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Strong low-ionization transitions like the Mg  ii |$\lambda 2796,2804$|  Å doublet are believed to produce a detectable 'metal-line forest', if metals pollute the neutral intergalatic medium (IGM). We measure the autocorrelation of the Mg  ii forest transmission using 10 ground-based |$z \ge 6.80$| quasar spectra probing the redshift range |$5.96 \lt z_{\rm Mg\, {\small II}} \lt 7.42$| (⁠|$z_{\rm Mg\, {\small II}, median} = 6.47$|⁠). The correlation function exhibits strong small-scale clustering and a pronounced peak at the doublet velocity (⁠|$\Delta v = 768~{\rm km\, s^{-1}}$|⁠) arising from discrete absorbers in the circumgalactic medium of galaxies. After these strong absorbers are identified and masked the signal is consistent with noise. Our measurements are compared to a suite of models generated by combining a large hydrodynamical simulation with a seminumerical reionization topology, assuming a simple uniform enrichment model. We obtain a 95 per cent credibility upper limit of |$[{\rm Mg/H}] \lt -3.73$| at |$z_{\rm Mg\, {\small II},median} = 6.47$|⁠ , assuming uninformative priors on [Mg/H] and the IGM neutral fraction |$x_{\rm {H\, {\small I}}}$|⁠. Splitting the data into low- z (⁠|$5.96 \lt z_{\rm Mg\, {\small II}} \lt 6.47$|⁠ ; |$z_{\rm Mg\, {\small II},median} = 6.235$|⁠) and high- z (⁠|$6.47 \lt z_{\rm Mg\, {\small II}} \lt 7.42$|⁠ ; |$z_{\rm Mg\, {\small II},median} = 6.72$|⁠) subsamples again yields null detections and 95 per cent upper limits of |$[{\rm Mg/H}] \lt -3.75$| and |$\lt -3.45$|⁠ , respectively. These first measurements set the stage for making the Mg ii forest an emerging tool to precisely constrain the Universe reionization and enrichment history. [ABSTRACT FROM AUTHOR]

Climate change will bring about changes in meteorological and ecological factors that are currently used in global-scale models to calculate biogenic emissions. By comparing long-term datasets of biogenic compounds to modeled emissions, this work seeks to improve understanding of these models and their driving factors. We compare speciated biogenic volatile organic compound (BVOC) measurements at the Virginia Forest Research Laboratory located in Fluvanna County, VA, USA, for the year 2020 with emissions estimated by the Model of Emissions of Gases and Aerosols from Nature version 3.2 (MEGANv3.2). The emissions were subjected to oxidation in a 0-D box model (F0AM v4.3) to generate time series of modeled concentrations. We find that default light-dependent fractions (LDFs) in the emissions model do not accurately represent observed temporal variability in regional observations. Some monoterpenes with a default light dependence are better represented using light-independent emissions throughout the year (LDF α-pinene=0 , as opposed to 0.6), while others are best represented using a seasonally or temporally dependent light dependence. For example, limonene has the highest correlation between modeled and measured concentrations using an LDF =0 for January through April and roughly 0.74–0.97 in the summer months, in contrast to the default value of 0.4. The monoterpenes β -thujene, sabinene, and γ -terpinene similarly have an LDF that varies throughout the year, with light-dependent behavior in summer, while camphene and α -fenchene follow light-independent behavior throughout the year. Simulations of most compounds are consistently underpredicted in the winter months compared to observed concentrations. In contrast, day-to-day variability in the concentrations during summer months are relatively well captured using the coupled emissions–chemistry model constrained by regional concentrations of NOX and O3. [ABSTRACT FROM AUTHOR]

Logistic regression is a standard technique in public administration research. However, there are two inconvenient truths about logistic regression of which scholars should be aware. First, logistic regression results are difficult to interpret. Raw coefficients are expressed in an enigmatic log odds scale and odds ratios are regularly misinterpreted as risk ratios. Second, logistic regression results are non‐collapsible, which renders model comparisons invalid. A review of recent public administration articles reveals that these inconvenient truths still plague the discipline. This paper advocates the use of average marginal effects to reckon with both inconvenient truths. Average marginal effects are easy to comprehend because they measure effect sizes on a probability scale. And average marginal effects are collapsible, and hence facilitate valid model comparisons. These concepts are illustrated using data simulations and data from the 2017 Current Population Survey. The paper concludes with suggestions for improved research practice. [ABSTRACT FROM AUTHOR]

Temperate forest soils are considered significant methane (CH4) sinks, but other methane sources and sinks within these forests, such as trees, litter, deadwood, and the production of volatile organic compounds are not well understood. Improved understanding of all CH4 fluxes in temperate forests could help mitigate CH4 emissions from other sources and improve the accuracy of global greenhouse gas budgets. This review highlights the characteristics of temperate forests that influence CH4 flux and assesses the current understanding of the CH4 cycle in temperate forests, with a focus on those managed for specific purposes. Methane fluxes from trees, litter, deadwood, and soil, as well as the interaction of canopy-released volatile organic compounds on atmospheric methane chemistry are quantified, the processes involved and factors (biological, climatic, management) affecting the magnitude and variance of these fluxes are discussed. Temperate forests are unique in that they are extremely variable due to strong seasonality and significant human intervention. These features control CH4 flux and need to be considered in CH4 budgets. The literature confirmed that temperate planted forest soils are a significant CH4 sink, but tree stems are a small CH4 source. CH4 fluxes from foliage and deadwood vary, and litter fluxes are negligible. The production of volatile organic compounds could increase CH4's lifetime in the atmosphere, but current in-forest measurements are insufficient to determine the magnitude of any effect. For all sources and sinks more research is required into the mechanisms and microbial community driving CH4 fluxes. The variability in CH4 fluxes within each component of the forest, is also not well understood and has led to overestimation of CH4 fluxes when scaling up measurements to a forest or global scale. A roadmap for sampling and scaling is required to ensure that all CH4 sinks and sources within temperate forests are accurately accounted for and able to be included in CH4 budgets and models to ensure accurate estimates of the contribution of temperate planted forests to the global CH4 cycle. [ABSTRACT FROM AUTHOR]

