Research

Our Research

We have pioneered a range of techniques for drone surveys, evaluated the performance of these novel methods and assessed different techniques that can be used. Our research aims to maximise the potential of remote sensing data and develop analytical approaches to complex ecosystem monitoring. Our most recent publications:

Congtian L., Xiongwei H., Jiangning W., Tianyu X., & Liqiang J. (2021). Learning niche features to improve image-based species identification. https://doi.org/10.1016/j.ecoinf.2021.101217

Corcoran E., Winsen M., Sudholz A., & Hamilton G. (2021). Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods in Ecology and Evolution, 12(6), 1103-1114. doi:https://doi.org/10.1111/2041-210X.13581 

Corcoran E., Denman S., & Hamilton G. (2021). Evaluating new technology for biodiversity monitoring: Are drone surveys biased? Ecology and Evolution, 11(11), 6649-6656. doi:https://doi.org/10.1002/ece3.7518

Sudholz, A., Denman, S., Pople, A., Brennan, M., Amos, M., & Hamilton, G. (2021). A comparison of manual and automated detection of rusa deer (Rusa timorensis) from RPAS-derived thermal imagery. Wildlife Research. https://doi-org.ezp01.library.qut.edu.au/10.1071/WR20169

Associate Prof. Grant Hamilton, Ms. Evangeline Corcoran, Ms. Megan Winsen and Dr Simon Denman. Automated Detection of Koalas on Kangaroo Island, South Australia. (September 2020). Final Report for Department for Environment and Water, South Australia.

Associate Prof. Grant Hamilton, Ms. Megan Winsen and Ms. Evangeline Corcoran. Preliminary assessment of koalas on Kangaroo Island: drone surveys and automated detection. (August 2020). Report for Department for Environment and Water, South Australia.

Corcoran E., Denman S., & Hamilton G. (2020). New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys. Ecology and Evolution, 10(15), 8176-8185. doi:https://doi.org/10.1002/ece3.6522

Hamilton G., Corcoran E., Denman S., Hennekam M. E., & Koh L. P. (2020). When you can’t see the koalas for the trees: Using drones and machine learning in complex environments. Biological Conservation, 247, 108598. doi:https://doi.org/10.1016/j.biocon.2020.108598

Corcoran E., Denman S., Hanger J., Wilson B., & Hamilton G. (2019). Automated detection of koalas using low-level aerial surveillance and machine learning. Scientific Reports, 9(1), 3208. doi:https://doi.org/10.1038/s41598-019-39917-5

Corcoran, E., Denman, S., & Hamilton, G. (2019). Modelling wildlife species abundance using automated detections from drone surveillance. 23rd International Congress on Modelling and Simulation – Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019. https://www.scopus.com/record/display.uri?eid=2-s2.0-85086441252&origin=inward&txGid=9ad939494833d8d48325a757837ac9e5

Baxter P. W. J., & Hamilton G. (2018). Learning to fly: integrating spatial ecology with unmanned aerial vehicle surveys. Ecosphere, 9(4), e02194. doi:https://doi.org/10.1002/ecs2.2194

Winsen M., Denman S., Corcoran E., & Hamilton G. (2021). Automated Detection of Koalas with Deep Learning Ensembles. https://doi.org/10.3390/rs14102432