Continental-scale ICESat canopy height modelling sensitivity and random forest simulations in Australia and Canada
Abstract:
Optimized GLAS data within Australian forests were employed as canopy height observations, and related to 6 predictor variables (landcover, cover fraction, elevation, slope, soils, and species) by random forest (RF) models. Fifty seven RF models were trained, varying by binomial combinations of predictor data, from 2 to 6 inputs. Trained models were separately utilized to predict Australia wide canopy heights; RF canopy height outputs were validated against spatially concurrent airborne LiDAR 95th percentile canopy heights from an all return point cloud for 10 sites, encompassing multiple ecosystems.
The best RF output was obtained from predictor data inputs: landcover, cover fraction, elevation soils, and species, yielding a RMSE=7.98 m, and R2=0.97. Results indicate inherent issues (noted in existing literature) in GLAS observations that propagate through RF algorithms, manifested as canopy height underestimations for taller vegetation (>45 m).
To extend this research to the Canadian boreal forest context, research is also targeting canopy height model development in the Northwest Territories, allowing investigations of time-variant phenology and landcover sensitivity due to wetland extent and growth, snow cover and other land cover changes common within boreal forest ecosystems.