The article explores the intricate relationship between basic human values and forest ecosystem services (FES). The study highlights the critical role that forests play in providing essential services for biodiversity, forest products, climate stabilization and human well-being, and emphasizes the need to understand and integrate human values into forest management and planning. Through a novel approach, this study explores how the concept of "forest" can elicit considerations of fundamental human values that diverge from conventional classifications and measurements of forest values. The study uses a comprehensive methodology, including surveys and content analysis, to uncover the hierarchical structure of human values associated with forests. This approach enables the identification of fundamental values that remain constant despite situational variations. The main results reveal a hierarchical structure of values, with Apollonian values being more prevalent than Dionysian ones. The study shows significant differences in the importance attributed to different FES, reflecting underlying value differences between residents. The study makes a novel contribution by systematically examining the links between human values and FES and proposing a profound and sustainable approach to forest management that takes into account the psychological dimensions of humanforest interactions. The study suggests that recognizing and incorporating the intrinsic human values into forest ecosystem service frameworks can improve sustainable forest management practices and ultimately foster a deeper connection between people and the forest environment. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net‐zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi‐automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net‐zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well‐documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi‐automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground‐based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground‐based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories. [ABSTRACT FROM AUTHOR]

Key message: Accurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points. Context: Forest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery. Aims: Our study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil. Methods: The components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date. Results: The best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models. Conclusion: The linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions. [ABSTRACT FROM AUTHOR]

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection. [ABSTRACT FROM AUTHOR]

Global Ecosystem Dynamics Investigation (GEDI) is a relatively new technology for global forest research, acquiring LiDAR measurements of vertical vegetation structure across Earth's tropical, sub-tropical, and temperate forests. Previous GEDI validation efforts have largely focused on top of canopy accuracy, and findings vary by geographic region and forest type. Despite this, many applications utilize measurements of vertical vegetation distribution from the lower canopy, with a wide diversity of uses for GEDI data appearing in the literature. Given the variability in data requirements across research applications and ecosystems, and the regional variability in GEDI data quality, it is imperative to understand GEDI error to draw strong inferences. Here, we quantify the accuracy of GEDI relative height metrics through canopy layers for the Brazilian Amazon. To assess the accuracy of on-orbit GEDI L2A relative height metrics, we utilize the GEDI waveform simulator to compare detailed airborne laser scanning (ALS) data from the Sustainable Landscapes Brazil project to GEDI data collected by the International Space Station. We also assess the impacts of data filtering based on biophysical and GEDI sensor conditions and geolocation correction on GEDI error metrics (RMSE, MAE, and Bias) through canopy levels. GEDI data accuracy attenuates through the lower percentiles in the relative height (RH) curve. While top of canopy (RH98) measurements have relatively high accuracy (R2 = 0.76, RMSE = 5.33 m), the accuracy of data decreases lower in the canopy (RH50: R2 = 0.54, RMSE = 5.59 m). While simulated geolocation correction yielded marginal improvements, this decrease in accuracy remained constant despite all error reduction measures. Some error rates for the Amazon are double those reported in studies from other regions. These findings have broad implications for the application of GEDI data, especially in studies where forest understory measurements are particularly challenging to acquire (e.g., dense tropical forests) and where understory accuracy is highly important. [ABSTRACT FROM AUTHOR]

Natural disturbances like windthrows or forest fires alter the provision of forest ecosystem services such as timber production, protection from natural hazards, and carbon sequestration. After a disturbance, forests release large amounts of carbon and therefore change their status from carbon sinks to carbon sources for some time. Climate-smart forest management may decrease forest vulnerability to disturbances and thus reduce carbon emissions as a consequence of future disturbances. But how can we prioritise the stands most in need of climate-smart management? In this study we adopted a risk mapping framework (hazard times vulnerability) to assess the risk to climate-related forest ecosystem services (carbon stock and sink) in forests prone to windthrow (in the Julian Alps, Italy) and forest fires (in the Apennines, Italy). We calculated hazard by using forest fire and windthrow simulation tools and examined the most important drivers of the respective hazards. We then assessed vulnerability by calculating current carbon stocks and sinks in each forest stand. We combined these values together with the calculated hazard to estimate "carbon risk" and prioritised high-risk stands for climate-smart management. Our findings demonstrate that combining disturbance simulation tools and forest carbon measurements may aid in risk-related decision-making in forests and in planning decisions for climate-smart forestry. This approach may be replicated in other mountain forests to enhance our understanding of their actual carbon vulnerability to forest disturbances. [ABSTRACT FROM AUTHOR]

Forest restoration thinning may accelerate the development of structural complexity toward old-growth conditions faster than a natural forest, yet associated changes in forest carbon (C) are poorly understood. Old-growth forests are characterized by high levels of sequestered C in aboveground biomass and soil C pools, yet active management has well-recognized negative impacts on stored C. Effects of forest restoration thinning on forest C can be determined using longitudinal measurements and modeling based on stand conditions and tree growth. At Ellsworth Creek Preserve in Southwest Washington, forest restoration efforts in a second-growth temperate rainforest have been monitored using permanent plots since 2007. Here, we compare repeat measurements from 2020, modeled forest C, and measurements of O-horizon C pools from 2022 to determine C impacts of silvicultural treatments for old-growth restoration. We found good general agreement between empirical measurements and models of forest C using the Forest Vegetation Simulator (FVS). However, treatment alone was not a strong indicator for C conditions; rather, forest age and age–treatment interactions better predicted soil C responses to restoration treatments. These data may indicate that "light" forest restoration thinning can accelerate old-growth development with minimal effects on soil carbon—a win-win conservation strategy for old-growth forests and the climate. [ABSTRACT FROM AUTHOR]

Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r |$^{2}$|⁠), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52–122 %, r |$^{2}$| decrease of 4–18 %). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21–118 %, r |$^{2}$| decrease of 5–14 % compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r |$^{2}$|⁠ , as long as no more than 12–346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r |$^{2}$| were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available. [ABSTRACT FROM AUTHOR]

