Search Results (823)

Forests, being the largest and most intricate terrestrial ecosystems, play an indispensable role in sustaining ecological balance. To effectively monitor forest productivity, it is imperative to accurately extract structural parameters such as the tree height and diameter at breast height (DBH). Airborne LiDAR technology, which possesses the capability to penetrate canopies, has demonstrated remarkable efficacy in extracting these forest structural parameters. However, current research rarely models different tree species separately, particularly lacking comparative evaluations of tree height-DBH models for diverse tree species. In this study, we chose sample plots within the Bila River basin, nestled in the Greater Hinggan Mountains of the Inner Mongolia Autonomous Region, as the research area. Utilizing both airborne LiDAR and field survey data, individual tree positions and heights were extracted based on the canopy height model (CHM) and normalized point cloud (NPC). Six tree height-DBH models were selected for fitting and validation, tailored to the dominant tree species within the sample plots. The results revealed that the CHM-based method achieved a lower RMSE of 1.97 m, compared to 2.27 m with the NPC-based method. Both methods exhibited a commendable performance in plots with lower average tree heights. However, the NPC-based method showed a more pronounced deficiency in capturing individual tree information. The precision of grid interpolation and the point cloud density emerged as pivotal factors influencing the accuracy of both methods. Among the six tree height-DBH models, a multiexponential model demonstrated a superior performance for both oak and ”birch–poplar” trees, with R² values of 0.479 and 0.341, respectively. This study furnishes a scientific foundation for extracting forest structural parameters in boreal forest ecosystems. Full article

The harvesting period is determined by forest maturity. However, there are few studies on the continuity of assessing cultivation duration based on both growth and wood quality, especially for Eucalyptus plantations. This study measures growth traits, such as the diameter at breast height (DBH), oblateness, and other characteristics, as well as wood properties like density and crystallinity, and axial surface growth strain levels at four age stages (6, 10, 22, and 34 years) of Eucalyptus cloeziana (E. cloeziana). By analyzing these factors, particularly the changes in growth strain throughout the tree’s development, the study aims to determine the optimal cultivation period for using E. cloeziana as solid wood. The survey revealed a two-stage pattern in the annual change rate of DBH, tree height, and oblateness: a decrease from 6 to 22 years followed by an increase from 22 to 34 years. In E. cloeziana, heartwood percentage and density rapidly declined during the first 6–10 years, then stabilized between 10 and 34 years. This suggested differential rates of growth and maturation. By analyzing the growth strain, it was observed that the growth strain of E. cloeziana exhibited an initial increase followed by a subsequent decrease with age. It reached its peak at 22 years and then gradually declined. Remarkably, at 34 years, the growth strain was even lower than that of 10-year-old E. cloeziana, measuring only 2148 με. This reduction in growth strain is advantageous for minimizing defects such as brittle core formation, cracking, and warping during harvesting. In practical cultivation aimed at solid wood utilization, harvesting can be conducted between 22 and 34 years based on management strategies to reduce operating costs. However, with close-to-nature management practices and sufficient financial resources, extending the cultivation period to 34 years or beyond may result in superior wood quality. We aim to achieve the sustainable utilization of resources, foster the long-term development of the wood processing and solid wood utilization industries, and guide the entire sector towards the goal of sustainable development. Full article

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 × 10⁴ km². The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R² value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R² value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model’s ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment. Full article

The diameter at breast height (DBH) is a fundamental index used to characterize trees and establish forest inventories. The conventional method of measuring the DBH involves using steel tape meters, rope, and calipers. Alternatively, this study has shown that it can be calculated automatically using image-based algorithms, thus reducing time and effort while remaining cost-effective. The method consists of three main steps: image acquisition using a fisheye lens, 3D point cloud generation using structure-from-motion (SfM)-based image processing, and improved DBH estimation. The results indicate that this proposed methodology is comparable to traditional urban forest DBH measurements, with a root-mean-square error ranging from 0.7 to 2.4 cm. The proposed approach has been evaluated using real-world data, and it has been determined that the F-score assessment metric achieves a maximum of 0.91 in a university garden comprising 74 trees. The successful automated DBH measurements through SfM combined with fisheye lenses demonstrate the potential to improve urban tree inventories. Full article

