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Forest management and sustainable timber production rely on forest measurements that include tree height. However, the fieldwork needed for tree height measurements is time-consuming, and several times, hard to obtain. The solution to this problem is the construction of reliable height-diameter (h-d) models that can provide accurate tree height predictions. To this direction, a modified Gompertz model that included dominant height and diameter at breast height was used to predict tree height. In order to investigate the most promising modelling method, six alternative approaches were evaluated; these involved both regression and artificial intelligence techniques and produced the (i) fixed-effects (FE), (ii) mixed-effects (ME), (iii) three-quantile regression (3QR), (iv) five-quantile regression (5QR), (v) general regression neural network (GRNN), and (vi) support vector regression (SVR) models. The accuracy of the developed h-d models was studied in this work for mixed-oak stands (Quercus cerris L., Quercus petraea (Matt.) Liebl. and Quercus frainetto Ten.) in Türkiye. For this purpose, 1735 trees were measured in 52 sample plots in total. The tree variability among sampling plots was incorporated in the constructed models through the dominant heights and the tree height variance in each sample plot, while the models were localized using one to five oak trees, as calibration samples, per plot. The study showed that the ME constructed model with root mean square value equal to 1.8331 was constantly superior to nonlinear regression methods used, while the GRNN and the SVR models with root mean square values equal to 1.8330 and 1.8279, respectively, showed similar predictive ability with the later to prevail, as compared to the rest of the models tested. Finally, five trees per plot were found to be an acceptable trade-off between sampling cost and predictive capacity and reliability. [ABSTRACT FROM AUTHOR]

Number preference, i.e., the human tendency to gravitate toward or away from specific numbers, is a potential source of measurement error in forest inventory. Identifying its presence is an important step to ensure unbiased results. This study evaluated U.S. national forest inventory data for number preference and identified factors that influence the proportion of tree cull volume, tree diameter, tree height, and seedling count observations ending with the digit zero or five (ED0,5) and seedling count observations that were multiples of four (M4). Two-sided hypothesis tests determined that ED0,5 occurred significantly more frequently than expected by chance for all metrics tested, though not in every inventory region of the country nor to the same degree. Consistently, tree-level ED0,5 was more likely when metrics were estimated visually rather than measured instrumentally. Logistic regression indicated that the effect of species class, species type, tree status, treetop status, and stem size on tree-level ED0,5 and the effect of plot-level water depth on seedling count ED0,5 also varied by region. Though the effect was small, findings suggest that some inventory regions may be employing an approved multiplicative shortcut that results in a greater-than-expected proportion of M4 observations among seedling counts. [ABSTRACT FROM AUTHOR]

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Accounting for the condensation of organic vapors along with water vapor (co-condensation) has been shown in adiabatic cloud parcel model (CPM) simulations to enhance the number of aerosol particles that activate to form cloud droplets. The boreal forest is an important source of biogenic organic vapors, but the role of these vapors in co-condensation has not been systematically investigated. In this work, the environmental conditions under which strong co-condensation -driven cloud droplet number enhancements would be expected over the boreal biome are identified. Recent measurement technology, specifically the Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an iodide-adduct Chemical Ionization Mass Spectrometer (I-CIMS), is utilized to construct a volatility distribution of the boreal atmospheric organics. Then, a suite of CPM simulations initialized with a comprehensive set of concurrent aerosol observations collected in the boreal forest of Finland during Spring 2014 is performed. The degree to which co-condensation impacts droplet formation in the model is shown to be dependent on the initialization of the updraft velocity, aerosol size distribution, organic vapor concentration and the volatility distribution. The predicted median enhancement in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics is 20 % (interquartile range 29–14 %). This corresponds to activating particles 12–16 nm smaller in dry diameter, that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (ΔCDNC) are predicted in the presence of a nascent ultrafine aerosol mode with a geometric mean diameter of ~40 nm and no clear Hoppel minimum, indicative of pristine environments with a source of ultrafine particles (e.g., via new particle formation processes). Such aerosol size distributions are observed 30–40 % of the time in the studied boreal forest environment in spring and fall when new particle formation frequency is the highest (six years of statistics). Five years of UK Earth System Model (UKESM1) simulations are further used to evaluate the frequencies to which such distributions are experienced by an Earth System Model over the whole boreal biome. The frequencies are substantially lower than those observed at the boreal forest measurement site (< 6 % of the time) and the positive values, peaking in spring, are modeled only over Fennoscandia and western parts of Siberia. For the aerosol size distribution regime simulated by UKESM1, offline simulations with the adiabatic parcel model reveal the ΔCDNC to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs). The magnitudes of ΔCDNC remain less affected by the more volatile vapors such as formic acid and extremely low and low volatility organic compounds (ELVOCs and LVOCs) in the CPM simulations. The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below cloud base and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce critical supersaturations needed for droplet activation. Suppression of the critical supersaturation caused by organic condensation is the main driver of the modeled ΔCDNC. The results highlight the potential significance of co-condensation in pristine boreal environments close to sources of fresh ultrafine particles. For accurate predictions of co-condensation effects on CDNC, the representation of the aerosol size distribution is of essence. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests. Field-based measurements of forest above-ground biomass (AGB) can be costly and difficult to conduct. Multi-source remote sensing data offers the potential to improve the accuracy of modelled AGB predictions. Here, four machine learning methods: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Trees (CART), and Minimum Distance (MD) were used to construct forest AGB models of Taiyue Mountain forest, Shanxi Province, China using single and multi-sourced remote sensing data and the Google Earth Engine platform. Results showed that the machine learning method that most accurately predicted AGB were GBDT and spectral index for coniferous (R2 = 0.99; RMSE = 65.52 Mg/ha), broadleaved (R2 = 0.97; RMSE = 29.14 Mg/ha), and mixed-species (R2 = 0.97; RMSE = 81.12 Mg/ha) forest types. Models constructed using bivariate variable combinations that included the spectral index improved the AGB estimation accuracy of mixed-species (R2 = 0.99; RMSE = 59.52 Mg/ha) forest types and reduced slightly the accuracy of coniferous (R2 = 0.99; RMSE = 101.46 Mg/ha) and broadleaved (R2 = 0.97; RMSE = 37.59 Mg/ha) forest AGB estimation. Overall, parameterizing machine learning algorithms with multi-source remote sensing variables can improve the prediction accuracy of mixed-species forests. [ABSTRACT FROM AUTHOR]

Ground-based forest inventories are reliable methods for forest carbon monitoring, reporting, and verification schemes and the cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else require manual intervention or offline processing to identify and measure tree trunks. In this paper, we designed an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate the trunk diameter automatically from a single image in complex and realistic field conditions. We implemented our design and built it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational costs to run on this commodity platform in near real-time. We evaluated our app in 3 different forests across 3 seasons and found that in a corpus of 97 sample tree images, our app estimated the trunk diameter with a RMSE of 3.7 cm ( R 2 = 0.97; 8.0% mean absolute error) compared to manual DBH measurement. It achieved a 100% tree detection rate while reducing the surveyor time by up to a factor of 4.6. Our work contributes to the search for a low-cost, low-expertise alternative to terrestrial laser scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating the trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurements outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions. [ABSTRACT FROM AUTHOR]

