Search Results (367)

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article

Ectomycorrhizal symbiosis is a cornerstone of ecosystem health, facilitating nutrient uptake, stress tolerance, and biodiversity maintenance in trees. Optimizing Pinus armandii–Tuber indicum mycorrhizal synthesis enhances the ecological stability of coniferous forests while supporting high-value truffle cultivation. This study conducted a pot experiment to compare the effects of three root nutrient regulations—Aolu 318S (containing N-P₂O₅-K₂O in a ratio of 15-9-11 (w/w%)), Aolu 328S (11-11-18), and Youguduo (19-19-19)—on the mycorrhizal synthesis of P. armandii–T. indicum. The results showed that root nutrient supplementation significantly improved the seedling crown, plant height, ground diameter, biomass dry weight, and mycorrhizal infection rate of both the control and mycorrhizal seedlings, with the slow-release fertilizers Aolu 318S and 328S outperforming the quick-release fertilizer Youguduo. The suitable substrate composition in this experiment was as follows: pH 6.53–6.86, organic matter content 43.25–43.49 g/kg, alkali-hydrolyzable nitrogen 89.25–90.3 mg/kg, available phosphorus 83.69–87.32 mg/kg, available potassium 361.5–364.65 mg/kg, exchangeable magnesium 1.17–1.57 mg/kg, and available iron 33.06–37.3 mg/kg. It is recommended to mix the Aolu 318S and 328S solid fertilizers evenly into the substrate, with a recommended dosage of 2 g per plant. These results shed light on the pivotal role of a precise N-P-K ratio regulation in fostering sustainable ectomycorrhizal symbiosis, offering a novel paradigm for integrating nutrient management with mycorrhizal biotechnology to enhance forest restoration efficiency in arid ecosystems. Full article

Accurate estimation of stem profiles is critical for forest management, timber yield prediction, and ecological modeling. However, traditional taper equations often fail to capture species-specific growth variability and exhibit significant biases, particularly in the upper stem regions. Machine learning regression models were applied to estimate Quercus mongolica stem profiles across South Korea, and performance was compared with that of a traditional taper equation. A total of 2503 sample trees were used to train and validate Random Forest (RF), XGBoost (XGB), Artificial Neural Network (ANN), and Support Vector Regression (SVR) models. Predictive performance was evaluated using root mean square error, mean absolute error, and coefficient of determination metrics, and performance differences were validated statistically. The ANN model exhibited the highest predictive accuracy and stability across all diameter classes, maintaining smooth and consistent stem profiles even in the upper stem regions where the traditional taper model exhibited significant errors. RF and XGB models had moderate performance but exhibited localized fluctuations, whereas the Kozak taper equation tended to overestimate basal diameters and underestimate crown-top diameters. Machine learning models, particularly ANN, offer a robust alternative to fixed-form taper equations, contributing substantially to forest resource inventory, carbon stock assessment, and climate-adaptive forest management planning. Full article

