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Previous studies have suggested that a major part of the observed variability in vegetation state might be associated with variability in climatic drivers during relatively short periods within the year. Identification of such critical climate periods, when a particular climate variable most likely has a pronounced influence on the vegetation state of a particular ecosystem, becomes increasingly important in the light of climate change. In this study, we present a method to identify critical climate periods for eight different semi-natural ecosystem categories in Hungary, in Central Europe. The analysis was based on the moving-window correlation between MODIS NDVI/LAI and six climate variables with different time lags during the period 2000–2020. Distinct differences between the important climate variables, critical period lengths, and direction (positive or negative correlations) have been found for different ecosystem categories. Multiple linear models for NDVI and LAI were constructed to quantify the multivariate influence of the environmental conditions on the vegetation state during the late summer. For grasslands, the best models for NDVI explained 65–87% variance, while for broad-leaved forests, the highest explained variance for LAI was up to 50%. The proposed method can be easily implemented in other geographical locations and can provide essential insight into the functioning of different ecosystem types. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a mandatory pool in national inventory reports on greenhouse gas (GHG) emissions and removals to the UNFCCC. Hence, its accurate assessment is important. Modelling SOC changes for national GHG reports is encouraged, but the uncertainty related to this pool still presents a significant challenge; thus, verifying modelling results with field observations is essential. We used the process-based model Biome-BGCMuSo and assessed its suitability for use in Croatia's GHG reporting. We modelled SOC stocks in the top 30 cm of the mineral soil layer (SOC30) for four different land-use (LU) categories (Deciduous/Coniferous Forest, Grassland and Annual Cropland) distributed in three biogeographical regions (Alpine, Continental and Mediterranean) and compared them with results of a national soil survey. A total of 573 plot level simulations were undertaken and results were evaluated at three stratification levels (LU, LU × biogeographical region, and plot). The model reproduced the overall country mean of SOC30 with no overall bias, and showed good performance at the LU level with no significant (p < 0.05) difference for all LUs except Deciduous Forest (11% overestimation). At finer stratifications, the model performance considerably worsened. Further model calibration, improvement and testing, as well as repeated soil survey are needed in order to assess the changes in SOC30 and to evaluate the potential of the Biome-BGCMuSo model for use in GHG reporting. [ABSTRACT FROM AUTHOR]

Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE% = 12.53%–13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level. [ABSTRACT FROM AUTHOR]

Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate-NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Under current environmental changes, forest management is challenged to foster contrasting benefits from forests, such as continuous wood supply while preserving biomass production, biodiversity conservation, and contribution to climate change mitigation through atmospheric carbon sequestration. Although being found as globally important, estimates of long-term forest C balance are still highly uncertain. In this context, the chronosequence experiments (space-for-time substitution) might fill this gap in even-aged forests, as they represent an approach that enables the assessment of forest net C balance in the long term. In this research, we explored the dynamics of C stocks and fluxes in different forest pools throughout the rotation period (140 years) of a Pedunculate oak (Quercus robur L.) forest in Croatia. For this purpose, we selected a chronosequence that was made up of seven forest stands with different age (5, 13, 38, 53, 68, 108, and 138 years). To address the issues of uncertainty in C balance estimates, we compared net ecosystem carbon balance (NECB) estimated while using two different approaches, which we name pool-change (from C stocks) approach and component-flux (from C fluxes) approach. Overall, the pool-change approach showed higher NECB estimate, with the greatest difference being observed in younger stands (<50 years). Component-flux approach showed significantly higher uncertainty. Throughout the rotation period, managed pedunculate oak stands become a C sink early in their development phase, between the age of 13 and 35 years according to pool-change and component-flux approach, respectively. During the 140 years, oak forest provided 187.2 Mg C ha−1 (604 m3 ha−1) through thinnings and 147.9 Mg C ha−1 (477 m3 ha−1) in the final cut, while preserving, on average, 88.9 Mg C ha−1 in mineral soil down to 40 cm, 18.2 Mg C ha−1 in dead wood, and 6.0 Mg C ha−1 in the forest floor. Soil C stocks in our chronosequence did not show any age-related trend, indicating that current management practice has no negative effect on soil C stocks. Finally, under current close-to-nature forest management, Pedunculate oak forest showed to be sustainable in providing both economic and ecological ecosystem services. [ABSTRACT FROM AUTHOR]

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 (RECO) using the online tool by Max Planck Institute for Biogeochemistry in Jena, Germany. Annual NEE, GPP, and RECO were correlated with main environmental drivers. Net primary productivity was estimated from EC (NPPEC), as a sum of −NEE and Rh obtained using a constant Rh:RECO ratio, and from independent periodic biometric measurements (NPPBM). 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 RECO 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 NPPEC and NPPBM showed a good overall agreement (R2 = 0.463, p = 0.03), although in all years NPPBM was lower than NPPEC, with averages of 680 (±88) gC m−2 year−1 and 819 (±89) gC m−2 year−1, respectively. Lower values of NPPBM 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. [ABSTRACT FROM AUTHOR]