Crown Area Research Articles

Microtopography-Guided precision restoration of sandy lands through UAV: A case study in Hunshandake Sandy Land, China

Increasing desertification rates have adversely affected biodiversity and ecosystem functioning in sandy lands. Ecological restoration is an effective way to combat desertification. Specific microtopographic characteristics may facilitate vegetation growth, enhancing the success of restoration efforts. However, limited research to date has explored how microtopography may guide the “precision restoration” of sandy lands by supporting spatially continuous patterns of vegetation growth. Here, high-resolution unmanned aerial vehicle (UAV) multispectral data were used to identify individual species and extract microtopographic variables, and the relationships between vegetation growth and microtopography were characterized for the Hunshandake Sandy Land in China. The distribution of three dominant shrub species and grasses was investigated by comparing the performance of five popular machine-learning methods. An auto-marking watershed algorithm was then developed to discriminate individual semi-shrubs (Artemisia desertorum). Finally, a new vegetation growth index (VGI), calculated from the UAV-derived crown area, normalized difference vegetation index (NDVI), and canopy height, was used to characterize the relationships between vegetation growth and several microtopographic variables (aspect, slope, and a topographic wetness index [TWI]). With the highest species classification accuracy (94.36%) and individual discrimination rate (81%), areas with high humidity (TWI), gentle and shady slopes were found to most strongly support vegetation growth; for grasses, VGI was lower in artificially-restored regions than naturally-developed regions with similar microtopographic characteristics. These findings provide valuable guidance on the use of UAV to support diverse ecological restoration solutions in sandy lands with a “precision restoration” strategy, thereby improving the survival of key vegetation for sand restoration, especially in remote areas.

Automated tree crown labeling with 3D radiative transfer modelling achieves human comparable performances for tree segmentation in semi-arid landscapes

Mapping tree crowns in arid or semi-arid areas, which cover around one-third of the Earth’s land surface, is a key methodology towards sustainable management of trees. Recent advances in deep learning have shown promising results for tree crown segmentation. However, a large amount of manually labeled data is still required. We here propose a novel method to delineate tree crowns from high resolution satellite imagery using deep learning trained with automatically generated labels from 3D radiative transfer modeling, intending to reduce human annotation significantly. The methodological steps consist of 1) simulating images with a 3D radiative transfer model, 2) image style transfer learning based on generative adversarial network (GAN) and 3) tree crown segmentation using U-net segmentation model. The delineation performances of the proposed method have been evaluated on a manually annotated dataset consisting of more than 40,000 tree crowns. Our approach, which relies solely on synthetic images, demonstrates high segmentation accuracy, with an F1 score exceeding 0.77 and an Intersection over Union (IoU) above 0.64. Particularly, it achieves impressive accuracy in extracting crown areas (r2 greater than 0.87) and crown densities (r2 greater than 0.72), comparable to that of a trained dataset with human annotations only. In this study, we demonstrated that the integration of a 3D radiative transfer model and GANs for automatically generating training labels can achieve performances comparable to human labeling, and can significantly reduce the time needed for manual labeling in remote sensing segmentation applications.

Elemental analysis and micromorphological patterns of tooth/restoration interface of three ion-releasing class V restorations

ObjectivesTo evaluate and compare the ion-releasing capability of three different restorative systems at the restoration/tooth interface elemental analysis using energy-dispersive X-ray technique. Additionally, micromorphological patterns of the restoration/tooth interfaces was investigated.Materials and methodsEighteen freshly extracted sound human premolars were collected for the study. The premolars were randomly assigned into 3 groups (n = 6) based on the type of restorative materials used: Giomer (Beautifill II), ion-releasing composite (Activa Presto), and RMGI (Riva Light Cure). Half of the specimens in each group were tested after 24 h (the “immediate group”), while the remaining half were tested after 6 months of storage in deionized water (the “delayed group”). Standardized box-shaped cavities along the cervical area of teeth crowns and restored them with the assigned restorative material following manufacturers’ instructions. The specimens were sectioned buccolingually into 2 halves. One half of each specimen was subjected to elemental analysis using energy-dispersive X-ray technique (EDX), while the remaining half was sputter coated and underwent micromorphological analysis of the restoration/tooth interface using a scanning electron microscope (SEM). The collected data from elemental analysis test were tabulated and subjected to statistical analysis.ResultsThe two-way ANOVA test showed significant differences in both phosphorus and calcium levels among the tested restorative systems (p < 0.05). In the immediate subgroup, RMGI recorded the highest phosphorus level (0.1527), followed by the ion-releasing composite (0.1172), while Giomer exhibited the least levels (0.0326) (p < 0.05). The ion-releasing composite group had the highest calcium level (0.2797), followed by RMGI (0.248), and Giomer (0.2385) respectively (p < 0.05). In the delayed subgroups, Giomer recorded the highest phosphorus level (0.1526), followed by the ion-releasing composite (0.1058), and RMGI group (0.0466) respectively (p < 0.05). RMGI had the highest calcium level (0.2801), followed by the ion-releasing composite (0.2659), and Giomer had the lowest level (0.1792) (p < 0.05). The micromorphological analysis of the restoration/tooth interfaces showed good adaptation between the composite and tooth substrate in different restorative groups.ConclusionsThe ion-releasing capability of the three restorative systems appears to be comparable. The rate of mineral release and diffusion is affected by time and composition.

