Next Issue
Volume 17, March
Previous Issue
Volume 17, January
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Forests
Volume 17
Issue 2
share announcement

Forests, Volume 17, Issue 2 (February 2026) – 139 articles

Cover Story (view full-size image): The aim of this research was to investigate the extractive content of bark beetle-attacked and dead wind-damaged Norway spruce trees relative to healthy trees, in order to assess their potential for extractives recovery. After harvesting, three discs were dissected along the stem height of each tree, and samples of sapwood, heartwood, knots, and bark were collected. Sequential extraction of the samples was performed using cyclohexane and acetone–water mixture in an accelerated solvent extractor. Lipophilic and hydrophilic extractives were determined gravimetrically, while total phenols and proanthocyanidins were measured by UV–Vis spectrophotometry. Results showed that knotwood contained the highest amounts of hydrophilic extractives and total phenols among investigated tissues. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
19 pages, 2762 KB  
Article
Assessing Spring Phenology Models with Photosynthesis Integration: Mechanistic Drivers of the Carbon–Frost Trade-Off
by Yating Gu, Qianhan Wu, Xiaorong Wang and Yantian Wang
Forests 2026, 17(2), 287; https://doi.org/10.3390/f17020287 - 23 Feb 2026
Viewed by 378
Abstract
Accurate prediction of spring phenology is critical for understanding ecosystem carbon and water dynamics under changing climates. In this study, we applied a revised optimality-based model (R-OPT) that integrates a mechanistic photosynthesis framework into the existing OPT model to simulate leaf unfolding date. [...] Read more.
Accurate prediction of spring phenology is critical for understanding ecosystem carbon and water dynamics under changing climates. In this study, we applied a revised optimality-based model (R-OPT) that integrates a mechanistic photosynthesis framework into the existing OPT model to simulate leaf unfolding date. We evaluated R-OPT alongside three widely used models—Growing Degree Days (GDD), Chilling–Forcing Trade-off (CFT), and Optimality-based (OPT) models—across multiple Plant Functional Types (PFTs) and sites using repeated 5-fold cross-validation. Findings reveal that R-OPT consistently outperforms the other models, achieving the lowest median RMSE (13.11 days), indicating enhanced predictive accuracy and explanatory power. Although the model incurs slightly higher complexity (median AIC = 13.44), the improvement in prediction justifies the trade-off. Our results highlight the importance of incorporating plant functional traits and environmental heterogeneity in phenological modeling. PFT-specific differences, such as the lower RMSEs for evergreen forbs and deciduous broadleaf PFTs versus larger uncertainties for drought-deciduous and semi-evergreen PFTs, underscore that current models may insufficiently capture key environmental drivers, including precipitation and partial leaf retention. Latitudinal and elevational variations in trade-off parameter a, and the prominence of leaf-level carbon assimilation traits (Aleaf) as drivers of phenology, demonstrate the critical role of physiological traits in shaping PFT-specific phenological timing. These findings have significant implications for large-scale ecosystem modeling. By linking phenology directly to photosynthetic processes, R-OPT enhances predictive skill and biological interpretability, supporting improved simulations of carbon and water fluxes. Overall, R-OPT offers a mechanistically grounded and robust framework for advancing predictive understanding of spring phenology and its ecological and climate-relevant consequences. Full article
(This article belongs to the Special Issue Climate Change and Forest Phenology: Ecophysiological Mechanisms and Impacts)
►▼ Show Figures

Figure 1

23 pages, 4270 KB  
Review
X-Ray Computed Microtomography and Investigations of Wood Structure and the Vascular Cambium
by David A. Collings and Ichirou Karahara
Forests 2026, 17(2), 286; https://doi.org/10.3390/f17020286 - 23 Feb 2026
Viewed by 581
Abstract
X-ray computed microtomography (µCT) provides an important complement to optical imaging for understanding the three-dimensional (3D) organization and function of xylem and wood. Unlike conventional sectioning, µCT is a non-destructive process that produces high-quality data sets that can be rotated, resliced and, following [...] Read more.
X-ray computed microtomography (µCT) provides an important complement to optical imaging for understanding the three-dimensional (3D) organization and function of xylem and wood. Unlike conventional sectioning, µCT is a non-destructive process that produces high-quality data sets that can be rotated, resliced and, following image segmentation, quantified. We highlight examples in which quantitative processing of 3D µCT sets has provided quantitative understanding of xylem and wood including the development and refilling of xylem embolisms, tree ring analyses and the development of interlocked grain. We also highlight two ways through which the µCT imaging of wood, and plants in general, will be improved. While the current staining protocols for plants are non-specific, developments in specific labeling techniques, including modifications of traditional electron microscopy stains for cell walls and recent developments in µCT imaging in non-plant specimens for studying antibody labeling and transgenes, should allow significant improvements in the imaging of xylem and wood by µCT. We also highlight machine learning which is already facilitating improvements in image segmentation and quantification of µCT data sets. When coupled with the recent advances in molecular genetics of the vascular cambium, these improvements in µCT should dramatically increase our understanding of xylem formation. Full article
(This article belongs to the Special Issue The Functional Anatomy of Tree Xylem: From Cell Structure to Ecological Roles)
►▼ Show Figures

Figure 1

17 pages, 1651 KB  
Article
Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea
by Han-Na Seo, Jae-Hyun Park, Ji-Young Ahn and Hyo-In Lim
Forests 2026, 17(2), 285; https://doi.org/10.3390/f17020285 - 23 Feb 2026
Viewed by 418
Abstract
Taxus cuspidata is a threatened subalpine conifer in South Korea, necessitating evidence-based restoration strategies to counter the impacts of climate change. In this study, we assessed 13 natural populations using 15 polymorphic nuclear simple sequence repeat (nSSR) markers developed in Taxus species and [...] Read more.
Taxus cuspidata is a threatened subalpine conifer in South Korea, necessitating evidence-based restoration strategies to counter the impacts of climate change. In this study, we assessed 13 natural populations using 15 polymorphic nuclear simple sequence repeat (nSSR) markers developed in Taxus species and spatial autocorrelation analysis to provide a scientific foundation for conservation. The results showed an intermediate level of genetic diversity, with the Mt. Gariwangsan population exhibiting higher diversity. This highlights its priority as a source for restoration materials. Bayesian clustering supported four distinct management units. Spatial autocorrelation analysis revealed significant positive genetic structure within approximately 50 m, indicating a localized genetic patch size. Based on these results, we suggest maintaining a minimum 50 m sampling distance during seed collection to avoid collecting closely related individuals and to reduce the risk of genetic homogeneity in restoration materials. Such restoration strategies informed by spatial genetic structure and broader genetic data are critical for enhancing the long-term resilience of T. cuspidata in the face of accelerating environmental shifts. Full article
(This article belongs to the Special Issue Population Genetic Diversity and Conservation in Forests)
►▼ Show Figures

