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Persistence of standing dead trees (snags) is an important determinant for their role for biodiversity and dead wood associated carbon fluxes. How fast snags fall varies widely among species and regions and is further influenced by a variety of stand‐ and tree‐level factors. However, our understanding of this variation is fragmentary at best, partly due to lack of empirical data.Here, we took advantage of the accruing time series of snag observations in the Finnish, Norwegian and Swedish National Forest Inventories that have been followed in these programs since the mid‐1990s. We first harmonized observations from slightly different inventory protocols and then, using this harmonized dataset of ca. 43,000 observations that had a consistent 5‐year census interval, we modelled the probability of snags of the main boreal tree species Pinus sylvestris, Picea abies and Betula spp. falling, as a function of tree‐ and stand‐level variables, using Bayesian logistic regression modelling.The models were moderately good at predicting snags remaining standing or falling, with a correct classification rate ranging from 68% to 75% among species.In general, snag persistence increased with tree size and climatic wetness, and decreased with temperature sum, advancing stage of decay, site productivity and disturbance intensity (mainly harvesting).Synthesis and applications: The effect of harvesting demonstrates that an efficient avenue to increase the amount of snags in managed forests is protecting them during silvicultural operations. In the warmer future, negative relationship between snag persistence and temperature suggests decreasing the time snags remain standing and hence decreasing habitat availability for associated species. As decomposition rates generally increase after fall, decreasing snag persistence also implies substantially faster release of carbon from dead wood. [ABSTRACT FROM AUTHOR]

Forest ecosystems are long-term exposed to dry periods in Europe, which leads to a significant loss of vitality and higher mortality, especially in coniferous forests. Identifying stress in the early stages when measures can be taken to protect the forest and living trees is crucial. Current detection methods are based on field surveys by forest workers or remote sensing methods to cover larger areas, which use changes in spectral reflectance of the forest canopy. In some cases, the attacked trees do not change their appearance, and based on calculations of vegetation indices from remote sensing data, the attack cannot be mapped. We present an innovative methodology based on non-optical analysis, namely identifying a group of volatile compounds and microclimate signs in forest stands that indicate stress factors in forest stands. An attacked tree by a bark beetle produces increased amounts of biogenic volatile organic compounds associated with defense, and the microclimate changes due to interrupted transpiration. In addition, the bark beetle uses the aggregation pheromone to attract more individuals and to attack the tree massively. In this study, we tested three electronic noses (Miniature Bosch sensor device with 25,419 samples, Sensory device for environmental applications with 193 samples, Handheld VOC Detector Tiger with 170 samples) in a freshly infested spruce stand. The measurement was conducted at ground level with the help of a human operator and was repeated six times to verify the detection capability of the electronic noses. To verify the capability of electronic noses to predict tree infestation, we used machine learning Random Forest. The results demonstrated that electronic noses can detect bark beetle infestation start (within 1 week of the first attack). The Miniature Bosch sensor device achieved the highest accuracy with a value of 95%, in distinguishing forest sections that are healthy and infested; the second most accurate electronic nose is the Sensory device for environmental applications, with an accuracy of 89%. Our proposed methodology could be used to detect bark beetle presence. [ABSTRACT FROM AUTHOR]

Tree regeneration shapes forest carbon dynamics by determining long‐term forest composition and structure, which suggests that threats to natural regeneration may diminish the capacity of forests to replace live tree carbon transferred to the atmosphere or other pools through tree mortality. Yet, the potential implications of tree regeneration patterns for future carbon dynamics have been sparsely studied. We used forest inventory plots to investigate whether the composition of existing tree regeneration is consistent with aboveground carbon stock loss, replacement, or gain for forests across the northeastern and midwestern USA, leveraging a recently developed method to predict the likelihood of sapling recruitment from seedling abundance tallied within six seedling height classes. A comparison of carbon stock predictions from tree and seedling composition suggested that 29% of plots were poised to lose carbon based on seedling composition, 55% were poised for replacement of carbon stocks (<5 Mg ha−1 difference) and 16% were poised to gain carbon. Forests predicted to lose carbon tended to be on steeper slopes, at lower latitudes, and in rolling upland environments. Although plots predicted to gain and lose carbon had similar stand ages, carbon loss plots had greater current carbon stocks. Synthesis and applications. Our results demonstrate the utility of considering tree regeneration through the lens of carbon replacement to develop effective management strategies to secure long‐term carbon storage and resilience in the context of global change. Forests poised to lose C due to climate change and other stressors could be prioritized for regeneration strategies that enhance long‐term carbon resilience and stewardship. [ABSTRACT FROM AUTHOR]

Inventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of Türkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (hmean), top height (htop), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson's correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the hmean - htop (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, hmean, htop, BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory. [ABSTRACT FROM AUTHOR]

In the Mediterranean area, wind-driven wildfires with crown fires are rising, causing an increment in atmospheric pollutant emissions. Quantifying gas emissions in these wildfires requires a better understanding of the components that contribute to the total emission estimate. Here, we aimed to analyze the differences in pre-fire available biomass distribution among layers of fuel types in Pinus halepensis and Quercus suber (hereafter, pine and oak) forests burned in one of the largest wildfires ("La Jonquera", 10,264 ha) of the past decades. This was done in order to try to unravel the differences in fire severity linked to the percentage of available biomass consumed in each layer and pollutant emissions (CO2, CO, CH4, PM2.5). We used field data (>100 post-fire plots) in which measures from crown, shrub and litter layers, fire severity and consumption assessments were combined with data from National Forest Inventories to quantify final atmospheric pollutant emissions. Total pre-fire available biomass among pine and oak forests showed different vertical distribution. Pine forests had a higher percentage of crown fine and shrub biomass for all fuel types while oak had more litter biomass. The fuel types with large trees and low tree density, together with fuel types with has lower tree density and vertical continuity had the highest non-charred fire severity in pine and oak. The presence of Erica arborea caused higher fire severity in oak stands. Fuel types of pine were more resistant to the effects of surface fires because they had taller trees than oak. Percent biomass consumption was higher in pine and oak stands in low fire severities because the taller trees could withstand surface fire at high intensities without increasing fire severity. The wildfire analyzed was a large fire with massive crown and high-intensity surface fires, but only a small amount of the finest crown biomass and coarse surface fuels were consumed. Fire severity was the main factor determining different amount of emissions without significant influence of fuel types, and only emissions of CO2 and CH4 were higher in pine than in oak in low fire severities. Although remote sensing technologies are extremely useful for biomass and wildfire severity assessments, field data is essential to quantify biomass consumption, atmospheric pollutant emissions from different fuel types and fuel layers. [ABSTRACT FROM AUTHOR]

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40'349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27'297 and 18'980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner. [ABSTRACT FROM AUTHOR]

Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification. [ABSTRACT FROM AUTHOR]