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Comprehending and quantifying local variability in plant phenology, alongside its impacts on tree growth, is challenging due to spatially and temporally heterogeneous environmental factors that interact to affect phenological events and periods. Although previous studies have focused on the climatic factors driving phenological events at the stand level, the influences of competition, neighborhood characteristics, species richness, and water availability on plant phenology remain unclear. In the present paper, we used hourly terrestrial LiDAR surveys performed between April 2020 and April 2021 to investigate the influence of local factors (neighborhood and topography) on the phenology of silver birch trees (Betula pendula Roth.). We also examined how phenological events affect growth in tree height and crown area. All response and explanatory variables were estimated using LiDAR time-series data and a field survey campaign. Our findings demonstrate a high within-species variability in plant phenology that is controlled by biotic and abiotic characteristics of the ecosystem. We found that between tree variation in leaf burst, completion of the leaf growth and length of canopy growth period were affected by tree size, neighborhood species richness, level of suppression and competition, which potentially indicate plant responses to light availability. The beginning of senescence, completion of leaf drop, and length of autumn phenology were mostly affected by topographic water index, which reflects water availability and can be linked to nutrient availability and exposure to wind. Furthermore, we demonstrated that an earlier leaf burst, and delayed beginning of senescence were associated with larger absolute growth in canopy area but found no clear relationship between height growth and phenology. Our study highlights the capability of dense, high-quality LiDAR time-series to detect significant phenological variation among individuals of the same species within the same locality, demonstrating the potential of LiDAR time-series as a tool for understanding phenology and its impacts.

Fiber-reinforced shotcrete lining for stabilizing rock blocks around underground cavities

Fiber-reinforced shotcrete is a high-performance material that presents some special characteristics, which can provide some suitable applications in the excavation of underground cavities. The presence of fibers induces an increase in the tensile strength, flexural strength and shear strength in the concrete, as well as allowing a ductile rather than brittle type of behavior. It can be used to create a lining of the underground cavity that allows the stabilization of rock blocks that show a tendency to slip or fall (from the side walls or from the crown area, respectively). In this work, some full-scale tests on the fiber-reinforced shotcrete lining are presented. From these tests, it was possible to measure the behavior of this material when it is loaded locally: it is the same type of action produced by the rock block when it is held back from falling or slipping. The results obtained have allowed to characterize this type of material from a mechanical point of view. A subsequent detailed analysis of the stability of rock blocks surrounding an underground cavity permitted to determine the static stabilizing contribution offered by the fiber-reinforced shotcrete lining, leading to the definition of the minimum thickness required, in relation to the type of block that is present (shape and size). It was possible to predict how a lining thickness of about 3.5 cm is able to stabilize (just 15 min after its spraying) rock blocks with an exposed surface area of up to 10 m2 and a distance of the internal vertex from the border of the cavity of up to 3 m

Research on the technical improvement of the turbine runner of a power station based on improving stability