In this paper, we present a model of inflation in the original form of Brans–Dicke (BD) theory without any modification and without adding any dark energy candidate. We consider a log function of the scale factor in the BD scalar field to discuss the inflationary era of the universe. We obtain the expressions for the deceleration parameter and the equation of state parameter, and plot their graphs against the cosmic scale factor. We observe that the universe starts with decelerated expansion and experiences an early time phase transition from a decelerated phase to an accelerated phase. After a very short period, the accelerated expansion ends in a decelerated expansion, showing the inflationary era of the cosmic evolution. To analyze the model on thermodynamic ground, we consider generalized second law of thermodynamics and observe that the law is satisfied within the model. Further, we study the stability of the model using the squared sound speed method. We plot the squared sound speed against cosmic time t and discuss the effects of the parameters on the stability of the model. We observe that for suitable values of the model parameters our model is stable. [ABSTRACT FROM AUTHOR]

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

In order to preliminarily evaluate the early growth of Pinus koraiensis and select excellent families, taking 117 full-sib family seedlings obtained by controlled pollination in the Affinity Seed Garden of Wangqing Forestry Branch of Changbaishan Forest Industry Group as materials, we observed their growth for three consecutive years and conducted a comprehensive evaluation after establishing a progeny measurement forest in the Affinity Seed Garden in the 91st forest class, class 4. The differences in seedling height and ground diameter of the offspring of P. koraiensis full-sib family lines from 6 to 8 years old were highly significant among family lines, and the interaction between family lines and district groups was highly significant (P<0. 01); the average seedling height at 6 years old was 39. 79 cm, the average annual growth rate at 8 years old was 25%, the average ground diameter at 6 years old was 11.11 mm, the average annual growth rate at 8 years old was 34%, and the phenotypic coefficient of variation of each trait ranged from 15. 84% to 25. 43%. The heritability of each trait was high, which was conducive to the further selection and evaluation of the family line. Using the Breggin multiple trait analysis method to select superior family lines, 24 superior family lines were from 117 familes with a benchmark of 20%. The excellent family lines selected by growth traits can provide the basis and materials for renewing the seed orchard generation and promoting the superiority and expansion of P. koraiensis. [ABSTRACT FROM AUTHOR]

Effective forest management and conservation are increasingly critical in addressing the challenges posed by climate change. Advances in remote sensing technologies, such as terrestrial laser scanning, offer promising tools for more accurate assessments in forestry research. This study explores the application of TLS in biomass estimation by conducting a bibliometric analysis of scientific articles indexed in Scopus and the Web of Science. By examining the literature from 2010 to 2024, the study identifies key trends, knowledge gaps, and emerging research opportunities, as well as practical applications in forest management and conservation. The analysis reveals a significant rise in scientific output on TLS, with an average annual growth rate of 8.16%. The most cited works address biomass estimation at the individual tree level using laser scanning data. China and the United States lead in the publication volume with 11 articles. The collaboration network highlights research disparities among regions such as Latin America. Overall, TLS has proven effective for the non-destructive measurement of forest variables and biomass. [ABSTRACT FROM AUTHOR]

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available. [ABSTRACT FROM AUTHOR]

Pre-tightening torque has a significant impact on the performance of a bolt-clamped high-frequency piezoelectric ultrasonic transducer widely used in wire bonding. To develop a vibratory response-based method for the determination of optimal pre-tightening torque, the vibration amplitude signals of the transducer under different excitation conditions were monitored, and the characteristic parameters of the response, involving the resonance frequency, settling time within the initial transient response, and vibration amplitude of the steady-state stage, were extracted. Based on a series of in-depth tests, analyses, and discussions, the influences that the pre-tightening torque has on the vibratory response properties were investigated. Then, the experimental measurement and the random forest method were combined to determine the optimal pre-tightening torque. The performance test results show that the developed method can give the optimal pre-tightening torque according to the desired vibrational response properties, and it is feasible, effective, and reliable as well as improves the adaptability and flexibility of the high-frequency ultrasonic transducer in the application. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required. [ABSTRACT FROM AUTHOR]

Quantifying nitrogen uptake rates across different forest types is critical for a range of ecological questions, including the parameterization of global climate change models. However, few measurements of forest nitrogen uptake rates are available due to the intensive labor required to collect in situ data. Here, we seek to optimize data collection efforts by identifying measurements that must be made in situ and those that can be omitted or approximated from databases. We estimated nitrogen uptake rates in 18 mature monodominant forest stands comprising 13 species of diverse taxonomy at the Morton Arboretum in Lisle, IL, USA. We measured all nitrogen concentrations, foliage allocation, and fine root biomass in situ. We estimated wood biomass increments by in situ stem diameter and stem core measurements combined with allometric equations. We estimated fine root turnover rates from database values. We analyzed similar published data from monodominant forest FACE sites. At least in monodominant forests, accurate estimates of forest nitrogen uptake rates appear to require in situ measurements of fine root productivity and are appreciably better paired with in situ measurements of foliage productivity. Generally, wood productivity and tissue nitrogen concentrations may be taken from trait databases at higher taxonomic levels. Careful sorting of foliage or fine roots to species is time consuming but has little effect on estimates of nitrogen uptake rate. By directing research efforts to critical in situ measurements only, future studies can maximize research effort to identify the drivers of varied nitrogen uptake patterns across gradients. [ABSTRACT FROM AUTHOR]

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar. [ABSTRACT FROM AUTHOR]

Semiarid regions and seasonally dry tropical forests play a critical role in global carbon exchange. In the Southern Hemisphere, these areas can contribute over 80% of positive global carbon storage in wet years due to their sensitivity to precipitation. It is therefore vital that climate models accurately represent land surface processes in these regions, including the contribution of vegetation seasonality to the water budget and carbon cycle. However, the simulation of phenological processes introduces new uncertainties associated with vegetation parameters, especially in biomes with a lack of field experiments, such as the Caatinga biome, a seasonally dry tropical forest. Furthermore, the global land cover maps used in land surface models and their associated parameters do not accurately reflect the diversity of vegetation in these regions. In this study, we improved the Noah-MP leaf area index simulation through parameter calibration and sensitivity analysis for the Caatinga biome in Brazilian semiarid region. To assess parameter uncertainty, we applied the generalized likelihood uncertainty estimation (GLUE) method with MODIS LAI as reference data. The best-performing models from GLUE improve the LAI simulation for BSA natural vegetation. The results indicated that the most sensitive parameters in LAI simulation are the field capacity, the specific leaf area index, and the leaf turnover rate. These parameters regulate water stress, conversion from leaf mass to LAI, and leaf carbon allocation to leaves. Additionally, it was observed that some vegetation parameters exhibit seasonal behavior, suggesting that allowing parameters to vary within the year could enhance the simulations. [ABSTRACT FROM AUTHOR]

