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The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. The precision of tree crown detection algorithms plays a critical role in providing reliable data for these applications, where even slight inaccuracies can lead to significant deviations in tree population estimates and ecological indicators. Various algorithmic parameters, such as pixel size and crown segmentation thresholds, can substantially impact tree crown detection accuracy. This study aims to explore the influence of tree stand features and parameters on the effectiveness of the individual tree crown detection method based on a watershed algorithm, leading to identifying optimal configurations that enhance the reliability of forest inventories and support sustainable management practices. Our analysis of the algorithm results shows that the features of the tree stand, such as tree height variance and tree crown size variance, significantly impact the algorithm's output in precisely estimating tree count. Consequently, adjusting the pixel size of a canopy height model in the context of tree stand features is necessary to minimize error. Additionally, our findings show that there is a need to carefully assess the criterion of membership of a detected tree crown in a circular sample plot, which we based on the point cloud. [ABSTRACT FROM AUTHOR]

The mixed estimator (ME) for annual forest inventory proposed by van Deusen (1999; Can. J. For. Res. 29: 1824–1828) is reformulated as a linear mixed model. This equivalent structure admits an interpretation of the ME as a polynomial regression on year with correlated year-specific random effects. It also uncovers the necessary criterion for maximum likelihood (ML) estimation. The improved performance of the ME under ML estimation is illustrated through simulations and application to inventory data from Montana, USA. Limitations of the ME relating to model-misspecification are also discussed. [ABSTRACT FROM AUTHOR]

The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns. [ABSTRACT FROM AUTHOR]

The concept of tree or stem form has been central to forest research for over a century, playing a vital role in accurately assessing tree growth, volume, and biomass. The form factor is an essential component for expressing the shape of a tree, enabling more accurate volume estimation, which is vital for sustainable forest management and planning. Despite its simplicity, flexibility, and advantages in volume estimation, the form factor has received less attention compared to other measures like taper equations and form quotient. This review summarizes the concept, theories, and measures of stem form, and describes the factors influencing its variation. It focuses on the form factor, exploring its types, parameterization, and models in the context of various tropical species and geographic conditions. The review also discusses the use of the form factor in volume estimation and the issues with using default or generic values. The reviewed studies show that tree stem form and form factor variations are influenced by multiple site, tree, and stand characteristics, including site quality, soil type, climate conditions, tree species, age, crown metrics, genetic factors, stand density, and silviculture. The breast height form factor is the most adopted among the three common types of form factors due to its comparative benefits. Of the five most tested form factor functions for predicting tree form factors, Pollanschütz's function is generally considered the best. However, its performance is often not significantly different from other models. This review identifies the "Hohenadl" method and mixed-effects modelling as overlooked yet potentially valuable approaches for form factor modelling. Using the form factor, especially by diameter or age classes, can enhance tree volume estimation, surpassing volume equations. However, relying on default or generic form factors can lead to volume and biomass estimation errors of up to 17–35%, underscoring the need to limit variation sources in form factor modelling and application. Further recommendations are provided for improving the statistical techniques involved in developing form factor functions. [ABSTRACT FROM AUTHOR]

Background: Estimating changes in fine fuel loads (FFL) is essential for carbon monitoring and fire management. Field measurements of post-fire fuel response are challenging, leading to reliance on generalised fuel types in operational models. Aims: This study presents a proof-of-concept for estimating fine fuel consumption and accumulation by integrating forest inventory and fire records, aiming to refine fuel dynamics estimates and enhance current practices. Methods: We estimated FFL changes across vertical strata in southeast Australian eucalypt forests, considering burn severity, fire type and forest cover. Fuel consumption was estimated by correlating pre-fire observations with combustion factors defined by burn severity. Fuel accumulation was predicted using modified Olson models with dynamic input parameters. Key results: Wildfires typically occurred in forests with higher FFL and consumed more fuels than prescribed burns. Closed forests experienced greater fuel loss compared with open and woodland forests. Increasing fire severity led to lower decomposition rates and a longer time to reach pre-fire FFL, with denser forests showing higher accumulation rates. Conclusions: Integrating forest inventory and fire history data offers valuable insights into fuel dynamics, potentially enhancing existing fuel hazard models. Implications: The approach is applicable in regions with mature forest inventories and advanced fire severity mapping. This study presents a proof-of-concept for estimating fine fuel consumption and accumulation by integrating forest inventory and fire records, aiming to refine fuel dynamics estimates and enhance current practices. We estimated FFL changes across different vertical strata in southeast Australian eucalypt forests, considering burn severity, fire type and forest cover. [ABSTRACT FROM AUTHOR]

