Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path

Byungwoo Chang; Byungmook Hwang; Wontaek Lim; Hankyu Kim; Wanmo Kang; Yong-Su Park; Dongwook W. Ko

doi:10.3390/drones9010052

journal article Open Access Jan 13, 2025

Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path

Byungwoo Chang

Byungmook Hwang

Wontaek Lim

Hankyu Kim Wanmo Kang

Yong-Su Park Dongwook W. Ko

Drones Vol. 9 No. 1 pp. 52 · MDPI AG

View at Publisher Save 10.3390/drones9010052

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 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.

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References

51

[1]

Brooks "Habitat Loss and Extinction in the Hotspots of Biodiversity" Conserv. Biol. (2002) 10.1046/j.1523-1739.2002.00530.x

[2]

The biodiversity of species and their rates of extinction, distribution, and protection

S. L. Pimm, Clinton N. Jenkins, R. Abell et al.

Science 2014 10.1126/science.1246752

[3]

Daskalova "Landscape-Scale Forest Loss as a Catalyst of Population and Biodiversity Change" Science (2020) 10.1126/science.aba1289

[4]

Doherty "Invasive predators and global biodiversity loss" Proc. Natl. Acad. Sci. USA (2016) 10.1073/pnas.1602480113

[5]

Silveira "Camera Trap, Line Transect Census and Track Surveys: A Comparative Evaluation" Biol. Conserv. (2003) 10.1016/s0006-3207(03)00063-6

[6]

O’Connell, A.F. (2011). Camera Traps in Animal Ecology: Methods and Analyses, Springer. 10.1007/978-4-431-99495-4

[7]

Foster "A Critique of Density Estimation from Camera-Trap Data" J. Wildl. Manag. (2012) 10.1002/jwmg.275

[8]

Meek "The Pitfalls of Wildlife Camera Trapping as a Survey Tool in Australia" Aust. Mammal. (2015) 10.1071/am14023

[9]

Anderson "Lightweight Unmanned Aerial Vehicles Will Revolutionize Spatial Ecology" Front. Ecol. Environ. (2013) 10.1890/120150

[10]

Kim, M., Chung, O.-S., and Lee, J.-K. (2021). A Manual for Monitoring Wild Boars (Sus scrofa) Using Thermal Infrared Cameras Mounted on an Unmanned Aerial Vehicle (UAV). Remote Sens., 13. 10.3390/rs13204141

[11]

Witt, R.R., Beranek, C.T., Howell, L.G., Ryan, S.A., Clulow, J., Jordan, N.R., Denholm, B., and Roff, A. (2020). Real-time drone derived thermal imagery outperforms traditional survey methods for an arboreal forest mammal. PLoS ONE, 15. 10.1371/journal.pone.0242204

[12]

Zhang "Thermal infrared imaging from drones can detect individuals and nocturnal behavior of the world’s rarest primate" Glob. Ecol. Conserv. (2020)

[13]

Menard "Wildlife Multispecies Remote Sensing Using Visible and Thermal Infrared Imagery Acquired from an Unmanned Aerial Vehicle (UAV)" Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. (2015)

[14]

Beaver "Evaluating the Use of Drones Equipped with Thermal Sensors as an Effective Method for Estimating Wildlife" Wildl. Soc. Bull. (2020) 10.1002/wsb.1090

[15]

Larsen, H.L., Møller-Lassesen, K., Enevoldsen, E.M.E., Madsen, S.B., Obsen, M.T., Povlsen, P., and Pagh, S. (2023). Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals. Drones, 7. 10.3390/drones7110680

[16]

Boccardo "UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications" Sensors (2015) 10.3390/s150715717

[17]

Srivastava, K., Pandey, P.C., and Sharma, J.K. (2020). An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs. Drones, 4. 10.3390/drones4030058

[18]

Ghelichi "Logistics for a Fleet of Drones for Medical Item Delivery: A Case Study for Louisville, KY" Comput. Oper. Res. (2021) 10.1016/j.cor.2021.105443

[19]

Xu, Y., Li, J., and Zhang, F. (2022). A UAV-Based Forest Fire Patrol Path Planning Strategy. Forests, 13. 10.3390/f13111952

[20]

Burke "Optimizing Observing Strategies for Monitoring Animals Using Drone-Mounted Thermal Infrared Cameras" Int. J. Remote Sens. (2019) 10.1080/01431161.2018.1558372

[21]

Ministry of Oceans and Fisheries (2005). Uninhabited Island Status Survey and Integrated Management Plan, Ministry of Oceans and Fisheries. (In Korean).

[22]

Šmilauer, P., and Lepš, J. (2014). Multivariate Analysis of Ecological Data Using CANOCO 5, Cambridge University Press. 10.1017/cbo9781139627061

[23]

Pollock "Large-Scale Wildlife Monitoring Studies: Statistical Methods for Design and Analysis" Environmetrics (2002) 10.1002/env.514

[24]

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Luigi Sedda, Eric R. Lucas, Luc S. Djogbénou et al.

Journal of The Royal Society Interface 2019 10.1098/rsif.2018.0941

[25]

Danz "Environmentally stratified sampling design for the development of great lakes environmental indicators" Environ. Monit. Assess. (2005) 10.1007/s10661-005-1594-8

[26]

Rietz "Drone-Based Thermal Imaging in the Detection of Wildlife Carcasses and Disease Management" Transbound. Emerg. Dis. (2023) 10.1155/2023/5517000

[27]

(2024, January 03). Subdivision Land Cover Map. Available online: https://egis.me.go.kr/.

