Search Results (821)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Elucidating the coexistence mechanisms of rapidly diverging species has long been a challenge in evolutionary biology. Genome-wide polymorphic loci are expected to provide insights into the speciation processes of these closely related species. This study focused on seven Chinese sclerophyllous oaks, represented by Quercus spinosa, Quercus aquifolioides, Quercus rehderiana, Quercus guyavifolia, Quercus monimotricha, Quercus semecarpifolia, and Quercus senescens, employing 27,592 single-nucleotide polymorphisms to examine their phylogenetic relationships at the genomic level. Combined with genetic structure analysis, phylogenetic trees revealed that the genetic clustering of individuals was influenced by both geographic distance and ancestral genetic components. Furthermore, this study confirmed the existence of reticulate evolutionary relationships among the species. Frequent gene flow and introgression within the seven species were primarily responsible for the ambiguous interspecies boundaries, with hybridization serving as a major driver of reticulate evolution. Additionally, the seven species exhibited distinct differences in niche occupancy. By reconstructing the climatic adaptability of ancestral taxonomic units, we found that the climatic tolerance of each species displayed differential responses to 19 climatic factors. Consequently, ecological niche differentiation and variations in habitat adaptation contributed to the preservation of species boundaries. This study provides a comprehensive understanding of the speciation processes in rapidly diverging genera and underscores the significance of both genetic and ecological factors in the formation and maintenance of species boundaries. Full article

Sequential drought and heat stress pose a growing threat to forest ecosystems in the context of climate change, yet systematic evaluation methods for woody plants remain limited. This study aimed to develop a comprehensive screening platform for identifying woody plant species tolerant to sequential drought and heat stress among 27 native species growing in Korea. A sequential stress protocol was applied: drought stress for 2 weeks, followed by high-temperature exposure at 45 °C. Physiological indicators, including relative water content (RWC) and electrolyte leakage index (ELI), were used for preliminary screening, supported by phenotypic observations, Evans blue staining for cell death, and DAB staining to assess oxidative stress and recovery ability. The results revealed clear differences among species. Chamaecyparis obtusa, Quercus glauca, and Q. myrsinaefolia exhibited strong tolerance, maintaining high RWC and low ELI values, while Albizia julibrissin was highly susceptible, showing severe membrane damage and low survival. DAB staining successfully distinguished tolerance levels based on oxidative recovery. Additional species such as Camellia sinensis, Q. acuta, Q. phillyraeoides, Q. salicina, and Ternstroemia japonica showed varied responses: Q. phillyraeoides demonstrated high tolerance, T. japonica showed moderate tolerance, and Q. salicina was relatively sensitive. The integrated screening system effectively differentiated tolerant species through multiscale analysis—physiological, cellular, and morphological—demonstrating its robustness and applicability. This study provides a practical and reproducible framework for evaluating sequential drought and heat stress in trees and offers valuable resources for urban forestry, reforestation, and climate-resilient species selection. Full article

Climate change poses a major threat to forests, impacting the distribution and viability of key species. Quercus pyrenaica Willd., a marcescent oak endemic to the Iberian Peninsula (Portugal and Spain) and southwestern France and a structural species in submediterranean forests, is particularly susceptible to shifts in temperature and precipitation patterns. Aiming to assess its potential loss of suitable area under future climate scenarios, we developed high-resolution spatial distribution models to project the future habitat suitability of Q. pyrenaica under two climate change scenarios (SSP3-7.0 and SSP5-8.5) for the periods 2070 and 2100. Our model, which has an excellent predictive performance (AUC of 0.971 and a TSS of 0.834), indicates a predominantly northward shift in the potential distribution of the species, accompanied by substantial habitat loss in southern and lowland regions. Long-term potential suitable area may shrink to 42% of that currently available. This, combined with the limited natural dispersal capacity of the species, highlights the urgency of targeted management and conservation strategies. These results offer critical insights to inform conservation strategies and forest management under ongoing climate change. Full article

