Forests

17 pages, 753 KiB

Open AccessArticle

Clonal Variation in Growth, Physiology and Ultrastructure of Populus alba L. Seedlings Under NaCl Stress

by Mejda Abassi, Mohammed S. Lamhamedi, Ali Albouchi, Damase Khasa and Zoubeir Bejaoui

Forests 2025, 16(5), 721; https://doi.org/10.3390/f16050721 - 23 Apr 2025

Afforestation and reforestation (A/R) of non-agricultural and marginal saline lands by promoting fast-growing and salinity-tolerant woody species are crucial strategies to overcome land degradation and vegetation cover scarcity. To obtain basic information before using Populus alba clones in such degraded areas, morpho-physiological and [...] Read more.

Afforestation and reforestation (A/R) of non-agricultural and marginal saline lands by promoting fast-growing and salinity-tolerant woody species are crucial strategies to overcome land degradation and vegetation cover scarcity. To obtain basic information before using Populus alba clones in such degraded areas, morpho-physiological and cellular responses to salt stress were investigated. The experiment was conducted in a nursery where cuttings of three P. alba clones (MA-104, MA-195 and OG) were grown for 90 days in 100 mM NaCl versus a non-saline control. A global approach highlighting clonal differences in terms of dry mass production and plant physiological performance was achieved by comparing plant water status, gas exchange, ionic selectivity, osmotic adjustment and chloroplast ultrastructure under the two treatments. Dry mass production and eco-physiological processes were reduced in response to salt stress, with substantial clonal variation. Clone MA-104 exhibited salinity-tolerant behaviour in contrast to clone MA-195 and OG’s medium or sensitive behaviour towards the stress. Tolerance mechanisms may be attributed to enhanced stomatal control and osmotic adjustment, thereby enabling the maintenance of turgor in plants subjected to salt stress. The chloroplast ultrastructure also showed modifications that are often involved in adaptation to salinity stress. Full article

(This article belongs to the Special Issue Physiological Mechanisms of Plant Responses to Environmental Stress)

19 pages, 1226 KiB

Open AccessArticle

Plant–Soil–Microbial Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry in Mongolian Pine-Planted Forests Under Different Environmental Conditions in Liaoning Province, China

by Hui Li, Yi Yang, Xiaohang Weng, Yongbin Zhou, Songzhu Zhang, Liying Liu and Jiubo Pei

Forests 2025, 16(5), 720; https://doi.org/10.3390/f16050720 - 23 Apr 2025

Abstract

Mongolian pine (Pinus sylvestris var. Mongolia) has been widely utilized as a key species for afforestation projects within the Three-North Shelterbelt of Liaoning Province in China. Its impressive ecological resilience has made it a favorite choice for this endeavor. However, as [...] Read more.

Mongolian pine (Pinus sylvestris var. Mongolia) has been widely utilized as a key species for afforestation projects within the Three-North Shelterbelt of Liaoning Province in China. Its impressive ecological resilience has made it a favorite choice for this endeavor. However, as the stands mature and climate conditions shift, some areas are experiencing premature decline or even mortality. Ecological stoichiometry is capable of uncovering the supply and equilibrium of plant and soil nutrients within ecosystems and is extensively utilized in the identification of limiting elements. Therefore, studying its ecological stoichiometry and internal stability dynamics is of crucial significance for clarifying the nutrient cycling process in the Mongolian pine region and alleviating the decline situation. The eastern and northwestern regions of Liaoning differ significantly in precipitation and soil nutrient availability. This study examines Mongolian pine plantations in both regions, analyzing the carbon (C), nitrogen (N), and phosphorus (P) content in plant tissues, soil, microbial biomass, and stoichiometric ratio under distinct environmental conditions. In order to provide a theoretical basis for alleviating the decline of artificial poplar forests and healthy management. Results indicate that (1) leaf C, N, and P contents in the eastern Liaoning region averaged 496.67, 15.19, and 1.66 g·kg⁻¹, respectively, whereas those in northwestern Liaoning were 514.16, 14.82, and 1.23 g·kg⁻¹, respectively. Soil C, N, and P concentrations exhibited notable regional differences, with eastern Liaoning recording 34.54, 2.62, and 0.48 g·kg⁻¹, compared to significantly lower values in northwestern Liaoning (7.74, 0.77, and 0.21 g·kg⁻¹). Similarly, microbial biomass C, N, and P were higher in eastern Liaoning (18.63, 5.09, and 7.72 mg·kg⁻¹) than in northwestern Liaoning (10.18, 3.46, and 4.38 mg·kg⁻¹). (2) The stoichiometric ratio of soil in the Mongolian pine plantations is higher than that in northwestern Liaoning, but the stoichiometric ratio of plants shows the opposite trend. Specifically, microbial carbon-to-nitrogen (MBC/MBN) ratios are higher in eastern Liaoning, whereas microbial carbon-to-phosphorus (MBC/MBP) and nitrogen-to-phosphorus (MBN/MBP) ratios are greater in northwestern Liaoning. Correlation analysis of plant–soil–microbe stoichiometry indicates that plant growth in both regions is co-limited by nitrogen, with Mongolian pine exhibiting strong internal stability. Full article