Figure 1

18 pages, 1994 KB  
Article
Bending Performance of Thermo-Hydro-Mechanically Densified Poplar Wood: Effects of Ultrasonic Pretreatment and Thermal Posttreatment at Different Compression Ratios
by Marko Veizović, Nebojša Todorović, Aleš Straže and Goran Milić
Forests 2026, 17(2), 284; https://doi.org/10.3390/f17020284 - 22 Feb 2026
Viewed by 334
Abstract
Thermo-hydro-mechanical (THM) densification is an effective method for improving the mechanical performance of low-density, fast-growing hardwoods such as poplar. This study examined the bending performance of THM-densified poplar wood at different compression ratios (CR = 0%, 50%, 60%, and 65%), with emphasis on [...] Read more.
Thermo-hydro-mechanical (THM) densification is an effective method for improving the mechanical performance of low-density, fast-growing hardwoods such as poplar. This study examined the bending performance of THM-densified poplar wood at different compression ratios (CR = 0%, 50%, 60%, and 65%), with emphasis on the effects of ultrasonic pretreatment (US) and thermal modification posttreatment (TM), applied individually and in combination. A paired sampling design was used to reduce material variability, and modulus of rupture (MOR) and modulus of elasticity (MOE) were evaluated using linear mixed-effects models (LMM). Bending tests were performed in accordance with EN 310:1993. Increasing the compression ratio led to substantial increases in MOR and MOE; compared with non-densified specimens, MOR increased by approximately 240% and MOE by about 140% at CR = 65%, confirming densification as the dominant factor controlling bending performance. US did not affect non-densified wood but significantly enhanced MOR and MOE after densification, particularly at CR = 50%. In contrast, TM consistently reduced MOR and, to a lesser extent, MOE across all compression ratios. The results demonstrate that the bending performance of densified poplar wood is governed by both compression ratio and compression-dependent treatment effects. Full article
(This article belongs to the Special Issue Wood Modification: Functionalisation, Optimisation and Characterisation of Modified Timbers)
►▼ Show Figures

Graphical abstract

19 pages, 5765 KB  
Article
Effects of Forestry Waste Mulching on Plantation Soil Fertility, Enzyme Activities, and Microbial Communities in China
by Zhihui Fan, Yi Zheng, Jixin Cao, Xiangyang Sun and Suyan Li
Forests 2026, 17(2), 283; https://doi.org/10.3390/f17020283 - 21 Feb 2026
Viewed by 341
Abstract
The application of forestry waste as organic mulch on soil represents an increasingly recognized management practice. However, studies on how different mulching strategies regulate soil fertility and microbial community responses remain limited. In this study, a field experiment was conducted in plantation forest [...] Read more.
The application of forestry waste as organic mulch on soil represents an increasingly recognized management practice. However, studies on how different mulching strategies regulate soil fertility and microbial community responses remain limited. In this study, a field experiment was conducted in plantation forest soil with four treatments: no mulching, fresh forestry waste mulching, composted mulching, and layered mulching. The results indicated that the layered mulching treatment significantly increased the soil comprehensive fertility index by 6.67% relative to the no mulching treatment. Both composted mulching and layered mulching treatments significantly reduced soil bulk density (2.26%–5.26%), increased pH (0.36%–0.48%) and organic matter content (21.90%–25.23%), and markedly enhanced urease (22.45%–26.41%) and protease activities (51.72%–62.68%). Under fresh forestry waste mulching, soil available phosphorus and available potassium increased by 23.21% and 27.07%, respectively, whereas improvements in the soil comprehensive fertility index, enzyme activities, and microbial communities were limited. Bacterial communities were highly responsive to mulching treatments, with composted mulching and layered mulching treatments significantly altering their structure, while fungal communities were comparatively stable across treatments. RDA and Mantel tests linked bacterial shifts mainly to total nitrogen, available potassium, and bulk density, and fungal variation mainly to total nitrogen (all p < 0.05). This study indicates that a layered mulching strategy simulating forest litter layers can enhance soil fertility and enzyme activity and provides an option for improving soil quality through the utilization of forestry waste. Full article
(This article belongs to the Special Issue Soil–Plant–Microbe Interactions in Forest Ecosystems)
►▼ Show Figures

Figure 1

17 pages, 3832 KB  
Article
Influence of Soil Fertility and Tree Characteristics on Heartwood and Specific Gravity in Dalbergia retusa and Platymiscium curuense Wood from Plantations in Costa Rica
by Roger Moya, Carolina Tenorio, Ricardo Lujan and José Corrales
Forests 2026, 17(2), 282; https://doi.org/10.3390/f17020282 - 21 Feb 2026
Viewed by 360
Abstract
Heartwood proportion (HWP) and specific gravity (SG) are two important properties of Dalbergia retusa and Platymiscium curuense wood, which is considered to be of high value. The objective of this study was to establish which morphological and soil fertility parameters present the greatest [...] Read more.
Heartwood proportion (HWP) and specific gravity (SG) are two important properties of Dalbergia retusa and Platymiscium curuense wood, which is considered to be of high value. The objective of this study was to establish which morphological and soil fertility parameters present the greatest influence on HWP and SG. For this, increment cores were extracted, and soil samples were collected. The results showed that D. retusa presented a lower HWP (22.65%) than P. curuense (28.75%), and D. retusa averaged a higher value (0.87) than P. curuense (0.63). The forward stepwise regression analysis for D. retusa showed that the magnesium content was the most important factor for SG, while for the HWP, the potassium content was the most important, followed by diameter at breast height (DBH). SG was most strongly influenced by total height in P. curuense, and HWP was most strongly influenced by DBH. Additional notable results showed that the SG of D. retusa was primarily determined by soil fertility conditions, whereas the SG of P. curuense was more strongly influenced by tree morphology. Meanwhile, the HWP in both species was mainly affected by DBH and total height, and to a lesser extent by soil fertility conditions. These results show that plantation management should be focused on trees with large diameters and HWP, since soil conditions demonstrated little effect on this property. Full article
(This article belongs to the Special Issue Tree Growth: Insights from Studies in Soil Nutrients)
►▼ Show Figures

Figure 1

21 pages, 2424 KB  
Article
Spatial Prediction of Forest Fire Occurrence Integrating Human Proximity: A Machine Learning Approach for Korea’s Eastern Coast
by Jeman Lee, Sujung Ahn and Sangjun Im
Forests 2026, 17(2), 281; https://doi.org/10.3390/f17020281 - 21 Feb 2026
Cited by 1 | Viewed by 325
Abstract
Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses [...] Read more.
Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
►▼ Show Figures

Figure 1

18 pages, 3969 KB  
Article
Effects of Industry-Inspired Storage Conditions on the Contents of Hydrophilic Extractives and Polyphenols in Silver Fir (Abies alba Mill.) Bark
by Peter Hrovatič, Urša Osolnik, Tomislav Levanič, Primož Oven, Ida Poljanšek and Viljem Vek
Forests 2026, 17(2), 280; https://doi.org/10.3390/f17020280 - 19 Feb 2026
Viewed by 396
Abstract
Silver fir bark (Abies alba Mill.) is an underutilized renewable resource containing valuable extractives and polyphenols of industrial importance. This study compared the influence of two storage methods on the extraction of total hydrophilic extractives content (TEC) and total polyphenols content (TPC) [...] Read more.
Silver fir bark (Abies alba Mill.) is an underutilized renewable resource containing valuable extractives and polyphenols of industrial importance. This study compared the influence of two storage methods on the extraction of total hydrophilic extractives content (TEC) and total polyphenols content (TPC) from silver fir bark samples. Bark samples were collected from two storage types: bark left on stem sections and stored under cover (B-D), and mechanically removed industrial bark stored outdoors (B-IS), over a 12-month period with monthly sampling and extraction, followed by measurements of TEC and TPC using gravimetric and spectrophotometric methods. B-D samples showed no statistically significant decrease in TEC or TPC during one year of storage, while B-IS samples exhibited substantial losses, with TEC decreasing by more than half (50.82%) and TPC by 65.68%, most rapidly within the first 3 months when precipitation-driven leaching and degradation processes were obviously most pronounced. These results demonstrate that bark removed before storage is much more susceptible to degradation and leaching of the hydrophilic extractives than bark retained on logs, confirming that mechanical disintegration and exposure to weathering accelerate the loss of valuable extractives and polyphenols. A strong TEC–TPC correlation (r = 0.67–0.81, p < 0.0001) provides a practical methodological approach for rapid biomass quality screening. Overall, the findings offer quantitative guidance for optimizing debarking timing and storage practices to preserve extractive yield and enhance the efficiency of bark-based biorefinery processes. Full article
(This article belongs to the Special Issue Tree and Shrub Bark: Management, Sustainable Production, and Opportunities in the Forest Bioeconomy)
►▼ Show Figures

