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The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R²) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain. Full article

The formation of animal breeds usually begins with a small subsample from their ancestral population. Deleterious mutations accumulate in the population under genetic drift, inbreeding, and artificial selection during the development and maintenance of traits desired by humans. White raccoon dogs are among the most popular breeds of farmed raccoon dogs, but white raccoon dogs are more susceptible to disease and have a lower reproductive ability. However, the accumulation of deleterious mutations in this white breed is largely unknown. By analyzing and comparing whole-genome sequencing data from 20 white raccoon dogs and 38 normal raccoon dogs, we detected an increased occurrence of loss-of-function (LoF) mutations in white raccoon dogs compared with normal raccoon dogs. With the finding of a significantly higher dosage of homozygous missense mutations in the white raccoon dog genome, we detected a greater fitness cost in white raccoon dogs. Although a much higher FROH level for ROH fragments longer than 1 Mb has been reported in white raccoon dogs, we did not detect a genetic signal of genetic purging in white raccoon dogs. This study provides valuable genomic resources and new insights into the accumulation of mutation loads in farmed raccoon dogs. Full article

Pedunculate oak (Quercus robur L.) is widely distributed across Europe and serves critical ecological, economic, and recreational functions. Investigating its responses to stressors such as drought, extreme temperatures, pests, and pathogens provides valuable insights into its capacity to adapt to climate change. Genetic and dendrochronological studies offer complementary perspectives on this adaptability. Tree-ring analysis (dendrochronology) reveals how Q. robur has historically responded to environmental stressors, linking growth patterns to specific conditions such as drought or temperature extremes. By examining tree-ring width, density, and dynamics, researchers can identify periods of growth suppression or enhancement and predict forest responses to future climatic events. Genetic studies further complement this by uncovering adaptive genetic diversity and inheritance patterns. Identifying genetic markers associated with stress tolerance enables forest managers to prioritize the conservation of populations with higher adaptive potential. These insights can guide reforestation efforts and support the development of climate-resilient oak populations. By integrating genetic and dendrochronological data, researchers gain a holistic understanding of Q. robur’s mechanisms of resilience. This knowledge is vital for adaptive forest management and sustainable planning in the face of environmental challenges, ultimately helping to ensure the long-term viability of oak populations and their ecosystems. The topics covered in this review are very broad. We tried to include the most relevant, important, and significant studies, but focused mainly on the relatively recent Eastern European studies because they include the most of the species’ area. However, although more than 270 published works have been cited in this review, we have, of course, missed some published studies. We apologize in advance to authors of those relevant works that have not been cited. Full article

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. Full article

Reliable prediction of tree stem volume is crucial for effective forest management and ecological assessment. Traditionally, regression models have been applied to estimate forest biometric variables, yet they often fall short when handling the complex, non-linear patterns typical of biological data, potentially introducing biases and errors. Tree stem volume, a critical metric in forest biometrics, is generally estimated through easily measured parameters such as diameter at breast height (d) and total tree height (h). This study investigates advanced machine learning (ML) techniques—Extreme Gradient Boosting (XGBoost), epsilon-Support Vector Regression (ε-SVR), and Random Forest regression (RFr)—to predict the stem volume of European black pine (Pinus nigra) on Mount Olympus, Greece, using basic field measurements. Machine learning (ML) approaches demonstrated substantial improvements in prediction accuracy compared to traditional non-linear regression-based models (RMs). Notably, XGBoost significantly enhanced predictive performance by reducing the Furnival index (FI) by as much as 42.3% (from 1.1859 to 0.1056) and 21.3% (from 0.1475 to 0.1161) in the test and fitting datasets, respectively, for the single-entry model. For the double-entry model, XGBoost achieved FI reductions of 40.5% (from 0.1136 to 0.0676) and 41.3% (from 0.1219 to 0.0715) in the test and fitting datasets, respectively. These findings highlight the potential of ML models to improve the accuracy of forest inventory predictions, thereby supporting more effective and data-driven forest management strategies. Full article

Basic inventory is required for proper understanding and utilization of Earth’s natural resources, especially with increasing soil degradation and species loss. Soil carbon is newly refined at >30,000 Gt C (gigatonnes C), ten times above prior totals. Soil organic carbon (SOC) is up to 24,000 Gt C, plus plant stocks at ~2400 Gt C, both above- and below-ground, hold >99% of Earth’s biomass. On a topographic surface area of 25 Gha with mean 21 m depth, Soil has more organic carbon than all trees, seas, fossil fuels, or the Atmosphere combined. Soils are both the greatest biotic carbon store and the most active CO₂ source. Values are raised considerably. Disparity is due to lack of full soil depth survey, neglect of terrain, and other omissions. Herein, totals for mineral soils, Permafrost, and Peat (of all forms and ages), are determined to full depth (easily doubling shallow values), then raised for terrain that is ignored in all terrestrial models (doubling most values again), plus SOC in recalcitrant glomalin (+25%) and friable saprock (+26%). Additional factors include soil inorganic carbon (SIC some of biotic origin), aquatic sediments (SeOC), and dissolved fractions (DIC/DOC). Soil biota (e.g., forests, fungi, bacteria, and earthworms) are similarly upgraded. Primary productivity is confirmed at >220 Gt C/yr on land supported by Barrow’s “bounce” flux, C/O isotopes, glomalin, and Rubisco. Priority issues of species extinction, humic topsoil loss, and atmospheric CO₂ are remedied by SOC restoration and biomass recycling via (vermi-)compost for 100% organic husbandry under Permaculture principals, based upon the Scientific observation of Nature. Full article