(This article belongs to the Section Forest Soil)

22 pages, 11540 KiB

Open AccessArticle

MTL-FSFDet: An Effective Forest Smoke and Fire Detection Model Based on Multi-Task Learning

by Chenyu Zhang, Yunfei Liu, Cong Chen and Junhui Li

Forests 2025, 16(5), 719; https://doi.org/10.3390/f16050719 - 23 Apr 2025

Abstract

Forest fires cause devastating damage to the natural environment, making prompt and precise detection of smoke and fires in forests crucial. When processing forest fire images based on ground and aerial perspectives, current object detection methods still encounter issues, such as inadequate detection [...] Read more.

Forest fires cause devastating damage to the natural environment, making prompt and precise detection of smoke and fires in forests crucial. When processing forest fire images based on ground and aerial perspectives, current object detection methods still encounter issues, such as inadequate detection precision, elevated false detection and omission rates, as well as difficulties in detecting small targets in complex forest environments. Multi-task learning represents a framework in machine learning where a model can handle detection and segmentation tasks concurrently, enhancing the accuracy and generalization capacity for object detection. Therefore, this study proposes a Multi-Task Learning-based Forest Smoke and Fire Detection model (MTL-FSFDet). Firstly, an improved Bilateral Filtering-Multi-Scale Retinex (BF-MSR) method for enhancing images was proposed, to lessen the effect of lighting on smoke images and improve the quality of the dataset. Secondly, a Hybrid Feature Extraction module, which integrates local and global information, was introduced to distinguish between targets and backgrounds, addressing smoke and fire detection in complex backgrounds. Furthermore, Dysample, a method utilizing point sampling, was designed to capture richer feature information when dealing with small targets. In addition, a feature fusion approach based on Context Gate Aggregation (CGA) was proposed to weightedly fuse low-level and high-level features, boosting the precision in detecting small targets. Finally, multi-task learning improves the capability to detect small targets and tackle complex scenarios by sharing the feature extraction module and leveraging refined supervision of the segmentation task. The findings from the experiments show that, in comparison to the baseline model, MTL-FSFDet improved the mAP@0.5 by 5.3%. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

14 pages, 1597 KiB

Open AccessArticle

Reproductive Ecology of Lecythis Pisonis in Brazilian Agroforestry Systems: Implications for Conservation and Genetic Diversity

by Zubaria Waqar, Acácia Brasil Rodrigues, Ciro Tavares Florence, Eduardo Mariano Neto and Fernanda Amato Gaiotto

Forests 2025, 16(5), 718; https://doi.org/10.3390/f16050718 - 23 Apr 2025

Abstract

Agroforestry systems are essential in sustainable land use in the face of the growing global food demand and climate change. The southern region of Bahia, Brazil, is one of the places in the world where the tree species is particularly in abundance, primarily [...] Read more.

Agroforestry systems are essential in sustainable land use in the face of the growing global food demand and climate change. The southern region of Bahia, Brazil, is one of the places in the world where the tree species is particularly in abundance, primarily in cocoa agroforestry systems, contributing to biodiversity conservation. Understanding their reproductive patterns is crucial for the survival and sustainability of these trees. This study dealt with Lecythis pisonis (Sapucaia) trees by applying microsatellite markers for mixed-mating mode and paternity analyses for pollen dispersal. In particular, it was found that Lecythis pisonis offspring are produced through outcrossing, as the case may be, while random crossings and no nearby tree fertilization are the remaining factors that play a crucial role in myriad genetic diversity inversions. This phenomenon was indicated by paternity in nine offspring, with full siblings being from the same parents. The average distance of pollen flow was 6 km, which is why the pollinator, the bee Xylocopa frontalis, has a flight range aligning with distance. These data show the influence of habitat fragmentation, the function of Cabruca, and the conservation strategy. Full article

(This article belongs to the Special Issue Genetic Diversity of Forest: Insights on Conservation)

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21 pages, 6600 KiB

Open AccessArticle

Validation of Satellite-Derived Green Canopy Cover in Rubber Plantations Using UAV and Ground Observations for Monitoring Leaf Fall Dynamics

by Masita Dwi Mandini Manessa, Anisya Feby Efriana, Farida Ayu, Fajar Dwi Pamungkas, Charlos Togi Stevanus, Tri Rapani Febbiyanti, Iqbal Putut Ash Shidiq, Rokhmatulloh Rokhmatulloh, Supriatna Supriatna, Retno Lestari, Kiwamu Kase, Minami Matsui, Abdul Azis As Sajjad, Dewo Mustiko Aji, Ariq Anggaraksa Riesnandar, Geraldo Nazar Prakarsa, Rakyan Paksi Nagara, Kuncoro Adi Pradono and Ramanatalia Parhusip

Forests 2025, 16(5), 717; https://doi.org/10.3390/f16050717 - 23 Apr 2025

Abstract

Accurate estimation of green canopy cover (GCC) in rubber plantations is crucial for monitoring vegetation health and assessing stress impacts. This study validates satellite-derived GCC estimates using unmanned aerial vehicle (UAV)-based remote sensing, ground observations, spaceborne remote sensing (satellite imagery), and supervised machine [...] Read more.