AbstractIn view of problems such as the narrow efficiency area, large hydraulic vibration area, pressure pulsation, and serious sediment wear of turbines at the Futang hydropower station, the technical transformation of turbine runners was carried out by modifying the blade shape and increasing the blade thickness, and a combination of numerical simulations based on shear stress transport k–ω turbulence model and tests was adopted to improve the operational stability of power station units. Calculation and testing demonstrate an enlargement of the high‐efficiency zone. Specifically, the optimal efficiency of the runner increases by 0.37%, while the rated efficiency rises by 0.19%. Significant reductions are observed in pressure pulsation within the draft tube and vaneless area decrease of approximately 50%. There is a high‐frequency pressure pulsation in the vaneless zone and the runner under low‐load conditions, and the influence of dynamic and static interference gradually weakens with the increase of opening. The draft tube is prone to eccentric vortex bands under partial working conditions, which causes the unit to be affected by low‐frequency pulsation. This optimization also leads to a notable decrease in runner blade wear, with the maximum sand and water velocity reduced from 45 to 40 m/s, resulting in a 30% reduction in sand wear. Moreover, there is a substantial enhancement in the runner's stiffness, with the thickness of the blade near the high stress area of the upper crown and lower ring increasing by over 50%, and the weight of each individual blade increasing by more than 50%. These research findings validate that modifying the runner blade effectively improves flow patterns, reduces eddy current generation, minimizes pressure pulsation, widens the high‐efficiency zone, decreases wear, and enhances the operational stability of the unit. The technical transformation method and research results of this study have important guiding significance for similar technical transformation of other power stations

Using Multispectral Data from UAS in Machine Learning to Detect Infestation by Xylotrechus chinensis (Chevrolat) (Coleoptera: Cerambycidae) in Mulberries

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts.

A Mechanistic Prediction Model of Resistance to Uprooting of Coniferous Trees in Heilongjiang Province, China.

Coarse roots and the root plate play an important role in tree resistance to uprooting. In this study, a qualitative mechanistic model was developed to analyze coniferous tree resistance to uprooting in relation to tree morphological characteristics. The sizes of the crown, stem, and root plate of twenty sample spruces and twenty sample Korean pines were individually measured for this purpose. Using Ground Penetrating Radar (GPR), the coarse root distribution and root plate size were detected. In the qualitative mechanistic model, a larger crown area increased the overturning moment, while higher DBH and root plate mass increased the resistance moment. The resistance coefficient (Rm) was calculated by comparing resistive and overturning moments, classifying samples into three uprooting hazard levels. Trees with smaller crown areas, larger stems, and root plates tend to have higher resistance to uprooting, as indicated by higher Rm values. This qualitative mechanistic model provides a useful tool for assessing coniferous standing tree uprooting resistance.

The space occupation and use by tree crowns explain variations of individual growth rates in an old-growth temperate forest in Japan

Relative growth rates (RGR), i.e., growth rates per unit biomass (ΔM/M, where M is plant mass and ΔM is the change in M over a period of time), reflect growth strategies of plant species. Partitioning of RGR to net assimilation rate (ΔM/leaf area) and leaf area ratio (leaf area/M) provides further insights into allocation strategies. To apply this analytical approach to large canopy tree species, we used crown area (Ac) as a proxy for leaf area to understand variations of RGR partitioned to space use efficiency (SUE, ΔM/Ac) and space occupation efficiency (SOE, Ac/M). With UAV imagery, we measured Ac of 226 co-occurring individuals of 14 canopy tree species in a 1-ha stand in a temperate old-growth mixed-forest in Japan, and analyzed how RGR was related to SUE and SOE. The results show that deciduous species exhibited higher SOE and lower SUE compared to evergreen species, even though their RGR values largely overlapped. Late successional species tended to have higher RGR through higher SUE than early-to-middle successional species. We also analyzed the relationship of absolute growth rates (AGR) with several functional traits including DBH (diameter at breast height), Ac, leaf- and stem traits. Both Ac and DBH were strong determinants of AGR across species. Low specific leaf area (SLA, leaf area per unit leaf mass) and high wood density positively contributed to AGR across species, offering long-term growth advantages for old-growth natural forest. The analytical framework introduced in this study may be useful to elucidate the variation of tree growth strategies of canopy trees in natural forest stands.