The application of lidar data to assist with forest inventory is common around the world. However, the determination of tree species is still somewhat elusive. Lidar data collected using UAS (uncrewed aircraft systems) platforms offer high density point cloud data for areas from a few to several hundred hectares. General point cloud metrics computed using these data captured differences in the crown structure that proved useful for species classification. For our study, we manually adjusted plot and tree locations to align field trees and UAS lidar point data and computed common descriptive metrics using a small cylindrical sample of points designed to capture the top three meters and leader of each tree. These metrics were used to train a random forest classifier to differentiate between two conifer species, Douglas fir and western hemlock, common in the Pacific Northwest region of the United States. Our UAS lidar data had a single swath pulse density of 90 pulses/m2 and an aggregate pulse density of 556 pulses/m2. We trained classification models using both height and intensity metrics, height metrics alone, intensity metrics alone, and a small subset of five metrics, and achieved overall accuracies of 91.8%, 88.7%, 78.6%, and 91.5%, respectively. Overall, we showed that UAS lidar data captured morphological differences between the upper crowns of our two target species and produced a classification model that could be applied over large areas. [ABSTRACT FROM AUTHOR]

Tree mortality is an objective forest health criterion and is particularly suitable for long-term and large-scale studies of forest condition. However, it is impossible to determine actual tree mortality in Central European managed forests where trees are removed for various reasons. In this case, the only way to approximate tree mortality is to define the range in which it occurs. This can be carried out by including in the mortality calculations either dead trees that remain in the stand at the end of the assessment period or additionally trees that have been removed from the stand. We used data from the annual forest monitoring surveys in Poland from 2009 to 2022 for pine, spruce, oak and birch to perform a survival analysis in which we included all removals or sanitary cuttings either as censored or complete observations. The differences between the calculated mortality rates were significant, indicating the importance of how removals are treated in the analysis. To assess which method used for mortality calculation was more appropriate, we compared values for last recorded defoliation and severity of damage from live, dead and thinned or salvaged trees. For all species studied, significant differences were found between dead trees or trees removed by sanitation cuts and living trees or trees removed by thinning, suggesting that not only dead trees remaining in the forest, but also trees removed by sanitation cuts, should be considered when calculating mortality in managed stands. We also recommend the use of survival analysis in forest monitoring as a routine method for assessing the health of stands. [ABSTRACT FROM AUTHOR]

Dead wood quality is typically recorded as a biodiversity indicator and as part of forest carbon estimates, using decay classification systems. In large-scale national forest inventories (NFIs), these systems typically differ among countries. Harmonizing them would allow analyzes over broader scales and the use of larger data sets. Here, we exemplified the development of a harmonized decay classification for the NFIs of Finland, Norway, and Sweden, using wood density as an objective harmonizing criterion. We sampled 441 Scots pine, Norway spruce, birch and trembling aspen in different conditions and decay classes, measured their density and developed the harmonized classification for each species and dead wood type (standing and fallen). The assignments relied on minimizing within-class variance in wood density in the harmonized classes. Assigned into three (standing dead wood) and four (fallen dead wood) classes, the harmonization led to modest losses of information, especially in the advanced decay stages. How the original decay classes were assigned into the harmonized classes differed slightly, depending on the tree species and type. This is an advantage over the expert assessment that typically treat all species and types of dead wood the same way despite clear differences in decomposition pathways. [ABSTRACT FROM AUTHOR]

The growth of trees generates a huge carbon sink, which is of great significance in alleviating environmental problems such as global warming caused by carbon emissions. To accurately assess the carbon sink of forests, a model of the annual growth rate of tree diameter at breast height (DBH) in 4 forms for 13 main tree species (groups) in Beijing was established based on the data from the 6th to 9th National Forest Inventory. This model predicted the future trend of DBH changes, thereby provided a computational basis for calculating carbon storage using the continuous function method of biomass conversion factors. Ultimately, it estimated the carbon storage and carbon density of deciduous forests in Beijing by 2050. The results showed that: 8 of the tree species (groups) had a DBH annual growth rate model R2 greater than 0.900, with the highest R2 of 0.960 for linden; except willow and Fraxinus mandshurica/Juglans mandshurica/Phellodendron amurense the RMSE of 11 tree species (groups) was less than 0.5 cm; except for poplars, other hard broad-leaved forests and elms, Bias was less than 1.0 cm. The overall R2 in the validation of DBH prediction accuracy was high, with the highest for black locusts (0.951) and the lowest for other hard broad-leaved forests (0.766). It was predicted the carbon stock of arbor forests in Beijing in 2050 was 42.71 TgC, and the carbon density is 43.35 MgC · hm-2. The study found that the tree growth simulation method based on the annual growth rate at breast height model could effectively improve the overall accuracy of future carbon sink potential assessment of tree forests in Beijing, which could provide a theoretical basis for formulating greenhouse gas reduction policies and achieving the 2060 carbon neutrality target. [ABSTRACT FROM AUTHOR]

White oak is a major commercial tree species and an important timber resource in Kentucky. However, current forest inventory trends from the Central Hardwood Forest Region (CHFR) reveal a sustainability threat from declining white oak regeneration and recruitment leading to a disproportionate inventory structure. Using Forest Inventory and Analysis (FIA) data together with the Forest Vegetation Simulator (FVS), we performed a base run analysis of projected inventory levels of white oak sawlogs to better understand whether the inventory level is sustainable to support the current harvest level of white oak in the future. The projections were further examined by tree grades to provide a general outlook on the quality of white oak timber growing in Kentucky's forests in posterity. By doing so, we generated results that indicate that projected inventory levels of white oak sawlogs cannot be considered sustainable to support current harvest levels from 2058 onwards. In addition, the long-term trends in inventory levels of high-quality white oak sawlogs would be continuously declining whereas that of low-quality sawlogs would be steadily increasing. On the brink of these significant inventory shifts, our study calls for proactive forest management approaches to stabilize the white oak timber resource supply in Kentucky and beyond. [ABSTRACT FROM AUTHOR]

Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46–0.59 and 0.62–0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P  < 0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models. [ABSTRACT FROM AUTHOR]

Due to the increased frequency of human–tick encounters and expanding ranges of ticks in the United States, there is a critical need to identify environmental conditions associated with tick populations and their likelihood to contact human hosts. In a passive tick surveillance partnership with the US Department of Agriculture Forest Inventory and Analysis (FIA) program, we identified environmental variables associated with tick encounters by forestry personnel. Ticks were identified by species and life stage, and site-specific variables were associated with each tick using FIA forest inventory datasets and generalized linear models with negative binomial distributions. Of the 55 FIA variables available, we identified biotic and abiotic environmental variables associated with Amblyomma americanum L. (carbon in litter material and standing dead tree aboveground dry biomass), Dermacentor variabilis Say (seedling species unevenness and elevation), and Ixodes scapularis L. (carbon in dead woody material and seedling species unevenness). We propose conducting future treatment–control studies using these forestry-related environmental variables to test their ability to alter tick abundance at sites. Land management decisions not only affect common flora and fauna, but changes to these habitats can also alter the way ticks parasitize hosts and use vegetation to find those hosts. These results can be used with land management decisions to prevent future human–tick encounters and highlight risk areas. [ABSTRACT FROM AUTHOR]

