Abstract
In this chapter, we present the results of several flight campaigns carried out in 2015 and 2016 using multirotor Unmanned Airborne Vehicles (UAVs) over Slender-billed Gull (Chroicocephalus genei) colonies in the Doñana Nature Space, south west Spain. The images were taken at different times during the breeding season. The requirements for the flight campaigns were to acquire sufficient visible and nadir pictures at 5 cm pixel resolution and to cover the entire nesting colony with maximum overlap. Although we carried out the flights under clear skies, low wind speed was not always possible, causing a few blurred pictures. After georeferencing and mosaicking the set of raw pictures, we adopted photo-interpretation as the first technique to identify and delineate birds, either lying, standing or flying. A nest position was assigned when the clear pattern of a lying birds was recognised. We then selected a set of breeding individuals (nests) to train a supervised classification in semi-automatic nest delineation. We applied two different algorithms and tested their accuracy in identifying gulls with an independent set of manually delineated individuals. We chose the best method according to the accuracy results and applied it to the whole colony. We found major issues for nest identification and delineation for nests under tree and shrub canopies. The different campaigns and flight characteristics were useful to improve bird identification accuracy. As a result, we provided estimates of the number of breeding pairs per year to managers and cross-checked these with estimates from the ground monitoring and colony sampling. As an added value, the spatial coordinates of nests can be used for spatial analysis and investigate nest aggregation, density and distribution in order to reveal spatial relationships with environmental factors such as distance to colony edges, distance to colony centroid, distance to predators, etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abd-Elrahman, A.: Development of pattern recognition algorithm for automatic bird detection from unmanned aerial vehicle imagery. Surv. Land Inf. Sci. 65, 37–45 (2005)
Anderson, K., Gaston, K.J.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11, 138–146 (2013). doi:10.1890/120150
Bakó, G., Tolnai, M., Takács, Á.: Introduction and testing of a monitoring and colony-mapping method for waterbird populations that uses high-speed and ultra-detailed aerial remote sensing. Sensors. 14, 12828–12846 (2014). doi:10.3390/s140712828
BirdLife International: Species factsheet: Larus genei, http://datazone.birdlife.org/species/factsheet/22694428
Camp-Valls, G., Bruzzone, L.: Kernel Methods for Remote Sensing Data Analysis. Wiley, New York (2009)
Chabot, D., Craik, S.R., Bird, D.M.: Population census of a large common tern colony with a small unmanned aircraft. Plos One. 10, e0122588 (2015). doi:10.1371/journal.pone.0122588
Chen, C.H.: Image Processing for Remote Sensing. CRC Press, Boca Raton (2007)
Chrétien, L.-P., Théau, J., Ménard, P.: Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildl. Soc. Bull. 40, 181–191 (2016). doi:10.1002/wsb.629
Del-Hoyo, J., Elliott, A., Sargatal, J.: Handbook of the birds of the world, vol. 3, Lynx Edicions edn. Hoatzin to Auks, Barcelona (1996)
Foody, G.M., Mathur, A.: A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosci. Remote Sens. 42, 1335–1343 (2004). doi:10.1109/TGRS.2004.827257
Frederick, P.C., Towles, T., Sawicki, R.J., Bancroft, G.T.: Comparison of aerial and ground techniques for discovery and census of wading bird (Ciconiiformes) nesting colonies. The Condor. 98, 837–841 (1996). doi:10.2307/1369865
Grenzdörffer, G.J.: UAS-based automatic bird count of a common gull colony. ISPRS – Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 1, 169–174 (2013). doi:10.5194/isprsarchives-XL-1-W2-169-2013
Hodgson, J.C., Baylis, S.M., Mott, R., Herrod, A., Clarke, R.H.: Precision wildlife monitoring using unmanned aerial vehicles. Sci. Rep. 6, 22574 (2016). doi:10.1038/srep22574
Jones, G.P., Pearlstine, L.G., Percival, H.F.: An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull.. 1973-2006. 34, 750–758 (2006)
Madroño, A., González, G.G., Atienza, J.C.: Libro rojo de las aves de España. Organismo Autónomo Parques Nacionales (2004)
Mañez, M., Arroyo, J.L., Chico, A., del Valle, J.L., García, L., Garrido, H., Martínez, A., Rodríguez, R.: Seguimiento de Aves Acuáticas. Espacio Natural de Doñana. Reproducción 2016. Estación Biológica de Doñana, CSIC, Sevilla, España (2015)
McEvoy, J.F., Hall, G.P., McDonald, P.G.: Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: disturbance effects and species recognition. Peer J. 4, e1831 (2016). doi:10.7717/peerj.1831
Oro, D., Tavecchia, G.: Gaviota picofina. In: Gaviotas cabecinegra, picofina, de Audouin y tridáctila, y gavión atlántico en España. Población en 2007 y método de censo. pp. 21–43. SEO/BirdLife, Madrid, España (2008)
Pal, M.: Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26, 217–222 (2005). doi:10.1080/01431160412331269698
Prosper, J., Hafner, H.: Breeding aspects of the colonial Ardeidae in the Albufera de Valencia, Spain: population changes, phenology, and reproductive success of the three most abundant species. Colon. Waterbirds. 19, 98–107 (1996). doi:10.2307/1521952
Ratcliffe, N., Guihen, D., Robst, J., Crofts, S., Stanworth, A., Enderlein, P.: A protocol for the aerial survey of penguin colonies using UAVs. J. Unmanned Veh. Syst. 3, 95–101 (2015). doi:10.1139/juvs-2015-0006
Richards, J.A.: Remote Sensing Digital Image Analysis: An Introduction. Springer Science & Business Media, Berlin, (2013)
Sardà-Palomera, F., Bota, G., Viñolo, C., Pallarés, O., Sazatornil, V., Brotons, L., Gomáriz, S., Sardà, F.: Fine-scale bird monitoring from light unmanned aircraft systems. Ibis. 154, 177–183 (2012)
Vas, E., Lescroël, A., Duriez, O., Boguszewski, G., Grémillet, D.: Approaching birds with drones: first experiments and ethical guidelines. Biol. Lett. 11, 20140754 (2015). doi:10.1098/rsbl.2014.0754
Acknowledgments
RECUPERA 2020 project partially funded Miguel Ferrer for this study. We are grateful to the Doñana Natural Processes Monitoring Team, especially José Luis Arroyo and Fernando Ibáñez, and also Luis García, Héctor Garrido, José Luis del Valle, Rubén Rodríguez, and Alfredo Chico. The authors want also to thank to Consejería de Medio Ambiente de la Junta de Andalucía for the funding of the Long-Term Monitoring Program of Doñana Natural Space and the annual census of Doñana breeding birds. Thanks to the owners of Veta La Palma for their cooperation and help in accessing the colonies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Díaz-Delgado, R., Mañez, M., Martínez, A., Canal, D., Ferrer, M., Aragonés, D. (2017). Using UAVs to Map Aquatic Bird Colonies. In: Díaz-Delgado, R., Lucas, R., Hurford, C. (eds) The Roles of Remote Sensing in Nature Conservation. Springer, Cham. https://doi.org/10.1007/978-3-319-64332-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-64332-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64330-4
Online ISBN: 978-3-319-64332-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)