Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning

For all but one of the 16 data sources parametric models were found to be more precise than non-parametric models. Inclusion of stand age as an explanatory variable improved the precision of all but one model. For parametric models that included stand age, the R2 and RMSE (in brackets) for models with (i) all metrics derived from satellite imagery, (ii) environmental surface variables, (iii) variables derived from satellite imagery and environmental surfaces, (iv) LiDAR metrics and (v) all available variables were, respectively, 0.237 (2.850 m), 0.613 (2.267 m), 0.716 (2.025 m), 0.883 (1.378 m) and 0.801 (1.672 m). These results show that LiDAR was the most useful data source for precise and unbiased prediction of Site Index. The parametric model created using variables derived from environmental surfaces and satellite imagery was also very precise showing that, in combination, these datasets may provide a useful alternative for predictions of Site Index when LiDAR data are not available.