[28]

Goodbody "sgsR: A Structurally Guided Sampling Toolbox for LiDAR-Based Forest Inventories" Forestry (2023) 10.1093/forestry/cpac055

[29]

R Core Team (2023). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https://www.R-project.org/.

[30]

IPGP (2024, January 07). csv2djipilot: From a CSV with Coordinates to a KML Waypoints to Import in DJI Pilot. Available online: https://github.com/IPGP/csv2djipilot.

[31]

Stachowicz, I., Ferrer-Paris, J.R., and Sánchez-Mercado, A. (2024). Leveraging Limited Data from Wildlife Monitoring in a Conflict-Affected Region in Venezuela. Sci. Rep., 14. 10.1038/s41598-024-52133-0

[32]

(2024, May 05). Aviation Safety Act (Ordinance of the Ministry of Land, Infrastructure and Transport, Subpara. 20396, 2024. 3. 19., All Revisions). Available online: https://www.law.go.kr/.

[33]

Bennie "Biogeography of time partitioning in mammals" Proc. Natl. Acad. Sci. USA (2014) 10.1073/pnas.1216063110

[34]

The influence of human disturbance on wildlife nocturnality

Kaitlyn M. Gaynor, Cheryl E. Hojnowski, Neil H. Carter et al.

Science 2018 10.1126/science.aar7121

[35]

Ministry of Environment (2022). Developing IT·ET·BT Convergence-Based Distribution and Spread Models for Introduced Exotic Species, Ministry of Environment. (In Korean).

[36]

Wearn "Snap Happy: Camera Traps Are an Effective Sampling Tool When Compared with Alternative Methods" R. Soc. Open Sci. (2019) 10.1098/rsos.181748

[37]

Delisle, Z.J., Flaherty, E.A., Nobbe, M.R., Wzientek, C.M., and Swihart, R.K. (2021). Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation. Front. Ecol. Evol., 9. 10.3389/fevo.2021.617996

[38]

Johansson, Ö., Samelius, G., Wikberg, E., Chapron, G., Mishra, C., and Low, M. (2020). Identification Errors in Camera-Trap Studies Result in Systematic Population Overestimation. Sci. Rep., 10. 10.1038/s41598-020-63367-z

[39]

Harris, G.M., Butler, M.J., Stewart, D.R., Rominger, E.M., and Ruhl, C.Q. (2020). Accurate Population Estimation of Caprinae Using Camera Traps and Distance Sampling. Sci. Rep., 10. 10.1038/s41598-020-73893-5

[40]

Royle "Modelling Occurrence and Abundance of Species When Detection Is Imperfect" Oikos (2005) 10.1111/j.0030-1299.2005.13534.x

[41]

Royle "Modeling Avian Abundance from Replicated Counts Using Binomial Mixture Models" Ecol. Appl. (2005) 10.1890/04-1120

[42]

Denes "Estimating Abundance of Unmarked Animal Populations: Accounting for Imperfect Detection and Other Sources of Zero Inflation" Methods Ecol. Evol. (2015) 10.1111/2041-210x.12333

[43]

Kidwai "Using N-Mixture Models to Estimate Abundance and Temporal Trends of Black Rhinoceros (Diceros bicornis L.) Populations from Aerial Counts" Glob. Ecol. Conserv. (2019)

[44]

Wevers "Modelling Species Distribution from Camera Trap By-Catch Using a Scale-Optimized Occupancy Approach" Remote Sens. Ecol. Conserv. (2021) 10.1002/rse2.207

[45]

Rooney "Direct and Indirect Effects of White-Tailed Deer in Forest Ecosystems" For. Ecol. Manag. (2003) 10.1016/s0378-1127(03)00130-0

[46]

Ecological Impacts of Deer Overabundance

STEEVE D. CÔTÉ, Thomas P. Rooney, Jean-Pierre Tremblay et al.

Annual Review of Ecology, Evolution, and Systemati... 2004 10.1146/annurev.ecolsys.35.021103.105725

[47]

Augustine "Defining Deer Overabundance and Threats to Forest Communities: From Individual Plants to Landscape Structure" Ecoscience (2003) 10.1080/11956860.2003.11682795

[48]

Morris "Mapping Resource Selection Functions in Wildlife Studies: Concerns and Recommendations" Appl. Geogr. (2016) 10.1016/j.apgeog.2016.09.025

[49]

Lecours, V. (2017). On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary). Front. Mar. Sci., 4. 10.3389/fmars.2017.00288

[50]

Kushwaha "Geospatial Technology for Wildlife Habitat Evaluation" Trop. Ecol. (2002)

Showing 50 of 51 references

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Details

Published: Jan 13, 2025
Vol/Issue: 9(1)
Pages: 52
License: View

Authors

B

Byungwoo Chang

Department of Forest Resources, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

B

Byungmook Hwang

Department of Forest Resources, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

W

Wontaek Lim

Industry-Academic Cooperation Foundation, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

H

Hankyu Kim

Department of Biology, Kyung Hee University, Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea

W

Wanmo Kang

Department of Forest, Environment, and Systems, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

Y

Yong-Su Park

Restoration Data Team, National Institute of Ecology, 23, Gowol-gil, Yeongyang-eup, Yeongyang-gun 36531, Republic of Korea

D

Dongwook W. Ko

Department of Forest, Environment, and Systems, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea

Funding

Korea Forest Service (Korea Forestry Promotion Institute) Award: RS-2024-00404816

Cite This Article

Byungwoo Chang, Byungmook Hwang, Wontaek Lim, et al. (2025). Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path. Drones, 9(1), 52. https://doi.org/10.3390/drones9010052

Enhancing Wildlife Detection Using Thermal Imaging Drones: Designing the Flight Path

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