Overstory and fuel traits drive moisture dynamics of mesophytic and pyrophytic leaf litter and 10-h woody debris fuels in a mixed longleaf pine-hardwood woodland

BackgroundFollowing decades of fire exclusion, many open pine and oak forests across the central and eastern US are shifting to closed-canopy forests that are increasingly dominated by shade-tolerant, fire-sensitive species (i.e., mesophytes). As mesophytes encroach into historically pyrophytic landscapes, changes in crown traits and understory microclimate may interact with fine fuel traits to influence fuel moisture retention, and ultimately, fire behavior. To better understand potential interactions among overstory trees and underlying fine fuels that occur during mesophyte encroachment, we measured in situ drying rates of leaf litter and 10-h woody debris of three functional groups (pyrophytic pine, pyrophytic oak, and mesophytic oak) in gaps and beneath overstory trees of each functional group within a longleaf pine-mixed oak woodland along with crown (area, volume, cover), leaf litter (curling, thickness, specific leaf area, volume), and woody debris (density) traits of each functional group and understory microclimate (vapor pressure deficit (VPD)).ResultsWe found that leaf litter from pyrophytic and mesophytic oaks had higher initial moisture content than pyrophytic pines, but pyrophytic pine and pyrophytic oak leaf litter dried 1.5 times faster than that of mesophytic oaks, likely due to their greater leaf curl, thickness, and volume. Initial moisture content of mesophytic oak woody fuels was lower than that of pyrophytic pine and pyrophytic oak, potentially because of higher wood density, but there were no differences in fuel drying rates. Regardless of fuel functional type, leaf litter and woody fuels dried 1.5 times faster in gaps and underneath pyrophytic pine compared to mesophytic oaks, likely due to the more open conditions in these areas. Notably overstory functional group and time of the day interacted to influence VPD, with VPD increasing throughout the day for all groups, but more so for gaps and beneath pyrophytic pines than either oak functional group.ConclusionsThus, fuel and crown traits differentially impacted understory microclimate and leaf litter and 10-h woody debris drying rates, leading to slower drying of fuels of encroaching mesophytes compared to pyrophytic pines and oaks, which could lead to reduced forest flammability, and consequently, the continued encroachment of mesophytic species into fire-dependent pine and oak forests.

Individual canopy tree species maps for the National Ecological Observatory Network.

The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km2 shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling.

Rainfall-induced Landslide in the Lesser Himalaya: A Case Study of the Pallotari Landslide, Lamjung, Central-West Nepal

The Pallotari area, located in the Marsyangdi Rural Municipality, Lamjung, central-west Nepal, within the Lesser Himalaya, was impacted by a series of rainfall-induced landslides in July 2020. This study focuses on the Pallotari landslide, one of the major landslides in the area. Geological and engineering geological studies were performed to identify the causes of this landslide, and its impacts to the local community. Our results indicate the major causative factors to be intensive rainfall, steep topography with loose colluvial soil, and some anthropogenic activities such as the construction of the rural roads through the crown area. The study area still has hanging colluvial material on the crown as well as on the left flank of the landslide, and the numerical model for slope stability analysis indicates it to be unstable even in the unsaturated condition. The colluvial material in the middle portion of the landslide also has the risk of failure in the saturation condition which can be due to intensive rainfall. However, the rock slope stability analysis using the kinematic analysis does not show any contribution to the orientation of discontinuities on the slope failure. The presence of several transverse and longitudinal cracks in the surrounding of the landslide are signs of future failure.

Agro-technological aspects of growing branched annual apple nursery plants

The article presents an analytical review of Russian publications reporting the results of studies that search for optimal ways to affect apple plants in order to obtain branched annual plants. Since it has been proved that nursery plants of a certain age and quality are preferable for each type of intensive planting, as their use promotes the early maturity of trees thus ensuring a quick economic return on capital investments, it is currently relevant to study the production of apple tree planting material with specified biometric parameters. The use of mechanical impact on the above-ground part of grafted plants to suppress apical dominance is considered; the effectiveness of stimulating premature shoots in the crown area is confirmed. Much attention is paid by researchers to the chemical treatments of plants in the fields of fruit tree nurseries to improve the quality of annual plants. The use of cytokinin preparations and gibberellic acids is shown to be highly effective. The maximum effect on obtaining branched annual plants in most works was achieved in pomological apple varieties that are prone to the formation of premature shoots, with the budding on vegetatively propagated rootstocks of a first-grade commercial variety at a height of over 15 cm above the soil surface. One of the alternative ways of obtaining branched annual plants is wood grafting in spring and cultivation both in the field and in protected ground with a root-ball system. According to most researchers, the greatest effectiveness of the combined use of various techniques can be achieved only with strict adherence to conventional technologies for each method of cultivation.

