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Bushfires significantly influence forest structure, function, and recovery in fire-prone areas, making it essential to monitor how landscapes rebound after wildfires. Understanding of the ecological impacts, including tree recruitment, changes in biomass, patterns of forest succession, and carbon fluxes etc. following the fires is crucial, as it informs strategies to reduce wildfire risks and enhance forest resilience. Landsat, airborne LiDAR, and infrared imagery has been widely used for these research purposes. This study will investigate spectral recovery with proxy of NDVI and NBR using remote sensing and machine learning to examine post-fire ecological responses in the Churchill region of Victoria, Australia, which was heavily affected by the 2009 Black Saturday fires. The study will integrate satellite imagery, Airborne LiDAR data, and machine learning on the Google Earth Engine (GEE) platform to better estimate fuel loads and assess burn severity. This approach offers more precise insights into potential high-risk areas than satellite data alone, guiding targeted fire management strategies. The study will further investigate floristic biodiversity shifts in temperate rainforests affected by the fires, using Airborne LiDAR and infrared imagery to map changes in species composition and community structure. Finally, the study will explore how altered vegetation cover influences carbon dioxide fluxes and forest energy balance. By observing vegetation indices over time, the study will illustrate how forests transition from a net carbon source immediately after fire to a potential carbon sink as they regenerate.

For access and password, please contact grs@unisq.edu.au.