Forests

32 pages, 8155 KiB

Open AccessArticle

Moisture Seasonality Dominates the Plant Community Differentiation in Monsoon Evergreen Broad-Leaved Forests of Yunnan, China

by Tao Yang, Xiaofeng Wang, Jiesheng Rao, Shuaifeng Li, Rong Li, Fan Du, Can Zhang, Xi Tian, Wencong Liu, Jianghua Duan, Hangchen Yu, Jianrong Su and Zehao Shen

Forests 2025, 16(7), 1167; https://doi.org/10.3390/f16071167 (registering DOI) - 15 Jul 2025

Abstract

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial [...] Read more.

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

(This article belongs to the Section Forest Biodiversity)

21 pages, 1566 KiB

Open AccessArticle

Environmental Degradation and Its Implications for Forestry Resource Efficiency and Total Factor Forestry Productivity in China

by Fuxi Wu, Rizwana Yasmeen, Xiaowei Xu, Heshan Sameera Kankanam Pathiranage, Wasi Ul Hassan Shah and Jintao Shen

Forests 2025, 16(7), 1166; https://doi.org/10.3390/f16071166 (registering DOI) - 15 Jul 2025

Abstract

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity [...] Read more.

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity (TFFP) in China’s 31 provinces between 2001 and 2021. Using the data envelopment analysis (DEA) model through the slack-based measure (SBM framework) and Malmquist–Luenberger index (MLI), we examine the efficiency and productivity growth of forestry, both with and without accounting for carbon emissions. The study reveals that when carbon emissions are not taken into account, traditional measures of productivity tend to overstate both efficiency and total factor forestry productivity (TFFP) growth, resulting in an average of 7.7 percent higher efficiency and 1.6 percent of additional TFFP growth per year. If we compare the regions, coast provinces with stricter technical regulations have improved efficiency in usage, but places like Tibet and Qinghai, with more vulnerable ecosystems, endure harsher consequences. Regardless of incorporating bad output into the TFFP estimation, China’s growth in forestry productivity primarily depends on efficiency change (EC) rather than technological change (TC). Full article

(This article belongs to the Special Issue Economic Assessment Research in Agroforestry Products, Environmental, and Renewable Resources Issues)

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20 pages, 3380 KiB

Open AccessArticle

Resilience of Mangrove Carbon Sequestration Under Typhoon Disturbance: Insights from Different Restoration Ages

by Youwei Lin, Ruina Liu, Yunfeng Shi, Shengjie Han, Huaibao Zhao and Zongbo Peng

Forests 2025, 16(7), 1165; https://doi.org/10.3390/f16071165 (registering DOI) - 15 Jul 2025

Abstract

Typhoons are major climate disturbances that significantly impact coastal ecosystems, particularly mangrove forests. This study examines the effects of typhoons on mangrove communities at different stages of recovery, focusing on how environmental factors influence carbon storage and net ecosystem exchange (NEE). Three mangrove [...] Read more.

Typhoons are major climate disturbances that significantly impact coastal ecosystems, particularly mangrove forests. This study examines the effects of typhoons on mangrove communities at different stages of recovery, focusing on how environmental factors influence carbon storage and net ecosystem exchange (NEE). Three mangrove sites were selected based on their recovery age: young, moderately restored, and mature. The results revealed that typhoons had the most pronounced effect on young mangroves, resulting in significant reductions in both above-ground and soil carbon storage. In contrast, mid-aged and mature mangroves demonstrated greater resilience, with mature mangroves recovering most rapidly in terms of community structure and carbon storage. Key factors such as wind speed, heavy rainfall, and changes in photosynthetically active radiation (PAR) contributed to carbon storage losses, particularly in young mangrove forests. This study underscores the importance of recovery age in determining mangrove resilience to extreme weather events and offers insights for enhancing restoration and conservation strategies to mitigate the impacts of climate change on coastal carbon sequestration. Full article

(This article belongs to the Section Natural Hazards and Risk Management)

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19 pages, 2494 KiB

Open AccessArticle

Assessing Forest Structure and Biomass with Multi-Sensor Remote Sensing: Insights from Mediterranean and Temperate Forests

by Maria Cristina Mihai, Sofia Miguel, Ignacio Borlaf-Mena, Julián Tijerín-Triviño and Mihai Tanase

Forests 2025, 16(7), 1164; https://doi.org/10.3390/f16071164 (registering DOI) - 15 Jul 2025

Abstract

Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of [...] Read more.

Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of remote sensing sensors for estimating biophysical variables in Mediterranean and temperate forests that can be easily translated into an operational context. Aboveground biomass (AGB), canopy height (CH), and forest canopy cover (FCC) were estimated using a combination of optical (Sentinel-2, Landsat) and radar sensors (Sentinel-1 and TerraSAR-X/TanDEM-X), along with records of past forest disturbances and topography-related variables. As a reference, lidar-derived AGB, CH, and FCC were used. Model performance was assessed not only with standard approaches such as out-of-bag sampling but also with completely independent lidar-derived reference datasets, thus enabling evaluation of the model’s temporal inference capacity. In Mediterranean forests, models based on optical imagery outperformed the radar-enhanced models when estimating FCC and CH, with elevation and spectral indices being key predictors of forest structure. In contrast, in denser temperate forests, radar data (especially X-band relative heights) were crucial for estimating CH and AGB. Incorporating past disturbance data further improved model accuracy in these denser ecosystems. Overall, this study underscores the value of integrating multi-source remote sensing data while highlighting the limitations of temporal extrapolation. The presented methodology can be adapted to enhance forest variable estimation across many forest ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)