Att-Mask R-CNN: an individual tree crown instance segmentation method based on fused attention mechanism

Tree detection and canopy area measurement are important and difficult tasks in forest inventory, which are important for understanding forest stand structure. This study utilized remotely piloted aircraft (RPA) aerial photography technology to collect remote sensing images of forests in Xiong County, China, creating a dataset comprising 1200 images of six tree species. Based on this dataset, the paper proposes an optimized model, Att-Mask R-CNN, for canopy detection and segmentation. Att-Mask R-CNN outperforms the original models (Mask R-CNN and MS R-CNN) by achieving 65.29% mean average precision for detection, 80.44% mean intersection over union for segmentation, and 90.67% overall recognition rate for the six tree species. In addition, a pixel statistics method based on segmentation masks is introduced for estimating the vertical projected area of individual tree crowns, and comparisons between the measured and predicted vertical projected area of the crowns of six tree species (100 trees of each class) show an overall goodness-of-fit R2 of 85% and a relative root-mean-square error rRMSE of 12.81%. By using remote sensing images from RPAs and optimizing existing deep learning models, the detection and segmentation of individual tree canopies can be achieved, resulting in a more accurate understanding of forest structure, which provides scientific support for forest management and resource monitoring.

Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud

Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

Drivers of cocoa yield and growth in young monoculture and agroforestry systems

CONTEXTThe expansion of cocoa farming has been linked to deforestation and biodiversity loss in West Africa. Agroforestry systems could potentially increase the sustainability of cocoa production. However, despite the long history of cocoa cultivation in agroforestry systems, the exact mechanism or the combination of factors that drive cocoa growth and yield in these agroecosystems, especially at the cocoa establishment stage, is unclear. OBJECTIVEThe present study aims to analyse how resource availability, stand characteristics and cocoa tree morphology interact to determine the performance of cocoa during the establishment phase among different cocoa cultivation systems. METHODSWe studied seven different agroforestry systems and one monoculture system in 53 plots located in a 12-ha cocoa agroforestry trial recently established in Côte d'Ivoire. We characterized each system with 16 variables that described five system components: 1) cocoa yield (number of pods, pods weight, number of productive trees) and cocoa growth (basal diameter), 2) cocoa tree morphology (crown depth, diameter, area, and volume), 3) stand characteristics (number and size of shade trees), 4) light (leaf area index) and 5) water (soil volumetric water content). We used a Structural Equation Modelling (SEM) approach to understand and quantify interactions between the five components. RESULTS AND CONCLUSIONSThe models were able to largely explain cocoa early yield and juvenile growth variability (respective determination coefficients: 0.45 and 0.92). We observed improved cocoa establishment in certain designs of agroforestry systems compared with monoculture. However, SEM revealed that stand characteristics (i.e. increase in the number and height of shade trees) had a negative effect on cocoa yield and growth via changes in cocoa tree morphology, having path coefficients of −0.48 and − 0.72, respectively. Conversely, we found a positive relationship between the cocoa tree morphology and cocoa early yield, for which the path coefficient was 0.74, the strongest relation. Cocoa tree morphology was more important than stand characteristics, leaf area index, and soil volumetric water content in controlling cocoa yield and growth. Taken together, our results suggest a competition for space between shade trees and cocoa plants. Finding the optimal design of agroforestry systems can help enhance cocoa establishment in the first years, improving the long-term performance of the system. SIGNIFICANCEThis finding suggests that the competition effect depends on spatial planting pattern and species composition, offering an opportunity to meet the goals of a sustainable intensification of cocoa plantations through the design and management of optimal agroforestry systems.

Common field measures and geometric assumptions of tree shape produce consistently biased estimates of tree and canopy structure in mixed Mediterranean forests

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

Accuracy, adaptation and margin quality of monolithic zirconia crowns fabricated by 3D printing versus subtractive manufacturing technique: A systematic review and meta-analysis of in vitro studies