Stem volume is a critical factor in managing and evaluating forest resources. At present, stem volume is commonly estimated indirectly by constructing a taper model that utilizes sampling, diameter at breast height (DBH), and tree height. However, these estimates are constrained by errors arising from spatial and stand environment variations as well as uncertainties in height measurements. To address these issues, this study aimed to accurately estimate stem volume using light detection and ranging (LiDAR) technology, a key tool in modern precision forestry. LiDAR data were used to build comprehensive three-dimensional models of forests with multi-platform LiDAR systems. This approach allowed for precise measurements of tree heights and stem diameters at various heights, effectively mitigating the limitations of earlier measurement methods. Based on these data, a Kozak taper curve was developed at the individual tree level using LiDAR-derived tree height and diameter estimates. Integrating this curve with LiDAR data enabled a hybrid approach to estimating stem volume, facilitating the calculation of diameters at points not directly identifiable from LiDAR data alone. The proposed method was implemented and evaluated for two economically significant tree species in Korea: Pinus koraiensis and Larix kaempferi. The RMSE comparison between the taper curve-based approach and the hybrid volume estimation method showed that, for Pinus koraiensis, the RMSE was 0.11 m³ using the taper curve-based approach and 0.07 m³ for the hybrid method, while for Larix kaempferi, the RMSE was 0.13 m³ and 0.05 m³, respectively. The estimation error of the hybrid method was approximately half that of the taper curve-based approach. Consequently, the hybrid volume estimation method, which integrates LiDAR and the taper model, overcomes the limitations of conventional taper curve-based approaches and contributes to improving the accuracy of forest resource monitoring. Full article

Given a graph G = (V, E), the edge set can be partitioned into k dimensions, for a positive integer k. The set of all i-dimensional edges of G is a subset of E(G) denoted by E_i. A Hamiltonian cycle C on G contains all vertices on G. Let E_i(C) = E(C) ∩ E_i. For any 1 ≤ i ≤ k, C is called a dimension-balanced Hamiltonian cycle (DBH, for short) on G if ||E_i(C)| − |E_j(C)|| ≤ 1 for all 1 ≤ i < j ≤ k. The dimension-balanced cycle problem is generated with the 3-D scanning problem. Graph G is called p-node q-edge dimension-balanced Hamiltonian (p-node q-edge DBH) if it has a DBH after removing any p nodes and any q edges. G is called h-fault dimension-balanced Hamiltonian (h-fault DBH, for short) if it remains Hamiltonian after removing any h node and/or edges. The design for the network-on-chip (NoC) problem is important. One of the most famous NoC is the toroidal mesh graph T_m,n. The DBC problem on toroidal mesh graph T_m,n is appropriate for designing simple algorithms with low communication costs and avoiding congestion. Recently, the problem of a one-fault DBH on T_m,n has been studied. This paper solves the one-node one-edge DBH problem in the two-fault DBH problem on T_m,n. Full article

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a non-destructive approach based on Terrestrial Laser Scanning (TLS) and Quantitative Structure Models (QSMs) that offers the unique advantage of investigating changes in complex tree parameters, such as volume and branch length parameters that have not been previously reported for savanna trees. Leaf-off multi-scan TLS point clouds were acquired during the dry season, using a Riegl VZ1000 TLS, in September 2015 and October 2019 at the Skukuza flux tower in Kruger National Park, South Africa. These three-dimensional (3D) data covered an area of 15.2 ha with an average point density of 4270 points/m² (0.015°) and 1600 points/m² (0.025°) for the 2015 and 2019 clouds, respectively. Individual tree segmentation was applied on the two clouds using the comparative shortest-path algorithm in LiDAR 360(v5.4) software. We reconstructed optimized QSMs and assessed tree structural parameters such as Diameter at Breast Height (DBH), tree height, crown area, volume, and branch length at individual tree level. The DBH, tree height, crown area, and trunk volume showed significant positive correlations (R² > 0.80) between scanning periods regardless of the difference in the number of points of the matched trees. The opposite was observed for total and branch volume, total number of branches, and 1st-order branch length. As the difference in the point densities increased, the difference in the computed parameters also increased (R² < 0.63) for a high relative difference. A total of 45% of the trees present in 2015 were identified in 2019 as damaged/felled (75 trees), and the volume lost was estimated to be 83.4 m³. The results of our study showed that volume reconstruction algorithms such as TreeQSMs and high-resolution TLS datasets can be used successfully to quantify changes in the structure of savanna trees. The results of this study are key in understanding savanna ecology given its complex and dynamic nature and accurately quantifying the gains and losses that could arise from fire, drought, herbivory, and other abiotic and biotic disturbances. Full article