Abstract Key message International forest reporting processes and increasing forest disturbances delineate new requirements regarding the information delivered by national forest inventories (NFI), with implications on their sampling strategies. An original comparative review of the sampling designs of 6 pioneer NFI programs being both annual and 5-year periodic evidences a set of common principles used to meet these demands, but also marked implementation differences, and open questions. Bases for a common framework and persisting research needs are highlighted. Developing virtual forest sampling simulation facilities at large scale is a critical challenge. Context National forest inventories (NFI) rely on diverse sampling strategies. In view of international forest reporting processes, these surveys are increasingly adopting a 5-year periodicity (their bar). The increased need for delivering updated representative statistics in the context of the environmental crisis is making annual forest inventory (their beat) a growing standard of the forest monitoring approach. To meet both objectives, spatially balanced sampling designs resulting in samples that can be split into yearly systematic subsamples have been devised. They ground the grid-based interpenetrating panel design principle that has generated various ingenious designs, however never presented nor reviewed to date. Aims The purpose of this review was to explore how the interpenetrating panel design principle has been implemented by the NFIs that have turned annual. The aims were to describe and frame the diversity of their designs, highlight their common bases and differences, and compare their ability to address new reporting needs. A special emphasis was placed on the graphical representation of these sampling designs. The NFI programs of France, Norway, Poland, Romania, Sweden, and of the USA were considered. Results The interpenetrating panel design principle is effective in reviewed inventories and is associated with the 5-year moving-window estimator. Original and creative design developments were identified, causing substantial variations in its implementation. They concern panel geometry, unaligned sampling options, sampling unit status, and estimation methods, making no-two inventory designs identical among those reviewed. In these inventories, the notions of annual and cyclic inventory do not substitute for each other, but appear to complement themselves to serve distinct reporting purposes. Also, negative coordination among annual samples is observed, questioning their adequacy for trend monitoring purposes. Conclusions The review evidences that a core sampling design principle, used to simultaneously operate annual and 5-year periodic forest inventory, has given rise to a diversity of implementation options. While it offers an original benchmark for any survey transition toward an annual frequency, it demonstrates the absence of a standardized framework. Developing simulation facilities for the comparison and optimization of associated designs appears as a critical priority, especially in the context of the EC forest monitoring perspective.

Personal laser scanning (PLS) systems are gaining popularity in forest inventory research and practice. They are primarily utilized on circular or compact rectangular sample plots to mitigate potential instrument drift and enhance tree detection rates, and a closed-loop scan path is usually implemented to achieve these objectives, ensuring thorough coverage of the plot. This study introduced a novel approach by applying the distance-sampling framework to PLS data collected during walks along line transects. Modeling the distance-dependent probability of tree detection using PLS coupled with automatic routines for point cloud processing aimed to ascertain the optimal width of line-plots to maximize tree detection rates. The optimized plots exhibited tree detection rates exceeding 99%, which facilitated accurate estimates of tree density, basal area, and growing stock volumes. This proposed method demonstrated considerable potential for data collection while walking along line transects in forests. For instance, the otherwise unproductive working time of field crews moving between systematically arranged sample plots can be utilized for additional data collection without generating additional costs. This innovative approach not only enhances operational efficiency but also establishes a foundation for further advancements to explore PLS applications in forest management practices.

Key message: International forest reporting processes and increasing forest disturbances delineate new requirements regarding the information delivered by national forest inventories (NFI), with implications on their sampling strategies. An original comparative review of the sampling designs of 6 pioneer NFI programs being both annual and 5-year periodic evidences a set of common principles used to meet these demands, but also marked implementation differences, and open questions. Bases for a common framework and persisting research needs are highlighted. Developing virtual forest sampling simulation facilities at large scale is a critical challenge. Context: National forest inventories (NFI) rely on diverse sampling strategies. In view of international forest reporting processes, these surveys are increasingly adopting a 5-year periodicity (their bar). The increased need for delivering updated representative statistics in the context of the environmental crisis is making annual forest inventory (their beat) a growing standard of the forest monitoring approach. To meet both objectives, spatially balanced sampling designs resulting in samples that can be split into yearly systematic subsamples have been devised. They ground the grid-based interpenetrating panel design principle that has generated various ingenious designs, however never presented nor reviewed to date. Aims: The purpose of this review was to explore how the interpenetrating panel design principle has been implemented by the NFIs that have turned annual. The aims were to describe and frame the diversity of their designs, highlight their common bases and differences, and compare their ability to address new reporting needs. A special emphasis was placed on the graphical representation of these sampling designs. The NFI programs of France, Norway, Poland, Romania, Sweden, and of the USA were considered. Results: The interpenetrating panel design principle is effective in reviewed inventories and is associated with the 5-year moving-window estimator. Original and creative design developments were identified, causing substantial variations in its implementation. They concern panel geometry, unaligned sampling options, sampling unit status, and estimation methods, making no-two inventory designs identical among those reviewed. In these inventories, the notions of annual and cyclic inventory do not substitute for each other, but appear to complement themselves to serve distinct reporting purposes. Also, negative coordination among annual samples is observed, questioning their adequacy for trend monitoring purposes. Conclusions: The review evidences that a core sampling design principle, used to simultaneously operate annual and 5-year periodic forest inventory, has given rise to a diversity of implementation options. While it offers an original benchmark for any survey transition toward an annual frequency, it demonstrates the absence of a standardized framework. Developing simulation facilities for the comparison and optimization of associated designs appears as a critical priority, especially in the context of the EC forest monitoring perspective. [ABSTRACT FROM AUTHOR]

