An Integrating Multimodal Technologies and Ethical Frameworks in Addressing Challenges in Modern Wildlife Monitoring

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume :15 | Issue : 01 | Page : –
    By

    D. Dharani,

  • Ateetha Santhosh,

  • D. Hemavadhana,

  • N. Nachammai,

  1. Assistant Professor, Department of Robotics and Automation, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India
  2. Student, Department of Robotics and Automation, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India
  3. Student, Department of Robotics and Automation, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India
  4. Student, Department of Robotics and Automation, Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_160408’);});Edit Abstract & Keyword

Wildlife monitoring plays a crucial role in understanding and conserving biodiversity, but traditional methods often have limitations in scope, accuracy, and ethical impact. Advances in multimodal technologies such as drones, acoustic sensors, environmental DNA (eDNA), and camera traps offer new avenues for gathering rich, non-invasive data. However, the integration of these technologies comes with a range of challenges, particularly in terms of ethical concerns related to animal welfare, data management, and environmental impact. This paper explores how combining these advanced technologies with ethical guidelines can address these challenges, creating a balanced approach to wildlife monitoring. By emphasizing the use of non-invasive techniques, sustainability, and ethical principles, this paper aims to propose practical solutions for modern wildlife monitoring and conservation efforts, ensuring that technological advancements support both scientific progress and ethical responsibility. Poaching, species extinction, habitat loss, and climate change are just a few of the many issues facing wildlife monitoring today. Data collection and analysis have been transformed by the integration of multimodal technologies including unmanned aerial vehicles (UAVs), bioacoustics, artificial intelligence (AI), and remote sensing. However, a clear ethical framework is required due to the ethical implications of new technologies, which include privacy issues, data ownership, and the possible damage of natural environments. In order to promote responsible innovation that strengthens conservation efforts while maintaining ecological and ethical integrity, this paper examines the relationship between multimodal technology and ethical considerations in wildlife monitoring.

Keywords: Wildlife Monitoring, Multimodal Technologies, Ethical Frameworks, Environmental DNA, Acoustic Sensors, Drones, Camera Traps, Non-invasive Monitoring, Conservation, Data Privacy.

[This article belongs to Journal of Aerospace Engineering & Technology (joaet)]

How to cite this article:
D. Dharani, Ateetha Santhosh, D. Hemavadhana, N. Nachammai. An Integrating Multimodal Technologies and Ethical Frameworks in Addressing Challenges in Modern Wildlife Monitoring. Journal of Aerospace Engineering & Technology. 2025; 15(01):-.
How to cite this URL:
D. Dharani, Ateetha Santhosh, D. Hemavadhana, N. Nachammai. An Integrating Multimodal Technologies and Ethical Frameworks in Addressing Challenges in Modern Wildlife Monitoring. Journal of Aerospace Engineering & Technology. 2025; 15(01):-. Available from: https://journals.stmjournals.com/joaet/article=2025/view=0

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_160408’);});Edit

References

  1. Huang JH, Chen YY, Huang YT, Lin PY, Chen YC, Lin YF. Rapid Prototyping for Wildlife and Ecological Monitoring. IEEE Systems Journal. 2010;4(2):198–209p. doi:10.1109/JSYST.2010.2047294.
  2. Zhang M, Zhang L, Zhao C, Jin R, Guo J, Li X. Fetching Ecosystem Monitoring Data in Extreme Areas via a Drone-Enabled Internet of Remote Things. IEEE Internet of Things Journal. 2022;9(24):25052–25067p. doi:10.1109/JIOT.2022.3195302.
  3. Tripathi RN, Ramachandran A, Tripathi V, Badola R, Hussain SA. Optimizing Riparian Habitat Conservation: A Spatial Approach Using Aerial and Space Technologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024; 17:16932–16943p. doi:10.1109/JSTARS.2024.3454453.
  4. Li X, Huang H, Savkin AV. Autonomous Navigation of an Aerial Drone to Observe a Group of Wild Animals with Reduced Visual Disturbance. IEEE Systems Journal. 2022;16(2):3339–3348p. doi:10.1109/JSYST.2021.3135982.
  5. Babu V, Fuentes Pineda R, Bizan M, Wojak A, Wierzowiecki S, Gervásio J. Perovskite Solar Module Enabled IoT Asset Tracking for Wildlife Conservation. IEEE Journal of Photovoltaics. 2024;14(2):337–343p. doi:10.1109/JPHOTOV.2024.3355406.
  6. Camacho L, Baquerizo R, Palomino J, Zarzosa M. Deployment of a Set of Camera Trap Networks for Wildlife Inventory in Western Amazon Rainforest. IEEE Sensors Journal. 2017;17(23):8000–8007p. doi:10.1109/JSEN.2017.2760254.
  7. Ehsan S, Bradford K, Brugger M, Hamdaoui B, Kovchegov Y, Johnson D. Design and Analysis of Delay-Tolerant Sensor Networks for Monitoring and Tracking Free-Roaming Animals. IEEE Transactions on Wireless Communications. 2012;11(3):1220–1227p. doi:10.1109/TWC.2012.012412.111405.
  8. Tavares TF. Open-set Classification Approaches to Automatic Bird Song Identification: Towards Non-invasive Wildlife Monitoring in Brazilian Fauna. IEEE Latin America Transactions. 2022;20(11):2388–2394p. doi:10.1109/TLA.2022.9904764.
  9. Pereira E, Araújo Í, Silva LFV, Batista M, Júnior S, Barboza E. RFID Technology for Animal Tracking: A Survey. IEEE Journal of Radio Frequency Identification. 2023; 7:609–620p. doi:10.1109/JRFID.2023.3334952.
  10. Li M, Im J, Quackenbush LJ, Liu T. Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014;7(7):3143–3156p. doi:10.1109/JSTARS.2014.2304642.
Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 10/01/2025
Accepted 17/01/2025
Published 08/02/2025
149