Plastic covers protect fruits from cracking caused by pre-harvest rains in sweet cherry orchards; however, they can decrease the quality parameters of cherries, such as firmness, titratable acidity, color, and sugar content. This study evaluated the impact of a reflective ground film used for 21 or 34 DBH (days before harvest) in a commercial sweet cherry orchard (cv. Regina) grown under plastic cover in southern Chile. Our study showed that the exposition of cherry trees to the reflective film increased firmness and total soluble solid (TSS) content in fruits at harvest, homogenizing the concentration of sugars in fruits along the tree canopy. Additionally, using reflective film for 21 DBH increased the proportion of fruits greater than 32 mm in the upper canopy and the quantity of mahogany-colored cherries in the lower canopy, compared to trees un-exposed to the reflective film. Concerning fruit condition defects, the results reveal that using the reflective film increased the incidence of cracking in fruits in both the upper and lower zones of the canopy. Furthermore, we found that the incidence of orange skin and pitting in fruits decreased at post-harvest in trees exposed to the reflective film, but depending on the canopy zones. Moreover, fruits of trees exposed to the film for 34 DBH exhibited a higher incidence of browning pedicel post-harvest. Finally, according to our results, the antioxidant activity increased in fruits exposed to the reflective film for 21 DBH. Therefore, we can conclude that using reflective films on sweet cherry orchards can improve and homogenize the maturity parameters and the antioxidant activity of fruits; however, this practice can negatively impact the condition of fruits post-harvest. Full article

Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive and often lack the spatial detail required for contemporary decision-making. This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. During these campaigns, detailed measurements were recorded for every tree within selected plots. For each tree, DBH was measured directly, and tree heights were obtained either by direct measurement—using hypsometers or clinometers—or, when direct measurements were not feasible, by applying well-established DBH—height allometric relationships that have been calibrated for the local forest types. This comprehensive approach to ground data collection, supplemented by an independent dataset from Brasov County collected using the same protocols, allowed for robust training and validation of the machine learning models. This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Classification and Regression Trees (CART), and the Gradient Boosting Tree Algorithm (GBTA)—in predicting the forest attributes from Sentinel-2 satellite imagery. While Random Forest consistently delivered high R² values and low root mean square errors (RMSE) across all attributes, GBTA showed particular strength in predicting standing stock, and CART excelled in basal area estimation but was less reliable for other attributes. A sensitivity analysis across multiple spatial resolutions revealed that the performance of all algorithms varied significantly with changes in resolution, emphasizing the importance of selecting an appropriate scale for accurate forest mapping. By focusing on both the methodological advancements in machine learning applications and the rigorous, detailed empirical forest data collection, this study provides a clear solution to the problem of obtaining reliable, spatially detailed forest attribute maps. Full article

Riparian forests in the valley area of the Irtysh River Basin are capable of providing a variety of ecosystem services such as water conservation and biodiversity maintenance. Their health condition is an important reflection of their ability to maintain the stability of ecosystem structure and perform ecosystem functions. In this study, a comprehensive survey was conducted to observe the typical distribution areas of riparian forests in the valley of six tributaries and one main stream of the Irtysh River Basin. Twelve indicators were chosen from the three categories of vigor (i.e., productivity), organization (i.e., species diversity and structure complexity), and resistance (i.e., harmful factors and disturbances) to form an evaluation system. Expert-based and statistical weighting were applied to calculate the health scores of riparian forests in the valley and prioritized the health grades of seven rivers. Several criteria were used to further classify the unhealthy level of each river individually. The results of this study can be used as a foundation for future conservation and orderly development of riparian forests in the valley area. The results show that (1) the Kuyertes River was classified as healthy, while the Haba and Berezek Rivers were classified as unhealthy. (2) Among the three evaluation categories, the organization consistently achieved higher scores compared to vigor and resistance. (3) Unhealthy conditions were consistently observed in the midstream sections of each river. (4) Forest types such as Salix alba L. forests, Populus euphratica Oliv. forests, and Betula pendula Roth forests were particularly prone to poor health outcomes. The health of the riparian forests was relatively unsatisfactory due to the conflicting water resource allocation. The protection and restoration of riparian forests in the valley area of the Haba and Berezek Rivers should be prioritized in the future, as well as the middle reaches of each tributary. Additionally, it is necessary to pay attention to three key indicators: stand volume per unit area, stand density, and diameter at breast high (dbh) class structure to improve the health condition of riparian forests. Full article

Background: Pinus pinea L. (stone pine) produces pine nuts of high value. Its cultivation is carried out in forests and plantations, with intensive management techniques being studied to stimulate diameter growth, which is positively related to cone production. Aims: To evaluate the effect of fertilization in a 30-year-old plantation and to understand if adult trees respond to nutritional management. Methods: A trial with completely randomized block design was established with two treatments (fertilization/control) and three repetitions. The plantation, with a density of 204 trees/ha, is located in central Chile, on a sandy-loam soil with neutral pH, medium organic matter content, and a fertility condition that limits tree development. Fertilization considered the repeated application of macro (N, P, K, S, Mg) and micronutrients (B, Fe, and Zn). Periodic measurements of height, stem and crown diameter, and cone production were made up to age 36. Cone production was evaluated using mixed generalized linear models and growth variables using ANOVA (analysis of variance). Results: Significant effects of fertilization on DBH annual growth (35% higher than the control, p < 0.001) and in cone production (3 times higher, p < 0.0001) were found. Conclusions: Fertilization is a useful practice to improve the growth and cone productivity of the species. Full article