Accurate estimation of green canopy cover (GCC) in rubber plantations is crucial for monitoring vegetation health and assessing stress impacts. This study validates satellite-derived GCC estimates using unmanned aerial vehicle (UAV)-based remote sensing, ground observations, spaceborne remote sensing (satellite imagery), and supervised machine learning regression approaches. Sentinel-2 and Landsat imagery were utilized to derive spectral vegetation indices (SVIs) under varying stress conditions, while UAV-based GCC assessments provided high-resolution reference data for validation. The findings revealed that while certain SVIs exhibited strong correlations with canopy density under stable conditions, their predictive accuracy declined significantly during extreme stress events, such as Pestalotiopsis outbreaks and seasonal leaf fall periods. To improve estimation accuracy, supervised machine learning regression models were developed, with Random Forest (RF) outperforming Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), and Linear Regression (LR). RF achieved the highest predictive accuracy (R² = 0.82, RMSE = 6.48, MAE = 4.97), demonstrating its reliability in capturing non-linear interactions between canopy heterogeneity and environmental stressors. These results highlight the limitations of traditional vegetation indices and emphasize the importance of multi-sensor integration and advanced modeling techniques for more precise GCC monitoring. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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22 pages, 3961 KiB

Open AccessArticle

Predicting Glossiness of Heat-Treated Wood Using the Back Propagation Neural Network Optimized by the Improved Whale Optimization Algorithm

by Ying Cao, Wei Wang and Yan He

Forests 2025, 16(5), 716; https://doi.org/10.3390/f16050716 - 23 Apr 2025

Abstract

The properties of wood change after heat treatment, affecting its applications. Glossiness, a key aesthetic property, is of great significance in fields like furniture. Precise prediction can optimize the process and improve product quality. Although the traditional back propagation neural network (BPNN) has [...] Read more.

The properties of wood change after heat treatment, affecting its applications. Glossiness, a key aesthetic property, is of great significance in fields like furniture. Precise prediction can optimize the process and improve product quality. Although the traditional back propagation neural network (BPNN) has been applied in the field of wood properties, it still has issues such as poor prediction accuracy. This study proposes an improved whale optimization algorithm (IWOA) to optimize BPNN, constructing an IWOA-BPNN model for predicting the glossiness of heat-treated wood. IWOA uses chaos theory and tent chaos mapping to accelerate convergence, combines with the sine cosine algorithm to enhance optimization, and adopts an adaptive inertia weight to balance search and exploitation. A dataset containing 216 data entries from four different wood species was collected. Through model comparison, the IWOA-BPNN model showed significant advantages. Compared with the traditional BPNN model, the mean absolute error (MAE) value decreased by 66.02%, the mean absolute percentage error (MAPE) value decreased by 64.21%, the root mean square error (RMSE) value decreased by 69.60%, and the R² value increased by 12.87%. This model provides an efficient method for optimizing wood heat treatment processes and promotes the development of the wood industry. Full article

(This article belongs to the Special Issue Wood Properties: Measurement, Modeling, and Future Needs)

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21 pages, 3111 KiB

Open AccessArticle

Unraveling the Spatial Dynamics and Global Climate Change Response of Prominent Tropical Tree Species in Asia: Symplocos cochinchinensis and Beyond

by Haijun Li, Lihao Guo, Jingrui Zhang, Suile Li and Bo Liu

Forests 2025, 16(5), 715; https://doi.org/10.3390/f16050715 - 23 Apr 2025

Abstract

The tropical tree species Symplocos cochinchinensis plays a crucial role in ecological restoration and serves as a resource for traditional medicine, dyeing, and timber production. Assessing its distribution patterns and adaptive responses to global climate change is essential for maintaining ecosystems and developing [...] Read more.

The tropical tree species Symplocos cochinchinensis plays a crucial role in ecological restoration and serves as a resource for traditional medicine, dyeing, and timber production. Assessing its distribution patterns and adaptive responses to global climate change is essential for maintaining ecosystems and developing conservation strategies. This study elucidates the spatial distribution patterns and projects potential geographic shifts of the widely distributed tropical species S. cochinchinensis under climate change scenarios. A compilation of data from global and local herbaria and databases yielded 5050 occurrence records, covering the majority of its native range in the tropics and subtropics. We modeled the species’ potential habitats using the maximum entropy (MaxEnt) principle for current, 2050, and 2070 climate scenarios under high-emission SSP585. Our analysis reveals that sampling bias substantially influences the observed distribution patterns of S. cochinchinensis. Predictions indicate a decrease in barely suitable habitats and an increase in areas deemed highly suitable, suggesting climate change stress and an ecological niche shift towards areas with favorable microclimates with “Precipitation of Wettest Month” (Bio 13) and “Mean Temperature of Wettest Quarter” (Bio 8). Our findings reveal S. cochinchinensis’s adaptive resilience, offering valuable insights for developing strategies and conservation management in Southeast Asia, as well as a reference for the response of other common tropical species to climate change. Full article