The development of a model is highly crucial in cases where there are intricate geographical features, and conducting a forest inventory is both time‐consuming and expensive, requiring significant manual effort for measurement. Acquiring reliable data regarding the forest's condition and future progression is essential for making informed decisions about its management. Therefore, this research aimed to create an individual tree diameter growth model specifically for Terminalia alata (B. Heyne. ex Roth). This study was conducted in Terai Arc Landscape of Nepal, encompassing 14 districts in the Terai and Chure regions of Nepal. Individual tree data (diameter at breast height, tree height, crown height, crown cover, longitude, and latitude) from three different time periods (2011, 2017, and 2022) were obtained with 673 sample plots maintained for forest research assessment by Government of Nepal, and annual diameter growth was estimated. Multiple linear, linear mixed, and generalized additive models were employed to fit the growth modeling for individual tree diameter growth of T. alata. We observed higher mean diameter growth rates in 0–25 cm and 101–125 cm tree diameter classes (0.318 cm·yr−1). There were significant differences in diameter growth across tree quality classes, but no significant differences due to crown classes were observed. Although the generalized additive model (Adj. R2 = 0.32) performed better than the linear mixed model (adj. R2 = 0.23) and the multiple linear model (adj. R2 = 0.03), it still explained only a small proportion of the variance in diameter growth. This suggests that other factors, such as unmeasured environmental variables, biotic interactions, or complex nonlinear relationships, may play a significant role in explaining the variation. In addition, the low R2 values indicate that the models may need further refinement, possibly by incorporating interaction terms, random effects, or other possible nonlinear approaches. Future research should also consider the potential influence of spatial or temporal heterogeneity on the growth dynamics. [ABSTRACT FROM AUTHOR]

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. [ABSTRACT FROM AUTHOR]

Remote sensing makes it possible to understand the physical, chemical and biological characteristics of a forest system, indirectly, which has been a challenge in several studies. Correlating data from these sensors with field-acquired sample data, through forest formations collection and measurements, can be a highly effective strategy in comprehending forest structure and the entire fragment under study. A literature review on the subject enabled an analysis and discussion about methodologies used in academic studies, particularly vegetation indices extracted from satellite image data, as well as their correlation with primary forest inventory data, using the statistical model of linear regression. The practical development of the work took place in a forest fragment of a phytogeographic formation called Seasonal Deciduous Forest (Dry Forest) in Funilândia, Minas Gerais, Brasil, where 8 (eight) sampled plots were duly inventoried and subjected to statistical analyses in the office. In this research it was found a strong correlation between vegetation indices, especially the GNDVI (0,82), and the wood volumes of the samples collected in the field. This fact allowed the main objective of this research to be achieved, which was to extrapolate the volume of the entire forest fragment studied using the predictive model. [ABSTRACT FROM AUTHOR]

The study analysed 2019–2022 summertime canopy height predictions (HICESat) given in ICESat-2 ATLAS dataset ATL08 for hemiboreal forests growing on an area of 40,000 km2 in Estonia around 25.6° E, 58.8° N. In total 12,711 ATL08 20×20 m pixel observations were used from 3,065 forest stands with homogenous canopy structure. Regression modelling was used to explain variability in ground surface elevation estimates, and relationships of HICESat to basal area weighted mean tree height given in the forest inventory database (HFI) and to the 95th percentile of the vertical distribution of airborne laser scanning pulse return (HALS). The other explanatory variables were the ICESat-2 ATLAS observation geographic location, ICESat-2 ATLAS track and beam energy indicators, forest canopy cover, evergreen coniferous tree dominance indicator, and deep peat soil indicator. The linear model between the Estonian digital terrain model elevation and ATL08 ground elevation had a determination coefficient of R2=99.97% and residual standard error of δ=0.51 m when a geographic location was included. The HFI can be predicted from HICESat with R2=85% and δ=2.7 m. A comparison of means indicated that, on average, HICESat was about 0.3 m greater than HFI. All the predictive variables (except the geographic location) were significant in canopy height models, and the best models fitted HICESat with R2=95% and δ=1.6 m, however, there was no notable increase in R2 if more predictors than HALS were added in the models. In practical applications using ATL08 data for forest inventories, the inclusion of weak energy beam observations increases the number of observations, but the beam energy indicator has to be included in the models. [ABSTRACT FROM AUTHOR]