Bamboo, characterized by its high growth speed and short maturation period, occupies 0.875% of the global forest area and significantly contributes to terrestrial carbon cycling. The state of shoot growth can essentially indicate a bamboo forests’ health and productivity. This study explored the potential of backpack laser scanning (BLS) for monitoring the growth of Moso bamboo shoots (Phyllostachys edulis), a key economic species in subtropical China. Initially, the accuracy of BLS in extracting attributes of bamboo and shoots (including diameter at breast height (DBH), height, and real-world coordinates) was validated. An optimized method was developed to address the lower precision of BLS in extracting the DBH for thinner species. Subsequently, this research analyzed the impact of spatial structure and other indicators on shoot emergence stage and growth rate using a random forest model. The results indicate that BLS can accurately extract Moso bamboo and shoot height (RMSE = 0.748 m) even in dense bamboo forests. After optimization, the error in DBH extraction significantly decreased (RMSE = 0.835 cm), with the average planar and elevation errors for Moso bamboo being 0.227 m and 0.132 m, respectively. The main indicators affecting the coordinate error of Moso bamboo were the distance to the start (DS) and the distance to the trajectory (DT). The emergence time of shoots was mainly influenced by the surrounding Moso bamboo quantity, with the leaf area index (LAI) and competition index (CI) positively related to the growth rate of shoots. The importance ranking of spatial structure for the carbon storage of shoots was similar to that of the growth rate of shoots, with both identifying LAI as the most significant indicator. This study has validated the value of BLS in monitoring the growth of shoots, providing a theoretical support for the sustainable management and conservation of bamboo forests. Full article

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

This study focused on 16,101 Cunninghamia lanceolata trees across 133 plots in seven cities of Guangdong Province, China, to develop a comprehensive full growth cycle crown width (CW) model. We systematically analyzed the dynamic characteristics of CW and its multi-scale influencing mechanisms. A binary basic model, with the diameter at breast height (DBH) and height (H) as core predictor variables, effectively reflected tree growth patterns. The inclusion of age groups as dummy variables allowed the model to capture the dynamic changes in CW across different growth stages. Furthermore, the incorporation of a nested two-level nonlinear mixed-effects (NLME) model, accounting for random effects from the forest block- and sample plot-level effects, significantly improved the precision and applicability of the final model (R² = 0.731, RMSE = 0.491). This model quantified both macro- and micro-level effects of region and plot on CW. Our findings showed that the two-level NLME model, incorporating tree age groups, optimally accounted for environmental heterogeneity and tree growth cycles, resulting in the best-fitting statistics. The proposed full growth cycle CW model effectively enhanced the model’s efficiency and predictive accuracy for Cunninghamia lanceolata, providing scientific support for the sustainable management and dynamic monitoring of plantation forests. Full article

Parkinson’s disease (PD), the second most common neurodegenerative disorder, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor and non-motor symptoms. The neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) has been extensively used in different animal species to develop chemical models of PD. This study aimed to evaluate the effects of acute exposure to MPTP (3 × 150 mg/kg, intraperitoneally) on adult zebrafish by assessing the neurochemical, transcriptional, and motor changes associated with PD pathogenesis. MPTP treatment resulted in a significant decrease in brain catecholamines, including dopamine, norepinephrine, and normetanephrine. Additionally, a trend towards decreased levels of dopamine precursors (tyrosine and L-DOPA) and degradation products (3-MT and DOPAC) was also observed, although these changes were not statistically significant. Gene expression analysis showed the downregulation of dbh, while the expression of other genes involved in catecholamine metabolism (th1, th2, mao, comtb) and transport (slc6a3 and slc18a2) remained unaltered, suggesting a lack of dopaminergic neuron degeneration. Behavioral assessments revealed that MPTP-exposed zebrafish exhibited reduced motor activity, consistent with the observed decrease in dopamine levels. In contrast, the kinematic parameters of sharp turning were unaffected. A significant impairment in the sensorimotor gating of the ASR was detected in the MPTP-treated fish, consistent with psychosis. Despite dopamine depletion and behavioral impairments, the absence of neurodegeneration and some hallmark PD motor symptoms suggests limitations in the validity of this model for fully recapitulating PD pathology. Further studies are needed to refine the use of MPTP in zebrafish PD models. Full article