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9 pages, 1699 KiB

Open AccessArticle

Density and Modulus of Elasticity (MOE) Distribution and Grading of Flattened Bamboo Boards

by Xun Luo, Jiarui Xu, Yuquan Li, Zhiru Song, Zhen Jiang, Xiubiao Zhang, Chunping Dai, Hu Miao and Huanrong Liu

Forests 2025, 16(7), 1163; https://doi.org/10.3390/f16071163 (registering DOI) - 15 Jul 2025

Abstract

The standardization of physical and mechanical properties is critical for the large-scale application of engineered bamboo products. In this study, the distribution characteristics of density and modulus of elasticity (MOE) were systematically examined in a large sample of flattened bamboo boards. The density [...] Read more.

The standardization of physical and mechanical properties is critical for the large-scale application of engineered bamboo products. In this study, the distribution characteristics of density and modulus of elasticity (MOE) were systematically examined in a large sample of flattened bamboo boards. The density and MOE ranged from 0.46 to 1.12 g/cm³ and 5.60 to 22.18 GPa, respectively. Both exhibited a decreasing trend with increasing board thickness. Based on interquartile analysis, four density grades and five MOE grades were established. A strong positive correlation was identified between density and MOE, indicating that density—closely linked to fiber volume fraction—is the primary factor influencing mechanical performance. Notably, the graded bamboo boards demonstrated significantly higher modulus values than conventional wood veneers such as hemlock and poplar, highlighting their potential for high-performance structural applications. This study proposes a practical grading framework that contributes to the standardization and broader engineering utilization of flattened bamboo boards. Full article

(This article belongs to the Special Issue Wood Properties: Strength, Density, Hardness)

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Graphical abstract

32 pages, 1661 KiB

Open AccessReview

Modelling Wood Product Service Lives and Residence Times for Biogenic Carbon in Harvested Wood Products: A Review of Half-Lives, Averages and Population Distributions

by Morwenna J. Spear and Jim Hart

Forests 2025, 16(7), 1162; https://doi.org/10.3390/f16071162 (registering DOI) - 15 Jul 2025

Abstract

Timber and other biobased materials store carbon that has been captured from the atmosphere during photosynthesis and plant growth. The estimation of these biogenic carbon stocks in the harvested wood products (HWP) pool has received increasing attention since its inclusion in greenhouse gas [...] Read more.

Timber and other biobased materials store carbon that has been captured from the atmosphere during photosynthesis and plant growth. The estimation of these biogenic carbon stocks in the harvested wood products (HWP) pool has received increasing attention since its inclusion in greenhouse gas reporting by the IPCC. It is of particular interest for long service life products such as timber in buildings; however, some aspects require further thought—in particular the handling of service lives as opposed to half-lives. The most commonly used model for calculating changes in the HWP pool uses first order decay based on half-lives. However other approaches are based on average service lives and estimates of residence times in the product pool, enabling different mathematical functions to be used. This paper considers the evolution of the two concepts and draws together data from a wide range of sources to consider service life estimation, which can be either related to design life or practical observations such as local environmental conditions, decay risk or consumer behaviour. As an increasing number of methods emerge for calculating HWP pool dynamics, it is timely to consider how these numerical inputs from disparate sources vary in their assumptions, calculation types, accuracy and results. Two groups are considered: half-lives for first order decay models, and service life and residence time population distributions within models based on other functions. A selection of examples are drawn from the literature to highlight emerging trends and discuss numerical constraints, data availability and areas for further study. The review indicated that issues exist with inconsistent use of nomenclature for half-life, average service life and peak flow from the pool. To ensure better sharing of data between studies, greater clarity in reporting function types used is required. Full article

(This article belongs to the Special Issue Forest Biomass Utilization: Implications for Global Sustainable Development)

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23 pages, 2490 KiB

Open AccessArticle

Endophytic Bacterial Consortia Isolated from Disease-Resistant Pinus pinea L. Increase Germination and Plant Quality in Susceptible Pine Species (Pinus radiata D. Don)

by Frederico Leitão, Marta Alves, Isabel Henriques and Glória Pinto

Forests 2025, 16(7), 1161; https://doi.org/10.3390/f16071161 (registering DOI) - 14 Jul 2025

Abstract

The nursery phase is vital for forest regeneration, yet studies on plant growth-promoting (PGP) bacteria to enhance sustainable nursery production in forest species are scarce. This study explores whether endophytic bacteria from disease-resistant Pinus pinea L. can improve germination and seedling quality in [...] Read more.