The main aim of this paper was to determine the rate of erosion and aggradation within a forest road on different geological structures as well as a comparison of changes between roadbeds and road cutslopes. The research was conducted in the Lejowa Valley in the Tatra Mountains. The measurements of the forest road were performed using a Leica ScanStation C10 terrestrial laser scanner (TLS) with a set of six‐inch Leica High Definition Survey (HDS) targets. Based on the multi‐temporal digital elevation models (DEMs) two DEM of Differences (DoD) were created for the summer season of 2019 and the 2019–2020 period. The research has shown that there is a significant difference between the rate of relief change for roads constructed atop conglomerates and limestone. The net change for the road within conglomerates was an erosion of 432.37 m3ha‐1 yr‐1, while for the road within limestone it was a sediment deposition which amounted to 3.87 m3ha‐1 yr‐1. Research has shown that there exists a difference in the rate of erosion for road cutslopes and roadbeds constructed within conglomerates. It was shown that the erosion rate for the roadbeds was 1.7‐times larger than that for the studied road cutslopes in the period 2019–2020. Improperly designed and constructed forest roads may be subject to intensive relief transformations, even after they are no longer in use. [ABSTRACT FROM AUTHOR]

Forest growing stem volume (GSV) is regarded as one of the most important parameters for the quality evaluation and dynamic monitoring of forest resources. The accuracy of mapping forest GSV is highly related to the employed models and involved remote sensing features, and the criteria of feature evaluation severely affect the performance of the employed models. However, due to the linear or nonlinear relationships between remote sensing features and GSV, widely used evaluation criteria inadequately express the complex sensitivity between forest GSV and spectral features, especially the saturation levels of features in a planted forest. In this study, novel feature evaluation criteria were constructed based on the Pearson correlations and optical saturation levels of the alternative remote sensing features extracted from two common optical remote sensing image sets (GF-1 and Sentinel-2). Initially, the spectral saturation level of each feature was quantified using the kriging spherical model and the quadratic model. Then, optimal feature sets were obtained with the proposed criteria and the linear stepwise regression model. Finally, four widely used machine learning models—support vector machine (SVM), multiple linear stepwise regression (MLR), random forest (RF) and K-neighborhood (KNN)—were employed to map forest GSV in a planted Chinese fir forest. The results showed that the proposed feature evaluation criteria could effectively improve the accuracy of estimating forest GSV and that the systematic distribution of errors between the predicted and ground measurements in the range of forest GSV was less than 300 m3/hm2. After using the proposed feature evaluation criteria, the highest accuracy of mapping GSV was obtained with the RF model for GF-1 images (R2 = 0.49, rRMSE = 28.67%) and the SVM model for Sentinel-2 images (R2 = 0.52, rRMSE = 26.65%), and the decreased rRMSE values ranged from 1.1 to 6.2 for GF-1 images (28.67% to 33.08%) and from 2.3 to 6.8 for Sentinel-2 images (26.85% to 33.28%). It was concluded that the sensitivity of the optimal feature set and the accuracy of the estimated GSV could be improved using the proposed evaluation criteria (less than 300 m3/hm2). However, these criteria were barely able to improve mapping accuracy for a forest with a high GSV (larger than 300 m3/hm2). [ABSTRACT FROM AUTHOR]

The growing season length indices derived from air temperature are frequently used in climate monitoring applications as well as to predict the response of forest ecosystems to climate change. The indicator most widely used in the studies of forest ecosystems is the length of the thermal growing season (temp =5°C), less commonly the parameters of the forest growing season (temp =10°C). However, only a few studies used long-term series of temperature measurements in the forest. In this article, we determined the temporal changes in the parameters of the thermal (TGS) and forest (FGS) growing season in the Experimental Forests of the Warsaw University of Life Sciences in Rogów (51o40'N, 19o55'E, h = 194 m MSL) in the years 1951-2020. The analysis is based on the dataset (daily mean air temperature) obtained from a meteorological station located near the forest complex and from a forest under-canopy station located in a more than 120-year-old fresh mixed forest. The results show a significant extension of the growing season in 1951-2020, the TGS lasted on average 2.8 days/10 years, and the FGS 2.4 days/10 years. The extension of the TGS and FGS was a consequence of both its earlier start and later end. The start of the TGS was characterized by a statistically significant negative trend (1.3 days/10 years), and most changes were characteristic for the last three decades (4.4 days/10 years). The last 30 years were also characterized by a statistically significant trend towards the later end of the TGS. The TGS in 1991-2020 was longer than in 1951-1980 and 1971-2000 by 9 days, while in 1981-2010 by 5 days. Changes in the length of the TGS resulted primarily from its earlier beginning: in the multi-year period 1991-2020, the TGS started 7 days earlier than in 1951-1980. In the case of the FGS, these changes were weaker, although there was a statistically significant negative trend in the start dates and a positive trend in the FGS length. The FGS started almost a month later than the TGS (average on April 28) and ended 4 weeks earlier (average on October 5) and lasted 160 days. TGS in the forest was shorter than outside the forest by 3 days, and FGS by 1 day. The acceleration of the beginning of TGS during the last three decades was faster than the beginning of the frost-free period, indicating a possible increase in vegetation exposure to spring frost. This may pose a threat to the development of plants in the first phase of vegetation. [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]

