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Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning

Hauglin, Marius, Hansen, Endre, Sørngård, Erik, Næsset, Erik, Gobakken, Terje
Canadian journal of forest research 2018 v.48 no.8 pp. 913-922
data collection, forests, harvesters, lidar, models, prediction, regression analysis, trees, yield forecasting
Modern cut-to-length harvesters are recording information about each harvested tree, and with accurate positioning, this information can be used as field reference data, replacing manually measured reference data. In the present study, models developed from accurately positioned harvester data were compared with a reference model. A set of ∼55 000 accurately positioned trees was used as the basis for a division into 792 reference plots of 400 m² each. A set of manually measured field plots was used for validation. Regression models were developed based on the relationship between airborne laser scanning data and the reference plot volumes. Separate models were developed for two strata: medium and high site productivity. Several modelling methods were compared, including nonparametric models; at the plot level, predictions for the validation dataset yielded RMSEs of 32%–60% for the medium productivity stratum and 19%–22% for the high productivity stratum. A reference model was fitted to the manually measured validation data in each stratum, and RMSEs of 45% and 25% were obtained for the medium and high productivity strata, respectively. The results show that the models based on the harvester data yield prediction errors at the same level as the reference model.