The nursery phase is vital for forest regeneration, yet studies on plant growth-promoting (PGP) bacteria to enhance sustainable nursery production in forest species are scarce. This study explores whether endophytic bacteria from disease-resistant Pinus pinea L. can improve germination and seedling quality in susceptible Pinus radiata D. Don. Root endophytes were isolated, screened for PGP traits, and identified via 16S rRNA gene sequencing. Bacterial formulations were applied to P. radiata seeds to determine their impact on germination and plant quality indicators (photosynthetic pigments and other metabolites). Paenibacillaceae (19%) and Bacillaceae (13%) were predominant among 68 isolates, with 94% producing indole-3-acetic acid, and Burkholderiaceae showing the broadest PGP trait diversity. Seedlings inoculated with formulation C3 (Caballeronia R.M3R3, Rhodococcus T.M4R4, and Mesorhizobium R.M1R2) displayed an improved germination rate (89% compared to 71% from the uninoculated control), while those inoculated with formulation P4 (Paenibacillus T.M5R4, Bacillus R.M2R7, Acinetobacter T.M2R22, and Paraburkholderia R.M1R3) showed an improved germination rate (81%), increased amount of starch (0.4-fold), and free amino acids (1.5-fold). This study presents a comprehensive approach, from endophyte isolation to in vivo tests, highlighting two bacterial formulations as candidates for further proof-of-concept nursery trials. Ultimately, these bioinoculants represent eco-friendly strategies to enhance forest seedling establishment and support sustainable forest management. Full article

(This article belongs to the Section Forest Ecology and Management)

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19 pages, 3006 KiB

Open AccessArticle

Non-Linear Regression with Repeated Data—A New Approach to Bark Thickness Modelling

by Krzysztof Ukalski and Szymon Bijak

Forests 2025, 16(7), 1160; https://doi.org/10.3390/f16071160 (registering DOI) - 14 Jul 2025

Abstract

Broader use of multioperational machines in forestry requires efficient methods for determining various timber parameters. Here, we present a novel approach to model the bark thickness (BT) as a function of stem diameter. Stem diameter (D) is any diameter measured along the bole, [...] Read more.

Broader use of multioperational machines in forestry requires efficient methods for determining various timber parameters. Here, we present a novel approach to model the bark thickness (BT) as a function of stem diameter. Stem diameter (D) is any diameter measured along the bole, not a specific one. The following four regression models were tested: marginal model (MM; reference), classical nonlinear regression with independent residuals (M1), nonlinear regression with residuals correlated within a single tree (M2), and nonlinear regression with the correlation of residuals and random components, taking into account random changes between the trees (M3). Empirical data consisted of larch (Larix sp. Mill.) BT measurements carried out at two sites in northern Poland. Relative root square mean error (RMSE%) and adjusted R-squared (R2adj) served to compare the fitted models. Model fit was tested for each tree separately, and all trees were combined. Of the analysed models, M3 turned out to be the best fit for both the individual tree and all tree levels. The fit of the regression function M3 for SITE1 (50-year-old, pure stand located in northern Poland) was 87.44% (R2adj), and for SITE2 (63-year-old, pure stand situated in the north of Poland) it was 80.6%. Taking into account the values of RMSE%, at the individual tree level the M3 model fit at location SITE1 was closest to the MM, while at SITE2 it was better than the MM. For the most comprehensive regression model, M3, it was checked how the error of the bark thickness estimate varied with stem diameter at different heights (from the base of the trees to the top). In general, the model’s accuracy increased with greater tree height. Full article

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

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16 pages, 2025 KiB

Open AccessArticle

Coating Performance of Heat-Treated Wood: An Investigation in Populus, Quercus, and Pinus at Varying Temperatures

by Andromachi Mitani, Paschalina Terzopoulou, Konstantinos Ninikas, Dimitrios Koutsianitis and Georgios Ntalos

Forests 2025, 16(7), 1159; https://doi.org/10.3390/f16071159 (registering DOI) - 14 Jul 2025

Abstract

Thermal modification applies to a technique for the enhancement of biological durability, stability, and appearance of wood. Much is known about its effects on the chemical and physical attributes of wood. However, there is a knowledge gap concerning the effects of heat treatment [...] Read more.

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

(This article belongs to the Section Wood Science and Forest Products)

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22 pages, 9940 KiB

Open AccessArticle

Developing a Novel Method for Vegetation Mapping in Temperate Forests Using Airborne LiDAR and Hyperspectral Imaging

by Nam Shin Kim and Chi Hong Lim

Forests 2025, 16(7), 1158; https://doi.org/10.3390/f16071158 - 14 Jul 2025

Abstract

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach [...] Read more.

This study advances vegetation and forest mapping in temperate mixed forests by integrating airborne hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data, overcoming the limitations of conventional multispectral imaging. Employing a Digital Canopy Height Model (DCHM) derived from LiDAR, our approach integrates these structural metrics with hyperspectral spectral information, alongside detailed remote sensing data extraction. Through machine learning-based clustering, which combines both structural and spectral features, we successfully classified eight specific tree species, community boundaries, identified dominant species, and quantified their abundance, contributing to precise vegetation and forest type mapping based on predominant species and detailed attributes such as diameter at breast height, age, and canopy density. Field validation indicated the methodology’s high mapping precision, achieving overall accuracies of approximately 98.0% for individual species identification and 93.1% for community-level mapping. Demonstrating robust performance compared to conventional methods, this novel approach offers a valuable foundation for National Forest Ecology Inventory development and significantly enhances ecological research and forest management practices by providing new insights for improving our understanding and management of forest ecosystems and various forestry applications. Full article

(This article belongs to the Special Issue Applications of LiDAR in Forestry: Challenges, Opportunities and the Future)

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23 pages, 10215 KiB

Open AccessArticle

A Simplified Sigmoid-RH Model for Evapotranspiration Estimation Across Mainland China from 2001 to 2018

by Jiahui Fan, Yunjun Yao, Yajie Li, Lu Liu, Zijing Xie, Xiaotong Zhang, Yixi Kan, Luna Zhang, Fei Qiu, Jingya Qu and Dingqi Shi

Forests 2025, 16(7), 1157; https://doi.org/10.3390/f16071157 - 13 Jul 2025

Viewed by 128

Abstract

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the [...] Read more.

