WILDLIFE CONSERVATION LAB
Lamuel Chi Hay Chung
PhD Candidate
Lamuel is a PhD candidate in the Wildlife Conservation Lab at The University of Queensland, under the supervision of Dr April Reside and Professor Stuart Phinn. His research utilized cloud computation platforms, machine learning, and citizen science to model Australian mammals’ dynamic habitat suitability across the continent.
Lamuel received his BA in Anthropology from the Chinese University of Hong Kong (2016), and MEnvM in Conservation Biology from The University of Queensland (2019) where he developed an immense interest in spatial ecology and remote sensing. Before returning to the university in 2022 as a PhD candidate, Lamuel worked as a Research Assistant for the State Key Laboratory of Marine Pollution of The City University of Hong Kong (2021-2022) and Ren Chao's Urban Climate Lab of The University of Hong Kong (2019-2021). His research and publications came across multi-disciplines, including ecology, geography, urban studies, artificial intelligence, etc. He is also an internationally awarded photographer and a keen hiker.
Publications:
Hua, J., Cai, M., Shi, Y., Ren, C., Xie, J., Chung, L. C. H., Lu, Y., Chen, L., Yu, Z., & Webster, C.
(2022). Investigating pedestrian-level greenery in urban forms in a high-density city for urban planning. Sustainable Cities and Society, 80, 103755. https://doi.org/10.1016/j.scs.2022.103755
Xie, J., Ren, C., Li, X., & Chung, L. C. H. (2022). Investigate the urban growth and urban-rural
gradients based on local climate zones (1999–2019) in the Greater Bay Area, China. Remote Sensing Applications, 25, 100669. https://doi.org/10.1016/j.rsase.2021.100669
Chen, G., Xie, J., Li, W., Li, X., Chung, L. C. H., Ren, C., & Liu, X. (2021). Future "local climate
zone" spatial change simulation in Greater Bay Area under the shared socioeconomic pathways and ecological control line. Building and Environment, 203, 108077. https://doi.org/10.1016/j.buildenv.2021.108077
Chung, L. C. H., Xie, J., & Ren, C. (2021). Improved machine-learning mapping of local
climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine. Building and Environment, 199, 107879. https://doi.org/10.1016/j.buildenv.2021.107879