High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates metallic alloys comprised of several metals which individually possess antimicrobial properties, with the target of achieving broad-spectrum, rapid sanitation through synergistic activity. An entropy-motivated stabilization paradigm is proposed to prepare scalable alloys of copper, silver, nickel and cobalt. Using combinatorial sputtering, thin-film alloys were prepared on 100 mm wafers with ≈50% compositional grading of each element across the wafer. The films were then annealed and investigated for alloy stability. Antimicrobial activity testing was performed on both the as-grown alloys and the annealed films using four microorganisms—Phi6, MS2, Bacillus subtilis and Escherichia coli—as surrogates for human viral and bacterial pathogens. Testing showed that after 30 s of contact with some of the test alloys, Phi6, an enveloped, single-stranded RNA bacteriophage that serves as a SARS-CoV-2 surrogate, was reduced up to 6.9 orders of magnitude (> 99.9999%). Additionally, the non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E. coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7 log reduction in activity after 30, 20 and 10 min, respectively. Antimicrobial activity in the alloy samples showed a strong dependence on the composition, with the log reduction scaling directly with the Cu content. Concentration of Cu by phase separation after annealing improved activity in some of the samples. The results motivate a variety of themes which can be leveraged to design ideal antimicrobial surfaces. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) has been recognized as a reliable and accurate measurement tool in forest volume estimation, urban scene reconstruction and land cover classification, where LiDAR data provide crucial and efficient features such as intensity, elevation and coordinates. Due to the complex urban environment, it is difficult to classify land cover accurately and quickly from remotely sensed data. Methods based on the Dempster–Shafer evidence theory (DS theory) offer a possible solution to this problem. However, the inconsistency in the correspondence between classification features and land cover attributes constrains the improvement of classification accuracy. Under the original DS evidence theory classification framework, we propose a novel method for constructing a basic probability assignment (BPA) function based on possibility distributions and apply it to airborne LiDAR land cover classification. The proposed approach begins with a feature classification subset selected by single-feature classification results. Secondly, the possibility distribution of the four features was established, and the uncertainty relationship between feature values and land cover attributes was obtained. Then, we selected suitable interval cut-off points and constructed a BPA function. Finally, DS evidence theory was used for land cover classification. LiDAR and its co-registration data acquired by Toposys Falcon II were used in the performance tests of the proposed method. The experimental results revealed that it can significantly improve the classification accuracy compared to the basic DS method. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of ICESat-2 data capabilities for forest-height estimation and assessment of its spatial distribution over Russia. A brief overview of available current and historic lidar satellite-data products is carried out. The physical basis of the ATLAS instrument operation, as well as an approach to forming an ATL08 data product with information on vertical structure of vegetation, are described. An approach for automated download and preprocessing of ATL08 product is implemented, including filtering of missing/corrupted data, conversion to vector format, and delineation of borders of aggregated lidar footprints for which characteristics of vegetation-height distribution are provided. All the available ATL08 data over Russia for the period from October 14, 2018, to May 13, 2020, was processed to form a dataset of over 125 million lidar measurements of vegetation vertical structure, including over 50 million measurements of forest height. This study was performed at local and national spatial levels. Local-scale accuracy assessment of the ATL08 product (at the level of forest stands) included actualization of field survey data using models of forest growth, and satellite-data-based estimation of mean forest height and its uncertainty considering uncertainties of input lidar measurements and forest cover heterogeneity. Results of the local-scale accuracy assessment of ATL08 data are presented, showing agreement between ground-based and satellite measurements of mean forest height at the level of R2 = 0.67 and RMSE = 3.79 m. National level analysis included examples of potential use of the resulting dataset to study height and productivity of Russian forests. Estimates of mean forest-height spatial distribution over Russia, as well as distribution of mean height for different forest types, are presented. [ABSTRACT FROM AUTHOR]

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests. [ABSTRACT FROM AUTHOR]

A changing climate is altering ecosystem carbon dynamics with consequences for natural systems and human economies, but there are few tools available for land managers to meaningfully incorporate carbon trajectories into planning efforts. To address uncertainties wrought by rapidly changing conditions, many practitioners adopt resistance and resilience as ecosystem management goals, but these concepts have proven difficult to monitor across landscapes. Here, we address the growing need to understand and plan for ecosystem carbon with concepts of resistance and resilience. Using time series of carbon fixation (n = 103), we evaluate forest management treatments and their relative impacts on resistance and resilience in the context of an expansive and severe natural disturbance. Using subalpine spruce–fir forest with a known management history as a study system, we match metrics of ecosystem productivity (net primary production, g C m−2 year−1) with site‐level forest structural measurements to evaluate (1) whether past management efforts impacted forest resistance and resilience during a spruce beetle (Dendroctonus rufipennis) outbreak, and (2) how forest structure and physiography contribute to anomalies in carbon trajectories. Our analyses have several important implications. First, we show that the framework we applied was robust for detecting forest treatment impacts on carbon trajectories, closely tracked changes in site‐level biomass, and was supported by multiple evaluation methods converging on similar management effects on resistance and resilience. Second, we found that stand species composition, site productivity, and elevation predicted resistance, but resilience was only related to elevation and aspect. Our analyses demonstrate application of a practical approach for comparing forest treatments and isolating specific site and physiographic factors associated with resistance and resilience to biotic disturbance in a forest system, which can be used by managers to monitor and plan for both outcomes. More broadly, the approach we take here can be applied to many scenarios, which can facilitate integrated management and monitoring efforts. [ABSTRACT FROM AUTHOR]

The characteristics and extent of forest damage, and the subsequent patterns of recovery, reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using satellite measurements of vegetation damage and recovery patterns, following the 2015 explosive eruption of Calbuco, Chile, we assess the impact on surrounding temperate forests and how areas impacted by different deposit types recover post-eruption. The Calbuco eruption resulted in tephra deposition over hundreds of square kilometres, pyroclastic flows extending 6 km and lahars extending 15 km. We explore NDVI derived from optical imagery (June 2013–May 2023) as well as radar backscatter and phase coherence (October 2014–June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40 cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28 km3. Vegetation recovery rates relate to initial impact type and intensity, but also local topography, aspect and altitude. Our results demonstrate a novel application of optical and radar satellite remote sensing to determine eruption extents and magnitudes through vegetation disturbance. We show that measuring vegetation disturbance, particularly in remote and densely vegetated environments, can help refine field-based analyses in inaccessible or intensely damaged zones. • Satellite imagery captures the impact of volcanic eruptions on forests and the post-disturbance recovery trajectories. • Time series cluster analysis discriminates between the impact of tephra fall deposits and volcanic flow deposits. • Forest damage is a useful proxy for tephra deposit distribution and thickness, enabling re-estimation of eruption volumes. [ABSTRACT FROM AUTHOR]

This study introduces an innovative demountable steel-timber composite (STC) flooring system with potential for reuse. The flooring system consists of downstanding hot-rolled I-shaped steel profiles and laminated veneer lumber (LVL) slabs connected to the steel beams with novel shear connectors. This STC flooring system aimed at reducing embodied carbon and optimizing the use of resources, offer an alternative to traditional, carbon-intensive flooring systems. The novel shear connections implemented in the STC beams facilitate demountability, adaptability, reconfiguration, and relocation of structural components, aligning with circular economy principles. Moreover, this flooring system exhibits significant promise for modularization, standardization, and off-site serial production, making it ideal for prefabrication in standard sizes and modules. Two full-scale STC beams, each with a span of 10 m, an LVL slab width of 2.51 m and thickness of 144 mm connected to a steel profile IPE 400, were simply supported and tested in six-point bending to assess their flexural response. The two tested beams were identical in terms of geometry, materials, and shear connection distribution, but differed in the type of shear connection implemented. This contribution outlines the details of the innovative flooring system and the novel connections that enable demountability and reusability of the components. Specifics of the two STC beam specimens, their assembly, the test setup, the instrumentation, and the testing procedure are presented. The results of the two full-scale bending tests demonstrate their significant load-bearing and deformation capabilities as well as potential for reuse. Additionally, the results indicate the efficacy of the novel shear connections and the beams' overall structural integrity. Despite the extensive deformation of the STC beams, the shear connections remained undamaged and slip values of less than 7 mm were recorded in the last loading stages. Within the elastic range, maximum slip values of less than 1 mm were recorded, highlighting the structural components' potential for reuse. Midspan deflections exceeding l / 20 demonstrate good ductility. Strain measurements on the timber slabs revealed that the slabs' width was not fully utilized, leading to the determination of an effective width of l / 5.5. • Innovative STC beams show high load-bearing and deformation capabilities. • Demountable shear connections enable beam reusability. • Full-scale bending tests confirm STC beams' structural integrity. • STC beams offer sustainable alternative to traditional flooring systems. • Demountable STC beams align with circular economy principles in construction. [ABSTRACT FROM AUTHOR]

Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are expected not to produce identical weights and likelihood ratio (LR) values due to the Monte Carlo aspect. This paper reports a detailed precision study under reproducibility conditions conducted as a collaborative exercise across the National Institute of Standards and Technology (NIST), Federal Bureau of Investigation (FBI), and Institute of Environmental Science and Research (ESR). Replicate interpretations generated across the three laboratories used the same input files, software version, and settings but different random number seed and different computers. This work demonstrates that using different computers to analyze replicate interpretations does not contribute to any variations in LR values. The study quantifies the magnitude of differences in the assigned LRs that is only due to run-to-run MCMC variability and addresses the potential explanations for the observed differences. • This collaborative study is an exploration of precision under reproducibility conditions. • We explore the differences in the assigned LR values attributed solely to the stochasticity of the MCMC resampling method. • The majority of log 10 (LR) of the H 1 -true tests were within 0 to 1 order of magnitude. • Replicate runs that resulted in an LR difference of more than 1 order of magnitude on the log 10 scale are discussed. [ABSTRACT FROM AUTHOR]

System logs are an important data source for performance monitoring and anomaly detection. Analyzing logs for anomaly detection can improve service quality. At present, although machine learning algorithms for anomaly detection can achieve high accuracy, they lack robustness. The detection model cannot dynamically adapt to changes of logs when system logs contain noises owing to the casualness of the operators or log templates update. In face of this challenge, the paper proposes a robust log anomaly detection method based on contrastive learning and multi-scale Masked Sequence to Sequence (MASS). First, a log feature extraction model integrating the BERT model with contrastive learning is proposed. It can extract effective features by pulling two related normal logs together and pushing apart normal and abnormal logs to ensure that the semantic similarity between normal and abnormal log templates is lower than that between normal log templates, effectively remove abnormal log templates and distinguish log categories to which normal log templates and normal noise log templates belong rather than rudely treating the noise log templates as anomalies, which enhances the robustness of anomaly detection. Then, a multi-scale Masked Sequence to Sequence (MSMASS) model is proposed, the Attention mechanism of the MASS model is replaced with multi-scale Attention to fully learn the context information of different scales of the log sequence, which improves the accuracy of anomaly detection. Contrast experiments are conducted with four baseline methods on common datasets, and the results show that the method proposed in this paper is superior to most existing log-based anomaly detection methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

An estimation of the aboveground biomass of herbaceous plants is of significant value for the evaluation of carbon storage in subtropical herbaceous layers. To estimate the aboveground biomass of eight common herbaceous species in subtropical forests in Anhui Province, China, single and mixed multi-species allometric models were established using the average height, coverage, and complex variables (average height was multiplied by the coverage) as independent variables. The results showed that the coverage and complex variables were strongly correlated with the aboveground biomass of herbaceous plants, which were suitable predictors for modeling. The complex variables, except for Selaginella moellendorffii, revealed a good fit accuracy when used as the prediction variable, with R2 values ranging from between 0.649 and 0.904. Compared with the allometric model of specific species, the R2 of the mixed species model was lower (R2 = 0.49); however, the MPE (Mean Predictive Error, MPE) was low and within the allowable error range (MPE = -8.599%). The nine optimal allometric models established in this study proved that average height and coverage possessed high predictive capacities for herbaceous biomass. This provided a new approach for the measurement and monitoring of forest productivity in subtropical areas. [ABSTRACT FROM AUTHOR]

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta's forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International's LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software's capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented. [ABSTRACT FROM AUTHOR]

The effect of a large-scale windfall as a result of the Lionrock typhoon (2016), which caused complete destruction of forest plantations on the area of 36 000 ha within the Sikhote–Alin Nature Reserve, on carbon emission from the soil surface and on fine root stocks was considered. Sample areas on windfall and control sites were in Siberian pine and birch forests. Emission from the soil surface on the windfall site in the birch forest was 5.52 ± 1.12 t C/(ha yr}, which was 25% lower in comparison with the emission on the control plot because of the weakened root respiration. The annual carbon flux from soil at the windfall site in the Siberian pine forest was 8.02 ± 1.50 t C/(ha yr); in contrast to the birch forest, it was 29% higher than on the control plot. This can be explained by intensive decomposition of fine roots on the windfall in the Siberian pine forest during the measurement period, while birch roots predominantly decomposed before the start of measurements. The stock of fine roots after the windfall decreased by 20% in the Siberian pine forest and by 37% in the birch forest, while the stock of dead roots was 58% higher at the windfall site of the Siberian pine forest and 28% lower at the windfall site of the birch forest site as compared to the control. It was determined that windfall significantly increased soil moisture (p < 0.001). Temperature dependences of emission were plotted. Estimates of annual emission values obtained using data from the Ternei weather station were close to the values calculated from logger temperatures on sample plots. Our work experimentally showed a significant contribution of fine roots to soil emission on the windfall site. Against the background of large carbon losses from fires and logging, the territory of the Ternei forestry of Primorskii region became a powerful source of carbon. [ABSTRACT FROM AUTHOR]

The eddy covariance (EC) technique has been widely used as a micrometeorological tool to measure carbon, water and energy exchanges. When utilizing the EC measurements, it is critical to be aware of the long-term information on source areas. In China, large-scale forest plantations have become a dominant driver of greening and carbon sinks on the planet. However, the spatial representativeness of EC measurements on forest plantations is still not well understood. Here, an EC flux site of a coniferous plantation mixed with cropland in a subtropical monsoon climate was selected to evaluate the spatial representativeness of the two approaches. One is the fraction of target vegetation type (FTVT), which was used to detect to what degree the flux is related to the target vegetation. The other is the sensor location bias calculated from the enhanced vegetation index (EVI), which was used to detect to what spatial extent the flux can be upscaled. The results showed that the monthly footprint climatologies changed intensely throughout the year. The source area is biased toward the southeast in summer and northwest in winter. The study area was mainly a composite of coniferous plantations (70.08%) and double-cropped rice (27.83%). The double-cropped rice, with a higher seasonal variation of EVI than the coniferous plantation, was mainly distributed in the eastern areas of the study site. As a result of spatial heterogeneity and footprint variation, the FTVT was 0.89 when the wind direction was southwest; however, this reduced to 0.65 when the wind direction changed to the northeast and exhibited a single-peak seasonal variation during a year. The sensor location bias of the EVI also showed a significant monthly variation and ranged from −14.21% to 19.04% in a circular window with an increasing size from 250 to 3000 m. The overlap index between daytime and nighttime (Oday_night) can potentially be a quality flag for the GPP derived from the EC flux data. These findings demonstrate the joint effects of the monsoon climate and underlying surface heterogeneity on the spatial representativeness of the EC measurements. Our study highlights the importance of having footprint awareness in utilizing EC measurements for calibration and validation in monsoon areas. [ABSTRACT FROM AUTHOR]