Taper equations are indispensable tools for characterizing the stem profile of trees, providing valuable insights for forest management, timber inventory, and optimal assortments allocation. The recent progress in Terrestrial Laser Scanning (TLS) has revolutionized forest inventory practices by enabling nondestructive data collection. In this study, four taper models from three different model categories were established based on point cloud data of 219 Pinus nigra trees. The taper equations fitted with TLS data were used to predict the diameter at specific stem heights and the total stem volume. The results show that among fitted models, the Max and Burkhart segmented model calibrated by the means of a mixed-effects approach provided the best estimate of the diameter at different heights and the total stem volume evaluated for different diameter at breast height (DBH) classes. In numerical terms, this model estimated the diameter and the volume with a respective overall error of 0.781 cm and 0.021 m3. The predicted profile also shows that above a relative height of 0.7, the diameter error tends to increase due to the low reliability of data collected beyond the base of the crown primarily caused by interference from branches and leaves. Nevertheless, this study shows that TLS technology presents a compelling opportunity and a promising non-destructive alternative for generating taper profiles and estimating tree volume. [ABSTRACT FROM AUTHOR]

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

Accurate measurement and estimation of forest carbon sinks and fluxes are essential for developing effective national and global climate strategies aimed at reducing atmospheric carbon concentrations and mitigating climate change. Various errors arise during forest monitoring, especially measurement instability due to seasonal variations, which require to be adequately addressed in forest ecosystem research and applications. Seasonal fluctuations in temperature, precipitation, aerosols, and solar radiation can significantly impact the physical observations of mapping equipment or platforms, thereby reducing the data's accuracy. Here, we review the technologies and equipment used for monitoring forest carbon sinks and carbon fluxes across different remote sensing platforms, including ground-based, airborne, and spaceborne remote sensing. We further investigate the uncertainties introduced by seasonal variations to the observing equipment, compare the strengths and weaknesses of various monitoring technologies, and propose the corresponding solutions and recommendations. We aim to gain a comprehensive understanding of the impact of seasonal variations on the accuracy of forest map data, thereby improving the accuracy of forest carbon sinks and fluxes. [ABSTRACT FROM AUTHOR]

Individual-tree-based models (IBMs) have emerged to provide finer-scale operational simulations of stand dynamics by accommodating and/or representing tree-to-tree interactions and competition. Like stand-level growth model development, IBMs need an array of detailed data from individual trees in any stand through repeated measurement. Conventionally, these data have been collected through forest mensuration by establishing permanent sample plots or temporary measurement plots. With the evolution of remote sensing technology, it is now possible to efficiently collect more detailed information reflecting the heterogeneity of the whole forest stand than before. Among many techniques, airborne laser scanning (ALS) has proved to be reliable and has been reported to have potential to provide unparallel input data for growth models. This study utilized repeated ALS data to develop a model to project the annualized individual tree height increment (ΔHT) in a conifer plantation by considering spatially explicit competition through a mixed-effects modelling approach. The ALS data acquisition showed statistical and biological consistency over time in terms of both response and important explanatory variables, with correlation coefficients ranging from 0.65 to 0.80. The height increment model had high precision (RMSE = 0.92) and minimal bias (0.03), respectively, for model fitting. Overall, the model showed high integrity with the current biological understanding of individual tree growth in a monospecific Pinus radiata plantation. The approach used in this study provided a robust model of annualized individual tree height growth, suggesting such an approach to modelling will be useful for future forest management. [ABSTRACT FROM AUTHOR]

This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. The studies were categorized according to their goals, including log end grading, log side grading, individual log scaling, log pile scaling, and log segmentation. The studies were compared based on the input used, choice of model, model performance, and level of autonomy. This review found a preference for images over point cloud representations for logs and an increase in camera use over laser scanners. It identified three primary model types: classical image-processing algorithms, deep learning models, and other machine learning models. However, comparing performance across studies proved challenging due to varying goals and metrics. Deep learning models showed better performance in the log pile scaling and log segmentation goal categories. Cameras were found to have become more popular over time compared to laser scanners, possibly due to stereovision cameras taking over for laser scanners for sampling point cloud datasets. Classical image-processing algorithms were consistently used, deep learning models gained prominence in 2018, and other machine learning models were used in studies published between 2010 and 2018. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability. [ABSTRACT FROM AUTHOR]

Radiative transfer models of vegetation play a crucial role in the development of remote sensing methods by providing a theoretical framework to explain how electromagnetic radiation interacts with vegetation in different spectral regions. A limiting factor in model development has been the lack of sufficiently detailed ground reference data on both structural and spectral characteristics of forests needed for testing and validating the models. In this data description paper, we present a dataset on the structural and spectral properties of 58 stands in temperate, hemiboreal and boreal European forests. It is specifically designed for the development and validation of radiative transfer models for forests but can also be utilized in other remote sensing studies. It comprises detailed data on forest structure based on forest inventory measurements, terrestrial and airborne laser scanning, and digital hemispherical photography. Furthermore, the data include spectral properties of the same forests at multiple scales: reflectance spectra of tree leaves and needles (based on laboratory measurements), forest floor (based on in situ measurements) and entire stands (based on airborne measurements), as well as transmittance spectra of tree leaves and needles and entire tree canopies (based on laboratory and in situ measurements, respectively). We anticipate that these data will have wide use in testing and validating radiative transfer models for forests and in the development of remote sensing methods for vegetation. The data can be accessed at: Hovi et al. 2024a, https://doi.org/10.23729/9a8d90cd-73e2-438d-9230-94e10e61adc9 (for laboratory and field data) and Hovi et al. 2024b, https://doi.org/10.23729/c6da63dd-f527-4ec9-8401-57c14f77d19f (for airborne data). [ABSTRACT FROM AUTHOR]