Figure 1

20 pages, 2430 KB  
Article
Real-Time IoT-Enabled Decision Support for Forest Supply Chains: An Optimization-Simulation Approach to Mitigating Wildfire Risk
by Reinaldo Gomes, Bernardine Chigozie Chidozie, João C. O. Matias and Ruxanda Godina Silva
Forests 2026, 17(2), 279; https://doi.org/10.3390/f17020279 - 19 Feb 2026
Viewed by 358
Abstract
Climate change has intensified wildfire risk, creating an urgent need for integrated, data-driven tools that connect forest operations with fuel-reduction strategies. This paper introduces a real-time IoT-enabled Decision Support System (DSS) that unifies wood traceability with optimization–simulation planning for biomass collection and processing. [...] Read more.
Climate change has intensified wildfire risk, creating an urgent need for integrated, data-driven tools that connect forest operations with fuel-reduction strategies. This paper introduces a real-time IoT-enabled Decision Support System (DSS) that unifies wood traceability with optimization–simulation planning for biomass collection and processing. The system captures detailed operational data from harvesting, transportation, and processing through IoT devices and industry formats, enabling the continuous monitoring of wood flows and precise estimation of biomass residues that directly contribute to wildfire fuel loads. The DSS transforms these real-time streams into actionable planning outputs through an optimization–simulation module that generates efficient biomass harvesting and processing schedules while evaluating their robustness under wildfire-related constraints. By linking wood traceability with biomass logistics, the system provides the missing operational bridge between forest management decisions and wildfire-risk mitigation. Results show that the DSS not only improves operational efficiency but also enhances resilience by supporting risk-aware planning, prioritizing high-exposure areas, and reducing the accumulation of hazardous biomass. These insights demonstrate how digital traceability and robust planning can work together to lower ignition potential while maintaining service levels and operational continuity. Overall, this work presents a practical and scalable solution that strengthens forest supply chain resilience and provides a new pathway for integrating wildfire-risk mitigation into everyday operational planning. Full article
(This article belongs to the Special Issue Managing Forest Wildfires in Climate Changes: New Paradigms and Challenges—2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 12611 KB  
Article
Projecting the Potential Shift of Larix principis-rupprechtii in Response to Future Climate Change: A Regional Analysis of the Haihe Basin in Northern China
by Desheng Cai, Shengping Wang, Wenxin Li, Kewen Wang, Guoping Zhu, Zhiqiang Zhang, Siyi Qu and Yiyao Liu
Forests 2026, 17(2), 278; https://doi.org/10.3390/f17020278 - 19 Feb 2026
Viewed by 312
Abstract
Projections of species distribution shifts induced by climate change are essential for adaptive management, yet regional-scale projections that explicitly address uncertainty remain underexplored. Future habitat suitability for Larix principis-rupprechtii in the Haihe Basin is projected using ensemble MaxEnt analysis driven by 13 CMIP6 [...] Read more.
Projections of species distribution shifts induced by climate change are essential for adaptive management, yet regional-scale projections that explicitly address uncertainty remain underexplored. Future habitat suitability for Larix principis-rupprechtii in the Haihe Basin is projected using ensemble MaxEnt analysis driven by 13 CMIP6 climate simulations under contrasting emission scenarios (SSP1-2.6 and SSP5-8.5). The MaxEnt demonstrates strong performance, with a mean AUC of 0.874. Future scenarios show that climatically favorable habitat for larch expands by over 20% and shifts approximately 42 km southwestward relative to the baseline, while high-suitability areas increase by 109%–181%. However, substantial uncertainty, quantified by the coefficient of variation (CV), persists in the low-suitability areas and intensifies with longer time horizons and higher emission pathways. Crucially, local topographic heterogeneity (elevation, slope, and shallow soil moisture) explains over 84% of the distribution variance, overriding broad-scale climatic drivers. We conclude that adaptive revegetation strategies at the regional basin scale should prioritize topographic controls, while the uncertainty in habitat suitability induced by climate change must not be overlooked. Full article
(This article belongs to the Section Forest Meteorology and Climate Change)
►▼ Show Figures

Figure 1

24 pages, 5692 KB  
Article
Multi-Scenario Recognition and Detection Model in National Parks Based on Improved YOLOv8
by Xiongwei Lou, Zixuan Qin, Hanbao Lou, Xinyu Zheng, Linhao Sun, Faneng Wang, Dasheng Wu, Sheng Chen and Guangyu Jiang
Forests 2026, 17(2), 277; https://doi.org/10.3390/f17020277 - 19 Feb 2026
Viewed by 358
Abstract
With the advancement of unmanned aerial vehicle (UAV) technology, its use in ecological monitoring and safety management of national parks has expanded significantly. However, object detection in complex scenes remains challenging due to environmental complexity, background interference, and occlusion. To address these issues, [...] Read more.
With the advancement of unmanned aerial vehicle (UAV) technology, its use in ecological monitoring and safety management of national parks has expanded significantly. However, object detection in complex scenes remains challenging due to environmental complexity, background interference, and occlusion. To address these issues, this paper proposes two improved YOLOv8-based models, YOLOv8-StarNet-CGA and SCS-YOLOv8, for detecting pine wilt disease-infected trees, under-construction farmhouses, and forest fires. In YOLOv8-StarNet-CGA, the StarNet module and Content-Guided Attention (CGA) are integrated into the backbone to enhance global feature extraction and focus on critical regions through dynamic weight adjustment. In SCS-YOLOv8, the original CIoU loss is also replaced with SIoU loss to optimize shape and orientation consistency, improving robustness. Experiments on UAV datasets covering diverse national park scenes demonstrate the effectiveness of the models. Results show that the improved models substantially outperform the original YOLOv8 in Precision, Recall, and mAP50. For pine wilt disease caused by the pine wood nematode Bursaphelenchus xylophilus, YOLOv8-StarNet-CGA achieves 8.6% higher Precision and 11.7% higher mAP50, facilitating early diagnosis and intervention of the disease. In under-construction farmhouse scenarios, Precision rises by 11% and mAP50 by 10.1%, lowering annual inspection labor by nearly 30% and improving oversight. For forest fires, SCS-YOLOv8 is more effective, with Precision improved by 7.2% and mAP50 by 6.3%. The improved detection model enables earlier identification of fire spots, thereby providing additional response time for emergency intervention, helping to mitigate fire spread and reduce the loss of forest resources. Both models also reduce GFLOPs and computational complexity, striking a balance between efficiency and accuracy, and showing strong potential for UAV deployment. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
►▼ Show Figures

Figure 1

15 pages, 4969 KB  
Article
Interactions Between Snow Cover and Forest Composition Drive Seasonal and Regional Variability in Soil Thermal Regimes of Hemiboreal Forests in the Eastern Baltic Region
by Andris Seipulis, Kristīne Riekstiņa, Kārlis Bičkovskis, Didzis Elferts, Endijs Bāders, Roberts Matisons and Oskars Krišāns
Forests 2026, 17(2), 276; https://doi.org/10.3390/f17020276 - 18 Feb 2026
Viewed by 346
Abstract
Wind disturbance is the major driver of forest damage in Northern Europe, particularly during late autumn and winter when cyclonic activity might coincide with unfrozen soil conditions. We quantified the thermal regime of periodically waterlogged mineral soils in relation to snow cover thickness [...] Read more.
Wind disturbance is the major driver of forest damage in Northern Europe, particularly during late autumn and winter when cyclonic activity might coincide with unfrozen soil conditions. We quantified the thermal regime of periodically waterlogged mineral soils in relation to snow cover thickness (SCT) in hemiboreal forests of Latvia. The study was conducted in 15 forest stands dominated by birch (Betula spp.), Scots pine (Pinus sylvestris L.), and Norway spruce (Picea abies (L.) H. Karst.) during two contrasting winters (2023/2024 and 2024/2025) across two regions differing in local climatic conditions. Soil temperature was monitored at 0, 10, and 20 cm depths, while SCT was measured at five points per plot. Linear mixed-effects models were used to assess the effects of air temperature, precipitation, region, season, and species composition to snow cover thickness (SCT) and effect of the other parameters to soil temperatures. SCT varied strongly between regions and seasons. Snow accumulation was lower in pine- and spruce-dominated stands compared to birch stands. Formation of snow layer increased soil temperatures at the surface, whereas SCT had a more pronounced insulating effect at depths of 10–20 cm, especially during prolonged snow cover (F = 15.43 − 54.25, p < 0.001). Heat transfer from deeper layers further enhanced thawing under waterlogged conditions. Snow cover significantly insulates soil in a depth-dependent manner, with its magnitude varying across regions and seasons. Promoting mixed-species stands and selecting deep-rooted species, such as birch, can enhance the formation of frozen soil, and thus soil–root anchorage, reducing wind damage risk on periodically waterlogged soils. Full article
(This article belongs to the Section Forest Soil)
►▼ Show Figures