(This article belongs to the Section Forest Meteorology and Climate Change)

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18 pages, 4605 KiB

Open AccessArticle

Unveiling Key Factors Shaping Forest Interest and Visits: Toward Effective Strategies for Sustainable Forest Use

by Kimisato Oda, Kazushige Yamaki, Asako Miyamoto, Keita Otsuka, Shoma Jingu, Yuichiro Hirano, Mariko Inoue, Toshiya Matsuura, Kazuhiko Saito and Norimasa Takayama

Forests 2025, 16(5), 714; https://doi.org/10.3390/f16050714 - 23 Apr 2025

Abstract

This study investigates the factors influencing urban residents’ interest in and visits to forests and explores strategies to promote forest space utilization. A survey was conducted among 5000 residents of Tokyo’s 23 wards, one of the world’s most densely populated urban areas, using [...] Read more.

This study investigates the factors influencing urban residents’ interest in and visits to forests and explores strategies to promote forest space utilization. A survey was conducted among 5000 residents of Tokyo’s 23 wards, one of the world’s most densely populated urban areas, using an online questionnaire. The collected data were analyzed using least absolute shrinkage, selection operator (LASSO) logistic regression, and piecewise structural equation modeling (pSEM). The analysis revealed that nature experiences in current travel destinations, particularly scenic walks, had a significant positive effect on both forest interest (standardized path coefficient = 0.19) and forest visits (0.30). These experiences were also significantly influenced by childhood nature experiences and frequent local walks. Conversely, factors negatively affecting forest visits included the lack of private vehicle ownership (−0.13) and increasing age (−0.21). While previous studies suggest that older individuals tend to visit natural areas more frequently, our findings indicate the opposite trend. One possible explanation is the low car ownership rate among Tokyo residents, which may limit accessibility to forests. These findings provide valuable insights for policy design, particularly regarding strategies to enhance forest accessibility and engagement among urban populations. Full article

(This article belongs to the Special Issue Multiple-Use and Ecosystem Services of Forests—2nd Edition)

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32 pages, 6516 KiB

Open AccessSystematic Review

Evidence on the Social, Economic, and Environmental Impact of Interventions That Facilitate Bamboo Industry Development for Sustainable Livelihoods: A Systematic Map

by Lucy Binfield, Tamara L. Britton, Chunping Dai and John L. Innes

Forests 2025, 16(5), 713; https://doi.org/10.3390/f16050713 - 22 Apr 2025

Abstract

Bamboo’s perceived potential in livelihood development has led to development interventions that aim to strengthen the bamboo industry via activities such as training participants in bamboo management, strengthening institutions, and raising awareness. Using the Campaign for Environmental Evidence’s guidelines, we systematically map the [...] Read more.

Bamboo’s perceived potential in livelihood development has led to development interventions that aim to strengthen the bamboo industry via activities such as training participants in bamboo management, strengthening institutions, and raising awareness. Using the Campaign for Environmental Evidence’s guidelines, we systematically map the available evidence of the impact of these interventions. The evidence is scattered across peer-reviewed and grey literature, with no universal reporting standards. Search sources for this systematic evidence map include a bibliographic database, CABdirect (now known as CABI Digital Library); a search platform for peer-reviewed literature, the Web of Science Core Collection; a bibliographic database for academic literature on agriculture and related fields, SEARCH by the USDA National Agricultural Library; a public search engine for scholarly literature, Google Scholar; a general search engine, Google; and the websites of 37 organizations, with both proprietary search engines and Google used to search for pdf files. Overall, 36 documents are included in the final review, describing 28 unique interventions from 13 countries. Most evidence is found outside the peer-reviewed literature. Outcomes including income changes, increased participation and engagement, and policy changes are reported, with economic impacts dominating the evidence base. Very little evidence of negative outcomes is found, likely constrained by reporting bias. Reporting on evidence of these interventions is limited, with many interventions being excluded from the database due to a lack of identifiable evidence of outcomes or impact. Full article

(This article belongs to the Section Forest Economics, Policy, and Social Science)

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23 pages, 4503 KiB

Open AccessArticle

Moisture-Dependent Transverse Isotropic Elastic Constants of Wood S2 Secondary Cell Wall Layers Determined Using Nanoindentation

by Xavier Arzola-Villegas, Nayomi Z. Plaza, Nathan J. Bechle, Yikai Wang, Roderic Lakes, Donald S. Stone and Joseph E. Jakes

Forests 2025, 16(5), 712; https://doi.org/10.3390/f16050712 - 22 Apr 2025

Abstract

Moisture- and orientation-dependent mechanical properties of the S2 secondary cell wall layer are needed to better understand wood mechanical properties and advance wood utilization. In this work, nanoindentation was used to assess the orientation-dependent elastic moduli and Meyer hardness of the loblolly pine [...] Read more.