Accurate terrestrial evapotranspiration (ET) estimation is crucial for understanding land–atmosphere interactions, evaluating ecosystem functions, and supporting water resource management, particularly across climatically diverse regions. To address the limitations of traditional ET models, we propose a simple yet robust Sigmoid-RH model that characterizes the nonlinear relationship between relative humidity and ET. Unlike conventional approaches such as the Penman–Monteith or Priestley–Taylor models, the Sigmoid-RH model requires fewer inputs and is better suited for large-scale applications where data availability is limited. In this study, we applied the Sigmoid-RH model to estimate ET over mainland China from 2001 to 2018 by using satellite remote sensing and meteorological reanalysis data. Key driving inputs included air temperature (Ta), net radiation (Rn), relative humidity (RH), and the normalized difference vegetation index (NDVI), all of which are readily available from public datasets. Validation at 20 flux tower sites showed strong performance, with R-square (R²) ranging from 0.26 to 0.93, Root Mean Squard Error (RMSE) from 0.5 to 1.3 mm/day, and Kling-Gupta efficiency (KGE) from 0.16 to 0.91. The model performed best in mixed forests (KGE = 0.90) and weakest in shrublands (KGE = 0.27). Spatially, ET shows a clear increasing trend from northwest to southeast, closely aligned with climatic zones, with national mean annual ET of 560 mm/yr, ranging from less than 200 mm/yr in arid zones to over 1100 mm/yr in the humid south. Seasonally, ET peaked in summer due to monsoonal rainfall and vegetation growth, and was lowest in winter. Temporally, ET declined from 2001 to 2009 but increased from 2009 to 2018, influenced by changes in precipitation and NDVI. These findings confirm the applicability of the Sigmoid-RH model and highlight the importance of hydrothermal conditions and vegetation dynamics in regulating ET. By improving the accuracy and scalability of ET estimation, this model can provide practical implications for drought early warning systems, forest ecosystem management, and agricultural irrigation planning under changing climate conditions. Full article

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

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33 pages, 11613 KiB

Open AccessArticle

Assessing and Mapping Forest Fire Vulnerability in Romania Using Maximum Entropy and eXtreme Gradient Boosting

by Adrian Lorenț, Marius Petrila, Bogdan Apostol, Florin Capalb, Șerban Chivulescu, Cătălin Șamșodan, Cristiana Marcu and Ovidiu Badea

Forests 2025, 16(7), 1156; https://doi.org/10.3390/f16071156 - 13 Jul 2025

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Abstract

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning [...] Read more.

Understanding and mapping forest fire vulnerability is essential for informed landscape management and disaster risk reduction, especially in the context of increasing anthropogenic and climatic pressures. This study aims to model and spatially predict forest fire vulnerability across Romania using two machine learning algorithms: MaxEnt and XGBoost. We integrated forest fire occurrence data from 2006 to 2024 with a suite of climatic, topographic, ecological, and anthropogenic predictors at a 250 m spatial resolution. MaxEnt, based on presence-only data, achieved moderate predictive performance (AUC = 0.758), while XGBoost, trained on presence–absence data, delivered higher classification accuracy (AUC = 0.988). Both models revealed that the impact of environmental variables on forest fire occurrence is complex and heterogeneous, with the most influential predictors being the Fire Weather Index, forest fuel type, elevation, and distance to human proximity features. The resulting vulnerability and uncertainty maps revealed hotspots in Sub-Carpathian and lowland regions, especially in Mehedinți, Gorj, Dolj, and Olt counties. These patterns reflect historical fire data and highlight the role of transitional agro-forested landscapes. This study delivers a replicable, data-driven approach to wildfire risk modelling, supporting proactive management and emphasising the importance of integrating vulnerability assessments into planning and climate adaptation strategies. Full article

(This article belongs to the Section Natural Hazards and Risk Management)

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24 pages, 3294 KiB

Open AccessReview

Trends and Applications of Principal Component Analysis in Forestry Research: A Literature and Bibliometric Review

by Gabriel Murariu, Lucian Dinca and Dan Munteanu

Forests 2025, 16(7), 1155; https://doi.org/10.3390/f16071155 - 13 Jul 2025

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Abstract

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides [...] Read more.

