Figure 1. Study area for monitoring of egrets and grey herons populations in Breeding Ground. The black polygon is natural monument in Sinjeop-ri, Yeoju, South Korea
Figure 2. Breeding ground images taken with the drones at various altitudes, and the circle image is a partial enlargement of the original image(A: PHANTOM, B: MAVIC)
Figure 3. Marking of heron and egret individuals using CAD tool(The cross is a marking block)
Figure 4. Box plot of expression levels of escape distance by Mavic and Phantom. The plot illustrates that the sets are different from each other in terms of expression level. p-value denotes the result from pair-wised t-test.
Figure 5. Box plot of expression levels of individuals of heron and egret by image taken with drone and ground observation. The plot illustrates that the sets are different from each other in terms of expression level. p-value denotes the result from one-way ANOVA
Table 1. Drone sources
Table 2. The escape distance of the heron and egret measured by different models of drones
Table 3. The individuals of heron and egret by ground observation and drone image analysis
References
- 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 https://doi.org/10.1890/120150
- Austin, R. 2011. Unmanned aircraft systems: UAVs design, development and deployment (Vol. 54). John Wiley & Sons
- 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 https://doi.org/10.1109/TGRS.2008.2010457
- 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 https://doi.org/10.1038/s41598-017-18202-3
- 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 https://doi.org/10.1675/063.035.0119
- 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 https://doi.org/10.1111/jofo.12171
- 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 https://doi.org/10.1111/j.2041-210X.2011.00158.x
- 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 https://doi.org/10.1016/S0006-3207(98)00045-7
- 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
- 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 https://doi.org/10.1038/srep22574
- 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 https://doi.org/10.1111/2041-210X.12974
- 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 https://doi.org/10.1177/194008291200500202
- 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 https://doi.org/10.1109/TGRS.2008.2009355
- 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 https://doi.org/10.1016/j.tree.2009.03.005
- OpenDroneMap [Computer software]. 2017. Retrieved from https://github.com/OpenDroneMap/OpenDroneMap
- 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 https://doi.org/10.1016/j.tree.2010.06.016
- Milstein, P.L..I. Prestt and A.A. Bell. 1970. The breeding cycle of the Grey Heron. Ardea. 58 (17):1-257
- 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 https://doi.org/10.1371/journal.pone.0178448
- NIER. 2012. Egrets and herons in Korea. National Institute of Environmental Research: National Institute of Environmental Research Publishing (in Korean)
- 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 https://doi.org/10.1371/journal.pone.0185924
- 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
- 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
- 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 https://doi.org/10.1111/jav.01535
- Schiffman, R. 2014. Drones flying high as new tool for field biologists. Science 344 (6183): 459 https://doi.org/10.1126/science.344.6183.459
- 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 https://doi.org/10.1111/1365-2435.12930
- Thomas, L. 1996. Monitoring long‐term population change: why are there so many analysis methods?. Ecology. 77(1): 49-58 https://doi.org/10.2307/2265653
- 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
- Vermeulen, C..P. Lejeune.J. Lisein.P. Sawadogo and P. Bouche. 2013. Unmanned aerial survey of elephants. PloS one 8(2): e54700 https://doi.org/10.1371/journal.pone.0054700
- Wich, S.A. and L.P. Koh. 2018. Conservation Drones: Mapping and Monitoring Biodiversity. Oxford University Press
- Wilson, A.M.J. Barr and M. Zagorski. 2017. The feasibility of counting songbirds using unmanned aerial vehicles. The Auk 134(2): 350-362 https://doi.org/10.1642/AUK-16-216.1
- 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 https://doi.org/10.1111/jav.00619
- 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 https://doi.org/10.1007/s11119-012-9274-5
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