The reduction of resinous species in Portuguese forest areas has caused constraints to wood industry supplies. Portugal represents the extreme southwest of Scots pine (Pinus sylvestris) natural distribution and large gaps exist in the knowledge of its wood-quality characteristics. Understanding the relationship between these traits is important for recognizing which combination of wood properties is the most suitable for specific uses. To address these questions, we assessed wood-quality traits, namely, wood-density components (microdensitometric analysis), chemical composition (NIR spectrometry) and mechanical properties (bending tests) of wood samples collected at five representative forest sites in Portugal. Our results showed that Portuguese Pinus sylvestris has good radial growth and denser wood, higher extractive content and higher stiffness and strength than northern European provenances. The lignin content was within the range attributed to softwoods. Among the Portuguese stands, trees growing at lower-altitude sites exhibited denser wood and higher mechanical properties, while trees from high-elevations showed higher amounts of lignin. Ring density was more strongly correlated with earlywood than latewood density. A negative, non-significant correlation was found between ring density and width, supporting the assumption that the higher radial growth (ring width) does not negatively affect wood quality (density). In general, chemical properties had a weak relationship with physical and mechanical properties (MOE and MOR). Both mechanical traits were positively correlated with density and growth components, supporting the assumption that trees with high radial growth do not exhibit poorer mechanical performances.

In Europe, P. nigra wood presents a density pattern of longitudinal variation with an increase from east to west. However, no latitudinal tendencies were detected. Compared to other Portuguese resinous species, P. nigra revealed higher density, identical radial growth and intra-ring heterogeneity, which presents advantages for industry purposes. The environmental factors (Sites effect) manifest more strongly in the latewood components while the Trees/Sites effect is more strongly expressed in the earlywood components.

The forest inventory plays a crucial role in forest management planning, and it is the first step in planning actions for forest production. However, conducting an inventory can be expensive and complex. Forest inventory applications on smartphones have emerged as an alternative to traditional methods and they aim to make field data collection more accessible to non-professionals while ensuring accuracy in determining the volume of wood in a given area. This study evaluates the effectiveness of the Katam, Arboreal, and Trestima applications compared to traditional data collection methods. The study focuses on assessing the stand density and diameter of sampled trees—two key variables that are assessed in forest inventories. Two species, maritime pine (Pinus pinaster Aiton) and Eucalyptus spp. (mainly Eucalyptus globulus and Eucalyptus nitens), were used to evaluate the performance of the methods, with assessments performed in the stands of diverse dendrometric characteristics, specifically those regarding the tree age, stand density, and topographic conditions (flat or sloping terrain). For the purpose of comparison, goodness-of-fit statistics (R², RMSE, and BIAS) were calculated, and an analysis of the diameter distribution and comparison of the mean diameter, number of trees per hectare, and basal area were performed. In general, the applications were accurate, and the average basal area did not differ significantly from the traditional method. The diameter measurements showed good accuracy. The accuracy of the applications varied depending on the terrain and forest characteristics, with the applications performing better in areas with flat terrain, as well as with older forests that were regular and had low under-cover density. In contrast, the applications performed worse in younger, irregular forests with sloping terrain, high tree density, and those with a great deal of understory vegetation. The applications still need to evolve in evaluating other important variables (such as tree height or volume) as they are currently estimated from auxiliary variables through mathematical equations.