Dr Lulu He

Dr Lulu He is a postdoctoral researcher at the School of Civil, Environmental and Mining Engineering. She holds PhD degree in Disaster Risk Reduction. Her research is built on disaster risk reduction, geography, political science, and social sciences, as well as the Sendai Framework for DRR (2015-2030). Lulu has extensive working experience under an international environment. She gained field experience of collecting primary data in Nepal, China, and New Zealand.
Lulu's research has been recognised by the United Nations (UN). In 2018, she received the PhD Fellowship Award (the award is competitive as only six candidates are successful amongst hundreds of applicants worldwide every year), sponsored by the UN, to undertake a three-month research visit at the United Nations University in Helsinki, Finland.

At the University of Adelaide, Lulu works as part of a larger team that specialises in natural hazards, modelling, remote sensing, and risk reduction. She currently works as project manager for a SmartSat CRC project focused identifying areas of bushfire risk and how the risk is likely to change into future, with collaboration with South Australia Government, Western Australia Government, RMIT, and Shoal Group.

The project focuses on developing the Adaptive Analytical Bushfire Likelihood (AABL) tool. The tool will provide spatial intelligence and data that can support strategic, medium- to long-term bushfire risk reduction activities in a range of agencies (e.g. land management, risk reduction planning, land use planning, spatial planning etc.). This is achieved by integrating earth-observation data, the methods and approaches used in the Australian Fire Danger Rating System and models of ignition potential and suppression to provide spatially varying estimates of bushfire likelihood at a resolution of 100m x 100m. By combining this modelling approach with scenario analysis, the AABL tool will enable users to explore changes in the spatial and temporal distribution of bushfire likelihood under a range of plausible future conditions (e.g. climate change, urban expansion) from 3 months to multiple decades into the future in a user-friendly and computationally efficient manner.