We analysed 10 years (2008–2017) of continuous eddy covariance (EC) CO₂ flux measurements of net ecosystem exchange (NEE) in a young pedunculate oak forest in Croatia. Measured NEE was gap-filled and partitioned into gross primary productivity (GPP) and ecosystem reparation (R_ECO) using the online tool by Max Planck Institute for Biogeochemistry in Jena, Germany. Annual NEE, GPP, and R_ECO were correlated with main environmental drivers. Net primary productivity was estimated from EC (NPP_EC), as a sum of −NEE and R_h obtained using a constant R_h:R_ECO ratio, and from independent periodic biometric measurements (NPP_BM). For comparing the NPP at the seasonal level, we propose a simple model that aimed at accounting for late-summer and autumn carbon storage in the non-structural carbohydrate pool. Over the study period, Jastrebarsko forest acted as a carbon sink, with an average (±std. dev.) annual NEE of −319 (±94) gC m⁻² year⁻¹, GPP of 1594 (±109) gC m⁻² year⁻¹, and R_ECO of 1275 (±94) gC m⁻² year⁻¹. Annual NEE showed high inter-annual variability and poor correlation with annual average global radiation, air temperature, and total precipitation, but significant (R² = 0.501, p = 0.02) correlation with the change in soil water content between May and September. Comparison of annual NPP_EC and NPP_BM showed a good overall agreement (R² = 0.463, p = 0.03), although in all years NPP_BM was lower than NPP_EC, with averages of 680 (±88) gC m⁻² year⁻¹ and 819 (±89) gC m⁻² year⁻¹, respectively. Lower values of NPP_BM indicate that fine roots and grasses contributions to NPP, which were not measured in the study period, could have an important contribution to the overall ecosystem NPP. At a seasonal level, two NPP estimates showed differences in their dynamic, but the application of the proposed model greatly improved the agreement in the second part of the growing season. Further research is needed on the respiration partitioning and mechanisms of carbon allocation.

The main aim was to develop models for predicting diameter at breast height (DBH), merchantable tree volume (V), and aboveground biomass (AGB) of individual black pine (Pinus nigra Arn.) trees grown in Sub-Mediterranean Croatian pure even-aged forests, which will be suitable for remote sensing based forest inventories. In total, eight variables obtained from field measurement, existing database, and digital terrain model were candidates for independent variables in regression analysis. DBH, V, and AGB were modeled as linear function of each of the independent variables, and all possible linear combinations thereof. Goodness of fit of every model was then evaluated using R² statistic. Comparison between selected models showed that the variability of all dependent variables are explained best by models which include both crown diameter and tree height as independent variables with coefficients of determination of 0.83, 0.89, 0.82 for DBH, V, and AGB, respectively. Consequently, these models may be recommended as the most suited for DBH, V and AGB estimation of black pine trees grown in pure Sub-Mediterranean forest stands using high-resolution aerial images or high-density airborne laser scanning data. This assumption should be further validated by conducting remote sensing inventory and comparing the obtained results with field measurement results.