Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan.

In order to understand the effect of phytotoxic tropospheric ozone (O₃) on terrestrial vegetation, we quantified the impact of current O₃ concentration ([O₃]) on net ecosystem production (NEP) when compared to the conditions of the pre-industrial era. We compared and tested linear mixed-effects models based on [O₃] and stomatal O₃ flux (F_sto). The managed ryegrass–clover (Lolium perenne and Trifolium pratense) mixture was grown on arable land in the Czech Republic, Central Europe. Values of [O₃] and F_sto were measured and calculated based on resistance analogy, respectively, while NEP was calculated from eddy covariance CO₂ fluxes. We found the F_sto-based model more precise when compared to measured NEP. High F_sto was found even at low [O₃], while broad summer maximum of [O₃] was not necessarily followed by significant NEP decline, due to low soil water content leading to a low stomatal conductivity and F_sto. Comparing to low pre-industrial O₃ conditions, current levels of O₃ resulted in the reduction of cumulative NEP over the entire growing season, up to 29.7 and 13.5% when the [O₃]-based and F_sto-based model was applied, respectively. During the growing season, an O₃-induced reduction of NEP ranged between 13.1% in May and 26.2% in July when compared to pre-industrial F_sto levels. Looking to the future, high [O₃] and F_sto may lead to the reduction of current NEP by approximately 13.3% on average during the growing season, but may increase by up to 61–86.6% in autumn, indicating further O₃-induced acceleration of the senescence. These findings indicate the importance of F_sto and its inclusion into the models estimating O₃ effects on terrestrial vegetation. The interaction between environmental factors and stomatal conductance is therefore discussed in detail.

Central European forests suffered from severe, large-scale atmospheric depositions of sulfur and nitrogen due to coal-based energy production during the 20th century. High deposition of acid compounds distorted soil chemistry and had negative effects on forest physiology and growth. Since 1994, continuous data on atmospheric deposition and stream runoff fluxes have provided evidence of ecosystem recovery from acidification. In this study, we combined for the first time mass budget data (sulfur deposition and total dissolved inorganic nitrogen (DIN) export) from the GEOMON monitoring network of headwater catchments with annual trajectories of vegetation indices derived from Landsat remote sensing observations. Time series of selected vegetation indices was constructed from Landsat 5, 7, and 8 using Google Earth Engine. Linear regression between the field data and vegetation indices was analyzed using R software. Biogeochemical responses of the forested catchment to declining acid deposition (driven by SO₂ emission reduction) were consistent across all catchments covering various forest stands from different regions of the Czech Republic. Significant correlations were found with total sulfur depositions, suggesting that the forests are continuously and consistently prospering from reductions in acid deposition. Disturbance index (DI) was the only vegetation index that was well-related to changes in forest cover associated with salvage loggings (due to the forest decline) during the 1980s and 1990s. A significant relationship (R² = 0.82) was found between the change in DI and DIN export in stream water. Regrowth of young forests in these highly affected areas tracks the most pronounced changes in total DIN export, suggesting a prominent role of vegetation in nitrogen retention. With the Landsat-derived DI, we could map decennial changes in forest disturbances beyond the small scale of the catchments to the regional level (demonstrated here for two protected landscape areas). This analysis showed the peak in forest disturbances to have occurred around the mid-1990s, followed by forest recovery and regrowth. Despite the improvement in forest ecosystem functioning over the past three decades in mountainous areas, emerging threats connected to changing climate will shape forest development in the near future.

High-resolution 3D forest representations are essential for remote sensing applications such as above-ground biomass estimation using radiative transfer modelling. However, existing reconstruction approaches are often time-consuming and rely heavily on manual input. A comprehensive and largely automated end-to-end workflow is presented for reconstructing realistic 3D forest representations from terrestrial laser scanning (TLS) data. The workflow includes five main steps: segmentation of individual trees, semantic classification into wood and foliage using a custom-trained PointNet++ model, reconstruction of woody structures via Quantitative Structure Models, biologically realistic foliage placement, and spatial distribution of trees. Reconstructed forest plots from Central Europe were used to simulate airborne laser scanning (ALS) data in Helios++. The results were validated against real ALS acquisitions. The simulated data showed strong agreement with real ALS data across key forest structure metrics, with correlations ranging from R2 = 0.46 for height standard deviation to R2 = 0.96 for mean canopy height, with corresponding nRMSE values ranging between 23.2% and 12.6%. The largest discrepancies occurred in upper canopy regions due to TLS occlusion effects, where dense vegetation blocked the scanner's line of sight, resulting in these areas being underrepresented in the reconstructed 3D scenes. These results demonstrate that detailed 3D forest reconstructions can be achieved with minimal manual inputs, providing a robust basis for radiative transfer modelling and the generation of synthetic remote sensing datasets, which are critical for improving forest monitoring and carbon stock assessments.