Figure 1

16 pages, 2183 KB  
Article
Ecological Education in the City: Who, How, and with What Effect Educates the Residents of the Warsaw Metropolitan Area—Preliminary Research in Poland
by Natalia Korcz, Agnieszka Kamińska and Mariusz Ciesielski
Forests 2026, 17(2), 275; https://doi.org/10.3390/f17020275 - 18 Feb 2026
Cited by 1 | Viewed by 411
Abstract
The aim of this study was to examine the level of participation of residents of the Warsaw metropolitan area in environmental education and to identify the relationship between the frequency of forest visits and self-assessed environmental and forest-related knowledge. Understanding these relationships is [...] Read more.
The aim of this study was to examine the level of participation of residents of the Warsaw metropolitan area in environmental education and to identify the relationship between the frequency of forest visits and self-assessed environmental and forest-related knowledge. Understanding these relationships is important for improving the effectiveness of environmental education programs targeted at adult populations. The research was conducted as a pilot and exploratory study using an online survey administered via the Maptionnaire platform. The study included 218 adult respondents who had visited forest areas at least once in the previous year. The results indicate that more than two-thirds of respondents visited forests at least once a week, while slightly more than half reported participation in environmental education activities, mostly on an occasional basis and primarily in the form of field-based activities. Non-governmental organizations were more frequently identified as organizers of environmental education than forest-related institutions. A key finding is the differentiated self-assessment of knowledge: frequent forest visitors more often rated their environmental knowledge as average or low, whereas respondents visiting forests sporadically tended to assess their knowledge as good or very good. This pattern suggests that increased contact with nature may raise awareness of the complexity of environmental issues. The study provides practical insights for public institutions, State Forests, national parks, and non-governmental organizations involved in environmental education. The findings contribute to the development of more effective, diversified, and audience-oriented educational programs, with particular emphasis on field-based education and initiatives addressed to adult learners. Full article
(This article belongs to the Special Issue Forest Ecotourism: Balancing Recreation, Conservation, and Local Development)
►▼ Show Figures

Graphical abstract

18 pages, 20304 KB  
Article
Genome-Wide Identification of Members of the Juglans mandshurica Maxim. HD-Zip Gene Family and Their Responses to Light Intensity
by Xinye Gu, Dadi Liu, Wenbo Li, Shuai Zhu, Xinxin Zhang, Mulualem Tigabu, Xiaona Pei, Xiyang Zhao and Yuxi Li
Forests 2026, 17(2), 274; https://doi.org/10.3390/f17020274 - 18 Feb 2026
Viewed by 274
Abstract
Homeodomain-Leucine Zipper (HD-Zip) constitutes a distinct class of plant-specific transcription factors (TFs) that serves an essential function in mediating plant responses to environmental cues, with the HD-Zip II subfamily recognized as a major regulator of light-intensity adaptation and other environmental responses. [...] Read more.
Homeodomain-Leucine Zipper (HD-Zip) constitutes a distinct class of plant-specific transcription factors (TFs) that serves an essential function in mediating plant responses to environmental cues, with the HD-Zip II subfamily recognized as a major regulator of light-intensity adaptation and other environmental responses. However, the involvement of HD-Zip genes in regulating the light response of Juglans mandshurica Maxim. is largely unexplored. In this study, a genome-wide identification, classification, and expression analysis of the HD-Zip gene family in J. mandshurica was conducted. Furthermore, transcriptomic profiling under varying light-intensity conditions was performed to investigate the transcriptional regulation and potential functional networks of differentially expressed HD-Zip genes. The results showed that a total of 57 HD-Zip family genes were identified in J. mandshurica (named as JmHD-Zip) and classified into four subfamilies (HD-Zip I, HD-Zip II, HD-Zip III and HD-Zip IV). Gene structure and phylogenetic analyses indicated that members within the same subfamily exhibited analogous structural characteristics and shared strong homology with closely related species such as Juglans sigillata Dode and Populus trichocarpa. Torr. & A.Gray ex Hook. Promoter cis-acting element analysis revealed that the promoter regions of JmHD-Zip genes were enriched with multiple regulatory motifs associated with light responsiveness, hormone signaling, and stress regulation. Protein–protein interaction network analysis identified JmHDZ57 and JmHDZ43 as the central genes of the differentially expressed HD-Zip genes. Through validation of gene functions, JmHDZ43 promotes plant growth by coordinating shade-responsive morphogenesis via integration of light and hormone signaling pathways. This study offers a theoretical foundation and candidate gene resources for breeding initiatives and molecular investigations of light adaptation in J. mandshurica and potentially other woody species. Full article
(This article belongs to the Special Issue Forest Tree Genetics and Breeding in Response to Different Threats: 2nd Edition)
►▼ Show Figures

Figure 1

19 pages, 5527 KB  
Article
Aboveground Biomass Retrieval and Time Series Analysis Across Different Forest Types Using Multi-Source Data Fusion
by Yi Shen, Qianqian Chen, Tingting Zhu, Qian Zhang, Yu Zhang and Lei Zhao
Forests 2026, 17(2), 273; https://doi.org/10.3390/f17020273 - 18 Feb 2026
Viewed by 376
Abstract
Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that [...] Read more.
Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that leverages forest-type specific (coniferous vs. broadleaf) to enhance regional AGB retrieval. By refining established data fusion techniques with structural and compositional parameters, this approach seeks to mitigate systematic biases often found in generic regional assessments. Compared with 360 geo-referenced subplots, our stratified Support Vector Regression (SVR) model significantly outperformed non-classified counterparts, achieving an R2 of 0.76 and a reduced RMSE of 18.48 Mg/ha. This refined precision enabled a nuanced time-series analysis (2013–2020), revealing that while regional AGB increased from 157.13 to 192.23 Mg/ha, this trajectory was punctuated by a distinct sub-regional growth plateau between 2016 and 2018. By correlating these trends with disturbance data, we identified a 11.27% biomass decline in southwestern sectors linked to a tripling of burned area, pinpointing intensified fire regimes as the primary driver overriding recovery-driven carbon gains. These findings demonstrate that harmonizing multi-sensor signals with functional forest differentiation provides the necessary sensitivity to track carbon resilience, offering a scalable and robust tool for operational forest management and global carbon cycle research. Full article
(This article belongs to the Special Issue Applications of Optical and Active Remote Sensing in Forestry)
►▼ Show Figures