Errors in forest inventory data can lead to sub-optimal management decisions and dramatic economic losses. Forest inventory approaches are typically evaluated by their levels of precision and accuracy; however, this overlooks the specific usefulness of the data in decision-making. By evaluating the value of information (VoI), we can assess the usefulness of the data for specific decision-making problems. We evaluated the VoI through stochastic programming for four airborne laser scanning-based inventory approaches. The stochastic programming model explored the trade-off between the maximal net present value and the minimal conditional value at risk of meeting specified periodic income targets. We evaluated a range of periodic targets and risk aversion preference levels. To compare the performance of the inventory approaches, we used a reference dataset that was acquired using a forest harvester with precise positioning. For a wide range of the trade-offs, inventory approaches with higher-quality information provided the best overall performance. If only one of the extreme objectives was desired, less precise inventory approaches were sufficient to produce high-quality solutions. [ABSTRACT FROM AUTHOR]

Monitoring forest stands using sampling techniques offers a valuable alternative to conventional forest condition assessment methods in Central Europe. While these designs are optimized for assessing production parameters, their effectiveness for structural characteristics remains unclear. This study evaluates various plot designs to determine their reliability in estimating structural diversity indices, including the Gini index, Artenprofile index, and Shannon index. We compared ten fixed-radius (FR) sampling designs (plot sizes: 50–1250 m2) and a concentric circle (CC) design (500 m2) employed at the Mendel University Forest Enterprise (Křtiny, Czech Republic). The CC design proved adequate for assessing production parameters and structural diversity indices like Artenprofile and Shannon. However, it showed significant limitations for the Gini index (p < 0.01), due to a smaller number of sampled trees. For the Gini index, fixed-radius plots of at least 150 m2, with 200 m2 being the most cost-effective size, provided the most reliable estimates. Interestingly, the CC design may also be less suitable for production parameters, where smaller fixed-radius plots (50 m2) were more effective, requiring fewer total samples despite the need for more plots. [ABSTRACT FROM AUTHOR]

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management. [ABSTRACT FROM AUTHOR]

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R2 = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results. [ABSTRACT FROM AUTHOR]

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Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson's r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches). [ABSTRACT FROM AUTHOR]

Traditional field inventories have been the standard method for collecting detailed forest attribute data. However, these methods are often time-consuming, labor-intensive, and costly, especially for large areas. In contrast, remote sensing technologies, such as unmanned aerial vehicles (UAVs), have become viable alternatives for collecting forest structure data, providing high-resolution images, precision, and the ability to use various sensors. To explore this trend, a bibliometric review was conducted using the Scopus database to examine the evolution of scientific publications and assess the current state of research on using UAVs to estimate dendrometric variables in forest ecosystems. A total of 454 studies were identified, with 199 meeting the established inclusion criteria for further analysis. The findings indicated that China and the United States are the leading contributors to this research domain, with a notable increase in journal publications over the past five years. The predominant focus has been on planted forests, particularly utilizing RGB sensors attached to UAVs for variable estimation. The primary variables assessed using UAV technology include total tree height, DBH, above-ground biomass, and canopy area. Consequently, this review has highlighted the most influential studies in the field, establishing a foundation for future research directions. [ABSTRACT FROM AUTHOR]

Since it is impossible in practice to create a sampling frame for the population of forest trees, forest inventories have relied on indirect sampling methods. This indirect sampling uses two populations: the discrete populations of trees and the continuous population of points, from which trees are being sampled. Important works such as Mandallaz, Eriksson, and Stevens and Urquhart brought the fundamental elements in the formalization of the sampling of trees, by defining the duality principle that relates both populations. They led to the so-called continuous population approach where trees attributes are transformed into attribute density values. However, in these approaches, the trees quickly fade away despite being the target population, while their weight is calculated as the inverse of their inclusion probability. We explain how the generalized weight share method (GWSM) can be used to formalize the link between the two populations. GWSM allows to revisit previous concepts proposed to solve the question of how to produce estimations from tree-level attributes, under uniform random or more complex sampling designs. The principles of the method are explained, and its functioning is illustrated under a variety of points and trees sampling designs, including fixed-area, Bitterlich, and cluster sampling. [ABSTRACT FROM AUTHOR]

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m2 plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation v = 0.000085 D 2.122270 H 0.666217 , and biomass was estimated using the equation A G B = 0.0673 ρ D 2 H 0.976 . Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m3/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m3/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome. [ABSTRACT FROM AUTHOR]

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas. [ABSTRACT FROM AUTHOR]

