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.

Resin is a renewable non-timber forest product that is used as a raw material in a wide range of goods, thereby holding significant socioeconomic importance and relevance across multiple industrial sectors. This study aims to provide a comprehensive review of the main factors influencing natural resin production in Pinus stands, as well as to address the effects of these factors on tree growth dynamics and resin yield optimization. Among these factors, dendrometric characteristics, environmental conditions, and silvicultural practices, such as thinning, pruning, and prescribed burning, are particularly relevant. However, the scientific literature presents conflicting results regarding the influence of these factors on resin yield, as well as the impacts of resin tapping on tree growth and wood quality. These divergences highlight the complexity of the process and reinforce the need for further studies to clarify the interactions between silvicultural practices in Pinus stands and resin production. Understanding these practices is essential for the development and implementation of efficient silvicultural models aimed at optimizing resin tapping that are properly tailored to the specific conditions of each site. In this context, the development of management models that integrate both timber and resin production is fundamental for simulating management scenarios, generating yield forecasts, and supporting decision-making processes. It is worth noting that management models focused on maximizing resin production may differ from conventional approaches intended for pulpwood or sawtimber production. Nevertheless, integrating resin tapping with timber harvesting holds significant potential to increase the profitability of forest operations.

The maritime pine (Pinus pinaster Ait.) forest is an essential element of the Portuguese forest landscape due to its social, economic, and environmental importance. The sustainability of these forests in the Mediterranean region faces challenges due to recurrent forest fires and the absence of or delayed management. The species has a high capacity for regeneration, but the perpetuation of pine forests in sustainable conditions depends on adequate management to achieve high biomass production and assure fire resilience. This study aimed to analyse four management scenarios (C1 to C4) for the natural regeneration of maritime pine in six areas with stand ages ranging from 6 to 16 years and densities varying from 15,000 to circa 93,000 trees per ha. The same four scenarios were implemented in each of the six areas. The scenarios considered the evolution of forest growth according to different management prescriptions and were simulated using Modispinaster and PiroPinus models. Scenario C1 considered no intervention, with only the final cut. Scenario C2 considered a thinning schedule to maintain the stand within the 50–60% range of the Stand Density Index (SDI). Scenario C3 followed the area’s Forest Management Plan (PGF), which typically includes two or three thinning operations throughout the cycle. Scenario C4 was adapted from the MS1 silvicultural model of the National Institute for Nature Conservation and Forests—ICNF, which involves opening strips at earlier ages (3 and 6 years), with the selection of trees to remain in the wooded area carried out between 4 and 10 years of age and performing thinning whenever the Wilson Spacing Factor (FW) reaches 0.21. The final cutting age was assumed to be 45 years but could be lowered to 35 years in Scenario 3 if defined in the plan. Based on the indicators generated by the simulators, the results showed variations in the total volume of timber produced at the time of harvest depending on the silvicultural guidelines. Scenario C4 was the most effective in generating the highest individual tree volume at the end of the cycle and the total volume of timber collected throughout the cycle. The ability of the forest to resist fire was evaluated before and after the first treatment for density reduction. The treatments performed did not decrease the resistance to fire control.

Maritime Pine sector is an important agent for promoting economy and sustainable development in Portugal. Among the products explored in these forests are wood and resin. The objective of this work was to evaluate the technical and financial viability from the creation of three simulated exploration scenarios: Wood Exploration (W), Resin (R) and Wood + Resin (WR), for this it was defined a loss of 16% of wood volume in resined forest stands and created operational models that defined the costs, investments and revenues that made it possible to prepare cash flow for each scenario and apply the Internal Rate of Return (IRR) feasibility analysis indicators, Net Current Value (VAL), Net Profitability Index (NPI) and Discounted Pay Back (DPB). Scenario R presented the best indicators, however, with absolute values that were not attractive and that the W and WR scenario presented viability but with a DPB at 10 years of age. The loss of wood was not compensated by resin production. The indicators of the WR scenario fell short of those observed in scenario R. Increased market price of resin and scale gain can overcome wood loss and ensure added value in the joint exploration of wood and resin.

The northern inland region of Portugal has experienced significant population decline due to the exodus of younger generations and an aging population. This has led to the abandonment of traditional activities in these territories, contributing to territorial abandonment, degradation of local economic conditions, increased social vulnerability, and a heightened risk of rural fires. The presence of communal lands, known as “baldios”, is an important facilitator for implementing actions that revitalize local villages, making them more attractive to the community. Forests, which are abundant in the baldios of northern inland Portugal, have the potential to generate environmental, social, and economic value through carbon sequestration, job creation, population stabilization, and wealth generation in the villages. However, the viability of this asset as a driver for sustainable development depends on the forest management model implemented. This case study aims to demonstrate that different forest management models have varied impacts on sustainability indicators, particularly economic and environmental sustainability. Based on naturally regenerated forests in the Carvalhelhos baldio in the Tâmega Valley region, data were collected to simulate in software four management scenarios, varying the number (0 to 4) and age of thinnings until the final cut. The simulation allowed for the calculation of the following economic indicators: Internal Rate of Return (IRR), Net Present Value (NPV), and Net Profitability Index (NPI), as well as environmental indicators related to carbon capture and accumulation, such as Gross Carbon Accumulation, Net Carbon Accumulation, Carbon accumulated in dead and suppressed trees, and carbon balance per management model. The simulations indicate that, for the studied area, Scenario 2, which involves only one thinning, yielded the highest total wood volume (cubic meters per hectare) over the cycle, making it the most suitable for biomass production. Meanwhile, Scenario 4, with three thinnings, showed the best results for individual volume (cubic meters per tree), making it more suitable for producing higher-value logs. Scenario 5 presented the best economic results and carbon capture. In all simulations, Scenario 1 showed the worst performance in the analyzed indicators. It was found that the indicators varied among the studied crop plans, highlighting that the adoption of a silvicultural regime depends on the forest characteristics, objectives, exploitation conditions, and local population sensitivity to regional priorities.

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species.