The suitability of using Landsat sensor variables to predict key stand attributes, including stand average dominant/codominant tree height (HT), mean diameter at breast height (DBH), the number of trees per hectare (NT), basal area per hectare (BA), and stand density index (SDI), of intensively managed loblolly pine plantations in the Western Gulf Region at the plot/stand level was assessed. In total, thirty Landsat sensor variables including six original bands, three vegetation indices, three Tasseled Cap transformed indices, and eighteen texture measure variables were used as predictors. Field data of 125 permanent plots located across east Texas and western Louisiana were used as reference data. Individual trees of those plots were measured at plot establishment (referred to as the first cycle measurement; average about 4.5 years old) and remeasured in three-year intervals (the second cycle measurement at approximately seven years old and the third cycle measurement at approximately 10 years old). Thus, field reference data represent stand development from open- (first cycle) to closed-canopy (third cycle). Models to predict stand HT, DBH, NT, BA, and SDI were developed by cycle using multiple linear regression (MLR) and also random forests (RF) methods. Results indicated that the first cycle stands HT, DBH, BA, and SDI were well predicted using the Landsat sensor variables with R2 > 0.7 and low RMSEs. These relationships weakened with stand age, although still moderate with R2 being around 0.45 for the second cycle measurement and became practically useless (R2 < 0.30) for the third cycle measurement. For NT, no meaningful models were achieved regardless of the measurement cycle. The MLR and RF models were comparable in accuracy and had similar key predictors. Overall, the shortwave infrared bands, red band, and wetness index were the most important predictors, but their dominance declined with the cycle. Texture measure variables were relatively less important but a trend of increasing their importance with cycle was noted. Results show promise for operationally predicting stand variables for young pine plantations, an age class that typically presents significant challenges using conventional forest measurement methodologies. Potential methods to further improve model accuracy and how to use the results within the context of pine plantation management planning in the region were discussed. [ABSTRACT FROM AUTHOR]

The understanding of new particle formation and growth processes is critical for evaluating the role of aerosols in climate change. One of the knowledge gaps is the ion–particle interaction during the early growth process, especially in the sub-3 nm range, where direct observations are sparse. While molecular interactions would imply faster growth rates of ions compared to neutral particles, this phenomenon is not widely observed in the atmosphere. Here, we show field measurements in the boreal forest indicating a smaller apparent growth rate of the ion population compared to the total particles. We use aerosol dynamics simulations to demonstrate that this effect can be caused by the changing importance of ion-induced nucleation mechanisms during the day. We further compare these results with chamber experiments under similar conditions, where we demonstrate that this effect critically depends on the abundance of condensable vapors and the related strength of ion-induced nucleation. Our results imply that atmospheric ion growth rate measurements below 3 nm need to be evaluated very carefully as they do not represent condensational growth alone but are influenced by ion–particle population interactions. [ABSTRACT FROM AUTHOR]

Forest biomass measurement or estimation is critical for forest monitoring at the stand scale, but errors among different estimations in stand investigation are unclear. Thus, the Pinus densata natural forest in Shangri-La City, southwestern China, was selected as the research object to investigate the biomass of 84 plots and 100 samples of P. densata. The stand biomass was calculated using five methods: stand biomass growth with age (SBA), stem biomass combined with the biomass expansion factors (SB+BEF), stand volume combined with biomass conversion and expansion factors (SV+BCEF), individual tree biomass combined with stand diameter structure (IB+SDS), and individual tree biomass combined with stand density (IB+SD). The estimation errors of the five methods were then analyzed. The results showed that the suitable methods for estimating stand biomass are SB+BEF, M+BCEF, and IB+SDS. When using these three methods (SB+BEF, SV+BCEF, and IB+SDS) to estimate the biomass of different components, wood biomass estimation using SB+BEF is unsuitable, and root biomass estimation employing the IB+SDS method was not preferred. The SV+BCEF method was better for biomass estimation. Except for the branches, the mean relative error (MRE) of the other components presented minor errors in the estimation, while MRE was lower than other components in the range from −0.11%–28.93%. The SB+BEF was more appealing for branches biomass estimation, and its MRE is only 0.31% lower than SV+BCEF. The stand biomass strongly correlated with BEF, BCEF, stand structure, stand age, and other factors. Hence, the stand biomass growth model system established in this study effectively predicted the stand biomass dynamics and provided a theoretical basis and practical support for accurately estimating forest biomass growth. [ABSTRACT FROM AUTHOR]

A new three-parameter survival model is proposed using the Kavya–Manoharan (KM) transformation family and the exponentiated Weibull (EW) distribution. The shapes of the pdf for the new model can be asymmetric and symmetric shapes, such as unimodal, decreasing, right-skewed and symmetric. In addition, the shapes of the hrf for the suggested model can be increasing, decreasing, constant and J-shaped. Statistical properties are obtained: quantile function, mode, moments, incomplete moments, residual life time, reversed residual life time, probability weighted moments, order statistics and entropy. We discuss the maximum likelihood estimation for the model. The relevance and flexibility of the model are demonstrated using two real datasets. The distribution is very flexible, and it outperforms many known distributions, such as the three-parameter exponentiated Weibull, the modified Weibull model, the Kavya–Manoharan Weibull, the extended Weibull, the odd Weibull inverse Topp–Leone and the extended odd Weibull inverse Nadarajah–Haghigh model. A bivariate step-stress accelerated life test based on progressive type-I censoring (PTIC) using the model is presented. This pattern is noticed when a particular number of lifetime test units are routinely eliminated from the test at the conclusion of each post-test period of time. Minimizing the asymptotic variance of the MLE of the log of the scale parameter at design stress under PTIC yields an expression for the ideal test plan under PTIC. [ABSTRACT FROM AUTHOR]

Climate change is one of the most critical threats to the human population and other living organisms on Earth. REDD+ is developed as a mechanism to acquire a global fund for addressing climate change, deforestation, and protecting the forest ecosystem while maintaining the livelihood of local communities. As a response to the need for carbon stock measurement at the specific forest and land-use types, this research aimed to estimate the aboveground carbon stock at seven land-use types in KPHP (Forest management unit) Katingan Hulu Central Kalimantan Indonesia. This research was conducted from May to September 2019. The data collected in 91 observation plots included diameter at breast height, total height, and fresh weight of understory vegetation and litter. Using an allometric equation, this research estimated the above-ground carbon stock in trees, understory vegetation, and litter. It was found that AGC varied across different land-use types: secondary peat forest 135.30 Mg C/Ha, secondary forest 212.19 Mg C/Ha, shrub 47.41 Mg C/Ha, oil palm plantation 73.76 Mg C/Ha, rubber plantation 65.56 Mg C/Ha, and forest with rattan 75.98 Mg C/Ha. It was concluded that AGC in KPHP Katingan Hulu varied according to the type of land use system. The forests with less human intervention, such as secondary forests, had higher AGC compared with highly disturbed forests such as shrubs. The findings from this research could help decision-makers to develop the REDD programs to rehabilitate forests and contribute to community development. [ABSTRACT FROM AUTHOR]