While the use of large tropical trees to predict aboveground biomass (AGB) in forests has previously been studied, the applicability of this approach in arid regions remains unquantified. In the natural forests of arid mountains of Northwestern China, this study collected individual tree data from 105 plots across 11 sites through field measurements. The objective was to assess the feasibility of using large trees for predicting plot AGB in these natural forests of arid mountains. This entailed determining the contribution of large trees, based on which a plot AGB prediction model was constructed. This study also aimed to identify the optimal number of large trees needed for accurate AGB prediction. The findings indicate that within the natural forests of arid mountains, only seven large trees (approximately 12% of the trees in a plot) are necessary to account for over 50% of the plot AGB. By measuring 18 large trees within a plot, this study achieved a precise plot AGB estimation, resulting in a model rRMSE of 0.27. The regression fit R2 for the predicted AGB and the estimated AGB was 0.79, effectively aligning the predicted and measured AGB. In the Tianshan Mountains' natural forests, the prediction model yielded further improvements with an rRMSE of 0.13 and a remarkable regression R2 of 0.92 between predicted and estimated AGB. However, due to variances in tree size distribution and tree species biomass, the Altai Mountains' natural forest was found to be unsuitable for predicting plot AGB using large trees. This study establishes that large trees can effectively represent plot AGB in the natural forests of arid mountains. Employing forest surveys or remote sensing to collect data from a few large trees instead of the entire tree population enables accurate plot AGB prediction. This research serves as the initial quantification of large tree utilization for plot AGB prediction in the natural forests of arid mountains, carrying substantial implications for future arid forest inventories, carbon accounting, and the formulation of prudent conservation strategies. [ABSTRACT FROM AUTHOR]

Climate change‐triggered forest die‐off is an increasing threat to global forests and carbon sequestration but remains extremely challenging to predict. Tree growth resilience metrics have been proposed as measurable proxies of tree susceptibility to mortality. However, it remains unclear whether tree growth resilience can improve predictions of stand‐level mortality. Here, we use an extensive tree‐ring dataset collected at ~3000 permanent forest inventory plots, spanning 13 dominant species across the US Mountain West, where forests have experienced strong drought and extensive die‐off has been observed in the past two decades, to test the hypothesis that tree growth resilience to drought can explain and improve predictions of observed stand‐level mortality. We found substantial increases in growth variability and temporal autocorrelation as well declining drought resistance and resilience for a number of species over the second half of the 20th century. Declining resilience and low tree growth were strongly associated with cross‐ and within‐species patterns of mortality. Resilience metrics had similar explicative power compared to climate and stand structure, but the covariance structure among predictors implied that the effect of tree resilience on mortality could partially be explained by stand and climate variables. We conclude that tree growth resilience offers highly valuable insights on tree physiology by integrating the effect of stressors on forest mortality but may have only moderate potential to improve large‐scale projections of forest die‐off under climate change. [ABSTRACT FROM AUTHOR]

To further understand calicioid communities and their habitat and substrate requirements, we conducted a study on the Colville National Forest of northeastern Washington State, U.S.A. We hypothesized that calicioid composition would vary depending on age and type of substrate, and that species diversity would be higher on older trees and in older stands, as found by previous research. To test these hypotheses, we searched for calicioids on plots established by the USDA Forest Service's Forest Inventory and Analysis (FIA) program. Because individual trees on FIA plots are tagged, we could relate recorded tree species and age to calicioid composition. We investigated calicioid species diversity in relation to host species; live, dead or burnt trees; and dominant forest type. Our analyses show that calicioid abundance and diversity do increase with tree age. Thuja plicata hosted the highest number of calicioid species in our study and also had the highest rate of occupancy with 70% of all investigated boles inhabited. In an analysis of the effect of forest type (series), we found the Thuja plicata series to host the highest number of species while the Pseudotsuga menziesii series had the lowest number of species. Only 3.4% of burnt trees sampled hosted calicioids while 43.6% of unburnt trees did, suggesting that fire does negatively impact calicioid communities. Three species, Chaenotheca obscura, Chaenothecopsis haematopus, and C. nigra and were found only on snags. Of the 17 species of shrubs sampled, only Alnus, Amelanchier, Salix, and Holodiscus hosted calicioid lichen or fungi. Species that have rarely been reported from Washington include Chaenothecopsis haematopus and C. ochroleuca. [ABSTRACT FROM AUTHOR]

Forest inventory is the primary method for collecting and recording information regarding the volumetric quantification and yield of trees. However, this is difficult when the individual has been cut and only the stump remains, in which case inventory is achieved through regression techniques. The objective of this work was to adjust allometric equations for the estimation of normal diameter (dn), total height (at) and volume (v) using the stump diameter (dt) for Abies religiosa in different regions of Mexico. A total of 2,599 data points from 20 forest management units were used to analyze the variables and their respective predictions through eight allometric equations, which were evaluated with different statistical criteria. The adjusted coefficient of determination (R2 adj) explained 91 % of the normal diameter, together with the root mean square error (RMSE) of less than 4.75 cm with respect to the allometric equation (3). Another of the best options to estimate the total height is the Schumacher exponential (4) and the allometric (6) with values of R2 adj of approximately 50 %, accompanied by RMSE less than 5.03 m. However, for the volume of the forest, Schumacher's equation was considered pertinent since it has a RMSE ranging from 0.00001 to 0.0026 m3 with R2 adj that explains more than 74 % of the variation of the data observed in each UMAFOR. These regressions may be useful for the restoration of trees after an unplanned or unexpected felling event, as the results are statistically reliable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Forests provide a wide range of ecosystem services and information requirements on forests have grown considerably. Nevertheless, collecting information in the forest is expensive and for this reason assessment of forest resources strongly relies on statistical sampling. However, plot measurement remains essential even when remote-sensing data are used, and field assessments are still among the costliest components of forest inventories. Studies on the costs for plots survey are limited and usually based on expert evaluation rather than on data. This article analysed the relationship between the time needed for measuring forest plots and their site-related characteristics (slope, terrain roughness), stand features (number of trees, subsample trees, stumps, coarse woody debris, understorey vegetation) and protocol-related procedures, by means of univariate and multivariate analyses. Analyses showed that the time needed for measuring plots depends on the workload or the intensity of fieldwork. Especially, the number of variables surveyed matters, because the variables explained the measuring time variation by an additive effect, suggesting that within a complex field protocol the total number of measurements taken may not represent properly the overall intensity of work. Marking in an effective way permanent plots is recommendable because the retrieval success of the pins buried into the ground was the most important explanatory variable. Presence of understorey vegetation was more important than the number of individuals measured. The results obtained are consistent and logical, but the variance explained was limited, suggesting that predictability of the measuring time under complex field protocols might be intrinsically limited by the interactions among factors with opposite effects and especially by the adaptation of the surveyors to specific circumstances. For example, effects of physical tiredness were not detected in the days when two plots were measured; conversely, measuring a second plot reduced the measuring time of the first, an event most likely dependent on the surveyors’ behaviour. Under the conclusion that predictability is low by nature, the inference of studies based on simulation data and simplified protocols to practical applications was finally discussed. [ABSTRACT FROM AUTHOR]