Accurate identification of whitebark and limber pine has become increasingly important following the 2022 listing of whitebark pine as a threatened species under the Endangered Species Act. However, morphological similarities make identification of the two species difficult where ranges overlap. Using a genetic test that differentiates whitebark and limber pine, we compared field identification by Forest Inventory and Analysis field crews with genetic identification for needle samples from 371 trees. Field identifications were 100% correct for the 76 samples collected from outside regions of species' range overlap. A total of 83% of the field identifications were correct in regions of range overlap (89% for large trees, 88% for saplings, and 78% for seedlings). Field-identified samples were correct 60% of the time for limber pine and >99% for whitebark pine. Random forests analysis revealed that identification accuracy is influenced by crew experience, large (≥ 12.7cm diameter) limber or whitebark pines recorded by field crews on the plot, elevation, Julian day of sample collection, and habitat type. We found that whitebark pine has likely been underestimated, and limber pine overestimated, within their overlapping ranges. We provide insights on improving accuracy of future monitoring where these species overlap. Study Implications : Accurate identification of whitebark pine is critical for monitoring this threatened species, yet distinguishing whitebark from limber pine can be difficult. Genetic analysis determined accuracy of field identification by Forest Inventory and Analysis (FIA) crews was 83% where the species' ranges overlap. Virtually all individuals identified as whitebark pine were genetically confirmed to be whitebark pine, although nearly 40% of individuals identified as limber pine were actually whitebark pine. Thus, previous data underestimated whitebark and overestimated limber pine abundance in the species' range overlap. These results quantify reliability of FIA data for whitebark pine assessments and identify areas for improvement. [ABSTRACT FROM AUTHOR]

Accurate estimates of forest dynamics and above-ground forest biomass for the topographically challenging Himalaya are crucial for understanding carbon storage potential, assessing ecosystem services, and guiding conservation efforts in response to climate change. This dataset provides a manually delineated multi-temporal forest inventory and a comprehensive record of above-ground biomass (AGB) across the Kashmir Himalaya, generated from field observations, advanced remote sensing and machine learning. Data were collected and generated through remote sensing techniques and extensive in-situ measurements of 6220 trees (n=275 plots), including tree diameter at breast height, species composition, and tree density to map forest area and model AGB across varied terrain. The dataset captures major forest types and species-specific AGB variation influenced by elevation, slope, and aspect. Additionally, newly developed species-specific allometric models, improved through the integration of normalized difference vegetation index (NDVI) and topographical augmentation are provided to improve AGB estimation accuracy. This dataset serves as a crucial resource for forest management, carbon monitoring, and ecological modeling, with broad applications in regional conservation strategies, biodiversity planning, and climate policy development in mountainous ecosystems.

Aim of study: To predict the productivity potential of a managed conifer forest by estimating the site index from Light Detection and Ranging (LiDAR) data. Study area: Intensive Carbon Monitoring Site Atopixco, Hidalgo, Mexico. Material and methods: A total of 329 observations from five remeasurements in permanent forest inventory sampling units were used to generate site index curves and metrics derived from a 2013 LiDAR scan. LiDAR elevation metrics were statistically related to field-observed dominant height (DH). Three models were fitted to predict DH as a function of LiDAR metrics, while nine height growth models were developed using the algebraic difference approach, at a base age of 40 years, using the ordinary least squares method and mixed effects models (MEM). Main results: The 99th height percentile was the LiDAR metric that showed the greatest correlation with the observed DH. Its integration into a linear model was best suited to estimate DH with Adjusted Determination Coefficient (R2adj) of 0.97 and Root Mean Square Error (RMSE) of 0.31 m. The Hossfeld IV anamorphic model adjusted as MEM and autocorrelation corrected model showed the best performance for predicting DH growth with R2adj of 0.87 and RMSE of 2.11 m. The integration of both models into a Geographic Information System (GIS) allowed the spatially explicit construction of an accurate mosaic of the DH and site index to classify stand productivity in the study area. Research highlights: Of the total area managed for timber purposes, 87% is classified as a heigh (≥31 m) and average (26 m) site index, while areas dedicated to conservation contain 13% of the area classified with low site index (≤21 m).

Abstract Key message Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees. Context The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration. Aims The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots. Methods Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression. Results Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data. Conclusion The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.

Remote sensing is increasingly being used to create large-scale forest descriptions. In New Zealand, where radiata pine (Pinus radiata) plantations dominate the forestry sector, the current national forest description lacks spatially explicit information and struggles to capture data on small-scale forests. This is important as these forests are expected to contribute significantly to future wood supply and carbon sequestration. This study demonstrates the development of a spatially explicit, remote sensing-based forest description for the Gisborne region, a major forest growing area. We combined deep learning-based forest mapping using high-resolution aerial imagery with regional airborne laser scanning (ALS) data to map all planted forest and estimate key attributes. The deep learning model accurately delineated planted forests, including large estates, small woodlots, and newly established stands as young as 3-years post planting. It achieved an intersection over union of 0.94, precision of 0.96, and recall of 0.98 on a withheld dataset. ALS-derived models for estimating mean top height, total stem volume, and stand age showed good performance (R2 = 0.94, 0.82, and 0.94 respectively). The resulting spatially explicit forest description provides wall-to-wall information on forest extent, age, and volume for all sizes of forest. This enables stratification by key variables for wood supply forecasting, harvest planning, and infrastructure investment decisions. We propose satellite-based harvest detection and digital photogrammetry to continuously update the initial forest description. This methodology enables near real-time monitoring of planted forests at all scales and is adaptable to other regions with similar data availability.

