Deep learning models for estimating volume and Lorey's height across Nordic countries using optical and SAR satellite images

Spatially explicit information on forest resources and structure is essential for sustainable forest management and evidence-based policy-making. In the Nordic region, large-scale forest mapping often relies on integrating National Forest Inventory (NFI) field plots with airborne laser scanning (ALS) data. However, the infrequent coverage of nationwide ALS campaigns limits their use for continuous monitoring. Satellite imagery, with its high temporal and spatial resolution, provides a promising alternative. We evaluate UNet-based deep learning models trained on wall-to-wall ALS-derived forest resource maps for predicting volume and Lorey’s height in Norway using optical (Sentinel-2) and SAR (Sentinel-1, PALSAR-2) data. The UNet models, trained on both Finnish and Norwegian ALS maps, are benchmarked against kNN models based on Norwegian NFI data. Transfer learning is further explored by fine-tuning models using Norwegian NFI plots. Model uncertainties are assessed using 541 reserved NFI plots and 44 independent validation stands mainly including high-volume boreal forests (>200 m³ ha⁻¹). The best-performing UNet model, trained on Norwegian ALS data, achieved an R² of 0.63 for volume and 0.55 for Lorey’s height, consistently outperforming kNN. Fine-tuning improved model transferability, with gains in R² of up to 0.13 for volume and 0.46 for Lorey’s height, when adapting the Finnish model to Norwegian conditions. Utilizing SAR data alongside optical data enhanced model accuracy, with R² improvement ranging from 0.03 to 0.19. Overall, our findings demonstrate the potential of UNet models trained on wall-to-wall ALS maps, offering a transferable approach for forest resource mapping across Nordic countries.