Flowering street trees are important carriers of urban landscapes and ecological functions, as well as a significant boost to the construction of “Shanghai Flower City”. Most existing studies focus on the ornamental value or single ecological benefits, and there are insufficient systematic evaluations of the landscape–ecology synergistic effect, especially as there are few quantitative studies on the landscape value during the flowering period and long-term ecological benefits. Scientific assessment of multiple benefits is of great significance for optimizing tree species allocation and enhancing the sustainability of road landscapes. Taking flowering street trees in Shanghai’s central urban area as a case study, this paper verifies the feasibility of using digital technology to evaluate their landscape and ecological benefits and explores ways to enhance these aspects. Landscape, ecological, and comprehensive benefits were quantitatively assessed using digital images, the i-Tree model, and the entropy-weighted method. Influencing factors for each aspect were also analyzed. The results showed the following: (1) Eleven species or cultivars of flowering street trees from six families and ten genera were identified, with the majority flowering in spring, fewer in summer and autumn, and none in winter. (2) The landscape benefits model was: Scenic Beauty Estimation (SBE) = −0.99 + 0.133 × Flowering branches+ 0.183 × Degree of flower display + 0.064 × Plant growth + 0.032 × Artistic conception + 0.091 × Visual harmony with surrounding elements. Melia azedarach L., Prunus × yedoensis ‘Somei-yoshino’, and Paulownia tomentosa (Thunb.) Steud. ranked highest in landscape benefits. (3) Catalpa bungei C. A. Mey., Koelreuteria bipinnata Franch., and Koelreuteria bipinnata ‘integrifoliola’ (Merr.) T.Chen had the highest plant height, diameter at breast height (DBH), and crown width among the studied trees, and ranked top in ecological benefits. (4) Koelreuteria bipinnata, Catalpa bungei, and Melia azedarach showed the best overall performance. The comprehensive benefits model was: Comprehensive Benefits = 0.6889 × Ecological benefits + 0.3111 × Landscape benefits. This study constructs a digital evaluation framework for flowering street trees, quantifies their landscape and ecological benefits, and provides optimization strategies for the selection and application of flowering trees in urban streets. Full article

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R², R_a² = 0.79) and AGB (R_a² = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated. Full article

Michelia macclurei, a valuable native broad-leaved species with good ecological and economic benefits and a key afforestation tree in South China, is facing progressive stand degradation and soil fertility decline with age. To investigate age-dependent dynamics of stand structure and soil properties, this study examined five stands (5, 10, 15, 20, and 42 a) in Yunfu City, Guangdong Province. The results revealed that (1) spatial distribution shifted from aggregated in young stands (5–10 a) to random in mature stands (42 a), with diameter and height class distributions becoming more diverse with age. Notably, topsoil (0–20 cm) in near-mature stands (15–20 a) exhibited not only significantly higher capillary porosity, non-capillary porosity, and water-holding capacity compared to young stands but also increased bulk density, indicating soil physical degradation. (2) Soil nutrient decline was observed in over-mature stands (42 a), characterized by a reduction in soil total nitrogen to 1.08 ± 0.06 g·kg⁻¹ and total phosphorus to 0.16 ± 0.02 g·kg⁻¹ in the topsoil (0–20 cm layer), suggesting age-related soil nutrient degradation. (3) Correlation analysis revealed a significant negative correlation between total potassium content and crown uniformity indices (p < 0.01), while available phosphorus was significantly positively correlated with crown and tree growth (p < 0.05). These findings provide critical insights for developing stage-specific management strategies in Michelia macclurei plantations. Full article

The stem taper function is essential in predicting diameter outside bark (DOB) variations along the tree height, contributing to volume estimation, harvest planning, and precision forestry. Traditional taper models, such as the Kozak function, offer interpretability but often fail to capture nonlinear growth dynamics and regional variability, particularly in the upper stem segments. This study aimed to evaluate and compare the prediction accuracy of conventional and machine learning-based taper models using Pinus densiflora, a representative conifer species in Korea. Field data from two ecologically distinct regions (Gangwon and Central Korea) were used to build and test four models: the Kozak taper function, random forest, extreme gradient boosting, and an artificial neural network (ANN). Model performance was assessed using the RMSE, R², and MAE, along with stem profile visualizations for representative trees. The results showed that the ANN consistently achieved the highest prediction accuracy across both regions, particularly at an upper crown zone relative height (RH) > 0.8, while maintaining smooth and stable taper curves. In contrast, the Kozak model tended to underestimate the diameter of the upper stem. This study demonstrates that machine learning models, particularly ANNs, can effectively enhance the taper prediction precision and serve as practical tools for data-driven forest management and the implementation of precision forestry in Korea. Full article

