Australia-wide Machine Learning Evapotranspiration for Trees (AMLETT) model for Murray-Darling Basin wetlands
This dataset contains the monthly evapotranspiration (ET, mm/month) data at 30 meters spatial resolution from Feb 2000 to June 2024 for Murray-Darling Basin wetlands, Australia. A model was created using field data and machine learning to estimate floodplain tree ET (Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box)). This dataset will be updated at least annually in the future.
Doody, T.M., Gao, S., Vervoort, W., Pritchard, J., Davies, M., Nolan, M. and Nagler, P.L., 2023. A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management. Journal of Environmental Management, 332, p.117393.
https://doi.org/10.25919/nxr8-9z06
Fractional tree canopy cover for Murray-Darling Basin wetlands
This dataset contains yearly fractional tree canopy cover (FTCC, %) data at 20 meter spatial resolution from 2016 to 2022 for Murray-Darling Basin wetlands. A machine learning model was created to predict FTCC using optical and radar satellite. This model was subjected to validation using tree canopy cover data extracted from LiDAR data. The model achieved a remarkable 85% explanatory capacity regarding FTCC variations, with an associated error rate of 8%. The fine-scale FTCC will be of value to catchment management concerns such as altered catchment water yields related to bushfires.
Gao, S., Castellazzi, P., Vervoort, R.W. and Doody, T.M., 2021. Fine scale mapping of fractional tree canopy cover to support river basin management. Hydrological Processes, 35(4), p.e14156.