Comparing nearest neighbor configurations in the prediction of species-specific diameter distributions

Overview & Synopsis

AbstractKey messageWe examine how the configurations in nearest neighbor imputation affect the performance of predicted species-specific diameter distributions. The simultaneous nearest neighbor imputation for all tree species and separate imputation by tree species are evaluated with total volume calibration as a prediction method for diameter distributions.ContextThis study considers the predictions of species-specific diameter distributions in Finnish boreal forests by means of airborne laser scanning (ALS) data and aerial images.AimsThe aim was to investigate different configurations in non-parametric nearest neighbor (NN) imputation and to determine how changes in configurations affect prediction error rates for timber assortment volumes and the error indices of the diameter distributions.MethodsNon-parametric NN imputation was used as a modeling method and was applied in two different ways: (1) diameter distributions were predicted at the same time for all tree species by simultaneous NN imputation, and (2) diameter distributions were predicted for one tree species at a time by separate NN imputation. Calibration to a regression-based total volume prediction was applied in both cases.ResultsThe results indicated that significant changes in the volume prediction error rates for timber assortment and for error indices can be achieved by the selection of responses, calibration to total volume, and separate NN imputation by tree species.ConclusionOverall, the selection of response variables in NN imputation and calibration to total volume improved the predicted diameter distribution error rates. The most successful prediction performance of diameter distribution was achieved by separate NN imputation by tree species.