Aboveground biomass models are useful for assessing vegetation conditions and providing valuable information on the availability of ecosystem goods and services, including carbon stock and forest/rangeland products. This study aimed to develop aboveground biomass estimation models for the common woody species found in Borana woodland. Multispecies and species-specific models for aboveground biomass were developed using 114 destructively sampled trees representing five species. The dendrometric variables selected as predictors of the trees’ aboveground dry biomass for both multispecies and species-specific models were diameter at breast height, tree height, wood basic density (ρ), crown area (ca) and crown diameter (cd). The distribution of biomass across trees’ aboveground components was estimated using destructively sampled trees. Most tree biomass is allocated to branches, followed by the stems. The tree diameter, wood basic density, and crown diameter were significant predictors in generic and species-specific biomass models across all tree components. Incorporating wood basic density into the model significantly improved prediction accuracy, while tree height had a minimal effect on biomass estimation. The stem and twig biomasses were the highest and least predictable plant parts, respectively. Compared with the existing models, our newly developed models significantly reduced prediction errors, reinforcing the importance of location-specific models for accurate biomass estimation. Hence, this study fills the geographic and ecological gaps by developing models tailored with the unique conditions of the Borana lowland forest. The accuracy of species-specific biomass models varied among tree species, indicating the need for species-specific models that account for variations in growth architecture, ecological factors, and bioclimatic conditions. Full article

Copal is a non-timber forest product of historical, cultural, and industrial significance in Mexico. The use of unsustainable harvesting methods and a lack of understanding of the factors influencing their production have led to a decline in natural populations of resin-producing species. This study aimed to identify the dendrometric, edaphoclimatic, physiological, and resin extraction method variables with the greatest influence on resin yield in Bursera bipinnata using correlation analysis and multiple linear regression. The research was conducted in the Los Sauces micro-watershed, Morelos, Mexico, with a randomly selected sample of 70 trees. Nineteen explanatory variables were categorized into dendrometric, edaphoclimatic, physiological, and extraction method parameters. Variables significantly correlated with resin yield were diameter at breast height, crown diameter, crown volume, altitude, resin tapping faces on the stem, resin tapping faces on branches, total resin tapping faces, resin tapping face height, total resin tapping area, and the Normalized Difference Moisture Index (NDMI) in October. The regression model revealed that resin yield increased significantly with total tapping area ($\beta =0.649$) but decreased with greater incision length ($\beta =-0.308$) and higher NDMI values in October ($\beta =-0.205$), explaining 43.8% of the variation in resin yield. Results highlight the importance of tissue damage intensity, tree physiological status, and water availability as determinants of resin production. The model provides practical guidelines for optimizing extraction techniques, enabling sustainable harvesting that maintains tree vitality and supports long-term productivity in resin-harvesting communities. Full article

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests. Full article

Stand density is a primary limiting factor affecting the accumulation of timber volume, growth, and development of trees in plantations. However, the impact of stand density on the spatial structure and developmental strategies of male and female plants in dioecious tree species remains unclear. In this study, we focused on female, male, and unknown-sex plants of Fraxinus mandshurica across four initial densities (1 m × 1 m, 1.5 m × 1.5 m, 2 m × 2 m, 3 m × 1.5 m). From 2018 to 2022, continuous observations were conducted to determine sex and growth traits (tree height, diameter at breast height, and crown width) with measurements taken annually during the peak growing season. In 2022, in the same season, we measured the morphology and nutrient contents of vegetative organs (shoots, leaves, and absorptive roots) in plants of different genders and assessed the soil properties of their rhizosphere soil. The competition intensity among female plants at high density (D₄) increased significantly by 46.32% compared to low density. The gender mingling between female and male plants remained relatively stable across all densities and was greater than 0.7, and the plants occupied a sub-dominant position within their spatial structure. As density increases, the annual growth in height and crown width of female, male, and unknown-sex plants significantly decreases (p ≤ 0.05), while the annual timber volume growth of males and unknown-sex plants also experiences a significant reduction (p ≤ 0.05). Density was a primary factor affecting the ratio of the leaf area, branch thickness, diameter of the absorbing roots, and root tissue density in female and male plants. It also significantly influenced the changes in nitrogen (negatively) and phosphorus (positively) levels within the vegetative organs (p ≤ 0.05). Collectively, these changes were related to the moisture content, ammonium nitrogen, and total phosphorus levels in the rhizosphere soil. These findings emphasize the important of density and spatial structure in shaping the interactions between male and female plants, with the density influencing their growth and reproductive strategies. Research findings provide important insights into the cultivation strategies for dioecious tree species in plantations. Full article