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Reactive nitrogen negatively affects terrestrial ecosystems by excessive deposition. Nitric acid gas (HNO3), a component of reactive nitrogen, is readily deposited on ground surfaces due to its high reactivity. However, there have been recent cases in which suppressed deposition fluxes, including upward fluxes, were observed above forests. As the mechanisms of HNO3 dry deposition on forest surfaces are not fully understood, the accuracy of dry deposition estimates remains uncertain. To reduce uncertainties in the estimation, we investigated dry deposition of HNO3 by 1-year measurement in a forest. We measured the vertical profiles of HNO3, nitrate, and sulfate in PM2.5 in a deciduous forest in suburban Tokyo (FM Tama). We observed their concentrations above the forest canopy (30 m) and near the forest floor (2 and 0.2 m) using the denuder/filter pack from October 2020 to September 2021. The HNO3 concentration decreased significantly from 30 to 2 m. However, the decrease in HNO3 was not as significant, and occasionally, emission profiles were produced between 2 and 0.2 m. This was likely caused by HNO3 generated by the volatilization of NH4NO3 near the forest floor, which was warmed by sunlight during daytime in both leafy and leafless periods. Conversely, HNO3 concentrations at 30 m were much higher than those at 2 m and 0.2 m, indicating that the forest acted as a sink for HNO3 from a long-term perspective. It is presumed that HNO3, generated just above the forest canopy, could cause an upward flux if a temperature difference of several degrees occurs between 25 and 20 m. [ABSTRACT FROM AUTHOR]

Trestima® is a computer vision-based smartphone application that utilises relascope theory to obtain estimates of forest attributes from smartphone photographs. The aim of this study was to investigate the accuracy of Trestima es - timation and evaluate whether it is sufficiently accurate for operational use in forestry. Our data consisted of 37 forest stands, encompassing 73.5 ha in southeastern Finland, where Trestima estimates were obtained by forestry professionals during their work. The results were compared with harvester data obtained from clear-cut stands. The number of photographs taken per stand ranged between 1-29 (average: 7.3; standard deviation: 5.0). The total amount of industrial roundwood harvested from the stands was 21,531 m3 and the average harvest removal per hectare was 282 m3 . The accuracy of Trestima estimation was relatively good when ≥ 10 photographs per stand were taken. In this case, the root mean square error percent (RMSE%) value associated with roundwood volume was 17.7%. When the number of photographs per stand was < 10, the accuracy of Trestima was much weaker (RMSE% 22.7- 55.3%). On average, Trestima underestimated harvested volumes in Scots pine (Pinus sylvestris L.) stands (Bias% 11.4-89.2), although the bias was smaller (Bias% –12.7-12.4) with Norway spruce (Picea abies [L.] Karst.) stands. The Trestima smartphone application is a possible option for traditional field measurements in operational forestry, provided that its usage instructions are strictly followed, which is not always the case in practice. [ABSTRACT FROM AUTHOR]

Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data to quantify and correct geolocation error in spaceborne sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR Hyperspectral and Thermal Imager (G‐LiHT) was used with a subset of LiDAR data from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel using spatially coincident G‐LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show a systematic geolocation error of 9.62 m in the southwest direction. In addition, estimated geolocation errors within GEDI footprints were highly variable, with results showing a ∼$$ \sim $$0.45 probability the true footprint center is within 20 m. Estimating and correcting geolocation error via the model outlined here can help inform subsequent efforts to integrate spaceborne LiDAR data, like GEDI, with other georeferenced data. [ABSTRACT FROM AUTHOR]

Current paper comprehensively establishes exact expressions for both single and product moments of lower generalized order statistics originating from scaled log-logistic distributions, popularly recognized as the Fisk Distribution-a distribution extensively utilized in reliability theory. Furthermore, we provide a characterization result for this specific class of distributions, employing the renowned Ruiz identity. This work aims to provide a thorough understanding of the statistical properties of lower generalized order statistics in relation to scaled log-logistic distributions, thereby enhancing the applicability and insights into reliability analysis and related fields. [ABSTRACT FROM AUTHOR]

Snow and watershed models typically do not account for forest structure and shading; therefore, they display substantial uncertainty when attempting to account for forest change or when comparing hydrological response between forests with varying characteristics. This study collected snow water equivalent (SWE) measurements in a snow-dominated forest in Colorado, the United States, with variable canopy structure. The SWE measurements were integrated with 1 m Lidar derived canopy structure metrics and incoming solar radiation to create empirical SWE offset equations for four canopy structure groupings (forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest) that varied in size compared to an open area. These simple equations indirectly integrate terrain shading and canopy shading and were able to estimate 40 to 70% of SWE variation in a heterogenous forested environment. The equations were then applied to a snow melt model with a 100 m grid size by applying the area-weighted average of SWE offsets from the four canopy structure groupings in each model cell. This tiled model configuration allowed for the model to better represent the subgrid heterogeneity of a forest environment that can be seen through an ensemble or range of potential outputs rather than a singular estimate. [ABSTRACT FROM AUTHOR]

Premise: To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators. Methods and Results: Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species‐classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species. Conclusions: This protocol is an adaptable tool for efficient and consistent identification of seed species or potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large‐scale monitoring projects in conservation biology. [ABSTRACT FROM AUTHOR]

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 volatility distributions 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 temperature, relative humidity, updraft velocity, aerosol size distribution, organic vapor concentration, and the volatility distribution. The predicted median enhancements in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics fall on average between 16 % and 22 %. This corresponds to activating particles 10–16 nm smaller in dry diameter that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (Δ CDNC) 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. To evaluate the frequencies with which such distributions are experienced by an Earth system model over the whole boreal biome, 5 years of UK Earth System Model (UKESM1) simulations are further used. 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 the western parts of Siberia. Overall, the similarities in the size distributions between observed and modeled (UKESM1) are limited, which would limit the ability of this model, or any model with a similar aerosol representation, to project the climate relevance of co-condensation over the boreal forest. For the critical aerosol size distribution regime, Δ CDNC is shown to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs), especially when the overall particle surface area is low. The magnitudes of Δ CDNC remain less affected by the more volatile vapors such as formic acid and extremely low- and low-volatility organic compounds (ELVOCs and LVOCs). The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below the cloud base, and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce the critical supersaturations enough for droplet activation to occur. A reduction in the critical supersaturation caused by organic condensation emerges as the main driver of the modeled Δ CDNC. 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, also in larger-scale models, an accurate representation of the aerosol size distribution is critical. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged. [ABSTRACT FROM AUTHOR]

