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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: 26 April 2026 | Viewed by 29040

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 250 words) can be sent to the Editorial Office for assessment.

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 (7 papers)

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Research

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11 pages, 3464 KB  
Article
Pre-Programming Thermal Sensors Improves Detection During Drone-Based Nocturnal Wildlife Surveys in Warm Weather
by Lori Massey, Aaron M. Foley, Jeremy Baumgardt, Randy W. DeYoung and Humberto L. Perotto-Baldivieso
Drones 2026, 10(2), 127; https://doi.org/10.3390/drones10020127 - 11 Feb 2026
Viewed by 270
Abstract
Improvements in thermal infrared imaging provide new opportunities for drone-based wildlife surveys. The use of thermal sensors can be limited by ambient temperatures and vegetation cover, which can limit opportunities to survey during optimal biological seasons. Pre-programming isotherm settings in thermal cameras has [...] Read more.
Improvements in thermal infrared imaging provide new opportunities for drone-based wildlife surveys. The use of thermal sensors can be limited by ambient temperatures and vegetation cover, which can limit opportunities to survey during optimal biological seasons. Pre-programming isotherm settings in thermal cameras has the potential to allow surveys during warmer environmental conditions. We evaluated night-time surveys of white-tailed deer (Odocoileus virginianus) using isotherm settings in a 102 ha enclosed property in South Texas during February (winter) and July (summer) 2022. Detection probabilities were 0.84 and 0.65 during winter and summer, respectively. Percent woody cover was 48.1% and 60.7% during these seasons, respectively. The seasonal pattern in detection probabilities met expectations in terms of visibility bias caused by canopy cover. Despite different detection probabilities among seasons, population estimates were similar because distance sampling accounted for visibility bias. The use of isotherm settings allowed us to survey during temperatures previously thought to be too warm for ideal contrast (~21 °C vs. 30 °C), which provides more opportunities to survey during biologically important seasons typically associated with warm temperatures (i.e., fawning and antlerogenesis). We recommend the use of distance sampling methods to evaluate and correct for visibility bias during thermal-based drone surveys because detections of focal species may vary with vegetation. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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16 pages, 1208 KB  
Article
The Efficacy of Drone-In-A-Box Technology for Marine Megafauna Surveillance off Coastal Beaches
by Kim I. Monteforte, Paul A. Butcher, Stephen G. Morris and Brendan P. Kelaher
Drones 2026, 10(2), 122; https://doi.org/10.3390/drones10020122 - 11 Feb 2026
Viewed by 498
Abstract
Drones are increasingly used in marine science for detecting and monitoring large megafauna in nearshore areas. Remotely operated, autonomous drone missions have the potential to improve the overall efficiency of drone-based research. We assessed the utility of autonomous drone operations by comparing real-time [...] Read more.
Drones are increasingly used in marine science for detecting and monitoring large megafauna in nearshore areas. Remotely operated, autonomous drone missions have the potential to improve the overall efficiency of drone-based research. We assessed the utility of autonomous drone operations by comparing real-time detection rates of marine megafauna (i.e., dolphins, rays, sharks, turtles) between a remotely operated Drone-In-A-Box (DIAB) system using pre-programmed missions and standard site-operated manual flight procedures. Megafauna were identified in real time during each drone mission, and missed detections were quantified through post-analysis of drone footage. A total of 71 missions were completed, with autonomous and manual flights operating concurrently at either 60 m or 80 m altitude, and a flight speed of 8 m/s. There were 107 and 117 real-time megafauna observations recorded for autonomous and manual operations, respectively. Post-flight analysis determined an overall missed detection of 52.4% for autonomous and 30.4% for manual operations, with undercounting higher for autonomous operations across all faunal groups. Dolphin detection in real time had the highest agreement with post-flight analysis, while real-time turtle detection proved the most difficult. Cloud cover, sea state, time of day, and water clarity significantly affected real-time false negative detection rates, though their relative importance varied across faunal groups and between flight procedures. Overall, remotely operated, autonomous drones have the potential to enhance long-term marine megafauna research, particularly when combined with post-flight analysis. Integrating artificial intelligence into autonomous drone operations will also be beneficial, especially for shark surveillance programs where real-time detection is essential for beach-user safety. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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15 pages, 2995 KB  
