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Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.

Abstract Pheochromocytoma and paraganglioma (PPGL) are rare chromaffin-cell tumors producing adrenaline and/or noradrenaline, or solely dopamine. A 52-year-old man presenting with hypertension (141/79 mm Hg) and weight loss (10 kg in 6 months) was admitted to our hospital. Computed tomography revealed a massive right adrenal mass (150 mm) with partial necrosis, accompanied by multiple liver nodules. These nodules showed a high signal intensity on T2-weighted magnetic resonance imaging. Subsequently, a diagnosis of PPGL was made based on elevated urinary excretion of adrenaline (355 µg/day [1937 nmol/day]; normal range: 3.4-26.9 µg/day; 18-146 nmol/day), noradrenaline (1690 µg/day [9989 nmol/day]; normal range: 48.6-168.4 µg/day; 287-995 nmol/day), and dopamine (53 000 µg/day [258 322 nmol/day]; normal range: 365-961.5 µg/day; 1779-4686 nmol/day). The 123I-metaiodobenzylguanidine scintigraphy and fluorodeoxyglucose positron emission tomography scan showed heterogenous uptake among the adrenal and the liver foci, respectively. Clustering analysis of previous PPGL cases highlighted the unique catecholamine profile of this case. These findings suggest a possibility that internodular heterogeneity between primary and metastatic foci on nuclear imaging may indicate varying differentiation grades and resultant catecholamine secretion. Further studies will be needed to verify these results and confirm this hypothesis.

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

Forests are ecologically complex, and trees play a structural and functional role in ecosystem dynamics. Tree height-DBH (diameter at breast height) relationships serve as a key indicator of forest productivity, competition, and succession, fundamental to sustainable forest management. This study develops height-DBH models for eight ecologically important tree species in boreal and mixed forests by applying nonlinear mixed-effects modelling approach to improve the predictive accuracy of height estimations. We evaluate height-DBH functions, including the two-parameter power function and Chapman-Richards function, incorporating stand-level variables-stand height based on dominant or co-dominant trees (SHT), basal area (BAH), and tree density (TPH) to refine predictions. Results indicate that mixed-effects models significantly improved model performance, with M4 (Chapman-Richards with mixed-effects) and M5 (Chapman-Richards function with mixed-effects and stand-level variables)-showing lowest AIC (Akaike Information Criterion) across species. Incorporating stand-level variables significantly enhanced performance, though improvements varied by species. The high accuracy of model M5 was further confirmed by validation process. Among stand-level variables, SHT contributed the most to height predictions (25.3 - 53.0%), while BAH (≤ 0.36%) and TPH (≤ 0.01%) had negligible effects. Still M4 can be a reliable alternative when stand-level variables are unavailable. This study highlights the effectiveness of a mixed-effects modelling framework complemented by stand-level variables in improving tree height estimation. Our research improves decision-making in growth and yield estimations of mixed stands and enhances the reliability of forest vegetation simulator outputs, thereby supporting ecological integrity.

Many leaf traits strongly vary with tree size and environmental factors, but the importance of these factors to intraspecific variations of leaf traits in forest trees has rarely been simultaneously evaluated. We measured needle longevity and specific leaf area (SLA) and nitrogen (N) content of every needle age (0‐ to 4‐year old) for 65 individuals with 0.3–100 cm diameter at breast height (DBH) for an evergreen coniferous species, Pinus koraiensis Sieb. et Zucc., in Northeast China. We simultaneously evaluated the effects of tree size (DBH or tree height) and environment factors (light intensity, soil N content and water availability) on the needle longevity, SLA, foliage N content as well as the slopes of regressions of SLA and foliage N content against needle age. All of the studied leaf traits and slopes of regressions of SLA and foliage N content against needle age were significantly related to tree size. Tree height had a greater impact on SLA and area‐based leaf N content (Narea), whereas DBH was more important for needle longevity and mass‐based leaf N content (Nmass). The environment variables, light intensity, soil N content and water availability, were rather minor factors for trait variations compared with tree size. Significant influence of light intensity was found only on needle longevity, and soil N and water availability had no effects on the leaf traits. Our study clearly showed that tree size is an important driver of intraspecific variations in the key leaf traits of P. koraiensis in a natural forest. We also emphasize the importance of DBH or tree height varies depending on leaf traits, suggesting various mechanisms of size effects on the intraspecific variations in leaf traits. We suggest that ecological significance of leaf trait variations needs reconsideration incorporating tree size effect. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