Climatic oscillations in the Quaternary are altering the performance of angiosperms, while the species’ distribution is regarded as a macroscopic view of these spatial and temporal changes. Oaks (Quercus L.) are important tree models for estimating the abiotic impacts on the distribution of forest tree species. In this study, we modeled the past, present, and future suitable habitat for two varieties of deciduous oaks (Quercus serrata and Quercus serrata var. brevipetiolata), which are widely distributed in China and play dominant roles in the local forest ecosystem. We evaluated the importance of environmental factors in shaping the species’ distribution and identified the “wealthy” habitats in harsh conditions for the two varieties. The ecological niche models showed that the suitable areas for these two varieties are mainly concentrated in mountain ranges in central China, while Q. serrata var. brevipetiolata is also widely distributed in the middle-east mountain range. The mean temperature of the coldest quarter was identified as the critical factor in shaping the habitat availability for these two varieties. From the last glacial maximum (LGM) to the present, the potential distribution range of these two sibling species has obviously shifted northward and expanded from the inferred refugia. Under the optimistic (RCP2.6), moderate (RCP 4.5)-, and higher (RCP 6.0)-concentration greenhouse gas emissions scenarios, our simulations suggested that the total area of suitable habitats in the 2050s and 2070s will be wider than it is now for these two varieties of deciduous oaks, as the distribution range is shifting to higher latitudes; thus, low latitudes are more likely to face the risk of habitat losses. This study provides a case study on the response of forest tree species to climate changes in the north temperate and subtropical zones of East Asia and offers a basis for tree species’ protection and management in China. Full article

Energy production is a global concern, encouraging the search for sustainable alternatives such as charcoal, a promising solid biofuel. This study evaluated the effects of temperature and carbonisation time on charcoal produced from Quercus wood. Carbonisation was carried out at 550 °C for 30 min, 700 °C for 30 min and under two progressive heating profiles: one starting at 550 °C for 30 min and increasing to 700 °C for a further 30 min, and another starting at 300 °C for 2 h and rising to 1000 °C for 10 min. Mass and volumetric yield, bulk density, proximate analysis, calorific value, energy yield and fuel ratio were determined. The results showed that carbonisation temperature affected charcoal properties. Mass and volumetric yields were highest at 550 °C (30.10% and 4.81 m³ t⁻¹) in Q. convallata and Q. urbanii. At higher temperatures, bulk density (0.56 g cm⁻³), fixed carbon (91.51%) and calorific value (32.82 MJ kg⁻¹) increased in Q. urbanii. Lower temperatures led to lower moisture levels (2.46%) and a higher energy yield (48.02%). Overall, temperatures above 700 °C improved energy properties, while those below 550 °C favoured higher yields. Species’ characteristics also influenced charcoal quality. These findings offer valuable insights into optimising the carbonisation of Quercus species and supporting the development of more efficient, sustainable charcoal production methods. Full article

The common oak (Quercus robur L.), though ecologically important and long-lived, has declined in Northern Europe due to historical land use and conifer-dominated forestry. In Latvia, where its distribution is limited, oaks support a rich biodiversity through features like tree-related microhabitats (TreMs) and diverse epiphytic communities. This study compared TreM and epiphyte diversity between planted mature oaks and relict champion oak trees across 16 forest stands. Epiphyte species were recorded using fixed-area frames on tree trunks, and TreMs were categorized following a hierarchical typology. Champion trees hosted significantly more TreMs and a greater variety, including 10 unique TreMs. While overall epiphyte diversity indices did not differ significantly, champion trees supported more specialist and woodland key habitat indicator species. The findings underscore the ecological value of legacy trees, which provide complex habitats essential for specialist taxa and indicators of forest continuity. Conserving such trees is vital for maintaining forest biodiversity and supporting ecosystem resilience in managed landscapes. Full article

Thermal modification applies to a technique for the enhancement of biological durability, stability, and appearance of wood. Much is known about its effects on the chemical and physical attributes of wood. However, there is a knowledge gap concerning the effects of heat treatment on surface coating performance of different wood species. The focus of this research is heat treatment regulation of 160 °C, 180 °C, and 200 °C for three commercially important wood species which are Populus (poplar), Quercus (oak), and Pinus (pine). These treatments were evaluated in relation to coating performance indicators adhesion, integrity, and visual stability during and after natural and artificial weathering. It was revealed that specific responses among species differences exist. Populus behaved differently and exhibited a steady loss in mass and volume. Quercus demonstrated gradual degradation alongside enhanced lignin stability. Pinus exhibited maintenance of volume and mass until 180 °C after which accelerated degradation was observed. Coating durability and adhesion exhibited dependence on thermal condition, wood species, porosity, surface chemistry and microstructural variations that occurred. The research results can be used to streamline finishing processes for thermally modified wood while underscoring the critical nature of precise treatment parameter adjustments guided by species-specific responses to ensure long-term stability. 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