Key message: Data acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees. Context: The effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration. Aims: The objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots. Methods: Tree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression. Results: Prediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data. Conclusion: The MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions. [ABSTRACT FROM AUTHOR]

Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution. [ABSTRACT FROM AUTHOR]

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The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass. [ABSTRACT FROM AUTHOR]

The Andean Patagonian forests are characterized, on the one hand, by a high degree of naturalness and, on the other hand, by signs of degradation by grazing, unsustainable timber use, the invasion of non-native tree species and anthropogenic fire. Considering the large extent of these forests and the variety of uses, traditional forestry practices should be revised in light of modern technologies. We employed Landsat time series from 1998 to 2020, accompanied by Landsat images from 1985, 1986, 1987, and 1992 to detect the fine-scale gap dynamics of Nothofagus pumilio forests in central Patagonia, Argentina. These gaps can occur naturally (i.e. death of old trees) or can be anthropogenically induced (i.e. after logging). In total, 41 permanently established study plots covering an area of 40 ha were investigated. Our aim was to test the viability of the TimeSync method for detecting fine-scale disturbances. This method was proposed in 2010, and up to date, it has only been employed to study vegetation disturbance of a scale that affects several pixels. Our adaptation of the TimeSync method proved to be useful in identifying the time and intensity of changes in tree structure at the pixel scale. Accordingly, it can support forest monitoring and management by providing a cost-effective proxy for natural dynamics. [ABSTRACT FROM AUTHOR]

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non‐destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory‐based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ‐400(i) TLS‐based D and H data, allowing accuracy assessment of local forest inventory‐based H:D allometries. Additionally, TLS‐based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory‐based H:D allometries underestimated H compared with TLS‐based allometries. For 30‐metre‐tall trees, these underestimations varied from −1.6 m (−5.3%) to −7.5 m (−25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as −31.7 m (−41.3%). We propose a TLS‐based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of −4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries. [ABSTRACT FROM AUTHOR]

Low-altitude aerial photogrammetry can be an alternative source of forest inventory data and a practical tool for rapid forest attribute updates. The availability of low-cost unmanned aerial systems (UASs) and continuous technological advances in terms of their flight duration and automation capabilities makes these solutions interesting tools for supporting various forest management needs. However, any practical application requires a priori empirical validation and optimization steps, especially if it is to be used under different forest conditions. This study investigates the influence of the main flight parameters, i.e., ground sampling distance and photo overlap, on the performance of individual tree detection (ITD) stand-level forest inventories, based on photogrammetric data obtained from budget unmanned aerial systems. The investigated sites represented the most common forest conditions in the Polish lowlands. The results showed no direct influence of the investigated factors on growing stock volume predictions within the analyzed range, i.e., overlap from 80 × 80 to 90 × 90% and GSD from 2 to 6 cm. However, we found that the tree detection ratio had an influence on estimation errors, which ranged from 0.6 to 15.3%. The estimates were generally coherent across repeated flights and were not susceptible to the weather conditions encountered. The study demonstrates the suitability of the ITD method for small-area forest inventories using photogrammetric UAV data, as well as its potential optimization for larger-scale surveys. [ABSTRACT FROM AUTHOR]

As the most important way to mitigate climate change, forest carbon storage has been the subject of extensive research. A comprehensive study was carried out to investigate the influence of elevation gradients and diameter classes on the forest growth, composition, diversity, and carbon pools of the Bagh Drush Khel Forest area. Research revealed that elevation gradients significantly influenced the composition, diversity, and carbon pools in forests. At lower elevations, Eucalyptus camaldulensis was the dominant species, with Olea ferruginea as a co-dominant species, whereas at higher elevations, Pinus roxburghii was the dominant species with Quercus incana as a co-dominant species. Regeneration was higher at higher elevations with the maximum number of saplings and seedlings of P. roxburghii. Species diversity association with elevation was negative (R2 = −0.44; p < 0.05—Shannon Index). Soil organic carbon (SOC association with elevation was non-significant while positive with DBH classes (R2 = 0.37; p < 0.05). Overall, carbon pool association with elevation and diameter at breast height (DBH) were negative (R2 = −0.73; p < 0.05) and (R2 = −0.45; p < 0.05). Litter biomass correlated positively with elevation (R2 = 0.25; p < 0.05) and DBH (R2 = 0.11; p < 0.05), while deadwood biomass correlated negatively with elevation gradients (R2 = −0.25; p < 0.05), and no effect was observed for DBH classes. The highest carbon stock (845.89 t C/ha) was calculated at low elevations, which decreased to (516.27 t C/ha) at high elevations. The overall carbon stock calculated was (2016.41 t C/ha) respectively. A total of six tree species were found at the study site. Future research is essential for forest health monitoring and understanding fine-scale impacts. This study offers a methodological framework for similar investigations in unexplored yet potentially significant forest regions worldwide. [ABSTRACT FROM AUTHOR]