Araucaria angustifolia (Bertol.) Kuntze, commonly known as Brazilian pine, is a significant tree species in the Brazilian flora that once covered an area of 200,000 km2 in the Southern region. During the 1970s, high-quality timber logs from this conifer became the primary export product of the country. However, the species is endangered due to uncontrolled exploitation and is subject to a harvesting ban. It is crucial, therefore, to explore sustainable cultivation methods for this species, which necessitates urgent research and scientific insights. In this study, we present a simulation of a management strategy for in situ conservation by manipulating growth space and crown size dynamics. Forest inventory data and mixed forest regression equations were employed to describe the horizontal dimensions of average and maximum potential crown growth, resulting in two management scenarios. The results presented in management diagrams show that both approaches required logging numerous trees to ensure adequate space for healthy tree growth and provide soil coverage and forest protection. Therefore, the absence of effective forest management initiatives for Araucaria forests may have further implications for the structure, production, conservation, and overall development. To address these challenges, we propose two hypotheses: firstly, that tree diameter depends on crown dimensions, which are in turn influenced by tree growth space, and, secondly, that crown dimensions serve as a reliable indicator of existing competition and can be utilized to simulate forest management practices. We urge that implementing sustainable forest management initiatives for Araucaria angustifolia at selected locations can contribute to expanding natural forest areas, mitigate deterioration caused by high competition, discourage illegal logging, and prevent overexploitation of their edible seeds, which hinders regeneration. Our results underscore the significant implications of the lack of forest management initiatives in rural properties, potentially resulting in irreversible deterioration. The exact consequences of this deterioration remain unclear, emphasizing the need for further studies to understand its eventual effects on the growth reaction of trees of different diameters, ages, and crown conditions after the liberation of their crowns. [ABSTRACT FROM AUTHOR]

With climate change, natural disturbances such as storm or fire are reshuffled, inducing pervasive shifts in forest dynamics. To predict how it will impact forest structure and composition, it is crucial to understand how tree species differ in their sensitivity to disturbances. In this study, we investigated how functional traits and species mean climate affect their sensitivity to disturbances while controlling for tree size and stand structure. With data on 130,594 trees located on 7617 plots that were disturbed by storm, fire, snow, biotic or other disturbances from the French, Spanish, and Finnish National Forest Inventory, we modeled annual mortality probability for 40 European tree species as a function of tree size, dominance status, disturbance type, and intensity. We tested the correlation of our estimated species probability of disturbance mortality with their traits and their mean climate niches. We found that different trait combinations controlled species sensitivity to disturbances. Storm-sensitive species had a high height-dbh ratio, low wood density and high maximum growth, while fire-sensitive species had low bark thickness and high P50. Species from warmer and drier climates, where fires are more frequent, were more resistant to fire. The ranking in disturbance sensitivity between species was overall consistent across disturbance types. Productive conifer species were the most disturbance sensitive, while Mediterranean oaks were the least disturbance sensitive. Our study identified key relations between species functional traits and disturbance sensitivity, that allows more reliable predictions of how changing climate and disturbance regimes will impact future forest structure and species composition at large spatial scales. [ABSTRACT FROM AUTHOR]

When wildlife species exhibit unexpected associations with vegetation, replication of studies in different locales can illuminate whether patterns of use are consistent or divergent. Our objective was to describe fine‐scale forest conditions used by Pacific martens (Martes caurina) at 2 study sites in northern California that differed in forest composition and past timber harvest. We identified denning and resting locations of radio‐marked martens and sampled structure‐ and plot‐level vegetation using standardized forest inventory methods between 2009–2021. Woody structures used by martens were significantly larger than randomly available structures across types (e.g., live tree, snag, log) and at both study sites. Den and rest structures occurred in areas characterized by higher numbers of logs and snags, lower numbers of live trees and stumps, larger diameter live trees and logs, and greater variation in live tree and log diameter. Features of denning and resting locations were largely consistent across study sites and were generally representative of fine‐scale forest heterogeneity and increased structural complexity, conditions that martens have been widely associated with at broader spatial scales (i.e., home range or landscape). The spatial occurrence of denning and resting locations may indicate that fine‐scale structural complexity facilitates marten foraging while reducing predation risk. Our work offers timely and directed information that can guide forest management in the context of increased landscape change. [ABSTRACT FROM AUTHOR]

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Standing deadwood is an important structural component of forest ecosystems. Its occurrence and dynamics influence both carbon fluxes and the availability of habitats for many species. However, deadwood is greatly reduced in managed, and even in many currently unmanaged temperate forests in Europe. To date, few studies have examined how environmental factors, forest management and changing climate affect the availability of standing deadwood and its dynamics.Data from five periods of the Austrian National Forest Inventory (1981–2009) were used to (I) analyse standing deadwood volume in relation to living volume stock, elevation, eco‐region, forest type, ownership and management intensity, (II) investigate the influence of forest ownership and management intensity on snag persistence and (III) define drivers of standing deadwood volume loss for seven tree genera (Abies, Alnus, Fagus, Larix, Picea, Pinus and Quercus) using tree‐related, site‐related and climate‐related variables, and predict volume loss under two climate change scenarios.Standing deadwood volume was mainly determined by living volume stock and elevation, resulting in different distributions between eco‐regions. While forest type and management intensity influenced standing deadwood volume only slightly, the latter exhibited a significant effect on persistence. Snag persistence was shorter in intensively managed forests than in extensively managed forests and shorter in private than in public forests.Standing deadwood volume loss was driven by a combination of diameter at breast height, elevation, as well as temperature, precipitation and relative humidity. Volume loss under climate change predictions revealed constant rates for moderate climate change (RCP2.6) by the end of the 21st century. Under severe climate change conditions (RCP8.5), volume loss increased for most tree genera, with Quercus, Alnus and Picea showing different predictions depending on the model used as the baseline scenario. We observed trends towards faster volume loss at higher temperatures and lower elevations and slower volume loss at high precipitation levels. The tree genera most susceptible to climate change were Pinus and Fagus, while Abies was least susceptible.Synthesis and applications. We recommend to protect standing dead trees from regular harvesting to ensure the full decomposition process. The consequences for decomposition‐dependent species must be taken into account to evaluate the influences of management and climate change on standing deadwood dynamics. [ABSTRACT FROM AUTHOR]

The volume models that have been used in Finland for the last 40 years, while generally well thought-out, exhibit an illogical behaviour for small trees. In recent studies, tree stem form was observed to have changed in time and also involve spatial variation attributable to environmental factors. It is yet unclear how the stem taper has actually changed. To overcome these problems, we fitted a completely new set of volume and taper curve models and examined whether this change is attributable to the changes in management and environmental factors rather than to measurement errors in the previously used datasets. For the latter, we added a dataset into the analysis, which was smaller but of higher quality due to the destructive nature of the stem taper measurements. We aim at (1) developing a new non-linear variable form factor volume function that works with trees of all sizes, (2) improving the description of the variation of the stem form in time and space by including temperature sum and soil type as predictors, (3) understanding the changes in the stem form by fitting new taper curve models and (4) improving the statistical properties of the predictions by using mixed model techniques and by addressing the effect of parameter uncertainty. To assess the impact of renewing the models, we (5) predicted the mean volume and its confidence interval with each model for forest inventory data at country level. The results show that the tree stem form has a spatial trend that can be described with the temperature sum. Moreover, the changes in stem form also have a spatial trend, with largest changes in Lapland. The difference is mostly observable in the lowest part of the stem, and it is especially large in the largest pines. We conclude that environmental variables can help to improve national stem taper functions in countries with pronounced environmental gradients. [ABSTRACT FROM AUTHOR]

