Assessing post-fire forested ecosystem by using Spaceborne LiDAR over south-east Australia | Natural Hazards Research Australia

Assessing post-fire forested ecosystem by using Spaceborne LiDAR over south-east Australia

Project type

Postgraduate research

Project status

In progress

This project proposes to develop accurate and efficient methods to identify burnt area and assess the ecological impacts on forested ecosystems after wildfire events by using Spaceborne LiDAR data over south-east Australia.

Project details

In Australia, fire has become part of the natural ecosystem, and through tens of thousands of years of evolution, most native species have been able to adapt to hill fires under specific fire-regime. However, extreme weather and climate has resulted changes in frequency and intensity of the fires. Severe fires have devastated Australia's unique forest ecosystems. This project proposes to develop accurate and efficient methods to identify burnt area and assess the ecological impacts on forested ecosystems after wildfire events by using Spaceborne LiDAR data over south-east Australia.

This project will be conducted in three phases:

  1. Burned area identification and fire severity analysis: using spaceborne LiDAR to derive spatially continuous burned area maps of wet/dry sclerophyll forests in southeast Australia, identifying fire severity level with vegetation structure data derived by spaceborne LiDAR and vegetation indices derived by multispectral images. This phase will provide new knowledge to develop methods for remote sensing application in the classification of burned and unburned areas.
  2. Above-ground biomass measurement and forest fuel load prediction: predict forest fuel load pre/post bushfire and monitor ecological functionality of different post-fire restoration treatments by integrating ICESat-2/GEDI data and multi-spectral data.
  3. Post-fire restoration assessment: understand ecosystem trajectory of disturbed forest in southeast Australia using satellite hyperspectral image and landscape function indices. 

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