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. Full article

Dehesas are mosaics of open grassland and standalone trees that are diversity reservoirs. However, they have recently faced abandonment and intensification, being replaced by plantations of fast-growing trees or subject to encroachment. Following a change in dehesa communities and structure, a change in soil microbial diversity and functionality in dehesas is expected, but dehesas’ microbial diversity is still a big unknown. In this work, we bring to light the soil prokaryotic taxonomic diversity in dehesa ecosystems and present a first approach to assessing their metabolic diversity through metabarcoding data. For this, we compared three dehesas dominated by different tree species: (i) one dehesa dominated by Quercus ilex; (ii) one dominated by Pinus pinea; and (iii) one dominated by a mixture of Q. ilex and Q. suber. At each dehesa, samples were taken under the canopy and in the open grassland, as well as through two seasons of peak vegetation productivity (autumn and spring). Our results show the following findings: (1) seasonality plays an important role in prokaryotic richness, showing higher values in autumn, and higher evenness in spring; (2) the effect of seasonality on the soil’s prokaryotic diversity is often modulated by the effect of tree species and canopy; (3) taxonomic diversity is driven mainly by the site effects, i.e., the opposite of the metabolic diversity that seemed to be driven by complex interactions among seasons, tree species, and canopies. Full article

Pedunculate oak (Quercus robur L.), an ecologically and economically important tree species has been significantly affected by oak dieback in recent years. Since one of the symptoms of oak dieback is crown defoliation, this research aimed to determine the quantity, quality, average tree value, and wood defects that influence grading in different stages of oak dieback indicated by tree crown defoliation degree. The research was conducted in a 62- and 116-year-old stand of the lowland Croatian forest. In total, 115 pedunculate oak trees were sampled and processed in 983 logs that were analyzed. The prescribed single-entry volume tables underestimate harvesting volume by 5.45% on site A and 6.16% on site B, while the calculation of net harvesting volume underestimates net volume by 0.26% on site A and overestimates net volume on site B by 4.59%. The analysis of wood defect presence showed that insect holes, rot, and covered knots were the main reasons for the degradation of quality class. Dead trees showed a decreased average tree value in DBH classes 32.5–42.5 cm compared to the healthy trees. Based on the findings of this research, tree crown defoliation degree could be used as a timber quality and average tree value indicator. Full article

The introduction of non-native species like Xylella fastidiosa to new environments can lead to potentially catastrophic ecological and economic repercussions. This work comprehended the prospection phase (insect sampling and submission of samples to the laboratory) from X. fastidiosa demarcated zones of Área Metropolitana do Porto; Sabrosa; Alijó; Baião; Mirandela; Mirandela II; and Bougado and the research phase (collecting and organizing data and statistical treatment). The results of this study showed the presence of the bacterium in some tested spittlebugs species captured in DZ of Área Metropolitana do Porto, which highlights the role of the vector in mediating the disease’s propagation. Most insects were found in public gardens and in nurseries/gardens where there is a diverse array of food sources, shelter, mating locations, and suitable substrates for egg laying that serve as ideal conditions for the population of Philaenus spumarius. We observed that most insects were found in the first trimester (36.5%), followed by the third trimester (23.2%). Finally, it was shown that, in our study, the most frequent host plants where insects were found included Lavandula dentata, Ulex minor, Ulex europaeus, Quercus suber, Plantago lanceolata. Our findings imply a robust connection between plant communities, ecological conditions, and insect populations with the occurrence of Xylella fastidiosa, particularly within the examined climatic context. Full article

Climate models forecast warmer winter conditions, which could lead to an earlier spring xylem phenology in trees. Localized stem heat experiments mimic this situation and have shown that stem warming leads to an earlier cambial resumption in evergreen conifers. However, there are still few comprehensive studies comparing the responses to stem heating in coexisting conifers and hardwoods, particularly in drought-prone regions where temperatures are rising. We addressed this issue by comparing the responses (xylem phenology, wood anatomy, growth, and sapwood concentrations of non-structural carbohydrates—NSCs) of two pines (the Eurosiberian Pinus sylvestris L., and the Mediterranean Pinus pinaster Ait.) and a ring-porous oak (Quercus pyrenaica Willd.) to stem heating. We used the Vaganov-Shashkin growth model (VS model) to simulate growth phenology considering several emission scenarios and warming rates. Stem heating in winter advanced cambial phenology in P. pinaster and Q. pyrenaica and enhanced radial growth of the three species 1–2 years after the treatment, but reduced the transversal lumen area of earlywood conduits. P. sylvestris showed a rapid and high growth enhancement, whereas the oak responded with a 1-year delay. Heated P. pinaster and Q. pyrenaica trees showed lower sapwood starch concentrations than non-heated trees. These results partially agree with projections of the VS model, which forecasts earlier growth onset, particularly in P. pinaster, as climate warms. Climate-growth correlations show that growth may be enhanced by warm conditions in late winter but also reduced if this is followed by dry-warm growing seasons. Therefore, forecasted advancements of xylem onset in spring in response to warmer winters may not necessarily translate into enhanced growth if warming reduces the hydraulic conductivity and growing seasons become drier. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article