In this paper, a co-aperture dual-polarized antenna array is proposed. The frequency band covers the P-band (0.4–0.7 GHz) and the X-band (9.3–9.9 GHz). The P-band array is designed in the form of an all-metal Vivaldi, while minimizing the impact of the P-band antenna array structure on the X-band electrical performance. The final profile design for the dual-band antenna structure is only 0.12 λ0 (λ0 corresponds to the wavelength of 0.4 GHz). The X-band design adopts the miniaturized waveguide slot structure, which can be well embedded in the P-band array structure. In order to verify the feasibility of the scheme, prototype processing and testing were carried out, and the test results are consistent with the simulation results. The active VSWRs of the overall design are less than 2.0. Additionally, the dual-band antenna array has favorable radiation performance in each frequency band. It can be used in forest biomass measurements and other interferometric applications. [ABSTRACT FROM AUTHOR]

Personal laser scanning devices employing Simultaneous Localization and Mapping (SLAM) technology have rightfully gained traction in various applications, including forest mensuration and inventories. This study focuses the inherent stochastic noise in SLAM data. An analysis of noise distribution is performed in GeoSLAM ZEB Horizon for point clouds of trees of two species, Norway spruce and European beech, to mitigate bias in diameter estimates. The method involved evaluating residuals of individual 3D points concerning the real tree surface model based on TLS data. The results show that the noise is not symmetrical regarding the real surface, showing significant negative difference, and moreover, the difference from zero mean significantly differs between species, with an average of −0.40 cm for spruce and −0.44 cm for beech. Furthermore, the residuals show significant dependence on the return distance between the scanner and the target and the incidence angle. An experimental comparison of RANSAC circle fitting outcomes under various configurations showed unbiased diameter estimates with extending the inlier tolerance to 5 cm with 2.5 cm asymmetry. By showing the nonvalidity of the assumption of zero mean in diameter estimation methods, the results contribute to fill a gap in the methodology of data processing with the widely utilized instrument. [ABSTRACT FROM AUTHOR]

Forests play key roles in climate regulation and essential environmental services for living organisms. This is why forests are the central focus of the United Nations (UN) Sustainable Development Goal (SDG 15). Thus, effective forest management is critical for forest sustainability and preservation. Remote sensing advancements have improved forest mensuration leveraging cost and time, contrary to the field surveying approach. Often, field data is required to validate remotely sensed results. However, circumstances in the forest may render field data collection impossible. This study applied LiDAR-derived L-moments to directly estimate and classify five forest plot decay levels, to understand forest growth dynamics in the absence of field data. Two L-moment-based rules were tested and evaluated for classifying the plot decay levels from ALS height returns. Our findings show that the first rule (Lcv = 0.5) classified decay Levels 1 and 2 at Lcv < 0.5 and Levels 3 to 5 at Lcv > 0.5, while the second rule (Lskew = 0) classified decay Level 1 at Lskew < 0, and Levels 2 to 5 at Lskew > 0. This indicates that, while discriminating plot decay levels, the L-moment-based rules can classify healthy forest areas and areas of deadwood of varying decay levels directly from ALS height returns. This can be convenient for forest managers to exploit for classifying plot decay levels and for mapping areas of large gaps for planning forest resources for effective forest management. Furthermore, the approach can equally be significant for assessing forest biomass, biodiversity, and carbon stock. [ABSTRACT FROM AUTHOR]

Minimal residual disease status in multiple myeloma is an important prognostic biomarker. Recently, personalized blood-based targeted mass spectrometry (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to measure minimal residual disease. However, quantification of MS-MRD requires a unique calibrator for each patient. The use of patient-specific stable isotope labelled (SIL) peptides is relatively costly and time-consuming, thus hindering clinical implementation. Here, we introduce a simplification of MS-MRD by using an off-the-shelf calibrator. SILuMAB-based MS-MRD was performed by spiking a monoclonal stable isotope labeled IgG, SILuMAB-K1, in the patient serum. The abundance of both M-protein-specific peptides and SILuMAB-specific peptides were monitored by mass spectrometry. The relative ratio between M-protein peptides and SILuMAB peptides allowed for M-protein quantification. We assessed linearity, sensitivity and reproducibility of SILuMAB-based MS-MRD in longitudinally collected sera from the IFM-2009 clinical trial. A linear dynamic range was achieved of over 5 log scales, allowing for M-protein quantification down to 0.001 g/L. The inter-assay CV of SILuMAB-based MS-MRD was on average 11 %. Excellent concordance between SIL- and SILuMAB-based MS-MRD was shown (R2>0.985). Additionally, signal intensity of spiked SILuMAB can be used for quality control purpose to assess system performance and incomplete SILuMAB digestion can be used as quality control for sample preparation. Compared to SIL peptides, SILuMAB-based MS-MRD improves the reproducibility, turn-around-times and cost-efficacy of MS-MRD without diminishing its sensitivity and specificity. Furthermore, SILuMAB can be used as a MS-MRD quality control tool to monitor sample preparation efficacy and assay performance. [ABSTRACT FROM AUTHOR]

In this article, a new three-parameter lifetime model called the Gull alpha power exponentiated exponential (GAPEE) distribution is introduced and studied by combining the Gull alpha power family of distributions and the exponentiated exponential distribution. The shapes of the probability density function (PDF) for the GAPEE distribution can be asymmetric shapes, like unimodal, decreasing, and right-skewed. In addition, the shapes of the hazard rate function (hrf) for the GAPEE distribution can be increasing, decreasing, and upside-down shaped. Several statistical features of the GAPEE distribution are computed. Eight estimation methods such as the maximum likelihood, Anderson-Darling, right-tail Anderson-Darling, left-tailed Anderson-Darling, Cramer-von Mises, least-squares, weighted least-squares, and maximum product of spacing are discussed to estimate the parameters of the GAPEE distribution. The flexibility and the importance of the GAPEE distribution were demonstrated utilizing three real-world datasets related to medical sciences. The GAPEE distribution is extremely adaptable and outperforms several well-known statistical models. A bivariate step-stress accelerated life test based on progressive type-I censoring using the model is presented. Minimizing the asymptotic variance of the maximum likelihood estimate of the log of the scale parameter at design stress under progressive type-I censoring yields an expression for the ideal test plan under progressive type-I censoring. [ABSTRACT FROM AUTHOR]

