Johannes Breidenbach
Head of Department/Head of Research
(+47) 974 77 985
johannes.breidenbach@nibio.no
Place
Ås H8
Visiting address
Høgskoleveien 8, 1433 Ås
Authors
Giovanni D’Amico Davide Botticelli Giacomo Marcelli Walter Mattioli Gherardo Chirici Elia Vangi Costanza Borghi Piermaria Corona Johannes Schumacher Johannes Breidenbach Yang Su Lauri Mehtätalo Saverio FranciniAbstract
This data article presents a multi-source dataset of satellite-based auxiliary data designed for forest modelling and monitoring. The dataset integrates annual medoid composites derived from Sentinel-1, Sentinel-2, and Landsat imagery, together with spectral indices, Landsat-based 3I3D change metrics, forest mask and forest type layers, and terrain variables derived from the Copernicus GLO-30 DEM, offering comprehensive information on forest cover, spectral behavior, and change metrics. It provides harmonized predictors across seven European countries, ensuring consistency, scalability, and ease of use for researchers developing or validating models to understand forest dynamics and estimate forest-related variables such as biomass or canopy recovery. A curated subset of the dataset is distributed via Zenodo, along with direct public access links to the complete multi-terabyte archive. The data support applications in forest biodiversity conservation, carbon monitoring, biomass modelling, and climate-change impact assessment.
Authors
Daniel Moreno-Fernández Patricia Adame Johannes Breidenbach Isabel Cañellas Christoph Fischer Kari T. Korhonen Jan Máslo Nerea Oliveira John Redmond Thomas Riedel Mitja Skudnik Iciar AlberdiAbstract
Forest diversity is a multidimensional concept comprising different components such as species diversity, functional diversity, structural diversity and genetic diversity. These diverse elements are recognised as being connected to the health and functioning of forest ecosystems and human well-being. However, information on forest diversity at broad spatial scales is scarce. Thus, the primary goal of this study is to quantify compositional diversity (i.e., tree species heterogeneity) and structural diversity (i.e., tree size heterogeneity) across a wide climatic gradient in European forest ecosystems, while also examining the influence of forest attributes and climatic variables on these two key components of forest diversity. Using harmonised data from eight European National Forest Inventories (n = 146,235 plots), we calculated Shannon’s Diversity Index as a measure of compositional and structural diversity. Finally, we estimated measures of forest diversity at three spatial scales ( α , β and γ -diversity) for each country. Basal area was positively related to compositional and structural diversity. In contrast, the quadratic mean diameter of the trees in each plot presented both positive and negative relationships with compositional and structural diversity, respectively. Climatic variables played a minor role, with precipitation and temperature showing a positive association with forest compositional and structural diversity. Furthermore, our findings revealed a positive link between compositional and structural diversity. Finally, the compound analyses of α , β , and γ-diversity emerged as key elements in interpreting compositional patterns at landscape scale. Results revealed strong scale dependence (from local to landscape level) in diversity metrics across countries, thereby highlighting the importance of reporting national forest information at multiple spatial scales.
Abstract
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.
