Conclusions
The aim of the reported study was to predict woody vegetation FTCC at 20
m resolution for floodplain vegetation and evaluate predictions using
LiDAR data. This study has shown that a combining predictor model was
able to explain up to 91% of FTCC variation, returning an acceptable
RMSE at our study sites. Individual models (RFYanga and
RFBarmah) displayed weaker correlations and larger
errors when compared to the combined model. Analysis of sensor band
importance suggests SWIR is the most important band which contributes
mostly to model training as it is sensitive to variation in leaf area
index and leaf water content. Additionally, Sentinel-1 (radar) band
contributions cannot be ignored for Random forest model training. Our
presented approach will prove useful in expanding knowledge of remote
sensing ET related directly to tree ET, improving estimations at a finer
spatial resolution. This study will be significant to further our
collective understanding of floodplain vegetation response to climatic
conditions and catchment water management.