| Abstract: | Abstract A model for prediction of stand basal area and diameters at 10 percentiles of a basal area distribution was estimated from small-footprint laser scanner data from primeval conifer forest using partial least squares regression. The regression explained 44–80% and 67% of the variability of the 10 percentiles and stand basal area, respectively. The predicted percentiles, scaled by the predicted stand basal area, were used to compute diameter distributions. A cross-validation showed that the mean differences between the predicted and observed number of stems by diameter class were non-significant (p>0.05) for 22 of 29 diameter classes. Moreover, plot volume was calculated from the predicted diameter distribution and cross-validation revealed a non-significant deviation between predicted and observed volume of ?3.3% (of observed volume). An independent validation showed non-significant mean differences for 20 of 21 diameter classes for data corresponding to the model calibration data. Plot volumes calculated from the predicted diameter distributions deviated from observed volume by ?4.4%. The model reproduced diameter distributions corresponding to the model calibration data (uneven-sized forest) well. However, the model is not flexible enough to reproduce normal and uniform diameter distributions. Volume estimates derived from predicted diameter distributions were generally well determined, irrespective of the observed distribution. |