Centrolobium ochroxylum Rose ex Rudd, known as Amarillo Guayaquil (AG), is a tropical tree species found in secondary vegetation or the wild in the western lowland region of Ecuador (WLRE). AG has heavy (0.78 g/cm 3 ) and durable wood, with whitish sapwood and orange-yellow heartwood, making it ideal for carpentry and construction. The International Union for Conservation of Nature in 2021 classified AG as a threatened and critically endangered tree species. However, information on the forest's growth and yield is limited. The primary objective of this study was to evaluate the first provisional models of growth, yield, site index (SI), volume, and diameter at breast height ( DBH ) - total height ( H ) relationships developed for AG planted in live fences in WLRE. A total of 415 sample plots, each measuring one ha in area, were surveyed. AG trees were arranged in live fences, and UTM coordinates and planting dates were recorded. H and DBH were measured in 160 trees per plot in 2004, 2009, 2012, 2016, and 2018. To model volume, diameters were measured at different heights on randomly selected trees in 195 study sites. Cross-validation revealed that the CR-GADA model, with its three parameters, achieved a better balance between fitness and generalisability than the CR-H model. The Spurr function was found to be the best model for determining the total volume. The linear model was selected to describe the H-DBH relationship in the study region because of its stability and statistical significance. However, the model of Larson showed better overall indicators of fit. Variation of the H-DBH relationship was observed according to the SI. The maximum MAI was 14.8 m 3 ha −1 yr −1 at age 26 years on the best sites, whereas, on less favorable sites, the maximum MAI was 4.4 m 3 ha −1 yr −1 at age 30 years. These models are preliminary and require validation with independent samples. Future studies should include data from mature plots and conduct economic analyses on silvopastoral systems, as well as study the carbon sequestration of AG to encourage reforestation.

Forest resource information has a hierarchical structure: individual tree attributes are summed at the plot level and then in turn, plot-level estimates are used to derive stand or large-area estimates of forest resources. Due to this hierarchy, it is imperative that individual tree attributes are measured with accuracy and precision. With the widespread use of different measurement tools, it is also important to understand the expected degree of precision associated with these measurements. The most prevalent tree attributes measured in the field are tree species, stem diameter-at-breast-height (dbh), and tree height. For dbh and height, the most commonly used measuring devices are calipers and clinometers, respectively. The aim of our study was to characterize the precision of individual tree dbh and height measurements in boreal forest conditions when using calipers and clinometers. The data consisted of 319 sample trees at a study area in Evo, southern Finland. The sample trees were measured independently by four trained mensurationists. The standard deviation in tree dbh and height measurements was 0.3 cm (1.5%) and 0.5 m (2.9%), respectively. Precision was also assessed by tree species and tree size classes; however, there were no statistically significant differences between the mensurationists for dbh or height measurements. Our study offers insights into the expected precision of tree dbh and height as measured with the most commonly used devices. These results are important when using sample plot data in forest inventory applications, especially now, at a time when new tree attribute measurement techniques based on remote sensing are being developed and compared to the conventional caliper and clinometer measurements.

Infection with the protozoan Toxoplasma gondii induces changes in neurotransmission, neuroinflammation, and behavior, yet it remains elusive how these changes come about. In this study we investigated how norepinephrine levels are altered by infection. TINEV (Toxoplasma-induced neuronal extracellular vesicles) isolated from infected noradrenergic cells down-regulated dopamine ß-hydroxylase ( DBH ) gene expression in human and rodent cells. Here we report that intracerebral injection of TINEVs into the brain is sufficient to induce DBH down-regulation and distrupt catecholaminergic signalling. Further, TINEV treatment induced hypermethylation upstream of the DBH gene. An antisense lncRNA to DBH was found in purified TINEV preparations. Paracrine signalling to induce transcriptional gene silencing and DNA methylation may be a common mode to regulate neurologic function.

Diameter at breast height (DBH) is a fundamental structural parameter for forest inventory and ecological analysis. However, field-based measurements (e.g., diameter tape surveys) are labor-intensive and inefficient for large-scale applications. Airborne light detection and ranging (LiDAR) provides an efficient alternative for individual-tree DBH estimation. Nevertheless, LiDAR-derived features—defined as statistical descriptors of point cloud structure and radiometric properties—are typically high-dimensional and redundant, which may degrade model performance. To address this issue, this study proposes an integrated framework combining Multi-Stage Feature Selection (MSFS) and Extreme Gradient Boosting (XGBoost) for DBH estimation. A total of 104 variables, including LiDAR-derived features (height, density, intensity, and canopy structure metrics) and structural parameters (tree height, crown diameter, and crown area), were used as predictors. The MSFS framework was applied to progressively reduce feature redundancy and identify an optimal subset, which was then used to train the XGBoost model. The results demonstrate that the MSFS–XGBoost model achieved the best performance, with a coefficient of determination (R2) of 0.901 and a root mean square error (RMSE) of 1.647 cm. Compared with models using the original feature set, R2 increased by 0.384 and RMSE decreased by 1.146 cm. These findings indicate that the proposed framework effectively improves DBH estimation accuracy and provides a reliable approach for individual-tree parameter estimation and large-scale forest resource monitoring using airborne LiDAR data.