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides the first comprehensive bibliometric and literature review focused exclusively on PCA applications in forestry. A total of 96 articles published between 1993 and 2024 were analyzed using the Web of Science database and visualized using VOSviewer software, version 1.6.20. The bibliometric analysis revealed that the most active scientific fields were environmental sciences, forestry, and engineering, and the most frequently published journals were Forests and Sustainability. Contributions came from 198 authors across 44 countries, with China, Spain, and Brazil identified as leading contributors. PCA has been employed in a wide range of forestry applications, including species classification, biomass modeling, environmental impact assessment, and forest structure analysis. It is increasingly used to support decision-making in forest management, biodiversity conservation, and habitat evaluation. In recent years, emerging research has demonstrated innovative integrations of PCA with advanced technologies such as hyperspectral imaging, LiDAR, unmanned aerial vehicles (UAVs), and remote sensing platforms. These integrations have led to substantial improvements in forest fire detection, disease monitoring, and species discrimination. Furthermore, PCA has been combined with other analytical methods and machine learning models—including Lasso regression, support vector machines, and deep learning algorithms—resulting in enhanced data classification, feature extraction, and ecological modeling accuracy. These hybrid approaches underscore PCA’s adaptability and relevance in addressing contemporary challenges in forestry research. By systematically mapping the evolution, distribution, and methodological innovations associated with PCA, this study fills a critical gap in the literature. It offers a foundational reference for researchers and practitioners, highlighting both current trends and future directions for leveraging PCA in forest science and environmental monitoring. Full article

(This article belongs to the Section Forest Ecology and Management)

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35 pages, 9338 KiB

Open AccessArticle

Early Response of Post-Fire Forest Treatments Across Four Iberian Ecoregions: Indicators to Maximize Its Effectiveness by Remote Sensing

by Javier Pérez-Romero, Manuel Esteban Lucas-Borja, Demetrio Antonio Zema, Rocío Soria, Isabel Miralles, Laura Blanco-Cano, Cristina Fernández and Antonio D. del Campo García

Forests 2025, 16(7), 1154; https://doi.org/10.3390/f16071154 - 12 Jul 2025

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Abstract

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate [...] Read more.

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS Utilization in Monitoring of Forest Ecosystems)

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22 pages, 1404 KiB

Open AccessProject Report

Implementation Potential of the SILVANUS Project Outcomes for Wildfire Resilience and Sustainable Forest Management in the Slovak Republic

by Andrea Majlingova, Maros Sedliak and Yvonne Brodrechtova

Forests 2025, 16(7), 1153; https://doi.org/10.3390/f16071153 - 12 Jul 2025

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Abstract

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS [...] Read more.

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

(This article belongs to the Special Issue Wildfire Behavior and the Effects of Climate Change in Forests)

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13 pages, 2599 KiB

Open AccessArticle

Enhancement of Dimensional Stability, Hydrophobicity, and Mechanical Strength of North American Red Alder Wood Through Silane Impregnation Combined with DES Pretreatment

by Yang Zheng, Ting Zhou, Chenyang Cai and Honghai Liu

Forests 2025, 16(7), 1152; https://doi.org/10.3390/f16071152 - 12 Jul 2025

Viewed by 99

Abstract

Wood is a green and renewable bio-based building material, but its hygroscopicity affects its dimensional stability, limiting its use in construction. Chemical modification can improve its properties, yet its effectiveness depends on wood permeability and traditional modifiers. This study first used a deep [...] Read more.

Wood is a green and renewable bio-based building material, but its hygroscopicity affects its dimensional stability, limiting its use in construction. Chemical modification can improve its properties, yet its effectiveness depends on wood permeability and traditional modifiers. This study first used a deep eutectic solvent (DES) to boost the permeability of North American alder wood. Then, methyl trimethoxysilane was impregnated under supercritical carbon dioxide (SCI), pressure (PI), vacuum (VI), and atmospheric pressure (AI) conditions. DES treatment damaged the cell structure, increasing wood permeability. Silane was deposited and polymerized in the cell lumen, chemically bonding with cell-wall components, filling walls and pits, and thickening walls. The VI group had the highest absolute density (0.59 g/cm³, +36.6%) and the lowest moisture absorption (4.4%, −33.3%). The AI group had the highest ASE (25%). The PI group showed the highest surface hardness (RL, 2592 N) and a water contact angle of 131.9°, much higher than natural wood. Overall, the VI group had the best performance. Silane reacts with cellulose, hemicellulose, and lignin in wood via hydrolysis and hydroxyl bonding, forming stable bonds that enhance the treated wood’s hydrophobicity, dimensional stability, and surface hardness. Full article

(This article belongs to the Section Wood Science and Forest Products)

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14 pages, 8367 KiB

Open AccessArticle

Anatomical Barriers to Impregnation in Hybrid Poplar: A Comparative Study of Pit Characteristics in Normal and Tension Wood

by Andreas Buschalsky, Holger Militz and Tim Koddenberg

Forests 2025, 16(7), 1151; https://doi.org/10.3390/f16071151 - 12 Jul 2025

Viewed by 119

Abstract

Fast-growing hardwoods like poplar often lack natural durability in outdoor use and require homogeneous impregnation with protective agents, though achieving homogeneity remains a known challenge. Various anatomical structures influence fluid transport in wood. This study compares characteristics of pits in libriform fibres, between [...] Read more.