Figure 1

22 pages, 2818 KB  
Article
Tree Geo-Positioning in Coniferous Forest Plots: A Comparison of Ground Survey and Laser Scanning Methods
by Lina Beniušienė, Donatas Jonikavičius, Monika Papartė, Marius Aleinikovas, Iveta Varnagirytė-Kabašinskienė, Ričardas Beniušis and Gintautas Mozgeris
Forests 2026, 17(2), 272; https://doi.org/10.3390/f17020272 - 18 Feb 2026
Viewed by 477
Abstract
Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based [...] Read more.
Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based field positioning system (TerraHärp), drone-based laser scanning, and mobile laser scanning (MLS). The analysis was conducted in five long-term experimental forest sites in Lithuania, comprising pine- and spruce-dominated stands with varying stand densities. Tree locations derived from legacy maps and the TerraHärp system were compared to assess systematic and random positional discrepancies. TerraHärp-derived tree positions were subsequently used as a reference to evaluate the laser scanning-based methods. Positional accuracy was assessed using Hotelling’s T2 test, root-mean-square error, and the National Standard for Spatial Data Accuracy (NSSDA), while spatial autocorrelation of deviations was examined using Moran’s I. The results indicated that discrepancies between TerraHärp and legacy maps were dominated by systematic horizontal shifts in the historical maps, whereas random positional variability was relatively small and consistent across stand types. Drone-based laser scanning showed a strong dependence of tree identification accuracy on stand density and mean tree diameter. Overall, CHM-based segmentation yielded more accurate tree identification than 3D point cloud segmentation, with mean F1-scores of 0.78 and 0.72, respectively. Positional accuracy varied by method, with the largest errors from CHM apexes and highest 3D point cloud points (mean NSSDA ≈ 1.8–2.0 m), improved accuracy using the lowest 3D cluster points (1.45–1.72 m), and the highest accuracy achieved using mobile laser scanning (mean NSSDA 0.76–0.90 m; >95% of trees within 1 m). These results demonstrate that pseudolite-based field mapping provides a reliable reference for high-precision tree location and for integrating field and laser scanning data in managed conifer stands. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
►▼ Show Figures

Figure 1

21 pages, 2608 KB  
Article
Integrating Remotely Sensed Data to Reconcile Gaps in Growing Stock Volume Accounting for National Forest Inventory
by Temitope Olaoluwa Omoniyi, Allan Sims, Ronald E. McRoberts and Mercy Ajayi-Ebenezer
Forests 2026, 17(2), 271; https://doi.org/10.3390/f17020271 - 18 Feb 2026
Viewed by 352
Abstract
National forest inventory (NFI) data are often collected over a 5-year or 10-year period, meaning some are already outdated by the time the complete results are available. This study assesses changes in growing stock volume (GSV, m3/ha) using hybrid estimation supported [...] Read more.
National forest inventory (NFI) data are often collected over a 5-year or 10-year period, meaning some are already outdated by the time the complete results are available. This study assesses changes in growing stock volume (GSV, m3/ha) using hybrid estimation supported by Sentinel-2 metrics. It focuses on constructing a model for estimating the change in GSV using NFI plot data and bitemporal remotely sensed auxiliary data, where such data are available for both points in time (t1 and t2), and unitemporal data for which remotely sensed auxiliary data are available only for t2. A machine-learning approach based on the random forests (RFs) algorithm was used to predict plot-level GSV change. The original data for t2 and t3 were first used to evaluate the accuracy of the change prediction at the plot level, after which the predicted changes were applied to update the plot-level GSV to predict plot-level GSV at t3, which was then assessed against the observed plot-level GSV at t3. Predicted change was assessed with the Mean Average Annual Volume Change (MAAVC) method, representing the average annual change in GSV over a given period. The results indicate that at the plot level, the bitemporal model produced GSV change estimates with low accuracy (R2 = 0.26, RMSE = 4.06 m3/ha, and MAE = 3.26 m3/ha), while the unitemporal model achieved R2 = 0.40, RMSE = 3.64 m3/ha, and MAE = 2.65 m3/ha when predicting the t1− t2 GSV change. Using the predicted change to predict plot-level GSV at t3, the MAAVC based on field data yielded R2 = 0.91 and RMSE = 45.11 m3/ha, while the RS unitemporal yielded R2 = 0.73 and RMSE = 83.79 m3/ha, and the bitemporal yielded R2 = 0.72 and RMSE = 83.61 m3/ha. Mean population GSV at t3, estimated from the RF models, was 254.61 and 255.19 m3/ha for the unitemporal and bitemporal models, respectively. Monte Carlo simulations with a novel stopping criterion were then used to estimate total standard errors, which were 10.48 and 10.40 m3/ha for the unitemporal and bitemporal models, respectively, incorporating both model prediction uncertainty and sampling variability. A test of significance revealed a significant effect of the proposed method on the estimated mean population GSV at t3 (p < 0.001). Conclusively, MAAVC and spatiotemporal RS methods provide a robust framework for predicting GSV at t3 using Estonian NFI and Sentinel-2 data. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry: 2nd Edition)
►▼ Show Figures

Figure 1

16 pages, 4310 KB  
Article
Bioactive Compounds in Cornus mas L. and Juniperus communis L.
by Anna Przybylska-Balcerek and Kinga Stuper-Szablewska
Forests 2026, 17(2), 270; https://doi.org/10.3390/f17020270 - 17 Feb 2026
Viewed by 448
Abstract
The growing interest in plant-derived bioactive compounds has intensified research on traditional but underexplored species as potential sources of health-promoting metabolites. This study aimed to compare the phytochemical profiles and antioxidant potential of fruits of Cornus mas L. (Cornelian cherry) and Juniperus communis [...] Read more.
The growing interest in plant-derived bioactive compounds has intensified research on traditional but underexplored species as potential sources of health-promoting metabolites. This study aimed to compare the phytochemical profiles and antioxidant potential of fruits of Cornus mas L. (Cornelian cherry) and Juniperus communis L. (common juniper) collected from two natural locations in Poland. Lyophilized fruits were subjected to combined alkaline and acid hydrolysis followed by extraction, and the released phenolic compounds were identified and quantified using UPLC–PDA. Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin carotenoid content, chlorophylls, organic acids, and antioxidant activity (ABTS•+ assay) were determined spectrophotometrically. The fruits of C. mas exhibited significantly higher TPC (3584–3641 mg GAE/100 g d.m.), TFC (875–895 mg RUTE/100 g d.m.), TAC (247–266 mg CAE/100 g d.m.), and antioxidant activity (1544–1698 µmol Trolox/kg d.m.) compared with J. communis. Chlorogenic acid and quercetin were the dominant phenolic constituents in C. mas, whereas J. communis was characterized by higher proportions of protocatechuic acid, catechin, and kaempferol. J. communis fruits contained higher total organic acids, mainly citric acid, while C. mas fruits showed elevated levels of shikimic acid. Strong positive correlations were found between TPC, TFC, and ABTS activity (r > 0.90), indicating that flavonoids are key contributors to antioxidant capacity. Principal component analysis clearly discriminated samples according to species, with minor effects of sampling location. Overall, C. mas fruits demonstrated a superior antioxidant potential associated with a rich and diverse phenolic profile. In contrast, J. communis fruits were distinguished by a higher content of organic acids and a species-specific phenolic pattern. These findings highlight the nutritional and functional value of both species, supporting their potential application in functional foods and nutraceuticals. Full article
(This article belongs to the Special Issue Exploring the Antioxidant Potential and Phenolic Diversity of Forest-Derived Plants)
►▼ Show Figures