Numerous forests in the eastern United States have been degraded due to past exploitative timber harvesting known as high grading. High graded forest stands may not improve without active rehabilitation and may require targeted silvicultural treatments. This study focuses on high graded mixed-oak (mixed- Quercus spp.) stands and aims to develop a model that can identify past high grading and to determine modifications that may improve forest management recommendations provided by the prominent decision support tool, SILVAH. We present a model that uses standard forest inventory measurements and does not require knowledge of preharvest stand conditions to predict with moderate to high accuracy whether a stand was high graded, which could be particularly useful for nonindustrial private forests. Results indicate that modifications to SILVAH may be necessary to improve its utility for prescribing silvicultural treatments in high graded stands. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) and structure-from-motion (SfM) image processing are promising tools for sustainable forest management as they allow for the generation of photogrammetrically derived point clouds from UAS images that can be used to estimate forest structure, for a fraction of the cost of LiDAR. The SfM process and the quality of products produced, however, are sensitive to the chosen flight parameters. An understanding of the effect flight parameter choice has on accuracy will improve the operational feasibility of UASs in forestry. This study investigated the change in the plot-level accuracy of top-of-canopy height (TCH) across three levels of flying height (80 m, 100 m, and 120 m) and four levels of forward overlap (80%, 85%, 90%, and 95%). A SenseFly eBee X with an Aeria X DSLR camera was used to collect the UAS imagery which was then run through the SfM process to derive photogrammetric point clouds. Estimates of TCH were extracted for all combinations of flying height and forward overlap and compared to TCH estimated from ground data. A generalized linear model was used to statistically assess the effect of parameter choice on accuracy. The RMSE (root-mean-square error) of the TCH estimates (RMSETCH) ranged between 1.75 m (RMSETCH % = 5.94%) and 3.20m (RMSETCH % = 10.1%) across all missions. Flying height was found to have no significant effect on RMSETCH, while increasing forward overlap was found to significantly decrease the RMSETCH; however, the estimated decrease was minor at 4 mm per 1% increase in forward overlap. The results of this study suggest users can fly higher and with lower levels of overlap without sacrificing accuracy, which can have substantial time-saving benefits both in the field collecting the data and in the office processing the data. [ABSTRACT FROM AUTHOR]

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Nitrogen-vacancy (NV) centers in diamond have been developed into essential hardware units for a wide range of solid-state-based quantum technology applications. While such applications require the long spin lifetimes of the NV centers, they are often limited due to decoherence. In this study, we theoretically investigate the decoherence of NV-spin ensembles induced by nitrogen impurities (P1 centers), which are one of the most dominant and inevitable magnetic field noise sources in diamond. We combined cluster correlation expansion and density functional theory to compute the Hahn-echo spin-coherence time of the NV centers for a broad range of P1 concentrations. Results indicate a clear linear dependence of T2 on P1 concentrations on a log scale with a slope of −1.06, which is in excellent agreement with previous experimental results. The interplay between the Jahn–Teller effect and the hyperfine interaction in the P1 center plays a critical role in determining the bath dynamics and the resulting NV decoherence. Our results provide a theoretical upper bound for the NV-spin T2 over a wide range of P1 densities, serving as a key reference for materials optimization and spin bath characterization to develop highly coherent NV-based devices for quantum information technology. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecastingmethodology and discuss its application to the production of global and regional risk maps. [ABSTRACT FROM AUTHOR]

Flux measurements over heterogeneous surfaces with growing vegetation and a limited fetch are a difficult task, as measurement heights that are too high or too low above the canopy adversely affect results. The aim of this study is to assess implications from measurement height in regard to low-pass filtering, footprint representativeness, and energy balance closure for a clear-cut site with regrowing vegetation of varying height. For this, measurements from two open-path eddy-covariance systems at different heights are compared over the course of one growing season. Particular attention is paid to low-pass-filtering corrections, for which five different methods are compared. Results indicate significant differences between fluxes from the upper and lower systems, which likely result from footprint differences and an insufficient spectral correction for the lower system. Different low-pass-filtering corrections add an uncertainty of 3.4% (7.0%) to CO2 fluxes and 1.4% (3.0%) to H2O fluxes for the upper (lower) system, also leading to considerable differences in cumulative fluxes. Despite limitations in the analysis, which include the difficulty of applying a footprint model at this study site and the likely influence of advection on the total exchange, the analysis confirms that information about the choice of spectral correction method and measurement-height changes are critical for interpreting data at complex sites. [ABSTRACT FROM AUTHOR]

Measurements in forest catchments in Fukushima Prefecture indicate that cesium‐137 (137Cs) leaching from forest litter affects dissolved 137Cs levels in river water. Existing simulations have been unsuccessful in reproducing this phenomenon due to mechanistic limitations in the models connecting the forest ecosystem and the river system. This paper introduces a new coupled watershed‐geochemical model to address this. We connected a forest ecosystem compartment model to the 3D watershed model General‐purpose Terrestrial Fluid‐Flow Simulator to describe 137Cs transfer between forests and the river system. The compartment model included nonleachable and leachable 137Cs sites in the organic layer. The latter sites model how leachable 137Cs stocks increase with ambient temperature owing to decomposing organic matter. The model was tested by simulating dissolved and particulate 137Cs discharges from a forested catchment upstream of the Ohta River, Fukushima Prefecture. The results for dissolved 137Cs concentrations in river water for 1 January 2014, to 31 December 2015, correlated well with catchment measurements. The simulations reproduced dissolved 137Cs concentration peaks that occurred during three typhoon events and the seasonal variations under baseflow conditions. The results support the theory that leaching from the organic layer in forests is a primary factor affecting river water dissolved 137Cs concentrations. The wider contribution of this study is it introduces a coupled watershed‐geochemical model, applicable not only for evaluating the dynamics of 137Cs, but also those of other substances that adsorb onto sediments and soil particles, and leach from forest organic matter. Plain Language Summary: Cesium‐137 (137Cs) derived from the Fukushima Daiichi Nuclear Power Plant is a critical radioisotope remaining from the fallout owing to its high activity, long half‐life (30.1 y), and potential impacts on humans and ecosystems. Approximately 70% of all land in Fukushima Prefecture is covered with forests, where 137Cs is predominantly present. The behavior of dissolved 137Cs in the environment must be investigated because this form of 137Cs is readily absorbed by living beings. Researchers have reported that dissolved 137Cs concentrations in river water under baseflow conditions vary seasonally and rise during storms, indicating that leaching from organic matter in forests is a dissolved 137Cs source. We developed a new simulation model for the dynamics of 137Cs between the forest ecosystem and the river system. The simulations reproduced the dissolved 137Cs concentration peaks that occurred during previous typhoons and the seasonal variations that occurred under baseflow conditions. Therefore, the model's results support the theory that leaching from the organic layer in forests is a primary factor affecting dissolved 137Cs concentrations in river water. The model introduced here can not only be used for simulating 137Cs, but also has wider applicability for simulating the transport mechanisms of other contaminants through forest watersheds. Key Points: A coupled watershed‐geochemical model is introduced for simulating dissolved contaminant transport from forests into river waterThe model reproduced dissolved 137Cs concentration trends in Ohta River, Fukushima Prefecture, in base‐ and stormflow periodsThe results supported the theory that the organic layer is an important contamination source for river water [ABSTRACT FROM AUTHOR]