Moisture- and orientation-dependent mechanical properties of the S2 secondary cell wall layer are needed to better understand wood mechanical properties and advance wood utilization. In this work, nanoindentation was used to assess the orientation-dependent elastic moduli and Meyer hardness of the loblolly pine (Pinus taeda) S2 layer under environmental conditions ranging from 0% to 94% relative humidity (RH). The elastic moduli were fit to a theoretical transverse isotropic elasticity model to calculate the longitudinal elastic modulus, transverse elastic modulus, axial shear modulus, and transverse shear modulus for the S2 layer at 0%, 33%, 75%, and 94% RH and 26 °C. The longitudinal elastic modulus was consistently higher than the transverse elastic modulus because of the orientation of the stiff cellulose microfibrils in the S2 layer. The axial shear modulus was consistently higher than the transverse shear modulus. The Meyer hardness had a much smaller orientation dependence than the elastic properties. Moisture generally plasticized the S2 layer. Over the range of RH tested, the longitudinal elastic modulus decreased by 30%, the transverse elastic modulus and transverse shear modulus decreased by 83%, the axial shear modulus did not have an observable trend with RH, and the hardness decreased by 68% to 82% with the hardness in the longitudinal direction softening less than in the transverse direction. Full article

(This article belongs to the Special Issue Wood Quality and Mechanical Properties: 2nd Edition)

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21 pages, 5330 KiB

Open AccessArticle

The Allelopathic Effect of the Epiphytic Lichen Physcia alnophila on Biochemical and Growth Processes in the Tissues of Larix gmelinii in the Cryolithozone

by Igor V. Sleptsov, Sakhaiana M. Rozhina, Ilya A. Prokopiev, Vladislav V. Mikhailov, Anna A. Mestnikova, Kirill V. Alekseev, Zhanna O. Zholobova and Daria A. Frolova

Forests 2025, 16(5), 711; https://doi.org/10.3390/f16050711 - 22 Apr 2025

Abstract

Epiphytic lichens are integral to boreal forest ecosystems, yet their allelopathic interactions with host trees, particularly in cryolithozone regions, remain poorly understood. This study elucidates the physiological and biochemical impacts of the epiphytic lichen Physcia alnophila on Larix gmelinii (Gmelin larch), a keystone [...] Read more.

Epiphytic lichens are integral to boreal forest ecosystems, yet their allelopathic interactions with host trees, particularly in cryolithozone regions, remain poorly understood. This study elucidates the physiological and biochemical impacts of the epiphytic lichen Physcia alnophila on Larix gmelinii (Gmelin larch), a keystone species in Siberian permafrost forests. By combining dendrochronology, GC–MS metabolomic analysis, and HPLC–ESI–MS/MS analysis, we demonstrate that the lichen’s primary metabolite, atranorin (ATR), systemically migrates from thalli into the host’s cambium, roots, and needles, with root accumulation reaching 36.3 µg g⁻¹ DW. Lichen-colonized trees exhibited severe radial growth inhibition (27%–51% reduction over five years) and suppressed apical growth, despite comparable heights to controls, indicating chronic phytotoxicity. Metabolomic profiling revealed lichen-specific polyols (e.g., arabitol, mannitol) in larch tissues, alongside elevated stress biomarkers (terpenes, sterols, phenolic acids), and significant disruptions to the tricarboxylic acid cycle and oxidative phosphorylation. These metabolic perturbations correlate with reduced monosaccharide availability and impaired energy production, directly linking ATR translocation to growth suppression. L. gmelinii exhibited compensatory responses, including increased fatty acids and arabinogalactan synthesis, suggesting adaptive mechanisms to mitigate lichen-induced stress. Our findings suggest P. alnophila as a biotic stressor that affects tree physiology in extreme climates, with implications for boreal forest resilience. This work provides an insight to the rarely pointed out species interactions, which, when combined with climate change, may alter carbon cycling and forest dynamics in permafrost ecosystems. Full article

(This article belongs to the Section Forest Ecophysiology and Biology)

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16 pages, 13219 KiB

Open AccessArticle

Three-Dimensional Visualization of Major Anatomical Structural Features in Softwood

by Meng Ye, Shichao Zhao, Wanzhao Li and Jiangtao Shi

Forests 2025, 16(5), 710; https://doi.org/10.3390/f16050710 - 22 Apr 2025

Abstract

Wood displays three-dimensional characteristics at both macroscopic and microscopic scales. Accurately reconstructing its 3D structure is vital for a deeper understanding of the relationship between its anatomical characteristics and its physical and mechanical properties. This study aims to apply X-ray micro-computed tomography (XμCT) [...] Read more.

