Optical satellite sensors, previously Landsat TM/ETM/OLI and now Sentinel-2 MSI, are the primary data sources for detection of clearing and regrowth in woodlands in Queensland. In this context, the Statewide Landcover and Tree Survey (SLATS) definition of woodlands is used, e.g. assemblages of woody plants, including stands of native vegetation, regrowth following clearing, plantations of native and exotic species, and woody weeds. Satellites using active imaging systems such as Synthetic Aperture Radar from the European Space Agency’s Sentinel-1 and its future BIOMASS mission, as well as the upcoming NISAR mission from NASA and India’s national space agency, have great potential to improve detection. These active sensors complement optical imagery as they: provide vegetation structural information, provide sub-canopy information, can collect imagery during day and night as well as in adverse weather conditions, and possess the ability to penetrate through clouds, smoke, and tree canopies. One of the major advantages to using imaging radar is the ability to detect structural information below the canopy, a feature inherent only to active sensors. This additional information paired with deep learning algorithms has the potential to improve the detection of woodland clearing and regrowth.

The Injune Landscape Collaborative Project will be used to test and develop clearing and regrowth methodologies. SLATS data, along with optical and radar imagery (i.e. Sentinel-2, Planet and Sentinel-1, ALOS PALSAR) will be the primary data used for development and validation. The outputs from this research contribute to vegetation management and environmental policies, as well as other initiatives including the Great Barrier Reef 2050 Quality program, the Land Restoration Fund, and bushfire management.