Background:Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models.We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species.Methods:We tested this hypothesis and an operational potential to improve the predictions of timber volumes,stratified to Scots pine,Norway spruce and deciduous trees,in a conifer forest dominated by the pine species.We derived predictor features from airborne laser scanning(ALS) data and used Most Similar Neighbor(MSN) and Seemingly Unrelated Regression(SUR) as examples of non-parametric and parametric prediction methods,respectively.Results:The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species.Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4%and 12.6-28.9%based on MSN and SUR,respectively,depending on the species.Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76%at best,which degraded the accuracies of the predicted volumes.Consequently,the predictions that did not consider the dominant species were more accurate than those refined with the predicted species.The MSN method gave slightly better results than models fitted with SUR.Conclusions:According to our results,incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes.Determining the dominant species based solely on ALS data is deemed challenging,but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species. Background: Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species- specific attributes in particular in study areas strongly dominated by certain species. Methods: We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning (ALS) data and used Most Similar Neighbor (MSN) and Seemingly Unrelated Regression (SUR) as examples of non-parametric and parametric prediction methods, respectively. Results: The relationships between the ALS features and the volumes of the the species were quite different depending on the dominant species.Incor porating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4% and 12.6-28.9% based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only only 76% at best, which degraded the accuracies of the predicted volumes.Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR .Conclusions: According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes .Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.