We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23/km2 when acquired for entire states), repeatedly (2–3 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (≈1 plot/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects. [ABSTRACT FROM AUTHOR]

Background--Forest carbon sinks is a new asset measured and reported in financial reporting system. However, current accounting standards at home and abroad do not regulate the accounting treatment of forest carbon sinks assets. As the core of financial accounting, the construction of accounting measurement model is the key issue to establish accounting standards for forest carbon sinks. (2) Methods--First, the particularity of forest carbon sinks assets affecting accounting measurement is summarized by using literature and data of forest carbon sinks CCER projects collated through the website of China Voluntary Emission Reduction Trading Information Platform. Then combining accounting theory and existing accounting standards, the accounting measurement model of forest carbon sinks assets including measurement objects, measurement attributes and measurement procedures is deduced. Finally, demonstrate the feasibility of the application of the measurement model by simulating the accounting practice cases. (3) Results--The accounting measurement object of forest carbon sinks should be divided into forest carbon sinks capacity assets and forest carbon sinks products. The economic essence of the former is forest carbon sinks function and belongs to an ecological asset, while the economic essence of the latter is forest carbon sinks physical quantity and belongs to a carbon emission right asset. Both the initial measurement and subsequent measurement of forest carbon sinks capacity assets should adopt the historical cost measurement attribute. The historical cost of initial measurement is the capitalized forestry expenditure and one-time development input of the project before the closure of young forest. The historical cost of the subsequent measurement should be amortized according to the "Carbon Sinks Method" for the historical cost of forest carbon sinks assets. The initial measurement and subsequent measurement of forest carbon sinks products are different before and after the issuance of carbon emission reduction credits. Before the issuance of carbon emission reduction credits, forest carbon sinks products should be measured by both historical cost and fair value, that is, historical cost is used for initial measurement to provide information on cost flow, and fair value is used for subsequent measurement to provide information on natural value added. The initial measurement and subsequent measurement of forest carbon sinks products after the issuance of carbon emission reduction credits should adopt the historical cost measurement attribute. Conclusions and Discussions--First, the systematization and coordination of internal logics of different accounting standards should be considered in the establishment of accounting measurement model of forest carbon sinks assets. Second, the consistency or comparability with other carbon sink assets with the same or similar characteristics should also be considered in terms of content in the accounting model of forest carbon sinks assets. Therefore, the premise of determining accounting measurement model is universality, inclusiveness and stability. Third, it requires the relevant financial personnel to have a higher professional competence. Therefore, financial personnel need to strengthen the knowledge accumulation of forestry, ecology and other related disciplines. Finally, we should improve the pricing mechanism of the carbon markets and the quality and transparency of the disclosure of trading information in the carbon markets. [ABSTRACT FROM AUTHOR]

Minimizing a convex function of a measure with a sparsity-inducing penalty is a typical problem arising, e.g., in sparse spikes deconvolution or two-layer neural networks training. We show that this problem can be solved by discretizing the measure and running non-convex gradient descent on the positions and weights of the particles. For measures on a d-dimensional manifold and under some non-degeneracy assumptions, this leads to a global optimization algorithm with a complexity scaling as log (1 / ϵ) in the desired accuracy ϵ , instead of ϵ - d for convex methods. The key theoretical tools are a local convergence analysis in Wasserstein space and an analysis of a perturbed mirror descent in the space of measures. Our bounds involve quantities that are exponential in d which is unavoidable under our assumptions. [ABSTRACT FROM AUTHOR]

Current study evaluates the vegetal development of Schizolobium parahyba var. amazonicum (Huber x Ducke) Barneby), commonly known as paricá, in pasturelands in the process of restoration in the Mosaic of Conservations Units of Carajás PA Brazil. The area comprises the connectivity program of forest fragments. They are degraded areas, previously occupied by pastures, which are at present being recuperated with native forest. Evaluation of the development of parica plants comprised plant height, diameter at breast height, cubing and canopy projection. Experimental data underwent regression analysis and Pearson's co-relation. As from strict cubing by Smalian method, a mean factor equal to 0.55 and a strong positive co-relationship with diameters and heights (r=0.83 and r=0.86) were determined. Cubing data helped in the adjustment of a mathematical model of double entry (diameter at breast height and height as independent variables) for estimating form factor in which regression adjustment statistics resulted in relatively high rates for the coefficient of determination (R² = 0.78) and low rates for standard estimation error (R² = 0.05). By applying the Schumacher-Hall's logarithm method for volume estimates, good statistical adjustments were obtained with a high coefficient of determination (R² = 0.99), low standard estimate error (R² = 0.07) and residual dispersion without biases. [ABSTRACT FROM AUTHOR]

Prolonged drought conditions in semi‐arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below‐average total precipitation. We used the data to assess the effects of fore‐summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine‐scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand‐level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought. [ABSTRACT FROM AUTHOR]

Wild boar (Sus scrofa L.) is one of the most discussed game species, distributed across Europe, therefore the management of this species is considered important. This management should be based on data presented, population quality and preferences and craniometric dimensions show the development of the individual and its prosperity. A sample of 148 male and 153 female wild boar mandibles was studied to compare differences in craniometric measurements, especially to find out wild boar environmental demands and population trends. The width of the caput mandibulae and angle of the mandible showed significant difference between males and females. Measurements analysed with forest area size and other data also showed that larger craniometric dimensions were reached in hunting areas with at least 200 ha of forested area, which may be due to the wild boar’s need for safety and vegetative cover in the first months of piglet development with respect to its home range. The development of young wild boar is dependent on rest and shelter in the first months of life. A forest cover of at least 200 ha appears to be sufficient in this respect. Information on habitat preferences and individual development can lead to improvements in wild boar management. [ABSTRACT FROM AUTHOR]

In the last few years, a number of low-cost 3D scanning sensors have been developed to reconstruct the real-world environment. These sensors were primarily designed for indoor use, making them highly unpredictable in terms of their performance and accuracy when used outdoors. The Azure Kinect belongs to this category of low-cost 3D scanners and has been successfully employed in outdoor applications. In addition, this sensor possesses features such as portability and live visualization during data acquisition that makes it extremely interesting in the field of forestry. In the context of forest inventory, these advantages would allow to facilitate the task of tree parameters acquisition in an efficient manner. In this paper, a protocol was established for the acquisition of 3D data in forests using the Azure Kinect. A comparison of the resulting point cloud was performed against photogrammetry. Results demonstrated that the Azure Kinect point cloud was of suitable quality for extracting tree parameters such as diameter at breast height (DBH, with a standard deviation of 2.2cm). Furthermore, the quality of the visual and geometric information of the point cloud was evaluated in terms of its feasibility to identify microhabitats. Microhabitats represent valuable information on forest biodiversity and are included in Swiss forest inventory measurements. In total, five different microhabitats were identified in the Azure Kinect Point cloud. The measurements were therefore comparable to sensors such as terrestrial laser scanning and photogrammetry. Therefore, we argue that the Azure Kinect point cloud can efficiently identify certain types of microhabitats and this study presents a first approach of its application in forest inventories. [ABSTRACT FROM AUTHOR]