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly important. Completely novel in approach, this study proposes a methodology encompassing two bespoke deep learning models: (1) a single encoder, double decoder (SEDD) model to generate a species segmentation map, regularized by a distance map in training, and (2) an XGBoost model that estimates the diameter at breast height (DBH) based on tree species and crown measurements. These models operate sequentially: RGB images from the ReforesTree dataset undergo preprocessing before species identification, followed by tree crown detection using a fine-tuned DeepForest model. Post-processing applies the XGBoost model and custom allometric equations alongside standard carbon accounting formulas to generate final sequestration estimates. Unlike previous approaches that treat individual tree identification as an isolated task, this study directly integrates species-level identification into carbon accounting. Moreover, unlike traditional carbon estimation methods that rely on regional estimations via satellite imagery, this study leverages high-resolution, drone-captured RGB imagery, offering improved accuracy without sacrificing accessibility for resource-constrained regions. The model correctly identifies 67% of trees in the dataset, with accuracy rising to 84% for the two most common species. In terms of carbon accounting, this study achieves a relative error of just 2% compared to ground-truth carbon sequestration potential across the test set. Full article

Close-to-nature forest management is a sustainable forest management approach aimed at achieving a balance between ecological and economic benefits. The cultivation of future crop trees in the later successional stages following the removal of competitive trees is crucial for promoting positive development trajectories of succession. Understanding the dynamic process of growth investment strategies in future crop trees facilitates the rational planning of management cycles and scopes, ultimately enhancing the quality of tree cultivation. This study was conducted in a Pinus massoniana secondary forest with close-to-nature forest management in Ningbo City, Zhejiang Province, using handheld mobile laser scanning technology to precisely reconstruct the structure of future crop trees. Over a period of 2–5 years following the initial implementation of close-to-nature forest management, 3D point cloud data were collected annually from both managed and reference (non-managed) plots. Using these multi-temporal data, we analyzed the dynamics of the investment strategies, structural growth components, and crown competition of future crop trees. A linear mixed-effect model was applied to compare the temporal variations in these indices between the managed and control plots. Our results revealed that the height-to-diameter ratio of the future crop trees gradually declined over time, while the crown-to-diameter ratio initially increased and then decreased in the managed plots. These trends were significantly different from those observed in the control plots. Additionally, the height growth rates of the future crop trees in the managed plots were consistently lower than those in the control plots, whereas the crown and diameter at breast height (DBH) growth rates were higher. Furthermore, the crown gap area between the future crop trees and their neighboring trees gradually diminished, and the crown overlap progressively increased. These results suggest that the investment in height growth, initially driven by crown competition, shifted toward crown and DBH growth following close-to-nature forest management. In the initial stage after the removal of competitive trees, future crop trees benefited from ample crown radial space and minimal crown competition. However, as the crown radial space became increasingly limited, the future crop trees shifted their growth investment toward DBH to enhance mechanical stability and achieve a balanced tree structure. Understanding these dynamic processes and the underlying mechanisms of growth investment strategies contributes to predicting future forest community development, improving forest productivity, maintaining structural diversity, and ensuring sustainable forest management. Full article

