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, infrequent nationwide ALS campaign coverage 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 extreme gradient boosting (XGB) models. Transfer learning is further explored by finetuning models using Norwegian NFI plots. Model accuracies are assessed using 541 reserved test NFI plots and 44 independent forest stands, representing high‑volume mature boreal forests (>200 m³ ha⁻¹). The UNet model trained on Norwegian ALS‑based data achieved R2 values of 0.59 for both volume and Lorey's height when evaluated on NFI plots, and 0.70 and 0.59 for forest stands, respectively, outperforming the XGB models. Finetuning improved model transferability, yielding gains of up to 0.13 in R2 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. Overall, our findings demonstrate the potential of UNet models trained on wall-to-wall ALS maps for forest resource mapping across Nordic countries.