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The stand structure of a virgin forest situated at an average altitude of 1130 m a.s.l. in the Milea Viforâta Nature Reserve (Southern Carpathians, Romania) was investigated to determine the specific development phases of the forest and understand how they influence the stand structure, with the aim of providing optimal solutions and structural models for sustainable forest management. All trees with breast height diameter (dbh) ≥ 8 cm were inventoried in the study plot (1 ha), and the main dendrometrical variables were measured. Radial increment cores were taken from all the trees and were subsequently processed. A total of 317 trees from three species -European beech (Fagus sylvatica), silver fir (Abies alba) and Norway spruce (Picea abies)were sampled at different development phases (optimum, ageing, breakdown and dieback, rejuvenation). Testing stand structural diversity with the Gini index, a minimal stability was found in the rejuvenation development phase and a maximum stability in the ageing phase. No significant match was found between standard theoretical functions (Normal, Weibull, Gamma and Exponential) and the observed distribution of tree diameter. Also, it was confirmed that dominance of beech in all development phases is a consequence of its high competitive ability and its capacity to endure difficult environmental and biologically stressful conditions. The results revealed a series of structural models specific to these forest ecosystems, which can help managing forests under the selection system.

The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m 3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m 3 ).