In recent years, it has been found that the phenomenon of ‘only seedlings but no young trees’ is very serious in P. yunnanensis forest, which is very unfavourable to the natural regeneration and succession of seedlings in P. yunnanensis forest. Through research on the growth and non-structural carbohydrates (NSCs) content of various organs under different shading treatments, this study provides a theoretical basis for understanding the regeneration difficulties of P. yunnanensis and strengthening the scientific conservation of P. yunnanensis forests. In this study, we set up shade treatments for potted P. yunnanensis seedlings by constructing shade shelters and simulated sunflecks by opening the shade net at noon; we set up five treatments, namely the control (natural light), 80% shade with the net open at noon for 1 h (T80-1), 80% shade all the time (T80), 95% shade with the net open at noon for 1 h (T95-1), and 95% shade all the time (T95). The changes in seedling height and diameter and the NSCs content of various organs of P. yunnanensis seedlings were determined after shading. The results showed that 80% and 90% shading significantly inhibited the growth of P. yunnanensis seedlings and reduced the biomass of each organ. While the needle–biomass ratio of P. yunnanensis increased, the fine root–biomass ratio and root–crown ratio tended to decrease. The starch content and NSCs content of each organ decreased, and the soluble sugar–starch ratio of each organ tended to increase. Under the simulated sunfleckssunfleckstreatment, P. yunnanensis seedlings had increased aboveground biomass investment and also decreased storage of thick root starch, which was decomposed and invested into the aboveground part. This indicates that the transient high intensity of Sunfleckssunflecksmitigation alleviated the adverse effects of shading on seedling carbon reserves and increased the adaptability of P. yunnanensis seedlings to prolonged shading. Full article

The Satsuma mandarin, a prominent fresh citrus variety cultivated in Asia, is susceptible to fruit cracking, a physiological disorder that significantly impacts yield and economic efficiency. This phenomenon occurs during the fruit expansion phase. The present study sought to further elucidate the correlation between citrus fruit cracking and fruit peel development or mineral elements, as well as to propose efficacious management measures. The present experiment was conducted on Citrus unshiu Marc. cv. ‘Miyagawa Wase’ over two successive seasons—2022 and 2023. The dynamic changes in fruit morphology were recorded using calipers, and the peel strength was assessed via a Plus Texture Analyzer. Paraffin sectioning technology was used to observe the morphological structure of peel cells. At 10 days after full bloom (DAFB), the peel cells exhibited vigorous proliferation, and the fruit and peel thicknesses underwent rapid expansion. At 50–60 d after full bloom, the longitudinal and transverse diameters of the fruit exhibited a marked increase in the growth rate of the former over the latter. At 80 d after full bloom, both the peel thickness change and the fruit growth rate exhibited a marked deceleration, and the albedo layer cells began to show signs of perforation. The following two time points were preliminarily proposed as the key points for the control of citrus fruit cracking: key point one was 50–60 days after full bloom; and key point two was 80–90 days after full bloom. The nitrogen (N), phosphorus (P), and potassium (K) contents in the different orchards were measured via the semi-micro Kjeldahl nitrogen method, the molybdenum–antimony colorimetric method, and flame photometry, respectively. The determination of other mineral elements was conducted by means of inductively coupled plasma spectroscopy. Principal component analysis was employed to analyze the 21-parameter indices of mineral elements in soil and leaf samples from the three orchards with different levels of fruit cracking. The study found that high concentrations of leaf Fe, P, and soil Cu, as well as organic matter content, contributed negatively to the extent of fruit cracking. The impact of diverse control measures on the incidence of fruit cracking was subsequently observed, following the implementation of tree crown spray treatments. The application of 0.5% calcium superphosphate and 0.006% EDTA-Fe, in combination with 10 ppm GA₃ sprayed during two critical periods, significantly reduced fruit cracking and did not adversely affect the internal or external quality of the fruits. The study emphasises the necessity of customising management measures according to the developmental characteristics of citrus fruits, given the observed varietal and regional distinctions in susceptibility to cracking. These findings are pivotal for advancing research in the field of fruit cracking and promoting the healthy development of the industry. Full article