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http://dx.doi.org/10.13087/kosert.2019.22.1.73

The Use of Unmanned Aerial Vehicle for Monitoring Individuals of Ardeidae Species in Breeding Habitat: A Case study on Natural Monument in Sinjeop-ri, Yeoju, South Korea  

Park, Hyun-Chul (Spatial Ecology Institute RAUM Co.)
Kil, Sung-Ho (Department of Ecological Landscape Architecture Design, Kangwon National University)
Seo, Ok-Ha (Department of Ecological Landscape Architecture Design, Kangwon National University)
Publication Information
Journal of the Korean Society of Environmental Restoration Technology / v.22, no.1, 2019 , pp. 73-84 More about this Journal
Abstract
In this research, it is a basic study to investigate the population of birds using UAVs. The research area is Ardeidae species(ASP) habitat and has long-term monitoring. The purpose of the study is to compare the ASP populations which analyzed ground observational survey and UAVs imagery. We used DJI's Mavic pro and Phantom4 for this research. Before investigating the population of ASP, we measured the escape distance by the UAVs, and the escape distances of the two UAVs models were statistically significant. Such a result would be different in UAV size and rotor(rotary wing) noise. The population of ASP who analyzed the ground observation and UAVs imagery count differed greatly. In detail, the population(mean) on the ground observation was 174.9, and the UAVs was 247.1 ~ 249.9. As a result of analyzing the UAVs imagery, These results indicate that the lower the UAVs camera altitude, the higher the ASP population, and the lower the UAVs camera altitude, the higher the resolution of the images and the better the reading of the individual of ASP. And we confirmed analyzed images taken at various altitudes, the individuals of ASP was not statistically significant. This is because the resolution of the phantom was superior to that of mavic pro. Our research is fundamental compared to similar studies. However, long-term monitoring for ASP of South Korea's by ground observation is a barrier of the reliability of the monitoring result. We suggested how to use UAVs which can improve long-term monitoring for ASP habitat.
Keywords
Drone; Spatial ecology; Habitat environment; long-term monitoring; protected area;
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1 Sarda Palomera, F.G. Bota.N. Padilla.L. Brotons and F. Sarda. 2017. Unmanned aircraft systems to unravel spatial and temporal factors affecting dynamics of colony formation and nesting success in birds. Journal of Avian Biology 48(9):1273-1280   DOI
2 Anderson, K and K.J. Gaston. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11(3):138-146   DOI
3 Austin, R. 2011. Unmanned aircraft systems: UAVs design, development and deployment (Vol. 54). John Wiley & Sons
4 Berni, J.A..P. J. Zarco-Tejada.M. D. Suarez Barranco and E. Fereres Castiel. 2009. Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing 47(3):722-738   DOI
5 Brisson-Curadeau, E.D. Bird.C. Burke.D.A. Fifield.P. Pace.R.B. Sherley.K.H. Elliott. 2017. Seabird species vary in behavioural response to drone census. Scientific reports 7(1):17884   DOI
6 Chabot, D. and D.M. Bird. 2012. Evaluation of an off-the-shelf unmanned aircraft system for surveying flocks of geese. Waterbirds 35(1): 170-174   DOI
7 Chabot, D. and C.M. Francis. 2016. Computer-automated bird detection and counts in high‐resolution aerial images: a review. Journal of Field Ornithology 87(4): 343-359   DOI
8 Furness, R.W..J.J.D. Greenwood and P.J. Jarvis. 1993. Can birds be used to monitor the environment?. In Birds as monitors of environmental change. Springer. Dordrecht. pp.1-41