Fast-growing hardwoods like poplar often lack natural durability in outdoor use and require homogeneous impregnation with protective agents, though achieving homogeneity remains a known challenge. Various anatomical structures influence fluid transport in wood. This study compares characteristics of pits in libriform fibres, between ray–vessel interfaces, and between vessel-to-vessel connections in normal wood and tension wood of a hybrid poplar genotype (Populus × canadensis, ‘Gelrica’), including both impregnated (with an aqueous, dye-containing solution) and non-impregnated regions, to identify anatomical barriers to impregnation. Light and scanning electron microscopy revealed significant differences in pit morphology and frequency in libriform fibres between normal wood and tension wood. In non-impregnated regions, pits were often encrusted. Vessel–ray pits did not differ between normal wood and tension wood but showed distinct differences between impregnated and non-impregnated regions: in the latter, pits were occluded by tylose-forming layers. Intervessel pits differed in border and aperture size between earlywood and latewood in both normal wood and tension wood. Hence, fluid transport is strongly impeded by occluded vessel–ray pits and, to a lesser extent, by encrusted fibre pits. Full article

(This article belongs to the Special Issue Wood Anatomy and Evaluation of Wood Structures and Their Modifications—2nd Edition)

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25 pages, 4955 KiB

Open AccessArticle

Optimized MaxEnt Modeling of Catalpa bungei Habitat for Sustainable Management Under Climate Change in China

by Xiaomeng Shi, Jingshuo Zhao, Yanlin Wang, Guichun Wu, Yingjie Hou and Chunyan Yu

Forests 2025, 16(7), 1150; https://doi.org/10.3390/f16071150 (registering DOI) - 11 Jul 2025

Viewed by 104

Abstract

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to [...] Read more.

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

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

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37 pages, 3846 KiB

Open AccessArticle

The Development of a Forest Tourism Attractiveness Model and a Foundational Framework for Forest Climatic Spa Resorts: An Attributive Theory Approach

by Darija Cvikl

Forests 2025, 16(7), 1149; https://doi.org/10.3390/f16071149 - 11 Jul 2025

Viewed by 69

Abstract

To date, there has been a noticeable lack of a systematic and structured approach to the development of forest therapy tourism. This study addresses this problem by introducing a forest tourism attractiveness model and an evidence-based framework for the conceptual development of Forest [...] Read more.

To date, there has been a noticeable lack of a systematic and structured approach to the development of forest therapy tourism. This study addresses this problem by introducing a forest tourism attractiveness model and an evidence-based framework for the conceptual development of Forest Climatic Spa Resorts. Based on an attributive theory approach, a comprehensive set of forest tourism attractiveness attributes is defined, a model of forest tourism attractiveness is developed, and theoretical and conceptual foundations to support the criteria for the development of Forest Climatic Spa Resorts are presented. This research contributes to the ongoing discourse on sustainable tourism practices and emphasises the role of forest environments in promoting health and well-being in therapeutic tourism activities. Ultimately, our findings offer valuable insights for policymakers, tourism developers, and practitioners in the field of forest therapy tourism, providing a foundation for future initiatives aimed at enhancing the appeal and sustainability of forest-based tourism experiences. Full article

(This article belongs to the Section Urban Forestry)

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13 pages, 5309 KiB

Open AccessArticle

Fungi Associated with Dying Buckthorn in North America

by Ryan D. M. Franke, Nickolas N. Rajtar and Robert A. Blanchette

Forests 2025, 16(7), 1148; https://doi.org/10.3390/f16071148 - 11 Jul 2025

Viewed by 159

Abstract

Common buckthorn (Rhamnus cathartica L.) is a small tree that forms dense stands, displacing native plant species and threatening natural forest habitats in its introduced range in North America. Removal via cutting is labor intensive and often ineffective due to vigorous resprouting. [...] Read more.

Common buckthorn (Rhamnus cathartica L.) is a small tree that forms dense stands, displacing native plant species and threatening natural forest habitats in its introduced range in North America. Removal via cutting is labor intensive and often ineffective due to vigorous resprouting. Although chemical control methods are effective, they can negatively affect sensitive ecosystems. A mycoherbicide that selectively kills buckthorn would provide an additional method for control. In the present study, fungi were collected from dying buckthorn species (Frangula alnus Mill., Rhamnus cathartica, Ventia alnifolia L’Hér) located at 19 sites across Minnesota and Wisconsin for their potential use as mycoherbicides for common buckthorn. A total of 412 fungi were isolated from samples of diseased tissue and identified via DNA extraction and sequencing. These fungi were identified as 120 unique taxa belonging to 81 genera. Of these fungi, 46 species belonging to 26 genera were considered to be canker or root-rot pathogens of woody plants, including species in Cytospora, Diaporthe, Diplodia, Dothiorella, Eutypella, Fusarium, Hymenochaete, Irpex, Phaeoacemonium, and others. A future study testing the pathogenicity of these putative pathogens of buckthorn is now needed to assess their utility as potential mycoherbicide agents for control of common buckthorn. Full article

(This article belongs to the Special Issue Pathogenic Fungi in Forest)

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17 pages, 36560 KiB

Open AccessArticle

Comparative Calculation of Spectral Indices for Post-Fire Changes Using UAV Visible/Thermal Infrared and JL1 Imagery in Jinyun Mountain, Chongqing, China

by Juncheng Zhu, Yijun Liu, Xiaocui Liang and Falin Liu

Forests 2025, 16(7), 1147; https://doi.org/10.3390/f16071147 - 11 Jul 2025

Viewed by 74

Abstract

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire [...] Read more.