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. Full article

Wetlands play a crucial role in carbon sequestration. The integration of wetland carbon dynamics into landscape architecture, however, has been challenging, mainly due to gaps between scientific knowledge and landscape practice norms. While the carbon performance of different wetland types is well established in the ecological sciences literature, our study pioneers the translation of this scientific understanding into actionable landscape design guidance. We achieve this through a comprehensive, spatially explicit analysis of wetland carbon dynamics using 2024 National Wetlands Inventory data and other spatial datasets. We analyze carbon flux rates across 13 distinct wetland types in Illinois to help quantify useful information related to designing for carbon outcomes. Our analysis reveals that in Illinois, bottomland forests function as primary carbon sinks (709,462 MtC/year), while perennial deepwater rivers act as significant carbon emitters (−2,573,586 MtC/year). We also identify a notable north–south gradient in sequestration capacity, that helps demonstrate how regional factors influence wetland and other stormwater management design strategies. The work provides landscape architects with evidence-based parameters for evaluating carbon sequestration potential in wetland design decisions, while also acknowledging the need to balance carbon goals with other ecosystem services. This research advances the profession’s capacity to move beyond generic sustainable design principles toward quantifiable climate-responsive solutions, helping landscape architects make informed decisions about wetland type selection and placement in the context of climate change mitigation. Full article

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. Full article

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article

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. Full article

It is widely accepted that land use and land cover (LULC) is an important conditioning factor for landslide occurrence, especially when considering the role of tree roots in stabilizing slopes and consolidating the soil. However, it is still difficult to assess the impacts of a specific LULC type on landslide distribution. The objective of the present study is to reveal the relationship between bamboo and landslide distribution at the regional scale. We aim to answer the following question: do the areas covered by bamboo have a higher susceptibility to landslides? Wenzhou City in SE China was taken as the study area, and a landslide inventory containing 1725 shallow landslides was constructed. The generalized additive model (GAM) was employed to assess the significance of LULC and nine additional factors, all of which were generated using the GIS platform. The frequency ratio (FR) method was used to analyze and compare the landslide density in each LULC category. Machine learning models were applied to perform landslide susceptibility mapping of the region. The results show that in the Wenzhou region, LULC is the second most important factor for landslide occurrences after the slope factor, whereas bamboo has a relatively higher FR value than most other LULC categories. The accuracies of the landslide susceptibility maps obtained from the random forest and XGBoost models were 79.6% and 85.3%, respectively. Moreover, 23.8% and 25.5% of the bamboos were distributed in very-high- and high-susceptibility-level areas. The incidents and density of landslides in bamboo areas were significantly higher than those with debris flow and rock collapses, indicating a promotional effect of bamboo on slope failure in the study area. This work will improve our understanding regarding the role of geological and ecological conditions that affect slope stability, which may provide useful guidance for land use planning and landslide risk assessment and mitigation at the regional scale. Full article

Elms (Ulmus spp.) are widely spread in the forest, shelter belts, and urban landscaping. This research aimed to reveal the trends of Ulmus sp. health in Ukraine under biotic damage. The tasks included: (i) analyzing the presence of Ulmus sp. in the forests; (ii) studying the dynamics of Ulmus sp. health for 2001–2015 in the monitoring plots in the frame of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP-Forests); (iii) assessing the prevalence of the dominant biotic factors affecting elm health and the probability of tree death or recovery. As a result of research, elms were found in 3.58% of the area in the stands with other main forest-forming species in the forests subordinated to the State Specialized Forest Enterprise «Forests of Ukraine». Four elm species are present in the forests of all regions of Ukraine. In the Forest zone, U. minor predominates, U. glabra is more common in the western part of the country, and U. pumila in the southern and eastern regions. In the ICP-Forests monitoring plots for 2001–2015, a trend of elm deterioration in 2007–2012 was found. The highest incidence of trees with disease symptoms was recorded for U. pumila. In the sample plots for 2023–2024, the health of three elm species tended to deteriorate. In 2024, mortality occurred among all elm species with symptoms of Dutch elm disease (DED) and among U. pumila trees with symptoms of wetwood. However, several trees have recovered. The results show the gaps in our knowledge that need to be filled, particularly in identifying resistant individuals and using their progeny to create resistant stands. Full article

Gap dynamics are driving many important processes in the development of temperate forest ecosystems. What remains largely unknown is how often the regeneration processes initialized by endogenous mortality of dominant and co-dominant canopy trees take place. We conducted a study in the high mountain forests of the Central Western Carpathians, naturally dominated by the Norway spruce. Based on the repeated forest inventories in two localities, we quantified the structure and amount of deadwood, as well as the associated mortality of standing dead canopy trees. We determined the basic specific gravity of wood and anatomical changes in the initial phase of wood decomposition. The approach for estimating the rate of gap formation and the number of canopy trees per unit area needed for intentional gap formation was formulated based on residence time analysis of three localities. The initial phase of gap formation (standing dead tree in the first decay class) had a narrow range of residence values, with a 90–95% probability that gap age was less than 10 or 13 years. Correspondingly, a relatively constant absolute number of 12 and 13 canopy spruce trees per hectare died standing in 10 years, with a mean diameter reaching 50–58 cm. Maximum diameters trees (70–80 cm) were represented by 1–4 stems per hectare. The values of the wood-specific gravity of standing trees were around 0.370–0.380 g.cm⁻³, and varied from 0.302 to 0.523 g.cm⁻³. Microscopically, our results point out that gap formation is a continuous long-lasting process, starting while canopy trees are living. We observed early signs of wood degradation and bacteria, possibly associated with bark beetles, that induce a strong effect when attacking living trees with vigorous defenses. New information about the initial phase of gap formation has provided a basis for the objective proposal of intervals and intensities of interventions, designed to promote a diversified structure and the long-term ecological stability of the mountain spruce stands in changing climate conditions. Full article

Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. Full article