This paper presents a study of the application of action cameras, such as the GoPro, for the extraction of 3D models in a forest environment. These cameras are very small and can be used by any operator, even in other forestry works, to collect imagery for photogrammetric post processing. The method implemented uses the Structure from Motion approach in order to obtain the 3D model. The model becomes georeferenced by means of the camera positions which are recorded by the camera navigation GPS receiver. The aim of the study is to assess the geometric accuracy of such methodology, especially in relative terms, in the evaluation of object dimensions. Several tests were carried out both in open sky and under trees, with worse conditions for GPS positioning. The results pointed to scale accuracy of around 3%, which may be acceptable for many applications. Accuracy can be improved by using a scale bar of known length placed in the scene. The method is intended to be used in tree measurements for forest inventory or other outdoor measuring. [ABSTRACT FROM AUTHOR]

In the low deposition situation of today at many of the long-term Swedish forest monitoring sites, the deposition measured as precipitation to the open field, i.e. bulk deposition of oxidized sulphur (S) is often higher or equal to deposition of S sampled under the forest canopies, i.e. as throughfall. This suggests that the total S deposition estimated using throughfall is underestimated. The reason for this is direct exchange of S with the forest canopies, leading to an underestimation which becomes evident in low-deposition areas. We describe a new method to estimate the dry deposition of S to coniferous forest based on measurements with Teflon string samplers as surrogate surfaces, in combination with measurements of the net throughfall for sodium (throughfall subtracted with wet deposition). The wet deposition was estimated from bulk deposition measurements on the open field, corrected for dry deposition to the collectors. The method was applied for Norway spruce forests at monitoring sites across Sweden during nine years 2014–2022, and total deposition was calculated based on wet deposition and the estimated dry deposition. The estimated annual total deposition of S as a mean value for coniferous forests ranged between 0.8 and 5.2 kg S ha−1 yr−1 with lowest values in northern Sweden and highest in southwest Sweden. The share of dry deposition of the total S deposition was between 20 and 53%. The mean annual deposition of S measured as throughfall during 2014–2022 for three different regions in Sweden was between 16 and 41% lower compared to the corresponding total deposition estimated with the new method. The canopy exchange of S was analyzed on a monthly basis as the difference between the estimated total deposition and the measured throughfall deposition of S. At most sites, there was a canopy uptake of S during the summer months and a leakage of S during the winter months. This indicates that the canopy exchange of S is a phenomenon that involves some biological activity. [Display omitted] • Dry deposition of S to coniferous forests was estimated with surrogate surfaces and wet deposition of S was estimated from bulk deposition. • The deposition of S that was measured in throughfall was lower than the estimated total dry and wet deposition of S. • The canopy uptake of S to coniferous forests in Sweden was estimated to 0.1-0.8 kg S ha−1 yr−1 and was highest during summer. [ABSTRACT FROM AUTHOR]

Abstract The knowledge of tree attributes of the genus Copaifera sp. (copaiba), such as the height of the trunks, helps to estimate the productive potential of oleoresin and to propose more suitable ways of handling, aiming at optimizing production. This research aimed to test hypsometric equations and deterministic methods of Artificial Neural Networks (ANN) to estimate the total heights levels of the trunks of 31 copaiba trees of the Western Brazilian Amazon, at unknown ages. However, the ANN correlation coefficients obtained were greater than 0,99, demonstrating that they are appropriate for the estimation of height level (h100%). Among the ANN architectures, ANN 3 with 2 neurons in the hidden layer stood out. The application of ANN to estimate the total height of the trunk of Copaifera sp. native trees is a viable tool that can contribute to optimize modeling of the different important aspects to determine the productive potential of oleoresin.

• The temporal scale of the original Biome-BGC model was refined from a daily scale to an hourly scale. • A theoretical interception module was incorporated into the hourly-scale Biome-BGC model for correcting canopy interception, to further enhance the accuracy of carbon and water fluxes simulations in subtropical coniferous forest. • In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at the Qianyanzhou forest site, with lower annual average GPP, Re , ET, and canopy interception. • Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception, and soil moisture simulations. The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management. [ABSTRACT FROM AUTHOR]

• The first test of common ground-measured crown and canopy metrics against TLS data. • Common geometric assumptions of crown shape are consistently biased across species. • Common assumptions underestimate crown cover and overestimate overlap in forests. • TLS can inform best choices for ground measurement methods of tree crowns. Crown shape and canopy structure are crucial determinants of tree and forest performance, and forest-climate interactions, but methods of quantifying these from field measurements are based on untested assumptions. Historically, measurements of crown size, shape and interactions have been limited to what can be quantified using simple tools such as a tape measure or clinometer, which are then combined with simple geometric assumptions to calculate metrics including crown area, crown volume and canopy cover and overlap. Terrestrial Laser Scanning (TLS) captures the three-dimensional structure of trees and can derive such metrics directly, bypassing the need for geometric assumptions. Here we use TLS data from c. 2500 trees in mixed Mediterranean forests to present a first test of the validity of common assumptions of tree shape. We simulate field measurements from TLS, use these to estimate crown and canopy properties based on geometric assumptions and compare with direct TLS estimates. We find that commonly used methods produce consistently biased estimates of individual tree crown area and crown volume, and whole-plot crown projected area and crown overlap. Although TLS is often not the practical choice for forest measurement, we show that it can guide best practice and here present recommendations of ground measurements that minimise errors. [ABSTRACT FROM AUTHOR]

Fire exclusion was first implemented across large areas of California and other areas of western North America in the late 19th or early 20th centuries but few studies have investigated how forests in the early decades after this decision were impacted. Jeffrey pine-mixed conifer forests of northern Baja California, Mexico, offer an area where this can be examined since fire suppression did not begin until the early 1970's. We use repeated forest and fuel measurements taken over a 25-year period (1998–2023) and found significant changes in forest structure and fuel loads (increased tree density, fine fuel loads, large fuel loads, snag density, snag basal area) that together clearly demonstrate the cumulative effects of fire removal. Interestingly, Jeffrey pine became more dominant despite the lack of recent fire, which is counter to the shift towards more shade-tolerant tree species observed in many other fire-suppressed, frequent-fire forests in the western US. Although these changes in Baja California forests point towards increased fire hazard, they are still in relatively low hazard conditions compared to long-fire suppressed forests in the western US. Prescribed fire or managed wildfire could easily be applied to counter the increased fuel loads and tree densities detected in this work, without the need for mechanical manipulation. In addition to maintaining resilient forests, using fire will reduce the risk of losing the large astronomical observatory at this site to wildfire. Restoration and stewardship of resilient forest structures similar to those in Baja California is the only way forward to conserve similar forests of the Sierra Nevada, southern California mountains, and elsewhere in the western US. • Wildfires are impacting large areas of the western US forests. • Early fire suppression impacts are modest but are increasing hazards in the Sierra San Pedro Martir. • Prescribed fire can easily be used today in Baja California, Mexico, forests. • Prescribed fire can maintain high forest resiliency and protect the large telescope at this site. [ABSTRACT FROM AUTHOR]