Systems of whole‐stand yield models facilitate projections of forest attributes, but their inputs may be difficult to measure accurately. This study conducted sensitivity analyses to examine the effect of systematic and stochastic measurement errors on outputs from a representative system of equations. Simulated error was added to explanatory variables stand age, site index, or both. Results showed that large systematic error in one variable tended to produce moderate to large percent changes in all models, particularly the height and volume equations (often >50% change). Systematic error in both variables amplified this effect, especially for young, less productive stands. Stochastic error dramatically increased estimate variability (some relative standard errors >50%), particularly in the height and volume models at young ages and low site indices. These results suggest that measurement error may considerably alter projections and increase uncertainty when using whole‐stand yield models, highlighting the need for careful crew training. Recommendations for Resource Managers: Measurement error in input variables has the potential to significantly affect estimates from systems of whole‐stand yield models.Effects can be over‐ or underestimation of volume yields and financial returns or other forest attributes of interest.Careful crew training is paramount to ensure forest measurements remain consistent with the quality of the data used to fit the models. [ABSTRACT FROM AUTHOR]

Forests are one of the most important sources of negative oxygen ions (NAIs). NAIs have been recognized as beneficial for both physical and mental well–being, and higher concentrations of NAIs have been associated with improved health. However, the environmental factors that predominantly influence NAI concentration and their relationship with NAIs remain uncertain. This study aims to investigate the dominant factors and their impact on NAI concentration by observing NAIs and various environmental factors in two different environments (natural forest and urban forest) in the Beijing region over a one–year period. Through our investigation, we aimed to identify the key factor as well as other influential variables affecting NAI concentration. Our analysis encompassed the examination of dynamic concentration changes over multiple time scales, revealing uniform trends in both forest types. Notably, natural forests consistently demonstrated higher NAI concentration across these time scales, attributable to greater vegetation density and the stability of the forest microenvironment. By utilizing regression, correlation analysis, and structural equation analysis, we determined that relative humidity (RH) has the most significant effect on NAI concentration. Notably, both NAI concentration and RH displayed similar patterns across multiple time scales. When considering hourly average daily variation, the lowest values for both NAI concentration and RH were observed at noon, followed by an increase that persisted throughout the night. Seasonal average variation showed that both NAI concentration and RH peaked in the summer, followed by autumn. In terms of daily average annual variation, summer exhibited more days with high NAI concentration and high RH, which can be attributed to the increased rainfall during that season. Rainy weather was found to contribute to higher NAI concentration and RH levels. Furthermore, our findings revealed that on a daily scale, high RH and high NAI concentration occurred more frequently under conditions of high air temperature and low wind speed. However, the air quality index demonstrated only a minor effect in urban forest, while net radiation exhibited no significant influence on NAI concentration and RH. The fitted equations and trends of the aforementioned environmental factors with NAI concentration and RH were found to be comparable. The path analysis further corroborates these conclusions. The findings of this study indicate that RH is the primary factor driving the fluctuations in NAI concentration across various time scales, including hourly, daily, and seasonal variations. The study revealed that wind speed indirectly impacts NAI concentration by modulating RH. In contrast, air temperature influences NAI concentration both indirectly through RH and directly. The environmental factors affecting NAI concentration in the two types of forests are similar, but the degrees vary; in urban forests, wind speed, air quality index, and RH are slightly higher, while in natural forests, air temperature is slightly higher. This discovery further enhances our understanding of the underlying mechanisms and dynamic changes in NAI concentration within urban forests and natural forests. Moreover, it confirms the reliability and effectiveness of using RH as an indicator to monitor changes in NAI concentration over time. [ABSTRACT FROM AUTHOR]

The placement of wood in rivers is a common restoration method used to locally affect hydraulic and morphologic conditions to create habitat. Laboratory experiments demonstrated that wood placements can also promote surface declogging, that is, removal of fine sediment from a gravel bed, thereby restoring spawning grounds for fish. Logs of different size and submergence level were placed on a gravel bed clogged with fines. Surface declogging was observed in regions of elevated turbulence in the log wake and elevated velocity adjacent to the log. A criteria for declogging was identified based on a modified non‐dimensional Shields parameter combining mean and turbulent velocity at the bed. The footprint of declogged bed scaled with log dimensions. Emergent logs produced a larger declogging footprint compared to submerged logs of the same length, due to their stronger influence on the flow field. Logs were also shown to prevent clogging over similar areas. Plain Language Summary: Placing wood in rivers is a common way of improving river health and creating regions for fish and small aquatic animals to live. This method involves the placement of individual wood pieces (logs) at the side or in the center of a river. In this study, we explored how the positioning of logs could be used to remove fine sediments from gravel beds by changing the way water flows. This is important because excessive deposition of fine sediments in the bed, defined as riverbed clogging, can make the channel bed too compact. This makes it harder for fish to find good spots to lay their eggs. Using laboratory experiments, we observed that the logs generated regions with strong water turbulence (swirling) and faster flowing water in their surroundings, both of which removed fines from the gravel bed surface. We identified specific conditions necessary for effective removal of fines, which were related to both the turbulence and water velocity. The size of the cleaned area on the gravel bed was proportional to the size of the log. Key Points: Deliberate wood placement can promote declogging adjacent to and downstream of logsDeclogging was correlated with elevated turbulence downstream of logs and elevated velocity adjacent to logsEmergent logs declogged a greater surface area than submerged logs [ABSTRACT FROM AUTHOR]