One potential legacy of the widespread deforestation of the 1700-1800s and subsequent agricultural abandonment and reforestation across the eastern United States (U.S.) during the 1900s is the establishment of introduced apple trees (Malus domestica) exclusive of naturally occurring native crab apple species. As these apple trees might provide ecological services and a genetic reserve of long-forgotten apple cultivars, a region-wide forest inventory was used to estimate the current extent, condition, and changes in the population of apple trees across eastern forests of the U.S. It was found that hundreds of millions of apple trees currently exist in the forests of the northern U.S. with indications of population senescence as the abundance of seedlings and saplings decline while the abundance of the largest-sized trees and standing dead trees increase. The distribution of forest apple trees is only aligned with native crab apple distributions in limited areas, suggesting distinct feral apple populations derived from agricultural abandonment decades ago. As these extant apple trees are likely relics of former orchards and/or their naturally dispersed progeny, these populations may serve as an in-situ gene bank representing hundreds, if not thousands, of apple cultivars forgotten long ago but adapted to local conditions and perhaps of importance to ensuring the future resilience of domestic apple production in the context of global change. [ABSTRACT FROM AUTHOR]

African mahogany plantations are increasing in Brazil, especially those planted with the species Khaya grandifoliola C. DC. Despite the interest in establishing plantations, there is a lack of studies in management practices for the genus. This makes it difficult to improve methodologies that optimise forest inventory activities, which are essential to support forest management decisions. Therefore, this study aimed to evaluate the effect of the number of trees selected to determine the mean dominant height, and to verify its impact on site classification of African mahogany stands. This will then optimise height sampling and ensure correct site index estimation. To determine the dominant height, 11 treatments were tested, with the selection of 1st to 11th thickest trees per plot, representing between 9 and 102 dominant trees per hectare. The effect of the treatments was evaluated using the values of the mean dominant height and metrics regarding site index classification (stability, stratification capacity and relationship with stand growth). Treatments with inclusion of more dominant trees resulted in a more stable site classification at the expense of a lower correlation with stand growth. Treatments using fewer dominant trees resulted in a greater allocation of plots in the upper site classes, showing that the number of trees used to calculate the dominant height influences the distribution of plots in different site classes. We concluded that Treatments 3 (27 trees ha−1) to 6 (55 trees ha−1) had the best performance, guaranteeing adequate site index estimation. The selection of at least 27 and a maximum of 55 dominant trees per hectare is recommended for African mahogany plantations with low planting densities. [ABSTRACT FROM AUTHOR]

Forest communities—assemblages of tree species at stand- to landscape-scales—vary in their capacity to capture and store carbon, but previous attempts using discrete forest types to quantify this relationship often fail to consider gradual changes to the seedling, live tree, and standing dead tree demographic components of the community. We analyzed two decades of plot remeasurements (20,680 remeasurements of 5594 unique plots) from the United States Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) Program across the Great Lakes region of the eastern U.S. to assess shifts in forest communities using Latent Dirichlet Allocation, a continuous model of forest community composition. We tested for links between aboveground live and standing dead tree carbon dynamics and shifts in community composition (the relative proportion of different communities within a stand) of three demographic components (seedlings < 2.54 cm, live trees ≥ 2.54 cm, and standing dead trees ≥ 2.54 cm). Live tree carbon varied across six unique communities ranging from 5.2 Mg C ha−1 (SE = 0.77 Mg C ha−1) in the spruce-tamarack community to 88.4 Mg C ha−1 (SE = 0.65 Mg C ha−1) in the sugar maple-basswood community. On average, forests contained less standing dead tree carbon (2.2 Mg C ha−1) than live tree carbon (40.4 Mg C ha−1), because of lower stem density and fewer species of standing dead trees. Both live and standing dead tree carbon increased over time across the study area (on average 0.274 and 0.045 Mg C ha−1 year−1, respectively). Remeasurements of FIA plots revealed that shifts in community composition of the live and standing dead demographic components were associated with decreases in live tree carbon. Conversely, larger degree of divergence between the community composition of the live and standing dead demographic components and shifts in stand structure between remeasurements (mean, variance, and skewness of the live tree diameter distribution) were associated with increases in live tree carbon. Shifts in stand structure were significant yet weaker predictors of standing dead tree carbon dynamics. Instead, shifts in community composition of the standing dead component over time and degree of divergence between the community composition of the live tree and standing dead tree components were more closely linked to changes in standing dead tree carbon stocks. The effects of shifting communities on carbon dynamics are important for understanding carbon cycling in forests and management under continued climate change. [ABSTRACT FROM AUTHOR]

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height — "diameter"). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors (kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 ≈2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10–200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes. [ABSTRACT FROM AUTHOR]

In 2021, a large transect with 281 10 × 10 m grid cells was re-surveyed in a limestone European beech forest (Hordelymo-Fagetum lathyretosum) on the plateau of the Göttingen Forest after surveys in 1981, 1991, 2001, and 2011. The stand, which has not been managed by forestry since the beginning of the permanent plot study, initially showed an increasing cover of the tree layer and, as a consequence of game-proof fencing until 2005, also an increasing cover of the shrub layer. Since 2011, the tree layer cover has decreased due to small openings after drought years and winter storms. Also, the shrub layer cover declined significantly since 2011, mainly due to ash dieback and the growing impact of deer browsing. In both vegetation layers, Fagus sylvatica was able to expand its dominant role. In the herbaceous layer, the sharp decline and loss of numerous species is striking, among them typical forest species that have been classified as characteristic for species-rich European beech forests on limestone (e. g., Lathyrus vernus, Mercurialis perennis). The large number of losers (27 species) is matched by only a small number of winners (5 species) between 1981 and 2021. Especially Allium ursinum and Hedera helix, but also the two Orchidaceae Epipactis helleborine and Neottia nidus-avis have increased significantly since 1981. The increase of the highly competitive Allium ursinum, probably due to an earlier start and a longer lasting vegetation period, is accompanied by an increasing share of beech in the tree layer. At the same time, the species diversity decreased, and the herb layer composition became more homogeneous across the transect. Hedera helix seems to have benefited mainly from climate change (lack of severe winter frosts, CO2 increase), but also from the temporary exclusion of roe deer browsing. Based on the analysis of mean indicator values shade-tolerant, oceanic-distributed species, as well as nitrogen and moisture indicators have increased, while more light demanding species continuously decreased between survey years. Compared to intensively managed stands with massive thinning and/or soil disturbances, as in coppice-with-standards or large shelterwood systems, these species suffered from a closed canopy after management abandonment. The small-scale disturbances caused by drought and windthrow in the period 2012-2021 are obviously not sufficient to provide favourable conditions for these species, including a large number of species that have been classified as typical for European beech forests on limestone and that have been present in the beginning of the longterm study. Thus, the presented time series indicates that eutrophic beech forests without forest management are species poorer than previously reported. [ABSTRACT FROM AUTHOR]