Wood displays three-dimensional characteristics at both macroscopic and microscopic scales. Accurately reconstructing its 3D structure is vital for a deeper understanding of the relationship between its anatomical characteristics and its physical and mechanical properties. This study aims to apply X-ray micro-computed tomography (XμCT) for the high-resolution, non-destructive visualization and quantification of softwood anatomical features. Six typical softwood species—Picea asperata, Cupressus funebris, Pinus koraiensis, Pinus massoniana, Cedrus deodara, and Pseudotsuga menziesii—were selected to represent a range of structural characteristics. The results show that a scanning resolution of 1–2 μm is suitable for investigating the transition from earlywood to latewood and resin canals, while a resolution of 0.5 μm is required for finer structures such as bordered pits, ray tracheids, and cross-field pits. In Pinus koraiensis, a direct 3D connection between radial and axial resin canals was observed, forming an interconnected resin network. In contrast, wood rays were found to be distributed near the surface of axial resin canals but without forming interconnected structures. The three-dimensional reconstruction of bordered pit pairs in Pinus massoniana and Picea asperata clearly revealed interspecific differences in pit morphology, distribution, and volume. The average surface area and volume of bordered pit pairs in Pinus massoniana were 1151.60 μm² and 1715.35 μm³, respectively, compared to 290.43 μm² and 311.87 μm³ in Picea asperata. Furthermore, XμCT imaging effectively captured the morphology and spatial distribution of cross-field pits across species, demonstrating its advantage in comprehensive anatomical deconstruction. These findings highlight the potential of XμCT as a powerful tool for 3D analysis of wood anatomy, providing deeper insight into the structural complexity and interconnectivity of wood. Full article

(This article belongs to the Special Issue Measurement and Enhancement of Wood Mechanical and Chemical Properties, 2nd Edition)

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17 pages, 19313 KiB

Open AccessArticle

Determining a Safe Distance Zone for Firefighters Using a High-Resolution Global Canopy Height Dataset—A Case in Türkiye

by Zennure Uçar

Forests 2025, 16(4), 709; https://doi.org/10.3390/f16040709 - 21 Apr 2025

Abstract

Safety zones protect firefighters from bodily injury and death caused by exposure to dangerous heat levels. These zones are defined by maintaining a safe distance from combustible fuels, a safe separation distance (SSD) derived from flame height. This study aimed to determine safety [...] Read more.

Safety zones protect firefighters from bodily injury and death caused by exposure to dangerous heat levels. These zones are defined by maintaining a safe distance from combustible fuels, a safe separation distance (SSD) derived from flame height. This study aimed to determine safety zones, integrating an existing automated identification-of-safety-zone model with vegetation height derived from a freely available high-resolution global canopy height dataset for Manavgat Forest Management Directorate (FMD) in Türkiye. Flame height, terrain slope, size of a safety zone, and distance to the closest road were also used as input in this model. The results indicated that vegetation height from high-resolution global canopy height offered promising results for determining potential safety zones (SZs) associated with SSD. Integrating the global canopy height dataset into the existing model could assist in determining the safety zone in the absence of lidar. Thus, this spatial model would provide a framework for decision-makers to develop fire prevention and suppression strategies for higher fire risk areas, especially before and during a fire. Full article

(This article belongs to the Special Issue Advancements in Forest Engineering Technologies and Sustainable Practices)

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13 pages, 4454 KiB

Open AccessArticle

Seasonal Water Use Patterns of Eucalyptus with Different Ages in Southern Subtropical China

by Haijun Zuo, Qing Xu, Deqiang Gao, Wenbin Xu, Ke Diao and Beibei Zhang

Forests 2025, 16(4), 708; https://doi.org/10.3390/f16040708 - 21 Apr 2025

Abstract

Seasonal droughts induced by climate change pose a significant threat to the normal growth patterns of forests in the subtropical regions of southern China. Therefore, it is crucial to explore the response of tree water use patterns to seasonal drought to maintain tree [...] Read more.

Seasonal droughts induced by climate change pose a significant threat to the normal growth patterns of forests in the subtropical regions of southern China. Therefore, it is crucial to explore the response of tree water use patterns to seasonal drought to maintain tree physiological activities. However, it remains unknown whether changes in dry and wet seasons have an impact on the water use patterns of trees of different ages. In this study, a two-year experiment was conducted in Eucalyptus urophylla × Eucalyptus grandis (hereinafter referred to as Eucalyptus) plantations at three ages (4, 7, and 17 years). Specifically, the water use patterns of Eucalyptus in dry and wet seasons were calculated using hydrogen stable isotopes (including the isotopes in xylem water and 0–150 cm soil layers) coupled with MixSIAR. The results showed that there were notable variations in the proportions of water absorption from different soil layers by Eucalyptus during dry and wet seasons. During the dry season (April 2024), 4-year-old and 7-year-old Eucalyptus primarily utilized water from the 40–90 cm soil layer, while 17-year-old Eucalyptus mainly relied on deep soil water at depths of 60–150 cm, with a utilization ratio of 50.9%. During the wet season (August 2023), the depth of water uptake by Eucalyptus of different ages significantly shifted towards shallow layers, and the trees primarily utilized surface soil water from the 0–60 cm layer, with utilization ratios of 59.9%, 64.8%, and 61.6% for 4-year-old, 7-year-old, and 17-year-old Eucalyptus, respectively. The water sources of Eucalyptus during dry and wet seasons were variable, which allowed Eucalyptus to cope with seasonal drought stress. The differences in the water uptake strategies of Eucalyptus between dry and wet seasons can be attributed to their long-term adaptation to the environment. Our research revealed the differences in the water utilization of Eucalyptus with various ages between dry and wet seasons in subtropical China, providing new insights for a better understanding of the adaptive mechanisms of subtropical forests in response to alterations in water conditions caused by climate change. Full article