ObjectiveThe purpose of this systematic review and meta-analysis was to evaluate the accuracy (trueness and precision), marginal and internal adaptation, and margin quality of zirconia crowns made by additive manufacturing compared to subtractive manufacturing technology. MethodsThe investigation adhered to the PRISMA-ScR guidelines for systematic reviews and was registered at the Prospero database (n°CRD42023452927). Four electronic databases, including PubMed, Scopus, Embase, and Web of Science and manual search was conducted to find relevant studies published until September 2023. In vitro studies that assessed the trueness and precision, marginal and internal adaptation, and margin quality of printed crowns compared to milled ones were included. Studies on crowns over implants, pontics, temporary restorations, laminates, or exclusively experimental materials were excluded. ResultsA total of 9 studies were included in the descriptive reporting and 7 for meta-analysis. The global meta-analysis of the trueness (P<0.74,I2=90 %) and the margin quality (P<0.61,I2=0 %) indicated no significant difference between the root mean square of printed and milled zirconia crowns. The subgroup analysis for the printing system showed a significant effect (P<0.01). The meta-analysis of the crown areas indicated no significant difference in most of the areas, except for the marginal (favoring milled crowns) and axial (favoring printed crowns) areas. For precision and adaptation, both methods showed a clinically acceptable level. ConclusionsAdditive manufacturing technology produces crowns with trueness and margin quality comparable to subtractive manufacturing. Both techniques have demonstrated the ability to produce crowns with precision levels, internal discrepancy, and marginal fit within clinically acceptable limits. Clinical significance3D printing emerges as a promising and potentially applicable alternative method for manufacturing zirconia crowns, as it shows trueness and margin quality comparable to restorations produced by the subtractive method.

Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data

Urban trees and forests can contribute to climate change mitigation by sequestering carbon in their living tissues, with aboveground biomass (AGB) playing a pivotal role. This study explores the capability of UAV-borne hyperspectral and LiDAR data for estimating AGB in tropical urban forests. Structural attributes of trees, such as diameter at breast height (DBH), total height, and wood density, were collected from over 5600 individuals, forming a comprehensive AGB dataset. Our methodology included two primary AGB estimation strategies: an area-based strategy that correlated AGB with hyperspectral and canopy height data across various grid sizes and an individual tree crown (ITC)-based method that integrated canopy height, spectral signatures of individual trees and crown area. The findings indicate that increasing the grid size from 10 m to 50 m improved the R2 from 0.24 ± 0.04–0.61 ± 0.13, mainly due to reduced border effects. Furthermore, integrating canopy height and hyperspectral data enhanced the R2 of AGB estimates from 0.61 ± 0.13–0.70 ± 0.09 for a 50 × 50 m grid. Crucially, wavelengths centered at the green peak and red-edge were identified as key bands in AGB retrieval. Integrating hyperspectral and LiDAR data did not significantly enhance results for individual trees, where AGB was closely linked to tree height and crown area. This study underscores the potential of utilizing integrated UAV-borne sensors for biomass assessment in urban forest settings.

Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion

Increasingly, dry conifer forest restoration has focused on reestablishing horizontal and vertical complexity and ecological functions associated with frequent, low-intensity fires that characterize these systems. However, most forest inventory approaches lack the resolution, extent, or spatial explicitness for describing tree-level spatial aggregation and openings that were characteristic of historical forests. Uncrewed aerial system (UAS) structure from motion (SfM) remote sensing has potential for creating spatially explicit forest inventory data. This study evaluates the accuracy of SfM-estimated tree, clump, and stand structural attributes across 11 ponderosa pine-dominated stands treated with four different silvicultural prescriptions. Specifically, UAS-estimated tree height and diameter-at-breast-height (DBH) and stand-level canopy cover, density, and metrics of individual trees, tree clumps, and canopy openings were compared to forest survey data. Overall, tree detection success was high in all stands (F-scores of 0.64 to 0.89), with average F-scores > 0.81 for all size classes except understory trees (< 5.0 m tall). We observed average height and DBH errors of 0.34 m and − 0.04 cm, respectively. The UAS stand density was overestimated by 53 trees ha−1 (27.9%) on average, with most errors associated with understory trees. Focusing on trees > 5.0 m tall, reduced error to an underestimation of 10 trees ha−1 (5.7%). Mean absolute errors of bole basal area, bole quadratic mean diameter, and canopy cover were 11.4%, 16.6%, and 13.8%, respectively. While no differences were found between stem-mapped and UAS-derived metrics of individual trees, clumps of trees, canopy openings, and inter-clump tree characteristics, the UAS method overestimated crown area in two of the five comparisons. Results indicate that in ponderosa pine forests, UAS can reliably describe large- and small-grained forest structures to effectively inform spatially explicit management objectives.