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

(This article belongs to the Special Issue Wildfire Behavior and the Effects of Climate Change in Forests)

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18 pages, 2591 KiB

Open AccessArticle

The Impact of Compound Drought and Heatwave Events on the Gross Primary Productivity of Rubber Plantations

by Qinggele Bao, Ziqin Wang and Zhongyi Sun

Forests 2025, 16(7), 1146; https://doi.org/10.3390/f16071146 - 11 Jul 2025

Viewed by 167

Abstract

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which [...] Read more.

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which have simpler structures, to explore the impacts of CDHEs on their primary productivity. We used Pearson and Spearman correlation analyses to select the optimal combination of drought and heatwave indices. Then, we constructed a Compound Drought–Heatwave Index (CDHI) using Copula functions to describe the temporal patterns of CDHEs. Finally, we applied a Bayes–Copula conditional probability model to estimate the probability of GPP loss under CDHE conditions. The main findings are as follows: (1) The Standardized Precipitation Evapotranspiration Index (SPEI-3) and Standardized Temperature Index (STI-1) formed the best index combination. (2) The CDHI successfully identified typical CDHEs in 2001, 2003–2005, 2010, 2015–2016, and 2020. (3) Temporally, CDHEs significantly increased the probability of GPP loss in April and May (0.58 and 0.64, respectively), while the rainy season showed a reverse trend due to water buffering (lowest in October, at 0.19). (4) Spatially, the northwest region showed higher GPP loss probabilities, likely due to topographic uplift. This study reveals how tropical plantations respond to compound climate extremes and provides theoretical support for the monitoring and management of tropical ecosystems. Full article

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

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12 pages, 2220 KiB

Open AccessArticle

The Effects of Tree Species on Soil Organic Carbon Mineralization in Reservoir Water-Level Drawdown Zones

by Jiayi Zhang, Fang Wang, Jia Yang, Yanting Zhang, Li Qiu, Ziting Chen, Xi Wang, Tianya Zhang, Songzhe Li, Jiacheng Tong, Shunbao Lu and Yanjie Zhang

Forests 2025, 16(7), 1145; https://doi.org/10.3390/f16071145 - 11 Jul 2025

Viewed by 132

Abstract

Soil organic carbon (SOC) mineralization is the conversion of SOC to inorganic forms of carbon (C) by microbial decomposition and conversion. It plays an important role in global C cycling. Currently, most of the studies investigating the effects of different tree species on [...] Read more.

Soil organic carbon (SOC) mineralization is the conversion of SOC to inorganic forms of carbon (C) by microbial decomposition and conversion. It plays an important role in global C cycling. Currently, most of the studies investigating the effects of different tree species on SOC mineralization focus on forest ecosystems, and few have focused on reservoir water-level drawdown zones. In this study, we used an indoor incubation method to investigate SOC mineralization in the plantation soils of Glyptostrobus pensilis, Taxodium Zhongshanshan, Taxodium distichum and CK (unplanted plantation) in the reservoir water-level drawdown zones. We aimed to explore the effects of different tree species on the process of SOC mineralization in the reservoir water-level drawdown zones by considering both the biological and chemical processes of the soil. The results showed that the rates of SOC mineralization in the G. pensilis and T. Zhongshanshan plantations were 47% and 37%, respectively, higher than those in CK (p < 0.05), whereas the rate of SOC mineralization in T. distichum soils did not differ from that in CK. The structural equation model’s results showed microbial biomass carbon (MBC) is a key driver of SOC mineralization, while SOC and dissolved organic carbon (DOC) concentrations are also important factors that affect SOC mineralization and follow MBC. Compared to soil biochemical properties, the bacterial community composition has relatively little effect on SOC mineralization. Planted forests can, to a degree, change the biochemical properties of the soil in the reservoir water-level drawdown zones, effectively improving soil pH, and significantly increasing the amount of potential soil C mineralization, the content of SOC and the diversity of the soil bacteria (p < 0.05). Full article

(This article belongs to the Special Issue Forest Plant, Soil, Microorganisms and Their Interactions—2nd Edition)

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17 pages, 2075 KiB

Open AccessArticle

Chemical Profiles and Nitric Oxide Inhibitory Activities of the Copal Resin and Its Volatile Fraction of Bursera bipinnata

by Silvia Marquina, Mayra Antunez-Mojica, Judith González-Christen, Antonio Romero-Estrada, Fidel Ocampo-Bautista, Ninfa Yaret Nolasco-Quintana, Araceli Guerrero-Alonso and Laura Alvarez

Forests 2025, 16(7), 1144; https://doi.org/10.3390/f16071144 - 11 Jul 2025

Viewed by 192

Abstract

Bursera bipinnata (DC.) Engl. (B. bipinnata), commonly known as “copal chino,” is a widely distributed Mexican tree found in transitional zones between pine-oak and deciduous forests. It is valued for its high-quality copal resin, traditionally used in ceremonies and offerings. Additionally, B. bipinnata [...] Read more.