(This article belongs to the Special Issue The Relationship Between Forest Vegetation and Water and Its Regulation in Changing Environments)

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25 pages, 21982 KiB

Open AccessArticle

Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images

by Dan Chen, Xianyun Fei, Jing Li, Zhen Wang, Yajun Gao, Xiaowei Shen and Dongmei He

Forests 2025, 16(4), 707; https://doi.org/10.3390/f16040707 - 21 Apr 2025

Abstract

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four [...] Read more.

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random Forest algorithm with a decision tree model, constructing a hierarchical strategy based on multi-temporal feature combinations to progressively address the challenge of distinguishing vegetation types with similar spectral characteristics. Compared to traditional single-temporal classification methods, our approach significantly enhances classification accuracy through multi-temporal feature fusion and comparative experimental validation, offering a novel technical framework for fine-grained vegetation classification under complex land cover conditions. To validate the effectiveness of multi-temporal features, we additionally performed Random Forest classifications on the four individual remote sensing images. The results indicate that (1) for single-temporal images classification, the best classification performance was achieved with autumn images, reaching an overall classification accuracy of 72.36%, while spring images had the worst performance, with an accuracy of only 58.79%; (2) the overall classification accuracy based on multi-temporal features reached 89.10%, which is an improvement of 16.74% compared to the best single-temporal classification (autumn). Notably, the producer accuracy for species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, Pinus thunbergii Parl., and Castanea mollissima Blume all exceeded 90%, indicating a relatively ideal classification outcome. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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18 pages, 5286 KiB

Open AccessArticle

Daily Variation of Soil Greenhouse Gas Fluxes in Rubber Plantations Under Different Levels of Organic Fertilizer Substitution

by Wangxin Zhang, Qingmian Chen, Hongyu Ran, Wen Lu, Wenxian Xu, Waqar Ali, Qiu Yang, Wenjie Liu, Mengyang Fang and Huai Yang

Forests 2025, 16(4), 706; https://doi.org/10.3390/f16040706 - 21 Apr 2025

Abstract

It has been widely recognized that replacing chemical fertilizers with organic fertilizers (organic substitution) could significantly increase the long-term productivity of the land and potentially enhance resilience to climate change. Nevertheless, there is limited information on the accurate monitoring of soil greenhouse gas [...] Read more.

It has been widely recognized that replacing chemical fertilizers with organic fertilizers (organic substitution) could significantly increase the long-term productivity of the land and potentially enhance resilience to climate change. Nevertheless, there is limited information on the accurate monitoring of soil greenhouse gas (GHG) fluxes at different levels of organic substitution in rubber plantations. Before accurate estimation of soil GHG fluxes can be made, it is important to investigate diurnal variations and suitable sampling times. In this study, six treatment groups of rubber plantations in the Longjiang Farm of Baisha Li autonomous county, Hainan Island, including the control (CK), conventional fertilizer (NPK), and organic substitution treatments in which organic fertilizer replaced 25% (25%M), 50% (50%M), 75% (75%M), and 100% (100%M) of chemical nitrogen fertilizer were selected as study objectives. The soil GHG fluxes were observed by static chamber-gas chromatography for a whole day (24 h) during both wet and dry seasons. The results showed the following: (1) There was a significant single-peak daily variation of GHGs in rubber plantation soils. (2) The soil GHG fluxes observed from 9:00–12:00 are closer to the daily average fluxes. (3) Organic fertilizer substitution influenced soil CO₂ and N₂O fluxes and had no significant effect on soil CH₄ fluxes. Fluxes of soil CO₂ and N₂O increased firstly and then decreased gradually when the substitution ratios exceeded 50% or 75%. (4) Soil CO₂ and N₂O fluxes were positively correlated with soil temperature and soil moisture, and CH₄ fluxes were negatively correlated with soil temperature and soil moisture in both wet and dry seasons. The study indicated that understanding the daily pattern of GHG changes in rubber forest soils under different levels of organic fertilizer substitution and the optimal observation time could improve the accurate assessment of long-timescale observation studies. Full article

(This article belongs to the Section Forest Soil)

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16 pages, 6825 KiB

Open AccessArticle

Phylogenomics and Floristic Origin of Endiandra R.Br (Lauraceae) from New Caledonia

by Jiayi Song, Chengyan Shao, Zhi Yang and Yong Yang

Forests 2025, 16(4), 705; https://doi.org/10.3390/f16040705 - 20 Apr 2025

Abstract

New Caledonia is a biodiversity hotspot with flora closely related to that of Australia and has received considerable attention. Endiandra (Cryptocaryeae; Lauraceae) is distributed from tropical Asia to Oceania, including New Caledonia, with northeastern Australia and New Guinea as diversity centers, but the [...] Read more.