To obtain suitable methods for accurately investigating the stand aboveground biomass (AGB) and determining plot size for biomass measurement, the plot-size gradient was set to 100 m2, 400 m2, 600 m2, 900 m2, and 1500 m2 in the 0.3 hm2 clear-cut plot of Pinus kesiya var. langbianensis natural forest of Mojiang county, Puer City, Yunnan Province, China. The AGB of each tree in the plot was investigated by a destructive investigation method. The AGB in the different plot sizes was calculated using the biomass model (BM), the biomass-volume regression (BV), the average standard tree of the diameter classes (SDC), the average standard tree of the tree classification (STC), the average standard tree of the stand (STS) and the measurement method (MD). Then, the error differences between these estimation methods were compared with the different plot sizes. The results showed that 1) with the increase in sample size, the mean absolute relative errors (MAREs) of all the methods tended to decrease, and the error values were stable if the plot size was greater than or equal to 400 m2. Thus, the plot size for a forest biomass survey should not be less than 400 m2. 2) BM, BV, and SDC had a lower mean error (ME) and MARE values. Especially, compared to those of the observed data for the 0.3 hm2 sample plots where the MAREs of the BM and BV were less than 15%, and the MARE of SDC was approximately 15% for wood and total AGB, but higher than 20% for the other three components. Therefore, the BM and BV should be used to estimate stand AGB if reliable biomass models or biomass-volume functions are available, the SDC is recommended for measuring AGB without biomass models, and the STS could be used for wood biomass and total AGB. [ABSTRACT FROM AUTHOR]

Airborne LiDAR has been extensively used for estimating and mapping forest attributes at various scales. However, most models have been developed separately and independently without considering the intrinsic mathematical relationships and correlations among the estimates, which results in the mathematical and biophysical incompatibility of the estimates. In this paper, using the measurement error model approach, the error-in-variable simultaneous equation (SEq) for airborne LiDAR-assisted estimations of four forest attributes (stand volume, V; basal area, G; mean stand height, H; and diameter at breast height, D) for four forest types (Chinese fir, pine, eucalyptus, and broad-leaved forest) is developed and compared to the independence models (IMs). The results indicated that both the SEqs and IMs performed well, and the rRMSEs of the SEqs were slightly larger than those of the IMs, while the increases in rRMSE were less than 2% for the SEqs. There were statistically significant differences (α = 0.05) in the means of the estimates between SEqs and IMs, even though their average differences were less than ±1.0% for most attributes. There were no statistically significant differences in the mean estimates between SEqs, except for the estimates of the D and G of the eucalyptus forest. The SEqs with H and G as the endogenous variables (EVs) to estimate V performed slightly better than other SEqs in the fir, pine, and broad-leaved forests. The SEq that used D, H, and V as the EVs for estimating G was best in the eucalyptus forests. The SEq ensures the definite mathematical relationship among the estimates of forest attributes is maintained, which is consistent with forest measurement principles and therefore facilitates forest resource management applications, which is an issue that needs to be addressed for airborne LIDAR forest parameter estimation. [ABSTRACT FROM AUTHOR]

We compile a data set of forest surveys from expeditions to the northeast of the Russian Federation, in Krasnoyarsk Krai, the Republic of Sakha (Yakutia), and the Chukotka Autonomous Okrug (59–73 ∘ N, 97–169 ∘ E), performed between the years 2011 and 2021. The region is characterized by permafrost soils and forests dominated by larch (Larix gmelinii Rupr. and Larix cajanderi Mayr). Our data set consists of a plot database describing 226 georeferenced vegetation survey plots and a tree database with information about all the trees on these plots. The tree database, consisting of two tables with the same column names, contains information on the height, species, and vitality of 40 289 trees. A subset of the trees was subject to a more detailed inventory, which recorded the stem diameter at base and at breast height, crown diameter, and height of the beginning of the crown. We recorded heights up to 28.5 m (median 2.5 m) and stand densities up to 120 000 trees per hectare (median 1197 ha -1), with both values tending to be higher in the more southerly areas. Observed taxa include Larix Mill., Pinus L., Picea A. Dietr., Abies Mill., Salix L., Betula L., Populus L., Alnus Mill., and Ulmus L. In this study, we present the forest inventory data aggregated per plot. Additionally, we connect the data with different remote sensing data products to find out how accurately forest structure can be predicted from such products. Allometries were calculated to obtain the diameter from height measurements for every species group. For Larix, the most frequent of 10 species groups, allometries depended also on the stand density, as denser stands are characterized by thinner trees, relative to height. The remote sensing products used to compare against the inventory data include climate, forest biomass, canopy height, and forest loss or disturbance. We find that the forest metrics measured in the field can only be reconstructed from the remote sensing data to a limited extent, as they depend on local properties. This illustrates the need for ground inventories like those data we present here. The data can be used for studying the forest structure of northeastern Siberia and for the calibration and validation of remotely sensed data. They are available at https://doi.org/10.1594/PANGAEA.943547. [ABSTRACT FROM AUTHOR]

Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In this study, 104 rectangular plots of 900 m2 (subdivided into nine quadrats with an area of 10 × 10 m) in subtropical planted forests (Chinese fir, pine, eucalyptus, and broad-leaved forest, 2–56 years old) were used to establish four datasets with six different plot sizes (100, 200, 300, 400, 600, and 900 m2) by combining quadrats. The differences in the LiDAR-derived metrics and forest attributes between plots of different sizes were statistically analyzed. Based on the multivariate power models with stable structures, the differences in estimation accuracies of the stand volume (VOL) and basal area (BA) using plot data of different sizes were compared. The results indicated that: (1) the mean differences in LiDAR-derived metrics of the plots of different sizes in all forest types were small, and most of them had no statistically significant differences (α = 0.05) between the plots of different sizes and the 900 m2 plots; however, the standard deviation of the difference increased rapidly with decreasing plot size; (2) except for the maximal tree height of the plots, the other forest attributes, including the mean tree height, diameter at breast height, BA, and VOL of all forest types, showed no statistically significant differences between the plots of different sizes and the 900 m2 plots; and (3) with increasing plot size, the accuracies of VOL and BA estimations improved markedly, and the effects of plot size on the estimation accuracies of the different forest attributes and different forest types were essentially the same. Spatial averaging resulted in the variations in the independent variables (LiDAR variables) and dependent variables (forest attributes) decreasing gradually with the increasing plot size, which was the main reason for the model's accuracy improving. In applying airborne LiDAR to a large-scale subtropical planted forest inventory, the plot size should be at least 600 m2 for all forest types. [ABSTRACT FROM AUTHOR]

The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, 10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, 10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, 10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, 10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions. [ABSTRACT FROM AUTHOR]