High densities of native and introduced deer hamper the regeneration of temperate forests worldwide. Sport hunting is often the sole means of deer control, but whether it can restore forest regeneration remains uncertain.We assessed the potential to restore forest regeneration using unrestricted sport hunting alongside commercial harvesting and government‐funded culling of introduced sika deer (Cervus nippon) across a 594 km2 landscape in North Island, New Zealand. We used six decades of repeated measurements of forest regeneration and deer presence, alongside monitoring of tagged stems in a 20‐year paired exclosure experiment, to determine whether deer control restored regeneration.In our exclosure experiment, mountain beech (Fuscospora cliffortioides) seedling and sapling density, growth and survival were variable but consistently higher when deer were excluded by fencing. Sapling counts in unfenced plots were ≈3–10 times lower after 60 years of deer control compared with unfenced plots, before sika colonisation and other mountain beech forests without sika deer. This result suggests canopy replacement remains at risk despite government‐funded culling and encouragement of sport hunting.Individual‐based demographic models show that mountain beech is unlikely to regenerate following canopy gap formation in our study landscape unless deer impacts are reduced from current levels. These demographic models predicted present‐day forest regeneration far better than two widely used proxies of deer impact: plot‐based counts of saplings and estimates of deer densities from faecal pellet counts.Synthesis and applications. Here, we show that intensive culling beyond that achievable by sport hunting is needed to reduce deer densities enough to assure canopy regeneration. These interventions will be necessary in the many places worldwide where sport hunting is being relied upon to protect forests but appears to be failing. Given the controversy associated with deer culling, and the need for clear evidence to justify its implementation, we suggest managers could strengthen the evidence base for their interventions by collecting data to build demographic models. [ABSTRACT FROM AUTHOR]

In the process of manufacturing various components using direct energy deposition (DED) with multiple layers and tracks, the accumulation of heat can result in high residual tensile stresses within the parts. Understanding these stresses and their physical properties is crucial to producing parts with optimal resistance. This paper examines the effects of melting and heat on residual stresses in multi-joint, multi-track, and multi-layer geometries. A hexagonal geometry with a material of AISI 1018 steel with multiple joints was fabricated by laser wire direct energy deposition with one-layer and five-layer depths, and their mechanical properties were measured. Additionally, three samples with the same process parameters and geometrical parameters were built in the form of a cube geometry with five layers, using 316L steel as the deposited material. Two commercial finite element platforms were used to evaluate physical assumptions in modeling DEDs. According to the numerical solution, failure to account for the penetration/mixing between the first layer and the substrate results in a shift in the predicted location of compressive residual stress depths. Nonetheless, The numerical method is proficient in forecasting the behavior of stress variation in accordance with the experimentally measured values. Statistical analysis of the three samples of the cube parts indicates that both depth and location have a significant impact on the local residual stress values. To conclude with rapid-predictor models, two machine learning methods were investigated as rapid-predictor models. The first approach employed a Long Short-Term Memory (LSTM) neural network to predict local stress using temperature history. The second approach involved training a Random Forest regression model to predict the residual stress based on the mathematical characteristics of the temperature history curves, rather than using the entire time-sequential temperature histories as inputs. The latter approach is advantageous over the former approach in terms of accuracy and reduced training resources. [ABSTRACT FROM AUTHOR]

The Elliott State Research Forest comprises 33,700 ha of temperate, Douglas-fir rainforest along North America's Pacific Coast (Oregon, United States). In 2015, naturally regenerated stands at least 92 years old covered 49% of the research area and sawtimber plantations younger than 68 years another 50%. During the winter of 2015–2016, a forest wide inventory sampled both naturally regenerated and plantation stands, recording 97,424 trees on 17,866 plots in 738 stands. The resulting dataset is atypical for the area as plot locations were not restricted to upland, commercially harvestable timber. Multiage stands and riparian areas were therefore documented along with plantations 2–61 years old and trees retained through clearcut harvests. This dataset constitutes the only open access, stand-based forest inventory currently available for a large area within the Oregon Coast Range. The dataset enables development of suites of models as well as many comparisons across stand ages and types, both at stand level and at the level of individual trees. DataSet: https://doi.org/10.7267/xp68kq79g DataSet License: CC-BY-NC-SA [ABSTRACT FROM AUTHOR]

Methane emissions from plant foliage may play an important role in the global methane cycle, but their size and the underlying source processes remain poorly understood. Here, we quantify methane fluxes from the shoots of Scots pine trees, a dominant tree species in boreal forests, to identify source processes and environmental drivers, and we evaluate whether these fluxes can be constrained at the ecosystem-level by eddy covariance flux measurements. We show that shoot-level measurements conducted in forest, garden, or greenhouse settings; on mature trees and saplings; manually and with an automated CO2-, temperature-, and water-controlled chamber system; and with multiple methane analyzers all resulted in comparable daytime fluxes (0.144 ± 0.019 to 0.375 ± 0.074 nmol CH4 g-1 foliar d.w. h-1). We further find that these emissions exhibit a pronounced diurnal cycle that closely follows photosynthetically active radiation and is further modulated by temperature. These diurnal patterns indicate that methane production is associated with diurnal cycle of sunlight, indicating that this production is either a byproduct of photosynthesis-associated biochemical reactions (e.g., the methionine cycle) or produced through nonenzymatic photochemical reactions in plant biomass. Moreover, we identified a light-dependent component in stand-level methane fluxes, which showed order-of-magnitude agreement with shoot-level measurements (0.968 ± 0.031 nmol CH4 g-1 h-1) and which provides an upper limit for shoot methane emissions. [ABSTRACT FROM AUTHOR]

Deadwood in forests is becoming an important topic globally for forest management and carbon markets. While deadwood volume is now routinely assessed by forest inventories and can be even monitored using remote sensing, data about deadwood carbon and density depending on the extent of decay is scarce. We propose a simple concept for estimating deadwood carbon using data from a pilot study in selected coniferous and broadleaf forests covering different climate zones in Austria. We measured deadwood volume and decay classes and calculated deadwood density and carbon fraction from destructive field samples. We found that decay reduces deadwood density significantly, while carbon fraction increases moderately with decay. Partitioning the explained variation confirmed that size and site parameters are more important for explaining deadwood carbon fraction than for deadwood density. These patterns were consistent for coniferous and broadleaf tree species. We also show how to estimate deadwood carbon stocks using standard forest inventory measurements or country statistics, without information on decay classes. We found differences in decay classes of standing versus lying deadwood, and our proposed method allows considering this in large-scale deadwood carbon assessments. We highlight research gaps and missing deadwood information, to provide more accurate carbon assessments of deadwood. [ABSTRACT FROM AUTHOR]