New Caledonia is a biodiversity hotspot with flora closely related to that of Australia and has received considerable attention. Endiandra (Cryptocaryeae; Lauraceae) is distributed from tropical Asia to Oceania, including New Caledonia, with northeastern Australia and New Guinea as diversity centers, but the genus in New Caledonia remains understudied. Here, four species of Endiandra native to New Caledonia were sequenced, and their complete plastome sequences were analyzed. A plastome-based phylogenomic tree of Cryptocaryeae was reconstructed, and divergence times were estimated. The phylogenomic tree supports the monophyly of Endiandra. Interestingly, the species of Endiandra from New Caledonia were grouped into two separate subclades, with one subclade including three species and the other subclade containing only one species. The stem and crown ages of the first subclade were 33.18 Ma and 14.5 Ma, respectively, and the second subclade diverged by approximately 10.36 Ma. The structural characteristics of the newly sequenced plastomes were compared with those of Beilschmiedia species from different continents. The results indicate that the plastome sequences of the four species of Endiandra are longer than those of Beilschmiedia. Additionally, Endiandra has more simple sequence repeats (SSRs) than Beilschmiedia, though the difference is slight. The Guanine-Cytosine (GC) content of Endiandra was lower than that of Beilschmiedia. Five highly variable regions were identified, including matK-rps16, ycf1, petA-psbJ, petN-psbM, and ndhF. The Endiandra species in New Caledonia originated through long-distance dispersal followed by local divergence, rather than vicariance. Additionally, we identified at least two instances of floristic exchange between New Caledonia and Australia. Our study provides further evidence for understanding the biogeographic history between these two regions. Full article

(This article belongs to the Special Issue Forest Tree Breeding: Genomics and Molecular Biology)

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45 pages, 2074 KiB

Open AccessReview

Advancements in Artificial Intelligence Applications for Forest Fire Prediction

by Hui Liu, Lifu Shu, Xiaodong Liu, Pengle Cheng, Mingyu Wang and Ying Huang

Forests 2025, 16(4), 704; https://doi.org/10.3390/f16040704 - 19 Apr 2025

Abstract

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, [...] Read more.

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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17 pages, 12380 KiB

Open AccessArticle

Research on the Construction of Health Risk Assessment Model for Ancient Banyan Trees (Ficus microcarpa) in Fuzhou City

by Huibin Liu, Wenjian Xu, Yangbin Yu, Xinrui Wang, Wenhao Liu, Zuxing Wei, Lingyan Chen and Donghui Peng

Forests 2025, 16(4), 703; https://doi.org/10.3390/f16040703 - 19 Apr 2025

Abstract

Constructing a scientific health risk assessment system for ancient trees is crucial for preserving cultural heritage and tree resources. As Fuzhou’s city tree, ancient banyan trees (Ficus microcarpa) with expansive canopies and aerial roots have shaped local ecology and history over [...] Read more.

Constructing a scientific health risk assessment system for ancient trees is crucial for preserving cultural heritage and tree resources. As Fuzhou’s city tree, ancient banyan trees (Ficus microcarpa) with expansive canopies and aerial roots have shaped local ecology and history over millennia. However, urbanization-induced habitat loss and structural vulnerabilities (e.g., root damage and branch injuries) increasingly threaten their health. Current generic tree evaluation standards inadequately address banyan trees’ unique aerial root physiology. This study developed a tailored assessment model using 140 ancient banyan trees from Fuzhou’s urban core and Minhou County. The researchers analyzed 12 tree health indicators (crown, trunk, visible roots, etc.) and two environmental factors through structural equation modeling (SEM) and cluster analysis. Key findings: (1) The SEM demonstrated strong data fit (CMIN/DF = 1.575, RMSEA = 0.064, TLI = 0.927, and CFI = 0.945), validating model reliability. (2) Mechanical damage to the visible root system (weight = 0.135) most significantly impacted health, while canopy closure (0.036) and crown saturation (0.034) showed minimal effects. (3) The site environment strongly correlated with trunk and visible root system health but not crown conditions. (4) In total, 60.71% of the sampled trees were healthy/sub-healthy, while 39.29% exhibited poor health. This methodology provides a replicable framework for ancient tree conservation, emphasizing species-specific evaluation criteria and environmental management strategies. The weighted indicator system enables precise health diagnostics and prioritized protection measures for vulnerable heritage trees. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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