The TreeMap 2016 dataset provides detailed spatial information on forest characteristics including number of live and dead trees, biomass, and carbon across the entire forested extent of the continental United States at 30 × 30m resolution, enabling analyses at finer scales where forest inventory is inadequate. We used a random forests machine learning algorithm to assign the most similar Forest Inventory Analysis (FIA) plot to each pixel of gridded LANDFIRE input data. The TreeMap 2016 methodology includes disturbance as a response variable, resulting in increased accuracy in mapping disturbed areas. Within-class accuracy was over 90% for forest cover, height, vegetation group, and disturbance code when compared to LANDFIRE maps. At least one pixel within the radius of validation plots matched the class of predicted values in 57.5% of cases for forest cover, 80.0% for height, 80.0% for tree species with highest basal area, and 87.4% for disturbance. A new feature of the dataset is that it includes linkages to select FIA data in an attribute table included with the TreeMap raster, allowing users to map summaries of 21 variables in a GIS. TreeMap estimates compared favorably with those from FIA at the state level for number of live and dead trees and carbon stored in live and dead trees. Study Implications: TreeMap 2016 provides a 30 × 30 m resolution gridded map of the forests of the continental United States. Attributes of each grid cell include a suite of forest characteristics including biomass, carbon, forest type, and number of live and dead trees. Users can readily produce maps and summaries of these characteristics in a GIS. The TreeMap also includes a database containing, for each pixel, a list of trees with the species, diameter, and height of each tree. TreeMap is being used in the private sector for carbon estimation and by land managers in the National Forest system to investigate questions pertaining to fuel treatments and forest productivity as well as Forest Plan revisions. [ABSTRACT FROM AUTHOR]

Surface albedo is a dominant factor affecting the earth energy balance. Bidirectional reflectance distribution function (BRDF) describes the anisotropic surface reflection of solar radiation. Bidirectional effect is especially noticeable in the forest canopies with three-dimensional canopy structure. Thus, it is challenging to evaluate the canopy structural effects on BRDF using traditional radiative transfer modelling methods, where leaf in canopies is generally assumed to be homogeneous. LESS, a ray-tracing-based three-dimensional (3D) radiative transfer model can effectively simulate bidirectional reflectance factor while fully considering multi-component spectral and structural characteristics of vegetation. In this study, we applied the LESS model to evaluate the impacts of changes in leaf area index (LAI) on canopy reflectance in a Picea crassifolia forest. First, canopy structural parameters obtained from forest inventory were used to construct forest scenes and drive the LESS model. Then, we validated the simulation accuracy of the LESS model against BRDF calculated from MODIS BRDF parameter product using kernel-driven Ross-Li bi-directional reflectance function. Finally, we constructed forest scenes with different LAI to investigate the canopy structural effects on BRDF. We found that the BRDF simulated by the LESS model is in good accordance with the MODIS BRDF data in the red (R2 = 0.97, RMSE = 0.03) and NIR (R2 = 0.94, RMSE = 0.05) bands. The simulated canopy reflectance in the red band displayed 'dome' shape and decreased with increasing LAI. In contrast, canopy reflectance in the NIR band displayed 'bowl' shape and increased with increasing LAI. Our study highlighted the sensitivity of canopy BRDF to changes in canopy structure and have implications for retrieving LAI from BRDF data. [ABSTRACT FROM AUTHOR]

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests. [ABSTRACT FROM AUTHOR]

Traditional forest inventories are based on field surveys of established sample plots, which involve field measurements of individual trees within a sample plot and the selection of proper allometric equations for tree volume calculation. Thus, accurate field measurements and properly selected allometric equations are two crucial factors for providing high-quality tree volumes. One key problem is the difficulty in accurately acquiring tree height data, resulting in high uncertainty in tree volume calculation when the diameter at breast height (DBH) alone is used. This study examined the uncertainty of tree height measurements using different means and the impact of allometric models on tree volume estimation accuracy. Masson pine and eucalyptus plantations in Fujian Province, China, were selected as examples; their tree heights were measured three ways: using an 18-m telescopic pole, UAV Lidar (unmanned aerial vehicle, light detection and ranging) data, and direct measurement of felled trees, with the latest one as a reference. The DBH-based and DBH–height-based allometric equations corresponding to specific tree species were used for the calculations of tree volumes. The results show that (1) tree volumes calculated from the DBH-based models were lower than those from the DBH–height-based models. On average, tree volumes were underestimated by 0.018 m3 and 0.117 m3 for Masson pine and eucalyptus, respectively, while the relative root-mean-squared errors (RMSEr) were 24.04% and 33.90%, respectively, when using the DBH-based model; (2) the tree height extracted from UAV Lidar data was more accurate than that measured using a telescopic pole, because the pole measurement method generally underestimated the tree height, especially when the trees were taller than the length of the pole (18 m in our study); (3) the tree heights measured using different methods greatly impacted the accuracies of tree volumes calculated using the DBH–height model. The telescopic-pole-measured tree heights resulted in a relative error of 9.1–11.8% in tree volume calculations. This research implies that incorporation of UAV Lidar data with DBH field measurements can effectively improve tree volume estimation and could be a new direction for sample plot data collection in the future. [ABSTRACT FROM AUTHOR]

The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model's accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management. [ABSTRACT FROM AUTHOR]

Recent landscape‐scale wildfires in eastern Australia have made apparent the need for a greater understanding of the flammability dynamics of forested ecosystems. Fire severity is a measure of the impact of a fire on vegetation, but little is known about the landscape‐scale response of the fire‐prone dry sclerophyll forests of eastern Australia to different levels of fire severity. Species in these forests have multiple responses to fire, which can be dependent on the fire severity. In this study, we aimed to determine the effect of fire severity on the vegetation structure, and therefore flammability, of these forests. We addressed two hypotheses that 1) High severity fire would result in a denser understory than low severity fire after 5 years and that 2) High severity fire would reduce the vertical separation between understory and canopy after 5 years. Field surveys of 38 forest sites with differing fire severity but standardised time since fire and forest type, in Sydney region of New South Wales, Australia, were used to test these hypotheses. We found lower canopy cover and greater understory cover (0.5–4 m height) after high severity fire compared with sites which burnt at low severity. Vertical separation was less between the canopy and understory at sites after high severity fire than after low severity fire. The greater quantity of understory fuel and greater vertical continuity in fuel structure observed suggests a potential increase in forest flammability after high severity fire compared with lower severity fires in these forest types. [ABSTRACT FROM AUTHOR]

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Forest fragmentation is a widespread phenomenon that directly or indirectly affects the processes that take place both in forest ecosystems and in their immediate surroundings. So far, many studies confirm its negative effects, especially on biodiversity. On the other hand, there are few studies that address the effects of forest fragmentation on the amount of accumulated biomass or carbon, as well as on the characteristics of wood resources in managed forests. Therefore, issues related to timber production, which are important from the point of view of multifunctional forest management, are omitted. The aim of our research was to add to the knowledge in this area. In particular, we focused on assessing the impact of forest fragmentation on wood resources based on an analysis of edge effects in forest patches (units formed by combining forest fragments characterized by structural connectivity). Vector data describing the topography of forest fragments in Poland and the results of the National Forest Inventory (NFI) from 2015–2019 were used as material for solving this problem. The results of our research showed that the effects of fragmentation on managed pine stands depend on the age of the stand and the fertility of the habitat. In young stands growing on barren or strongly barren habitats, growing stock volume turned out to be significantly higher in the edge zone. In older stands, especially on moderately fertile habitats, significantly higher resources were found in the interior zone of forest patches. Habitat quality also had a significant effect on the amount of carbon accumulated. In strongly barren habitats, higher carbon mass was found in edge zones, while in moderately fertile habitats, stands had higher carbon volume in the interior zone. Our results illustrate that forest fragmentation is a very complex process that can increase or reduce wood resources, depending on the age of the stand and the quality of the habitat. From the standpoint of measurable benefits, it was concluded that protection from the negative effects of fragmentation should focus primarily on older stands and more fertile habitats. [ABSTRACT FROM AUTHOR]

The article focuses on effects of numbers of observations and predictors for various model types on the performance of forest inventory with airborne laser scanning.

