Bird monitoring intelligence: Integrating Thermal UAV Imagery and Deep Learning Tools
Keywords:
Automatic bird detection, Computer Vision, Deep learning, Image and Video Processing, Thermal imagery, Subtropical wetland.Abstract
Birds are excellent indicators of biodiversity and due to their selective association, are ideal for providing insights into the diversity of vegetation, insects, and aquatic life. Bird census, therefore, is an important tool for ecological monitoring. Birds, however, particularly migratory birds, often flock together in large numbers and bird count estimation of such congregations are herculean tasks, subject to large error margins. Computer vision tasks, such as object detection, tracking, and counting, immensely aid environmental monitoring. Despite many innovative techniques, there is still a lot scope for simplifying and improvising the count estimation process, especially through employing technology and artificial intelligences (AI). The integration of AI into drones for on-the-fly problem-solving is an evolving trend. This paper endeavors to offer a comprehensive compilation of potential studies on wildlife using low-altitude UAVs equipped with thermal sensor datasets found in the literature. Further, we tested the ability of a thermal drone to identify and count water birds in a fresh water habitat. The Unmanned Aerial System with an optical and thermal sensor was integrated with widely accepted detection models such as Detection Transformer, Yolo V7 and Yolo V8 to delineate and count the birds. Thermal imagery was found to be excellent in highlighting birds as bright/hot pixels especially against the cooler waterbody. Among the models, DETR achieved the highest precision score of 91.4%, followed closely by YOLOv8 with a precision score of 84.1%. Additionally, DETR exhibited a notable mAP of 89.2%, demonstrating its efficacy in object detection tasks. Interestingly thermal images are also effective in detecting birds even through canopy that otherwise camouflage well in vegetation. The birds didn't show much response to the presence of UAS particularly at late hours of the day. There is a huge scope of applications and research in the field of ecology. Our study illustrates how UAS, thermal imagery, and automated detection algorithms can be combined to efficiently detect and count birds, thereby offering a critical solution towards population count estimation essential for wildlife management.
Downloads
References
C. J. Ralph, J. R. Sauer, and S. Droege, Monitoring bird populations by point counts. Pacific Southwest Research Station, 1995.
S.-J. Hong, Y. Han, S.-Y. Kim, A.-Y. Lee, and G. Kim, “Application of deep-learning methods to bird detection using unmanned aerial vehicle imagery,” Sensors, vol. 19, no. 7, p. 1651, 2019.
D. J. Prosser et al., “A Comparison of Direct & Indirect Survey Methods for Estimating Colonial Nesting Waterbird Populations,” Waterbirds, vol. 45, no. 2, pp. 189–198, 2023.
S. E. Svensson, “Efficiency of two methods for monitoring bird population levels: breeding bird censuses contra counts of migrating birds,” Oikos, pp. 373–386, 1978.
C. Irigoin-Lovera, D. M. Luna, D. A. Acosta, and C. B. Zavalaga, “Response of colonial Peruvian guano birds to flying UAVs: Effects and feasibility for implementing new population monitoring methods,” PeerJ, vol. 7, p. e8129, 2019.
K. Whitehead et al., “Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: scientific and commercial applications,” Journal of unmanned vehicle systems, vol. 2, no. 3, pp. 86–102, 2014.
L. F. Gonzalez, G. A. Montes, E. Puig, S. Johnson, K. Mengersen, and K. J. Gaston, “Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation,” Sensors, vol. 16, no. 1, p. 97, 2016.
L. Jiao, D. Dong, X. Zhao, and P. Han, “Compensation method for the influence of angle of view on animal temperature measurement using thermal imaging camera combined with depth image,” Journal of Thermal Biology, vol. 62, pp. 15–19, 2016.
The Nature Trail- Tales of the forest, ESRI Story map 2021. https://www.esri.in/en-in/esri-news/maps/the-nature-trail-tales-of-the-forest. Environmental Systems Research Institute. Accessed on January 2024.
R. N. Tripathi, M. G. Ghazi, R. Badola, and S. A. Hussain, “Feasibility study of UAV based ecological monitoring and habitat assessment of cervids in the floating meadows of Keibul Lamjao National Park in Manipur, India,” Measurement, vol. 229, p. 114411, 2024, doi: https://doi.org/10.1016/j.measurement.2024.114411.