Graphical abstract

22 pages, 3677 KB  
Article
Genotypic and Silvicultural Controls on Wind Damage, Failure Mode, and Productivity in a Radiata Pine Trial Following Cyclone Gabrielle
by Michael S. Watt, Kate Halstead, Tommaso Locatelli, Nicolò Camarretta, Sadeepa Jayathunga and Juan C. Suárez
Forests 2026, 17(2), 269; https://doi.org/10.3390/f17020269 - 17 Feb 2026
Viewed by 420
Abstract
Storm damage poses an increasing risk to radiata pine (Pinus radiata D. Don) plantations in New Zealand as extreme wind events intensify under climate change. This study quantified wind damage following ex-tropical Cyclone Gabrielle in a seven-year-old genetics trial comprising 12 genotypes [...] Read more.
Storm damage poses an increasing risk to radiata pine (Pinus radiata D. Don) plantations in New Zealand as extreme wind events intensify under climate change. This study quantified wind damage following ex-tropical Cyclone Gabrielle in a seven-year-old genetics trial comprising 12 genotypes grown under four stand configurations defined by contrasting stocking (833 and 1282 stems/ha) and cultivation (with and without cultivation) treatments. The genotypes comprised a Pinus attenuata × P. radiata var. cedrosensis hybrid, ten anonymised radiata pine clones and an industry-standard radiata pine seedlot. Field assessments and unmanned aerial vehicle UAV laser scanning were used to classify damage into stem breakage and overturning and to derive structural metrics, including tree diameter, height, slenderness, volume, crown width and crown volume. Overall, 16.7% of trees were damaged, with stem breakage (10.2%) occurring more frequently than overturning (6.5%). Averaged across the four treatments, total damage significantly ranged from 10.4% in the high stocking cultivated treatment to 23.5% in the low stocking no cultivation treatment. Variation between the 12 genotypes was highly significant, with breakage, overturning and total damage ranging from 3.3%–25.4%, 1.4%–15.0% and 6.6%–29.5%, respectively, between the 12 genotypes. Two radiata pine clones with high growth rates and low to moderate wind damage had the highest post-storm total stem volume per hectare, which greatly exceeded that of the hybrid or the widely planted radiata pine seedlot. These findings highlight the potential of clones that combine high growth rates and resistance to wind damage to maintain high productivity under a changing climate with a greater frequency of extreme weather events. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
►▼ Show Figures

Figure 1

25 pages, 1285 KB  
Review
Climate-Smart Forestry and Its Strong Correlation with Forest Genetic Resources: Current State and Future Actions
by Ermioni Malliarou, Eleftheria Dalmaris and Evangelia V. Avramidou
Forests 2026, 17(2), 268; https://doi.org/10.3390/f17020268 - 16 Feb 2026
Viewed by 754
Abstract
Climate-smart forestry (CSF) is a comprehensive approach that aims to sustainably enhance wood productivity (production), improve forest resilience and adaptation, sequester carbon (mitigation), and support broader development goals. This strategy is profoundly linked with Forest Genetic Resources (FGR), which are crucial for the [...] Read more.
Climate-smart forestry (CSF) is a comprehensive approach that aims to sustainably enhance wood productivity (production), improve forest resilience and adaptation, sequester carbon (mitigation), and support broader development goals. This strategy is profoundly linked with Forest Genetic Resources (FGR), which are crucial for the adaptive capacity and long-term sustainability of forest ecosystems in the face of the escalating climatic changes. Climate change presents significant risks, including increased air temperatures, altered precipitation regimes, and a rise in extreme weather events, leading to tree mortality, shifts in vegetation distribution, and a potential loss of critical forest functions and services, such as carbon sequestration capacity. While forests have inherent resilience, the rapidity and magnitude of projected changes may exceed their natural adaptive capacity, potentially resulting in local extinction and degradation of ecosystems. This review explores various facets of the interplay between CSF and FGR, emphasizing their role in sustainable forest management. Key areas of focus include: (1) Genetic Diversity, (2) Genotype Selection and Breeding, (3) Modern Breeding Techniques, (4) Molecular Breeding, (5) Genomic Prediction (GP), (6) Breeding Programs, (7) Silvicultural Practices, (8) Adaptation Mechanisms, (9) Phenotypic Plasticity, (10) Migration, particularly Assisted Gene Flow (AGF) and (11) Reproductive Material Management. Ultimately, the study highlights the crucial role of FGR in the resilience of forest ecosystems and proposes future actions for their integration into CSF strategies, including in situ and ex situ conservation, assisted migration, advanced research and development, community involvement, and supportive policy frameworks, all vital for the long-term sustainability and vitality of forest ecosystems in a changing climate. Full article
(This article belongs to the Special Issue Forest Genetic Resources: State, Pressure, Benefit and Response to Global Change)
►▼ Show Figures

Figure 1

17 pages, 2577 KB  
Article
Forest Type Regulates Soil Aggregate Stability and Soil Organic Carbon Stabilization in Subtropical Plantations
by Xinyu Wei, Jie Xiao, Yuan Gong, Jiaming Chang, Lulu Xia, Ye Hu, Wei Liu and Xiang Nong
Forests 2026, 17(2), 267; https://doi.org/10.3390/f17020267 - 16 Feb 2026
Viewed by 327
Abstract
The influence of forest type on soil aggregates distribution, stability, and the contribution of aggregate-associated carbon (C) to bulk soil organic carbon (SOC) remains poorly understood. This may be crucial for the accumulation and persistence of SOC in subtropical ecosystems. In this study, [...] Read more.
The influence of forest type on soil aggregates distribution, stability, and the contribution of aggregate-associated carbon (C) to bulk soil organic carbon (SOC) remains poorly understood. This may be crucial for the accumulation and persistence of SOC in subtropical ecosystems. In this study, we examined soil aggregate distribution and stability at two depths (0–15 and 15–30 cm) in 10-, 20-, and 30-year-old Cryptomeria japonica (Japanese cedar) and Chimonobambusa quadrangularis (square bamboo) plantations. We further assessed the contribution of carbon (C) associated with distinct aggregate fractions to bulk SOC. Across all stand ages and soil depths, macroaggregates accounted for 19%–56% of total soil aggregates in Japanese cedar plantations, whereas their proportion was 30%–337% higher in square bamboo plantations. In contrast, fine aggregates constituted 3%–67% of total aggregates in Japanese cedar plantations, but were 29%–94% lower in square bamboo plantations than in Japanese cedar plantations. Compared with Japanese cedar plantations, aggregate mean weight diameter (MWD) and geometric mean diameter (GMD) increased by 17%–88% and 35%–152%, respectively, in square bamboo plantations. In Japanese cedar soils, C and nitrogen (N) were primarily concentrated in coarse macroaggregates and fine macroaggregates, whereas in square bamboo plantations, C and N were mainly associated with coarse macroaggregates only. Both aggregate-associated soil C and N varied significantly with aggregate size and forest type, and Japanese cedar soils exhibited higher aggregate C/N ratios, particularly in older stands. Bulk SOC was positively correlated with macroaggregate-associated C in both forest types and with the silt and clay fractions in Japanese cedar plantations. MWD increased with higher macroaggregate C content and declined as the proportion of C in smaller aggregate fractions increased. These findings indicate that forest type plays a critical role in regulating soil aggregation and SOC stabilization pathways, with square bamboo plantations enhancing C sequestration by promoting macroaggregate formation and stability. Full article
(This article belongs to the Special Issue Carbon, Nitrogen, and Phosphorus Storage and Cycling in Forest Soil—2nd Edition)
►▼ Show Figures

