Research Highlights: Forests, due to their aesthetic properties, have huge recreational potential, but their management must take into account the requirements of all parties interested in these services. Background and Objectives: We sought to determine the main indicators that characterize the structural diversity of a recreational mountain forest, with relevance for the management of these forests, given that they fulfill multiple functions. Materials and Methods: The structure of 446 stands was investigated and the Shannon (H) diversity index was applied at the level of species (Hsp), age (Hage), tree diameter (Hd_g), and tree height (Hh_g). Results: Beech occupied 49% of the forest area and fir and spruce 16% each. Generations of trees older than 100 years occupied 71% of the forest area and those older than 150 years occupied 10%. At an age of 120 years, the beech reached a diameter (d, at breast height) of 45 cm and the fir 52 cm. At the forest level, Hsp had a value of 1.63, Hd_g of 3.17, and Hh_g of 2.76. At the stand level, Hsp reached 1.54, while Hd_g and Hh_g reached 1.72. Mixed beech–coniferous stands had the greatest diversity. High values of 1.00 for Hsp were determined for 18% of the stands, for Hd_g 38%, and for Hh_g 35%. Conclusions: Stand structures are in a continuous state of change, so diversity indices can be used to monitor structural and species diversities and to evaluate the recreational potential of stands and forests. A compatibility between the aesthetic qualities of Romanian forests, which is a priority, and the other protection and production services they offer can be achieved by leading the forest stands toward a selection system.

Forests play an important role in biodiversity conservation, being one of the main providers of ecosystem services, according to the Economics of Ecosystems and Biodiversity. The functions and ecosystem services provided by forests are various concerning the natural capital and the socio-economic systems. Past decades of remote-sensing advances make it possible to address a large set of variables, including both biophysical parameters and ecological indicators, that characterize forest ecosystems and their capacity to supply services. This research aims to identify and implement existing methods that can be used for evaluating ecosystem services by employing airborne and terrestrial stationary laser scanning on plots from the Southern Carpathian mountains. Moreover, this paper discusses the adaptation of field-based approaches for evaluating ecological indicators to automated processing techniques based on airborne and terrestrial stationary laser scanning (ALS and TLS). Forest ecosystem functions, such as provisioning, regulation, and support, and the overall forest condition were assessed through the measurement and analysis of stand-based biomass characteristics (e.g., trees’ heights, wood volume), horizontal structure indices (e.g., canopy cover), and recruitment-mortality processes as well as overall health status assessment (e.g., dead trees identification, deadwood volume). The paper, through the implementation of the above-mentioned analyses, facilitates the development of a complex multi-source monitoring approach as a potential solution for assessing ecosystem services provided by the forest, as well as a basis for further monetization approaches.

Research Highlights: Management of the risks forests are exposed to is based on the dynamics of the composition and structure of the stands and the forest. Background and Objectives: This study aimed to document the dynamics of the composition and structure of stands and forest in the Romanian Carpathians over the last five decades, as well as estimate the forecast composition of the forest in the near future (i.e., 2070). Materials and Methods: The obtained results were based on long-term monitoring and analysis of the species and structures in the stands in long-term research areas (over five decades). We performed an inventory of all the trees (on 7.5 ha) in order to characterize the stand structure in sampling plots of 0.25–1.0 ha, located in representative stands of five forest formations. Bitterlich sampling was performed in order to determine the composition of each stand (on 2930.4 ha). The future composition was established in accordance with the characteristics of the natural forest types and was based on seedling dynamics and forest management plans. Results: In mixed beech–coniferous stands, over the last five decades, the area of beech has increased by 38%, while conifers have decreased proportionally—fir by 31% and spruce by 5%. The seedling area increased from 23% to 65%, with fir contributing 22% to the composition and beech 42%. Stand density decreased by an average of 14%, with the current increment decreasing by 3.8%. The slenderness index for fir decreased from 73 to 61. In the near future, there will be an increase in the proportion of fir, from 15 to 33%, and a reduction in beech, from 49 to 45%. The proportion of spruce will be reduced from 17 to 12%. Conclusions: Based on the forest dynamics, management adaptation strategies need to be developed to improve the stability of the forest ecosystems.

Accurate measurement of tree height and diameter at breast height (DBH) is essential for forest inventory, biomass estimation, and habitat assessment but remains challenging in structurally complex, multi-layered forests. This study evaluates the accuracy and operational feasibility of Unmanned Aerial Vehicle (UAV) LiDAR and Mobile Laser Scanning (MLS) for estimating tree height and DBH in such stands with a diverse structure in the Romanian Carpathians. Field measurements from six plots encompassing mixed-species (Fagus sylvatica L., Abies alba Mill., Picea abies (L.) H.Karst.) and single-species (Picea abies) stands were compared against UAV- and MLS-derived metrics. MLS delivered near-inventory-grade DBH accuracy across all species (R² up to 0.98) and reliable height estimates for intermediate and suppressed trees, while UAV LiDAR consistently underestimated tree height, especially in dense, multi-layered stands (R² < 0.2 in mixed plots). Voxel-based occlusion analysis revealed that over 93% of area under canopy and interior crown volume was captured only by MLS, confirming its dominance below the canopy, whereas UAV LiDAR primarily delineated the outer canopy surface. Species traits influenced DBH accuracy locally, but structural complexity and canopy layering were the main drivers of height underestimation. We recommend hybrid UAV–MLS workflows combining UAV efficiency for canopy-scale mapping with MLS precision for stem and sub-canopy structure. Future research should explore multi-season acquisitions, improved SLAM robustness, and automated data fusion to enable scalable, multi-layer forest monitoring for carbon accounting, biodiversity assessment, and sustainable forest management decision making.

Research highlights: In this study, the possibility of developing predictive models for both individual trees and forest stands, based on information derived from digital surface models (DSMs), was evaluated. Background and objectives: Unmanned aerial systems (UASs) make it possible to obtain digital images with increased spectral and spatial resolution at a lower cost. Based on the variables extracted by means of the digital representation of surfaces, we aimed at generating mathematical models that would allow the prediction of the main biometric features of both individual trees and forest stands. Materials and methods: Forest stands are characterized by various structures. As such, measurements may address upper-level trees, but most often are oriented towards those belonging to the mean tree category, randomly selected from those identifiable from digital models. In the case of grouped trees, it is the best practice to measure the projected area of the entire canopy. Tree and stand volumes can be determined using models based on features measured in UAS-derived digital models. For the current study, 170-year-old mixed sessile oak stands were examined. Results: Mathematical models were developed based on variables (i.e., crown diameter and tree height) extracted from digital models. In this way, we obtained results characterized by root mean square error (RMSE) values of 18.37% for crown diameter, 10.95% for tree height, and 8.70% for volume. The simplified process allowed for the estimates of the stand volume using crown diameter or diameter at breast height, producing results with RMSE values of 9%. Conclusions: The accuracy of the evaluation of the main biometric features depends on the structural complexity of the studied plots, and on the quality of the DSM. In turn, this leads to the necessity to parametrize the used models in such a manner that can explain the variation induced by the stand structure.