T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 9243–9275, Mar. 2023, doi: 10.1007/s11042-022-13644-y.
M. Roy, J. Bhaduri, T. Kumar, and K. Raj, “WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection,” Ecological Informatics, vol. 75, p. 101919, 2023, doi: https://doi.org/10.1016/j.ecoinf.2022.101919.
F. Sultana, A. Sufian, and P. Dutta, “A review of object detection models based on convolutional neural network,” Intelligent computing: image processing based applications, pp. 1–16, 2020.
J. T. Beaver, R. W. Baldwin, M. Messinger, C. H. Newbolt, S. S. Ditchkoff, and M. R. Silman, “Evaluating the use of drones equipped with thermal sensors as an effective method for estimating wildlife,” Wildlife Society Bulletin, vol. 44, no. 2, pp. 434–443, 2020.
Christensson and A. Flodell, “Wildlife surveillance using a uav and thermal imagery.” 2016.
R. Stander, D. J. Walker, F. C. Rohwer, and R. K. Baydack, “Drone nest searching applications using a thermal camera,” Wildlife Society Bulletin, vol. 45, no. 3, pp. 371–382, 2021.
L. G. Howell et al., “Drone thermal imaging technology provides a cost-effective tool for landscape-scale monitoring of a cryptic forest-dwelling species across all population densities,” Wildlife Research, vol. 49, no. 1, pp. 66–78, 2021.
H. L. Larsen et al., “Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals,” Drones, vol. 7, no. 11, p. 680, 2023.
E. D. McCarthy, J. M. Martin, M. M. Boer, and J. A. Welbergen, “Drone-based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats,” Remote Sensing in Ecology and Conservation, vol. 7, no. 3, pp. 461–474, 2021.
Z. J. Delisle, P. G. McGovern, B. G. Dillman, and R. K. Swihart, “Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors,” Remote Sensing in Ecology and Conservation, vol. 9, no. 2, pp. 222–234, 2023.
S. Pagacz and J. Witczuk, “Estimating ground surface visibility on thermal images from drone wildlife surveys in forests,” Ecological Informatics, p. 102379, 2023.
A. Rahman and Y. Setiawan, “Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring–preliminary results,” in E3S Web of Conferences, EDP Sciences, 2020.
B. Dilli and M. Suguna, “Early Thermal Forest Fire Detection using UAV and Saliency map,” in 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, 2022, pp. 1523–1528.
J. Rietz et al., “Drone-Based Thermal Imaging in the Detection of Wildlife Carcasses and Disease Management,” Transboundary and Emerging Diseases, vol. 2023, 2023.
Saunders, A. R. Pople, and S. McLeod, “Studying wildlife from the air,” Wildlife Research in Australia: Practical and Applied Methods, p. 96, 2022.
J. Zhan, Y. Hu, G. Zhou, Y. Wang, W. Cai, and L. Li, “A high-precision forest fire smoke detection approach based on ARGNet,” Computers and Electronics in Agriculture, vol. 196, p. 106874, 2022, doi: https://doi.org/10.1016/j.compag.2022.106874.
H. Lyu, F. Qiu, L. An, D. Stow, R. Lewision, and B. Eva, “Deer survey from drone thermal imagery using enhanced faster R-CNN based on ResNets and FPN,” Ecological Informatics, p. 102383, 2023.
Santangeli, Y. Chen, E. Kluen, R. Chirumamilla, J. Tiainen, and J. Loehr, “Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land,” Scientific reports, vol. 10, no. 1, p. 10993, 2020.
S. Srivastava, S. Gupta, O. Dikshit, and S. Nair, “A Review of UAV Regulations and Policies in India,” in Proceedings of UASG 2019, K. Jain, K. Khoshelham, X. Zhu, and A. Tiwari, Eds., Cham: Springer International Publishing, 2020, pp. 315–325.
S. A. Collins, G. J. Giffin, and W. T. Strong, “Using flight initiation distance to evaluate responses of colonial-nesting Great Egrets to the approach of an unmanned aerial vehicle,” Journal of Field Ornithology, vol. 90, no. 4, pp. 382–390, 2019.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