Figure 1

19 pages, 6412 KB  
Article
Changes in Potentially Suitable Habitats and Priority Conservation Zones of Prunus sibirica L. in China Under Climate Change
by Junxing Chen, Lin Wang, Dun Ao, Ming Ma, Ru Yi, Shuning Zhang and Wenquan Bao
Forests 2026, 17(2), 266; https://doi.org/10.3390/f17020266 - 16 Feb 2026
Viewed by 379
Abstract
Prunus sibirica L. is a key ecological and economic tree species in northern China that is threatened by habitat degradation due to climate change and human activities. To address the gaps of incomplete historical dynamics and lack of conservation integration in existing studies, [...] Read more.
Prunus sibirica L. is a key ecological and economic tree species in northern China that is threatened by habitat degradation due to climate change and human activities. To address the gaps of incomplete historical dynamics and lack of conservation integration in existing studies, we integrated MaxEnt and Zonation v4.0 to predict its suitable habitat across five periods (LIG to 2090s) and three CMIP6 SSP scenarios, identifying key drivers and priority conservation zones. The model showed high prediction accuracy (mean AUC > 0.9). Results indicated that Human Footprint (HFP), Precipitation Seasonality (Bio15), Annual Mean Temperature (Bio1), Elevation (ELEV), and Mean Temperature of the Coldest Quarter (Bio11) were the key environmental factors (cumulative contribution 91.4%), with Bio1, Bio15, Temperature Seasonality (Bio4), and HFP confirmed as major drivers (AUC > 0.8) via jackknife test. Spatiotemporally, the species’ suitable habitat contracted from the Last Interglacial to the Last Glacial Maximum and expanded to the current total suitable area of 506,620.1 km2. Under future SSP scenarios, suitable habitats expanded continuously under SSP126 and SSP245 but showed a “first expansion then contraction” trend under SSP585, with a persistent northeastward migration of the habitat centroid. The vertical (altitudinal) distribution of P. sibirica showed a trend of moving to higher elevations under future warming scenarios, especially in the SSP585 scenario. High-priority conservation zones are concentrated in northern China with insufficient existing protection. It is emphasized that this study contributes to improving the adaptive capacity and genetic characterization of P. sibirica almond populations to future climate. Full article
(This article belongs to the Section Forest Ecology and Management)
►▼ Show Figures

Figure 1

19 pages, 17253 KB  
Article
ALS and SfM Field Data Survey as a Basis of Forest Road Design
by Ivica Papa, Luka Hodak, Maja Popović, Andreja Đuka, Tibor Pentek and Mihael Lovrinčević
Forests 2026, 17(2), 265; https://doi.org/10.3390/f17020265 - 16 Feb 2026
Viewed by 437
Abstract
Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, [...] Read more.
Field data of high accuracy and precision is the basis for creating the high-quality design of a forest road. In this study, three survey methods for collecting field data were tested: ALS UAV, LiDAR data of the Republic of Croatia, collected by airplane, and UAV SfM. A total of three detailed forest road projects were created based on the collected data. The designed forest roads had the same horizontal and vertical development, thus eliminating the human factor from the design process. Four important forest road parameters were tested: earthwork cut and fill volume, cross-terrain slope, and carriageway value. No significant statistical difference was found for any of the tested parameters between designs. The design based on ALS data had a total number of earthworks of 1026.03 m3, the amount was 1449.56 m3 for SfM design, and the number of earthworks for the State Geodetic Administration LiDAR data was 889.02 m3. The calculated amount of cut volume was significantly affected by the error of the carriageway value for the State Geodetic Administration LiDAR data-based design. The results indicate the possibility of using all used methods on terrain with a moderate slope, but there is a need for further testing on different terrain slope classes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Forest Resource Monitoring and Management)
►▼ Show Figures

Figure 1

24 pages, 4324 KB  
Article
Mapping Spatial Drivers of Predicted Active Fires Kernel Density with Geographically Weighted Regression in Mexico
by Norma Angélica Monjarás-Vega, Daniel José Vega-Nieva, Carlos Ivan Briones-Herrera, Jaime Briseño-Reyes, José Javier Corral-Rivas, Pablito Marcelo López-Serrano, Marín Pompa-García, Julián Cerano-Paredes, Diego Rafael Pérez Salicrup, William Matthew Jolly and Ernesto Alvarado-Celestino
Forests 2026, 17(2), 264; https://doi.org/10.3390/f17020264 - 16 Feb 2026
Viewed by 519
Abstract
Despite the need to understand the spatial variation in human and biophysical drivers of fire spread, studies aimed at predicting remotely sensed fire kernel density at multiple scales are still relatively scarce. The current study aimed at the prediction of MODIS and VIIRS [...] Read more.
Despite the need to understand the spatial variation in human and biophysical drivers of fire spread, studies aimed at predicting remotely sensed fire kernel density at multiple scales are still relatively scarce. The current study aimed at the prediction of MODIS and VIIRS active fire kernel density (AFKD) using region-specific geographically weighted regression (GWR) in Mexico. GWR models overcame stationary models to predict AFKD at a selected 20 km optimum active fire kernel density bandwidth. Our observations confirmed, for the first time for remotely-sensed fire records, the regional variation and the non-stationarity of the human and fuel-related drivers of AFKD in Mexico. Aboveground biomass mainly showed positive relationships in low productivity areas and humped relationships in more productive areas. Contrary to previous observations for fire suppression records, road density mainly showed a negative relationship, and slope mainly showed a positive relationship with AFKD. This highlights the importance of monitoring fire spatial activity, not only from human-based suppression records, but also considering remotely-sensed AFKD, potentially improving fire prevention planning. The methodology shown in the current study can be replicated elsewhere for improving our understanding of the spatial drivers of remotely sensed fire activity. Full article
(This article belongs to the Section Natural Hazards and Risk Management)
►▼ Show Figures

Figure 1

21 pages, 1493 KB  
Article
Forest Area Collaborative Governance Path Based on Forest Birdwatching: A Case Study of Mingxi, China
by Tianle Liu, Suxin Hu and Wenhui Chen
Forests 2026, 17(2), 263; https://doi.org/10.3390/f17020263 - 15 Feb 2026
Viewed by 240
Abstract
Under strict ecological protection regimes, identifying development pathways that can be integrated into forest governance without undermining conservation boundaries remains a critical challenge. This study adopts a qualitative case-study approach to examine how forest birdwatching is governed as a form of low-disturbance forest [...] Read more.
Under strict ecological protection regimes, identifying development pathways that can be integrated into forest governance without undermining conservation boundaries remains a critical challenge. This study adopts a qualitative case-study approach to examine how forest birdwatching is governed as a form of low-disturbance forest use in Mingxi County, China. Based on semi-structured interviews, field observations, and governance-related materials, the analysis examines governance mechanisms and interaction processes shaping everyday regulatory practices. The findings indicate that forest birdwatching does not function as low-disturbance use by virtue of its activity type alone, but through its progressive embedding within routine forest governance under rigid institutional constraints. Institutional enforcement, spatial zoning, community-based benefit coordination, and media-supported normative regulation interact to stabilize behavioral boundaries, manage participation, and mitigate disturbance risks. The governance significance of forest birdwatching lies not in its direct replicability across regions, but in its value as an analytical reference for understanding how governance elements may be conditionally configured under specific institutional, organizational, and spatial contexts. By clarifying the minimum enabling conditions under which low-disturbance forest use can contribute to collaborative governance outcomes, this study provides a context-sensitive analytical framework for forest governance in ecologically valuable but development-constrained regions. Full article
(This article belongs to the Special Issue Forestry Economy Sustainability and Ecosystem Governance)
►▼ Show Figures

Figure 1

23 pages, 2497 KB  
Article
The Economic and Ecological Benefits of the Optimal Control Measures for Pine Wood Nematode Disease in Weihai City, China
by Qi Cai, Junhua Chen, Lina Jiang, Ning Ding, Tong Liu and Ming Yuan
Forests 2026, 17(2), 262; https://doi.org/10.3390/f17020262 - 15 Feb 2026
Viewed by 213
Abstract
Pine wilt disease poses a significant threat to forest ecosystems. This study evaluates the efficacy and economic–ecological benefits of its control measures in Weihai City, China, from 2019 to 2022. Employing disaster economics theory and a simultaneous equation model, we analyzed control performance, [...] Read more.
Pine wilt disease poses a significant threat to forest ecosystems. This study evaluates the efficacy and economic–ecological benefits of its control measures in Weihai City, China, from 2019 to 2022. Employing disaster economics theory and a simultaneous equation model, we analyzed control performance, influencing factors, and optimal strategies, estimating costs and losses under actual, optimal, and no-control scenarios. The results show that the optimal investment is 70.63 CNY per dead tree. Each additional treated hectare averts 119.6 tree deaths, and every 10,000 CNY invested prevents 88.5 mortalities. Economic benefits increased sharply from 2.169 to 94.749 billion CNY, while ecological benefits also grew substantially. However, control inputs in 2019 were insufficient, and subsequent years revealed opportunities for more efficient allocation, despite persistent constraints like limited funding and personnel. We recommend implementing a precision budgeting model with dynamic adjustment model and an integrated township-level management system to optimize control outcomes. Full article
(This article belongs to the Section Forest Ecology and Management)
►▼ Show Figures

