Conference Agenda

Airborne laser scanning (ALS) data enable the wall-to-wall estimation of forest aboveground biomass. However, the use of ALS data for biomass estimation is often limited by the lack of biomass reference data that are required to build prediction models, because the field work to collect these data is time-consuming and therefore costly. One approach to deal with missing in-situ reference data is a spatial model transfer: models trained with ALS and biomass reference data collected from other sites are applied to the ALS data from the site of interest, for which no reference data are available. Another approach to overcoming the need to collect reference data is to train models with computer-generated data. We investigated the performance of biomass models trained with simulated forest and ALS data in comparison to spatially transferred models. Simulated data were generated by combining a forest generator, real laser scanning point clouds of individual trees, and a laser scanning simulator (HELIOS++). Real datasets collected from forest sites in Poland, the Czech Republic, and Canada were used for training and testing the models.

We found that models trained with simulated data did not perform as well as models trained with real data collected from the same site the models were applied to. However, using simulated data as additional training data could improve model accuracies in terms of RMSE and r² when only a limited number of real training samples (12 – 346, depending on the study site) are available (Figure 1). For three of the four test datasets, training models with data collected from other sites resulted in higher model accuracies than when models were trained with simulated data.

Although the simulated data we generated cannot compete with real data, our study showed promising results for using simulated laser scanning data to train biomass models.