, MSC 218, Kingsville, TX 78363, USA
Interests: wildlife management; population estimation; survey methods; large mammal ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The second edition of this Special Issue "Drone-Based Wildlife Protection, Monitoring, and Conservation Management" focuses on the application of drones to develop methodologies to assist in the protection and management of wildlife. The use and application of drones for wildlife studies has increased significantly in the last decade. From individuals’ detection to population estimations and habitat assessments, drones are now an integral part of the wildlife professional toolbox.

We welcome research that examines the use of drone technology to improve our understanding of wildlife research and wildlife management. This Special Issue welcomes a variety of topics including the following:

  1. Species detection protocols;
  2. Methodological approaches to estimate wildlife populations;
  3. Geospatial approaches to assess wildlife populations;
  4. Wildlife monitoring;
  5. Remote data collection using drones and other sensors in the field;
  6. Habitat–wildlife relationships using data derived from drones;
  7. Sensors, wildlife, and habitat;
  8. AI methods of assessing imagery for wildlife management.

Dr. Humberto Perotto-Baldivieso
Dr. Aaron M. Foley
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. Drones is an international peer-reviewed open access monthly 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

  • habitat monitoring
  • LiDAR
  • population estimation
  • thermal
  • wildlife conservation
  • wildlife management
  • wildlife monitoring
  • wildlife species detection

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

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23 pages, 28505 KiB  
Article
Drone-Based Detection and Classification of Greater Caribbean Manatees in the Panama Canal Basin
by Javier E. Sanchez-Galan, Kenji Contreras, Allan Denoce, Héctor Poveda, Fernando Merchan and Hector M. Guzmán
Drones 2025, 9(4), 230; https://doi.org/10.3390/drones9040230 - 21 Mar 2025
Viewed by 465
Abstract
This study introduces a novel, drone-based approach for the detection and classification of Greater Caribbean Manatees (Trichechus manatus manatus) in the Panama Canal Basin by integrating advanced deep learning techniques. Leveraging the high-performance YOLOv8 model augmented with Sliced Aided Hyper Inferencing (SAHI) for [...] Read more.
This study introduces a novel, drone-based approach for the detection and classification of Greater Caribbean Manatees (Trichechus manatus manatus) in the Panama Canal Basin by integrating advanced deep learning techniques. Leveraging the high-performance YOLOv8 model augmented with Sliced Aided Hyper Inferencing (SAHI) for improved small-object detection, our system accurately identifies individual manatees, mother–calf pairs, and group formations across a challenging aquatic environment. Additionally, the use of AltCLIP for zero-shot classification enables robust demographic analysis without extensive labeled data, enhancing model adaptability in data-scarce scenarios. For this study, more than 57,000 UAV images were acquired from multiple drone flights covering diverse regions of Gatun Lake and its surroundings. In cross-validation experiments, the detection model achieved precision levels as high as 93% and mean average precision (mAP) values exceeding 90% under ideal conditions. However, testing on unseen data revealed a lower recall, highlighting challenges in detecting manatees under variable altitudes and adverse lighting conditions. Furthermore, the integrated zero-shot classification approach demonstrated a robust top-2 accuracy close to 90%, effectively categorizing manatee demographic groupings despite overlapping visual features. This work presents a deep learning framework integrated with UAV technology, offering a scalable, non-invasive solution for real-time wildlife monitoring. By enabling precise detection and classification, it lays the foundation for enhanced habitat assessments and more effective conservation planning in similar tropical wetland ecosystems. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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14 pages, 4547 KiB  
Article
Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path
by Byungwoo Chang, Byungmook Hwang, Wontaek Lim, Hankyu Kim, Wanmo Kang, Yong-Su Park and Dongwook W. Ko
Drones 2025, 9(1), 52; https://doi.org/10.3390/drones9010052 - 13 Jan 2025
Viewed by 1911
Abstract
Thermal imaging drones have transformed wildlife monitoring by facilitating the efficient and noninvasive monitoring of animal populations across large areas. In this study, an optimized flight path design was developed for monitoring wildlife on Guleopdo Island, South Korea using the DJI Mavic 3T [...] Read more.
Thermal imaging drones have transformed wildlife monitoring by facilitating the efficient and noninvasive monitoring of animal populations across large areas. In this study, an optimized flight path design was developed for monitoring wildlife on Guleopdo Island, South Korea using the DJI Mavic 3T drone equipped with a thermal camera. We employed a strata-based sampling technique to reclassify topographical and land cover information, creating an optimal survey plan. Using sampling strata, key waypoints were derived, on the basis of which nine flight paths were designed to cover ~50% of the study area. The results demonstrated that an optimized flight path improved the accuracy of detecting Formosan sika deer (Cervus nippon taiouanus). Population estimates indicated at least 128 Formosan sika deer, with higher detection efficiency observed during cloudy weather. Customizing flight paths based on the habitat characteristics proved crucial for efficient monitoring. This study highlights the potential of thermal imaging drones for accurately estimating wildlife populations and supporting conservation efforts. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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Review

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26 pages, 4680 KiB  
Review
Impact of Drone Disturbances on Wildlife: A Review
by Saadia Afridi, Lucie Laporte-Devylder, Guy Maalouf, Jenna M. Kline, Samuel G. Penny, Kasper Hlebowicz, Dylan Cawthorne and Ulrik Pagh Schultz Lundquist
Drones 2025, 9(4), 311; https://doi.org/10.3390/drones9040311 - 16 Apr 2025
Viewed by 466
Abstract
Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals [...] Read more.
Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, and physiological and behavioral responses. We found that drone altitude, speed, approach distance, and noise levels significantly influence wildlife responses, with some species exhibiting increased vigilance, flight responses, or physiological stress. Environmental context and visual cues are also involved in species detection of drones and disturbance thresholds. Although the short-term response to behavior change has been well documented, long-term consequences of repeated drone exposure remain poorly known. This paper identifies the necessity for continued research into drone–wildlife interactions, with an emphasis on the requirement to minimize disturbance by means of improved flight parameters and technology. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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