Article
Thermal Drones Aid to Uncover Nocturnal Subgrouping Patterns of a Diurnal Primate
by Eduardo José Pinel-Ramos, Denise Spaan, Serge Wich and Filippo Aureli
Drones 2026, 10(2), 114; https://doi.org/10.3390/drones10020114 - 5 Feb 2026
Viewed by 490
Abstract
Spider monkeys (Ateles spp.) have traditionally been described as strictly diurnal primates, with only low levels of activity during the night. Consequently, little attention has been given to the possibility of nocturnal movements and social dynamics occurring at sleeping sites. Recent advances [...] Read more.
Spider monkeys (Ateles spp.) have traditionally been described as strictly diurnal primates, with only low levels of activity during the night. Consequently, little attention has been given to the possibility of nocturnal movements and social dynamics occurring at sleeping sites. Recent advances in technologies, such as drone-based thermal infrared imaging (TIR), provide new opportunities to explore behavioral patterns that were previously undetectable through ground-based observations. In this study, we aimed to evaluate whether Geoffroy’s spider monkeys (Ateles geoffroyi) change their subgroup size once they are at their sleeping sites by comparing the numbers of monkeys detected after sunset with those detected before sunrise using TIR drone surveys. We conducted TIR drone flights over four sleeping sites of well-habituated Geoffroy’s spider monkey groups in Los Árboles Tulum in the Yucatán Peninsula, Mexico. We carried out 18 flight pairs—18 flights at sunset when the majority of individual spider monkeys were expected to have arrived at the sleeping sites, and 18 flights the next following morning at sunrise—before the monkeys began their daily movements. Our results revealed that in 12 out of the 18 flight pairs (67%), the number of monkeys counted at sunset differed from the number counted at sunrise. In 58% of these 12 flight pairs, more monkeys were counted at sunrise than at sunset. Furthermore, when changes in subgroup size occurred, they were more frequent (67%) when the subgroups at sleeping sites were larger (>10 monkeys). These changes in subgroup size are consistent with the occurrence of fissions and fusions continuing after dark. This study provides preliminary evidence that Geoffroy’s spider monkeys are more active during the night than generally assumed. Furthermore, our results highlight the value of TIR drones as an effective tool for studying primate social dynamics under low-light conditions. Unlike traditional ground-based observations, which depend on natural light, TIR drones allow for accurate and reliable monitoring throughout the night. By providing access to behavioral information that would otherwise remain hidden, this technology opens new possibilities for understanding the full temporal range of activity of diurnal species. 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, 6120 KB  
Article
Drones and Deep Learning for Detecting Fish Carcasses During Fish Kills
by Edna G. Fernandez-Figueroa, Stephanie R. Rogers and Dinesh Neupane
Drones 2025, 9(7), 482; https://doi.org/10.3390/drones9070482 - 8 Jul 2025
Cited by 2 | Viewed by 1501
Abstract
Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address [...] Read more.
Fish kills are sudden mass mortalities that occur in freshwater and marine systems worldwide. Fish kill surveys are essential for assessing the ecological and economic impacts of fish kill events, but are often labor-intensive, time-consuming, and spatially limited. This study aims to address these challenges by exploring the application of unoccupied aerial systems (or drones) and deep learning techniques for coastal fish carcass detection. Seven flights were conducted using a DJI Phantom 4 RGB quadcopter to monitor three sites with different substrates (i.e., sand, rock, shored Sargassum). Orthomosaics generated from drone imagery were useful for detecting carcasses washed ashore, but not floating or submerged carcasses. Single shot multibox detection (SSD) with a ResNet50-based model demonstrated high detection accuracy, with a mean average precision (mAP) of 0.77 and a mean average recall (mAR) of 0.81. The model had slightly higher average precision (AP) when detecting large objects (>42.24 cm long, AP = 0.90) compared to small objects (≤14.08 cm long, AP = 0.77) because smaller objects are harder to recognize and require more contextual reasoning. The results suggest a strong potential future application of these tools for rapid fish kill response and automatic enumeration and characterization of fish carcasses. Full article
(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)
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23 pages, 28505 KB  
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
Cited by 3 | Viewed by 1964
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 KB  
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
Cited by 7 | Viewed by 10076
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 KB  
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
Cited by 13 | Viewed by 12912
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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