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With the intensification of the effects of climate change, the urgent need to address their drivers, especially greenhouse gas emissions, has become essential. In this context, forests offer a robust solution, with their potential to store and mitigate carbon emissions. However, striking a balance is critical given the significant economic contribution of the forestry and wood-based industries, which account for about 5% of Romania's GDP and employ 6% (around 300 thousand) of its active workforce. This study, conducted in the Piatra Craiului National Park located in Romania's Southern Carpathians, we utilize the EFISCEN application to generate three distinct 50-year forest evolution scenarios based on harvest intensity, namely Business As Usual (BAU), Maximum Intensity (MAX), and No Harvest (MIN), on two historical different managed forests, i.e., conservation and production. The study aims to guide forest owners in decision making with scenario modeling tools, with the objectives of assessing the forest carbon sequestration potential and evaluating the economic feasibility. In the most probable scenario, the BAU scenario, the growing stock increases from 2.6 million m3 to 3.8 million m3 over 50 years, with a more than 40% increase. Comparing the carbon stock change for all tree harvest scenario types indicates that the MIN scenario has the highest carbon sink capacity in the next 50 years; the BAU scenario is a well-balanced option between carbon sink and wood provision and has an optimal EUR 3.7 million in annual revenue. The MAX scenario can boost the growth and increase the annual revenue from wood by 35% but is effective only for a short time and thus has the smallest calculated revenue in time. Achieving a win–win relationship between carbon sequestration and wood supply is imperative, as well as good planning and scenarios to contribute to climate mitigation and also as provisions for local communities and to sustain the local economy. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) has emerged as a powerful tool for acquiring detailed three-dimensional information about tree species. This study focuses on the development of models for tree volume estimation using TLS data for even aged Fagus sylvatica L. stands located in the western part of the Southern Carpathians, Romania. Both parametric and non-parametric modeling approaches were explored, leveraging variables extracted from TLS point clouds such as diameter at breast height (DBH), height, crown radius, and other relevant crown and height parameters. Reference data were collected through high-precision field measurements across 76 circular Permanent Sample Areas (PSA) spanning 500 m² each. A multi-scan approach was implemented for TLS data collection, involving four scanning stations within each PSA. Concurrently, parametric (regression equations) and non-parametric (Random Forest - RF) models were applied, leveraging all TLS-derived variables to explore potential enhancements in volume estimation accuracy. Among the parametric models, the most effective performer was the one featuring solely DBH as an input variable. The RF non-parametric model yielded more accurate stem volume estimates (RMSE=1.52 m³ *0.1ha-1; RRMSE=3.62%; MAE=1.22 m3 *0.1ha-1) compared to the best-performing regression model (RMSE=5.24 m³ *0.1ha-1; RRMSE=12.48%; MAE=4.28 m³ *0.1ha-1). Both types of models identified DBH as the most important predictive variable, while the RF model also included height and crown related parameters among the variables of importance. Results demonstrate the effectiveness of the non-parametric RF model in providing accurate and robust estimates of tree stem volume within even aged European beech stands. The integration of these models in operational forestry can enhance precision in biomass estimation and forest resource management. Future studies should aim to validate these models across diverse forest ecosystems to further refine and enhance their applicability. [ABSTRACT FROM AUTHOR]