9 Getzin, S..K. Wiegand and I. Schoning. 2012. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods in Ecology and Evolution 3:397-404   DOI
10 Gilchrist, H.G. 1999. Declining thick-billed murre Uria lomvia colonies experience higher gull predation rates: an inter-colony comparison. Biological Conservation 87(1):21-29   DOI
11 Hodgson, J.C..S.M. Baylis.R. Mott.A. Herrod and R. H. Clarke. 2016. Precision wildlife monitoring using unmanned aerial vehicles. Scientific reports. 6:22574   DOI
12 Hodgson, J.C..R. Mott.S.M. Baylis.T.T. Pham.S. Wotherspoon.A.D. Kilpatrick and L.P. Koh. 2018. Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution 9(5): 1160-1167   DOI
13 Koh, L.P and S.A. Wich. 2012. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical Conservation Science 5(2):121-132   DOI
14 Laliberte, A.S. and A. Rango. 2009. Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing 47(3): 761-770   DOI
15 Lindenmayer, D.B and G.E. Likens. 2009. Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends in Ecology & Evolution 24(9):482-486   DOI
16 NIER. 2012. Egrets and herons in Korea. National Institute of Environmental Research: National Institute of Environmental Research Publishing (in Korean)
17 OpenDroneMap [Computer software]. 2017. Retrieved from https://github.com/OpenDroneMap/OpenDroneMap
18 Magurran, A.E..S.R. Baillie.S.T. Buckland. J.M. Dick.D.A. Elston.E.M. Scott and A.D. Watt. 2010. Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends in ecology & evolution 25(10): 574-582   DOI
19 Milstein, P.L..I. Prestt and A.A. Bell. 1970. The breeding cycle of the Grey Heron. Ardea. 58 (17):1-257
20 Mulero-Pazmany, M..S. Jenni-Eiermann.N. Strebel.T. Sattler.J.J. Negro and Z. Tablado. 2017. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PloS one 12(6): e0178448   DOI
21 Pavlacky Jr, D.C..P.M. Lukacs.J.A. Blakesle y.R.C. Skorkowsky.D.S. Klute.B.A. Hahn.D.J. Hanni. 2017. A statistically rigorous sampling design to integrate avian monitoring and management within Bird Conservation Regions. PloS one 12(10): e0185924   DOI
22 R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available from: www.rproject. org
23 Ruddock, M. and D.P. Whitfield. 2007. A review of disturbance distances in selected bird species. A report from Natural Research (Projects) Ltd to Scottish Natural Heritage 181
24 Schiffman, R. 2014. Drones flying high as new tool for field biologists. Science 344 (6183): 459   DOI
25 Schofield, G.K.A. Katselidis.M.K. Lilley. R.D. Reina and G.C. Hays. 2017. Detecting elusive aspects of wildlife ecology using drones: new insights on the mating dynamics and operational sex ratios of sea turtles. Functional ecology 31(12): 2310-2319   DOI
26 Wich, S.A. and L.P. Koh. 2018. Conservation Drones: Mapping and Monitoring Biodiversity. Oxford University Press
27 Thomas, L. 1996. Monitoring long‐term population change: why are there so many analysis methods?. Ecology. 77(1): 49-58   DOI
28 Vas, E..A. Lescroel.O. Duriez.G, Boguszewski. D. Gremille. 2015. Approaching birds with drones: first experiments and ethical guidelines. Biology letters. 11(2): 2014075
29 Vermeulen, C..P. Lejeune.J. Lisein.P. Sawadogo and P. Bouche. 2013. Unmanned aerial survey of elephants. PloS one 8(2): e54700   DOI
30 Wilson, A.M.J. Barr and M. Zagorski. 2017. The feasibility of counting songbirds using unmanned aerial vehicles. The Auk 134(2): 350-362   DOI
31 Weissensteiner, M.H.J.W. Poelstra and J.B. Wolf. 2015. Low-budget ready-to-fly unmanned aerial vehicles: An effective tool for evaluating the nesting status of canopybreeding bird species. Journal of Avian Biology 46(4): 425-430   DOI
32 Zhang, C. and J.M. Kovacs. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision agriculture 13(6): 693-712   DOI