This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m2. We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management. [ABSTRACT FROM AUTHOR]

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics. [ABSTRACT FROM AUTHOR]

Forest owners need simple and efficient tools to capture and understand forest metrics. Additionally, they could benefit from emerging technologies in forest analysis. Light detection and ranging (LiDAR) sensors allow for precise measurements of different variables and can be used more easily in forestry settings thanks to their introduction into smartphones and tablets. ForestScanner and Arboreal Forest are two applications (apps) that allow for the measurement of tree diameter at breast height (DBH) on LiDAR-equipped devices. Our study sought to analyze and compare (1) the timing of traditional and LiDAR-based forest inventory methods and (2) the accuracy of traditional and LiDAR-based forest measurements. We established a series of plots to record and compare tree DBH and measurement time between a traditional diameter tape, ForestScanner, and Arboreal Forest. We found that DBH measurements from the apps were in good agreement with diameter tape measurements; however, both apps tended to underestimate DBH. Additionally, measurement time for both apps was found to be significantly shorter than traditional tape measurements. Further improvements in LiDAR apps can present a simple and efficient way for future forest analysis by seasoned foresters and private landowners. Study Implications : LiDAR smartphone applications represent a cost-effective and time-efficient method for wide-spread forest management, especially among landowners who have little access to forestry-specific equipment. As these technologies continue to advance, the incorporation of functions for plot establishment, height measurements, biomass estimations, and ecosystem dynamics could become valuable assets in the forest industry. We encourage individuals to further test LiDAR measurement applications themselves, as further training on these applications will help developers to improve application accuracy. [ABSTRACT FROM AUTHOR]

Sampling surveys are broadly employed in forest inventories due to their efficiency in quantifying forest characteristics. To this end, selecting an appropriate sampling met- hod is essential. This study introduces a new application of horizontal point sampling (HPS). We assess the statistical properties of HPS with variations in crown basal area factor (CBAF) and sample sizes (n). The study further conducts a comparative analysis between HPS and fixed-radius (FR) plot sampling methods. The study was done in an actual open coppice forest and in a simulated forest, using sampling simulations with a large number of repetitions. In HPS, a greater CBAF for a given sample size lead to a higher relative standard error (RSE %). Multiple regression model showed that both n and CBAF influence RSE %. There was an inverse relationship between n and RSE%, whereas a positive relationship was found between CBAF and RSE %. A combination of HPS and crown relascope is easily applicable in forest inventories. This combined method demonstrates efficiency, when compared to the traditional FR for estimating crown cover area in open coppice forests. [ABSTRACT FROM AUTHOR]

Stacked timber is quantified in-store units and then adjusted with a conversion factor for volume estimation in cubic meters, which is important for the wood trade in South America. However, measuring large quantities accurately can be challenging. Digital image processing and artificial intelligence advancements offer promising solutions, making research in this area increasingly attractive. This study aims to estimate conversion factors of stacked Eucalyptus grandis timber using supervised image classification with Artificial Neuronal Network (ANN). Measured data and photographs from an experiment involving thirty stacks of timber were used to achieve this. The conversion factor was determined using photographic methods that involved the applications of equidistant points and ANN and subsequently validated with values observed through the manual method. The ANN method produced more accurate conversion factor estimates than the equidistant points method. Approximately 97% of the ANN estimates were within the ±1% error class, even when using low-resolution digital photographs. [ABSTRACT FROM AUTHOR]

There is considerable interest in identifying and locating natural forests as accurately as possible, because they are deemed essential in preventing biodiversity loss. In the boreal region, natural forests contain a substantial amount of dead wood and exhibit considerable variation in tree age, size, and species composition. However, it is difficult to define natural forests in a quantitative manner. This is an issue, for example, in the Finnish national forest inventory. If naturalness could be related to the metrics derived from tree measurements, it would be easier to locate natural forests based on the inventory data. In this study, we investigated the value of metrics computed from tree locations and tree sizes for the characterization of a key aspect of naturalness, namely, structural naturalness as defined in the Finnish national forest inventory. We used L-moments, Gini coefficient, Lorenz asymmetry, and interquartile range to quantify the variations in tree size at the plot level. We summarized the spatial pattern of trees with a spatial aggregation index. We compared the structural metrics, species proportions, and stand age using the classes of structural naturalness described in the Finnish national forest inventory, which have been determined in the field without strict numerical rules. These categories are 'natural', 'near-natural', and 'non-natural'. We found that the forests evaluated as structurally natural had larger variations in tree size and species composition and showed a more clustered spatial pattern of trees on average, although the variation in the structural metrics was considerable in all three classes. In addition, we used the structural metrics to predict naturalness by employing a random forest algorithm. Based on the structural metrics, it was possible to obtain high precision in the classification only if we simultaneously accepted low recall, and vice versa; the link between the inspected metrics and naturalness evaluated in the field was weak. The stand age separated the three classes more clearly and it also improved the classification. [ABSTRACT FROM AUTHOR]

Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate‐mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies. Plain Language Summary: We investigated the role of western US forests as natural climate solutions by analyzing current forest carbon storage and trends from 2005 to 2019 across 19 forested regions. We found that the Pacific Northwest stores the most carbon, while the Southwest stores the least. Climate, wildfires, and human activities determined carbon amounts. For instance, climate is important in the Southwest and Lower Rockies, while wildfires impact the entire western US, but particularly the Upper Rockies and California. Human activities like harvesting and private ownership, decrease carbon, particularly in the Pacific Northwest. Since 2005, live carbon has declined in many western US areas, likely due to drought and fires. The study highlights the vulnerability of western US forests to climate‐related and human threats to carbon, providing crucial insights for effective climate change mitigation strategies. Understanding these dynamics is essential for scientists, policymakers, and educators working toward sustainable forest management and climate solutions in the region. Key Points: Live carbon has declined across much of western United States forests, likely due to drought and fire, resulting in an increase in dead carbonIn the Pacific Northwest (PNW), harvest led to reduced carbon, but recovery from past harvest likely caused carbon to increase since 2005Our results provide a baseline from which to evaluate future changes and inform management strategies [ABSTRACT FROM AUTHOR]

Accurate timber quantification is essential in forestry and the timber industry, impacting harvest planning, processing, pricing, and overall supply chain management. Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. The proposed procedure combines automatic log detection and diameter distribution models. Automatic log detection was achieved using advanced computer vision techniques, specifically the You Only Look Once version 9 (YOLOv9) model, which automates the identification and counting of individual logs within a stack. We used stem diameter distribution models to estimate total stack volume based on log counts and probability densities. This approach ensures high accuracy and efficiency, significantly reducing the time and effort required for volume estimation. The dataset used for this study includes diameter measurements from a pre-harvest inventory of eucalypt trees aged 6 and 7 years, alongside videos of stacked timber. The YOLOv9 model was trained to detect logs from these videos, achieving high precision in object detection and segmentation tasks. Performance metrics such as Box Precision, Box Recall, and mean Average Precision (mAP) were used to evaluate the model's effectiveness. The results indicate that the model generalizes well to new data, with high accuracy in both validation and test sets. Among the distribution models evaluated, the generalized extreme value (GEV) distribution provided the best fit for the stem diameter data, allowing for accurate volume predictions. This procedure, which integrates automatic log detection with diameter distribution models, offers a scalable solution applicable to large and complex timber stacks. Finally, a repository was established to allow users to test the proposed method. Future works will focus on refining the model's accuracy and expanding its applicability across different species, forest production and log conditions.

Phoenix acaulis (Roxb.), a common understory shrub in Nepal's Chure region, has remained largely understudied until now. Therefore, this study aims to examine the ecological impact of P. acaulis on Shorea robusta (Gaertn. f.) forest properties and to explore associated community perceptions in S. robusta forest of Udayapur district, Nepal. Stratified random sampling was adopted for this study where P. acaulis density (high, low and absent) was considered as basis of stratification. Altogether, 45 rectangular plots of 10 m x 10 m area (15 each category) were established for regeneration survey and soil sample collection (up to 30 cm). Soil quality index (SQI) method was used for soil quality assessment using indicators on the basis of prior studies conducted in Nepal. Using random sampling, a total of 52 households from the community forest user group were interviewed to gather their insights on the perceived effects of P. acaulis and its management. Highest S. robusta seedling and sapling density was observed in P. acaulis absent area (1132 ± 9.65 ha⁻¹ and 60 ± 0.63 ha⁻¹) where lowest in P. acaulis dense area (548 ± 7.4 ha⁻¹ and 4 ± 0.2 ha⁻¹). Similarly, higher SQI was in areas with high P. acaulis density (0.49) followed by low (0.45) and absent area (0.39). Most respondents advocated for removing P. acaulis from the forest, highlighting significant concerns among stakeholders. Our study suggests a positive impact P. acaulis on soil quality but indicates a negative impact on S. robusta regeneration. Therefore, further research to explore management strategies that balance the positive impact on soil quality with the observed negative influence on regeneration in P. acaulis presence areas is recommended.