Figure 1

17 pages, 2239 KB  
Article
Human Activities and Climate Jointly Shape the Old-Tree Diversity in Human-Dominated Landscapes of the Yellow River Basin, China
by Xin Wang, Jinfen Han, Pengcheng Liu, Donggang Guo and Meichen Jiang
Forests 2026, 17(2), 261; https://doi.org/10.3390/f17020261 - 15 Feb 2026
Viewed by 258
Abstract
Old trees function as enduring ecological legacies that preserve historical biodiversity within intensively human-modified landscapes, yet the relative influence of environmental versus anthropogenic drivers on their diversity remains unclear. Here, we aim to disentangle the joint effects of climate, urbanization intensity and cultural [...] Read more.
Old trees function as enduring ecological legacies that preserve historical biodiversity within intensively human-modified landscapes, yet the relative influence of environmental versus anthropogenic drivers on their diversity remains unclear. Here, we aim to disentangle the joint effects of climate, urbanization intensity and cultural preservation on old-tree density and community composition. We analyzed a province-wide census of 21,733 old-tree individuals across 115 counties in Shanxi Province, China, encompassing species origin (native vs. nonnative) and growth form (trees vs. shrubs). Old-tree density was assessed using spatial simultaneous autoregressive error models, while compositional dissimilarity was quantified using generalized dissimilarity modeling. In total, 131 species were recorded, with four dominant species comprising more than 75% of all individuals. Old-tree density increased with mean annual temperature, human population density, and cultural heritage abundance, but declined sharply with cropland coverage. Driver importance varied among groups: native species were primarily governed by climatic conditions, nonnative species by land-use intensity, and tree-form old trees were positively associated with cultural heritage abundance, an effect absent in shrub-form old trees. Compositional dissimilarity was driven mainly by climatic gradients and spatial distance, with additional contributions from human-related variables, particularly for nonnative assemblages. Our findings demonstrate that climate and spatial processes establish the regional framework of old-tree community composition, while cultural and demographic contexts promote local retention of old trees. By explicitly integrating ecological filters with socio-cultural drivers, this study advances old-tree research through a large-scale empirical framework, providing both scientific insight and socially relevant guidance for conservation under land-use intensification and climate warming. Full article
(This article belongs to the Section Forest Biodiversity)
►▼ Show Figures

Figure 1

26 pages, 14766 KB  
Article
Optimization of Planting Trees Can Improve Thermal Comfort in Historic Districts
by Suming Guo, Yuyan Lin, Meiling Feng, Mu He and Xinyi Zhu
Forests 2026, 17(2), 260; https://doi.org/10.3390/f17020260 - 15 Feb 2026
Viewed by 382
Abstract
Under the dual pressures of global climate change and rapid urbanization, historic districts face the challenge of improving livability and adapting to climate conditions while preserving their historical fabric. While street greening is recognized as a key mitigation strategy, the lack of quantitative, [...] Read more.
Under the dual pressures of global climate change and rapid urbanization, historic districts face the challenge of improving livability and adapting to climate conditions while preserving their historical fabric. While street greening is recognized as a key mitigation strategy, the lack of quantitative, spatially explicit guidelines often leads to indiscriminate planting and inefficient resource use in practice. Taking the historic districts of Nanjing—a representative city in China’s hot-summer and cold-winter region—as a case study, we systematically explored the comprehensive impacts of street orientation, height-to-width ratios (H/W), and spacing of street trees on the microclimate of the districts through empirical analysis and ENVI-met simulation. Then we constructed a typical street canyon model to simulate winter and summer conditions, and regression models were established to identify suitable SVF ranges for different street orientations. Results indicate that the recommended SVF ranges vary by street orientation: 0.3–0.5 for S–N, SE–NW, and NE–SW streets, and 0.4–0.6 for E–W streets. Crucially, denser planting does not always improve comfort. These evidence-based thresholds were applied to the renewal of Yongyuan Road. The study delivers spatially explicit guidelines in the form of quantitative planting thresholds to support climate-resilient street tree planning in historic districts, helping to enhance planting precision and resource efficiency. Full article
(This article belongs to the Special Issue Optimizing Urban Green Space Ecosystem Services for Resilient and Sustainable Cities)
►▼ Show Figures

Figure 1

18 pages, 2847 KB  
Article
Deep Soil as a Critical Nutrient Reservoir: Different C–N–P Stoichiometry and Drivers Between Surface and Subsoil in the Loess Plateau, China
by Yugang Guo, Tianyu Hao, Jianhao Song, Xiang Fan, Jingyue Xiao, Yuanyuan Wang, Dingning Wang, Jiahui Luo, Zhongke Bai and Ziqiang Liu
Forests 2026, 17(2), 259; https://doi.org/10.3390/f17020259 - 15 Feb 2026
Viewed by 371
Abstract
Soil stoichiometric characteristics serve as key indicators for assessing soil nutrient status and quality. Previous studies have predominantly focused on surface soil (0–40 cm), with limited understanding of deep soil (>40 cm) stoichiometric traits and their underlying drivers. In this study, we provide [...] Read more.
Soil stoichiometric characteristics serve as key indicators for assessing soil nutrient status and quality. Previous studies have predominantly focused on surface soil (0–40 cm), with limited understanding of deep soil (>40 cm) stoichiometric traits and their underlying drivers. In this study, we provide a case study of three typical restoration stands from China’s Loess Plateau, which compared differences between surface and deep soil layers in stoichiometric traits and influencing factors. Soil samples were systematically collected at 20 cm intervals down to bedrock to analyze the reserves and stoichiometric differences in C, N, and P between surface and deep soil layers, and to identify relevant environmental influencing factors. The results showed that: (1) Within this Loess Plateau case study, deep soil accounted for 33%–47% of the total profile storage of C, N, and P, representing a critical nutrient reservoir. (2) The differences from China’s average in C:N and C:P were markedly greater in deep soil (15.55 and 68.62, respectively) than in surface soil (11.63 and 8.31, respectively), indicating more pronounced nitrogen and phosphorus limitations in deep soil. (3) The factors influencing surface soil stoichiometry were mainly climate-related and biological interaction (altitude, soil water content and pH), while those for deep soil layers were factors related to nutrient storage and transport (soil thickness, soil bulk density and altitude). These results highlight that neglecting deep soil can lead to substantial underestimation of ecosystem nutrient reserves and misinterpretation of soil stoichiometry and its drivers. Therefore, we advocate incorporating deep soil into sampling designs in stoichiometric studies and attach more research attention to deep soil’s stoichiometry and its role in biogeochemical cycling. Full article
(This article belongs to the Section Forest Soil)
►▼ Show Figures

Figure 1

30 pages, 37480 KB  
Article
Machine Learning-Based Analysis of Forest Vertical Structure Dynamics Using Multi-Temporal UAV Photogrammetry and Geomorphometric Indicators
by Abdurahman Yasin Yiğit
Forests 2026, 17(2), 258; https://doi.org/10.3390/f17020258 - 15 Feb 2026
Viewed by 417
Abstract
Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by [...] Read more.
Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P95) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP95). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP95 increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R2 = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings. Full article
(This article belongs to the Special Issue Geospatial and Geomorphological Analysis of Forest Using Machine Learning)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-139
Previous Issue
Next Issue
Back to TopTop