Forest ecosystems strongly contribute to the mitigation of climate change impacts through the carbon stored in forests and through harvested wood products, such as sawed wood and furniture, which are obtained from many types of timber assortments. Timber assortments are defined as log sections of specific dimensions (log length and maximum/minimum end diameters), gathered from felled trunks, that have both specific commercial timber utilisation and economic value. However, it is challenging to discriminate and assess timber assortment types, especially within a forest stand before the forest has been harvested. Accurate estimations of timber assortments are a fundamental prerequisite in supporting forest holdings and assisting practitioners in the optimisation of harvesting activities and promoting forest wood chains, in addition to forest policy and planning. Based on the georeferenced points cloud tool, light detection and ranging (LiDAR) is a powerful technology for rapidly and accurately depicting forest structure, even if the use of LiDAR for timber assortments estimation is lacking and poorly explored. This systematic literature review aimed to highlight the state-of-the-art applications of the LiDAR systems (spaceborne; airborne, including unmanned aerial UASs; and terrestrial) to quantify and classify different timber assortment types. A total of 304 peer-reviewed papers were examined. The results highlight a constant increment of published articles using LiDAR systems for forest-related aspects in the period between 2000 and 2021. The most recurring investigation topics in LiDAR studies were forest inventory and forest productivity. No studies were found that used spaceborne LiDAR systems for timber assortment assessments, as these were conditioned by the time and sample size (sample size = ~12 m/~25 m of laser footprint and 0.7 m/60 m of space along the track for ICESat-2, GEDI and time = since 2018). Terrestrial LiDAR systems demonstrated a higher performance in successfully characterising the trees belonging to an understory layer. Combining airborne/UAS systems with terrestrial LiDAR systems is a promising approach to obtain detailed data concerning the timber assortments of large forest covers. Overall, our results reveal that the interest of scientists in using machine and deep learning algorithms for LiDAR processes is steadily increasing.

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

The most significant European forest-related strategies highlight the importance of multifunctional forests for human wellbeing, due to the provision of a wide range of goods and services. However, managing competing aims, such as timber production, economic drivers and biodiversity conservation is often difficult for practitioners. In order to assess the loss and gain of ecosystem services caused by forestry, trade-off evaluation has been increasingly used to aid decision-making. In this study, four silvicultural scenarios are simulated using the Marteloscope approach to evaluate the trade-offs between biodiversity conservation and timber production. Tree-related Microhabitats (TreMs) are used as a proxy to evaluate forest habitat value, while timber production is assessed by the number of harvested trees, biomass removal and economic income. This study takes an innovative approach by investigating TreMs using the Marteloscope in mixed Mediterranean forest. The main findings from this paper confirm that tree-related microhabitats can be considered ecological indicators effective in identifying important habitat trees, to assess forest habitat value and support tree marking for thinning operations and management.

In this work we present an innovative method based on the application of inverse yield models for producing spatially explicit estimations of forest age. Firstly, a raster growing stock volume map was produced using the non-parametric k-Nearest Neighbors estimation method on the basis of IRS LISS-III remotely sensed imagery and field data collected in the framework of a local forest inventory. Secondly, species specific inverted yield equations were applied to estimate forest age as a function of growing stock volume. The method was tested in 128.000 ha of even-aged forests in central Italy (Molise region). The accuracy of the method was assessed using an independent dataset of 305 units from a local standwise forest inventory. The results demonstrated that the forest age map was accurate, with a root mean square error of 15.8 years (30% of the mean of field values), thus at least useful for supporting forest management purposes, such as the assessment of harvesting potential, and of ecosystem services. Thanks to the use of remotely sensed data and spatial modeling, the approach we propose is cost-effective and easily replicable for vast regions.