Analysis of height growth of dominant trees is crucial for accurate evaluation of site quality and how this influences stand characteristics. This study explores, whether stand origin, tree species composition and stand density influence the growth process of stand dominant height. We used forest resource inventory data from 1283 permanent plots in Sichuan Province, China. Three dominant trees were selected from each permanent plot to calculate the average dominant height and diameter at breast height (DBH). The Chapman-Richards equation was used to construct a dominant height prediction model of each tree species based on DBH. The effects of the stand origin, species composition, and stand density on the upper asymptote parameter a, shape parameter b, and growth rate parameter c were analyzed using a dummy variable method. The results show that there was no statistically significant difference in parameters a, b, and c among different stand origins, species composition, and stand density. The upper asymptote parameters of the stand with a natural origin were greater than stands with an artificial origin. Species composition influenced the parameters among different coniferous species but was associated with large coefficients of variation, especially for broadleaf species. Stand density moderatly influenced the model parameters for all species. With an increase in stand density, the influence of stand density on the model parameters decreased gradually. Our results confirm that the relationship between DBH and height of dominant trees is useful to evaluate site productivity. However, this method has to consider stand origin and differences in stand density and species composition, especially for species with DBHheight relations sensitive to stand conditions. [ABSTRACT FROM AUTHOR]

In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud metrics and the estimation of forest attributes in balsam fir (Abies balsamea (L.) Mill.) dominated forests of western Newfoundland, Canada. We collected calibration data from ground plot locations representing varying scan angles from two flight lines: within 4° of nadir in one flight line, and either 11–20° from nadir (low scan angle plots: L), or 21–30° from nadir (high scan angle plots: H) in an adjacent flight line. We computed three sets of ALS point cloud metrics for each ground plot using ALS data from: individual flight lines (near-nadir and off-nadir) and data from all available flight lines (up to 4) combined (aggregated, as commonly used in an operational inventory context). We generated three sets of models for each of the L and H plots using the ALS metric sets, and applied the models to independent validation data. We analysed the effect of scan angle on both the ALS metrics and performance statistics for area-based models generated using the L and H datasets. Our results demonstrate that off-nadir scan angles significantly affected (P  < 0.05) specific metrics from both L (i.e. coefficient of variation (COVAR)) and H (i.e. maximum height, 95th percentile of height, mean height) plots, although the effects were trivial (mean absolute differences were ≤ 0.01 for COVAR and < 0.3 m for the height metrics). Forest attribute predictions using these and other metrics were also significantly affected (P  < 0.05), namely gross merchantable volume (GMV), total volume (TVOL) and aboveground tree biomass (AGB) from L ; and Lorey's mean height (HGT), mean diameter at breast height (DBH), and GMV from H. We further demonstrated that combining ALS data from all available flight lines significantly increased errors for the predictions of HGT, GMV, and TVOL using L , and significantly reduced errors of HGT using H when compared to errors resulting from models developed with near-nadir data. While the differences in prediction errors were significant, they were small, with differences in mean absolute prediction errors all <1.3 per cent. Based on our results, we concluded that the effects of large scan angles, up to 30° off-nadir, on area-based forest attribute predictions were minimal in this study, which used ALS metrics calculated from ALS returns with a height above ground >2 m for balsam fir-dominated forests. This result may provide for operational efficiencies in implementing enhanced forest inventories in this particular forest environment. [ABSTRACT FROM AUTHOR]

Wood discoloration was investigated in 721 paper birch (Betula papyrifera Marsh.) trees from 146 sites across the managed forest of Quebec, Canada. Discoloration was present at breast height in 85% of trees, but its impact was limited in terms of tree basal area and volume, with proportions of 6.4% and 3.6%, respectively. These two measures were strongly correlated. Discoloration changed wood appearance but had no effect on microfibril angle and modulus of elasticity. Discolored wood was nevertheless denser, probably due to the accumulation of colored extractives. Predictive models were also developed and applied to 415 711 paper birch trees from 51 689 inventory plots across the province to study regional variation. Higher proportions of discolored basal area were obtained in the southwestern areas of the province where the climate is warmer and drier. In these areas, paper birch trees are larger and, likely, support bigger branches which can cause larger columns of discoloration when broken. Lower proportions were found in eastern regions where snowfall is more abundant. Smaller, more flexible branches in trees growing in these areas could explain this result. This study confirms the feasibility of large-scale mapping of wood discoloration in standing trees based on forest inventory and climate data. [ABSTRACT FROM AUTHOR]

The overwhelming majority of information on historical forest conditions in western North America comes from public lands, which may provide an incomplete description of historical landscapes. In this study we made use of an archive containing extensive timber survey data collected in the early 1920s from privately owned forestland. These data covered over 50,000 ha and effectively represent a 19% sample of the entire area. The historical forest conditions reconstructed from these data fit the classic model of frequent‐fire forests: large trees, low density, and pine‐dominated. However, unlike other large‐scale forest reconstructions, our study area exhibited relatively low overall variability in forest structure and composition across the historical landscape. Despite having low variability, our analyses revealed evidence of biophysical controls on tree density and pine fraction. Annual climatic variables most strongly explained the range in historical tree densities, whereas historical pine fraction was explained by a combination of topographic and climatic variables. Contemporary forest inventory data collected from both public and private lands within the same general area, albeit not a direct remeasurement, revealed substantial increases in tree density and greatly reduced pine fractions relative to historical conditions. Contemporary forests exhibited a far greater range in these conditions than what existed historically. These findings suggest that private forestland managed with multiaged silviculture may be similar to public forestland with respect to departure in forest structure and compositions from that of historical forests. However, there may be differences between management objectives that favor timber production, more typical on private lands, vs. those that favor restoration, increasingly supported on public lands. [ABSTRACT FROM AUTHOR]

Prediction of aboveground wood volume is one of most important stages when conducting a forest inventory and making forest management decisions. This is more difficult for native forest than for plantations given the high variability of the former (trees of different species and age groups). The objective of this work was to evaluate different strategies to estimate aboveground standing tree wood volume (v) using its diameter at breast height (DBH) and total height (h), comparing the use of form factors, volume equations fitted by ordinary least squares and mixed modelling, as well as the options from the literature. To achieve this, 351 trees were scaled during field campaigns, with 158 felled trees and 193 measured using Criterion equipment. The data collected from each scaled tree was: h, DBH, v and identification of the botanical species. We found that the best application of form factors occurred when the trees were divided by diameter classes. However, regression models, regardless of the fitting technique, presented better volume estimates than form factors. Mixed models, with either the species or diameter class as the random variable, provided the lowest errors when estimating tree volume. Thus, we recommend the use of mixed models as the best strategy to estimate volume of standing trees. The following equation can be used to estimate aboveground wood volume for trees from vegetation types similar to the ones of this study: ln(v) = −9.06013 + 1.91756 ln(DBH) + 0.69846 ln(h). [ABSTRACT FROM AUTHOR]