Bursera bipinnata (DC.) Engl. (B. bipinnata), commonly known as “copal chino,” is a widely distributed Mexican tree found in transitional zones between pine-oak and deciduous forests. It is valued for its high-quality copal resin, traditionally used in ceremonies and offerings. Additionally, B. bipinnata is recognized for its significant value in traditional medicine, particularly in treating ailments associated with inflammation. In this work, the inhibition of nitric oxide (NO) production of the volatile fraction and resin of B. bipinnata in LPS-stimulated RAW 264.7 macrophage cells were demonstrated. In contrast, the volatile fraction exhibited 37.43 ± 7.13% inhibition at a concentration of 40 µg/mL. Chromatographic analyses of the total resin enabled the chemical characterization of eleven pentacyclic triterpenes belonging to the ursane, oleanane, and lupane series, as well as eight monoterpenes. Notably, the structures of compounds 15, 17, and 29–35 are reported for the first time from the resin of Bursera bipinnata. The anti-inflammatory activity observed for B. bipinnata resin in this study may be attributed to its high content of the triterpenes α-amyrin (15, 29.7%) and 3-epilupeol (17, 38.1%), both known for their anti-inflammatory properties. These findings support the traditional use of this copal resin. Full article

(This article belongs to the Special Issue Medicinal and Edible Uses of Non-Timber Forest Resources)

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23 pages, 48857 KiB

Open AccessArticle

A 36-Year Assessment of Mangrove Ecosystem Dynamics in China Using Kernel-Based Vegetation Index

by Yiqing Pan, Mingju Huang, Yang Chen, Baoqi Chen, Lixia Ma, Wenhui Zhao and Dongyang Fu

Forests 2025, 16(7), 1143; https://doi.org/10.3390/f16071143 - 11 Jul 2025

Viewed by 135

Abstract

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. [...] Read more.

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

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

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13 pages, 2240 KiB

Open AccessArticle

Multi-Annual Dendroclimatic Patterns for the Desert National Wildlife Refuge, Southern Nevada, USA

by Franco Biondi and James Roberts

Forests 2025, 16(7), 1142; https://doi.org/10.3390/f16071142 - 10 Jul 2025

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Abstract

Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in the western United States have experienced reduced fire frequency since Euro-American settlement, usually because of successful fire suppression policies and even without such human impacts at remote sites in the Great Basin [...] Read more.

Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in the western United States have experienced reduced fire frequency since Euro-American settlement, usually because of successful fire suppression policies and even without such human impacts at remote sites in the Great Basin and Mojave Deserts. In an effort to improve our understanding of long-term environmental dynamics in sky-island ecosystems, we developed tree-ring chronologies from ponderosa pines located in the Sheep Mountain Range of southern Nevada, inside the Desert National Wildlife Refuge (DNWR). After comparing those dendrochronological records with other ones available for the south-central Great Basin, we analyzed their climatic response using station-recorded monthly precipitation and air temperature data from 1950 to 2024. The main climatic signal was December through May total precipitation, which was then reconstructed at annual resolution over the past five centuries, from 1490 to 2011 CE. The mean episode duration was 2.6 years, and the maximum drought duration was 11 years (1924–1934; the “Dust Bowl” period), while the longest episode, 19 years (1905–1923), is known throughout North America as the “early 1900s pluvial”. By quantifying multi-annual dry and wet episodes, the period since DNWR establishment was placed in a long-term dendroclimatic framework, allowing us to estimate the potential drought resilience of its unique, tree-dominated environments. Full article

(This article belongs to the Special Issue Environmental Signals in Tree Rings)

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27 pages, 3843 KiB

Open AccessArticle

Phenotypic Variability of Juglans neotropica Diels from Different Provenances During Nursery and Plantation Stages in Southern Ecuador

by Byron Palacios-Herrera, Santiago Pereira-Lorenzo and Darwin Pucha-Cofrep

Forests 2025, 16(7), 1141; https://doi.org/10.3390/f16071141 - 10 Jul 2025

Viewed by 194

Abstract

Juglans neotropica Diels, an Andean native species classified as endangered by the IUCN, holds significant potential for reforestation and sustainable forest management programs. This study evaluated seed quality, phenotypic variability, and early establishment under nursery and field conditions in southern Ecuador. Three provenance [...] Read more.

Juglans neotropica Diels, an Andean native species classified as endangered by the IUCN, holds significant potential for reforestation and sustainable forest management programs. This study evaluated seed quality, phenotypic variability, and early establishment under nursery and field conditions in southern Ecuador. Three provenance sites—The Tundo, The Victoria, and The Argelia—were evaluated during the nursery phase, and two (The Tundo and The Victoria) in plantations, applying four pre-germination treatments: control, mechanical scarification, hot water, and water-sun exposure. Parameters assessed included seed weight, size, viability, germination, survival, and growth across three planting environments: secondary forest, riparian forest, and pasture. Significant differences in seed morphometry were observed among localities, while germination was influenced by treatment but not provenance. Seed viability remained high for up to six months, decreasing with a 2% loss of moisture. Survival reached 100% with urea application, and 96% of individuals exhibited straight stems after one year. No significant differences in growth were found between localities; however, basal diameter was highest in the pasture (13.2 mm/year⁻¹), and total height was greatest in the secondary forest (54.8 cm/year⁻¹). These findings provide key technical evidence to optimize the propagation and establishment of J. neotropica in ecological restoration and forest production contexts. Full article

(This article belongs to the Special Issue Tree Breeding: Genetic Diversity, Differentiation and Conservation)

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22 pages, 4083 KiB

Open AccessArticle

Employing Aerial LiDAR Data for Forest Clustering and Timber Volume Estimation: A Case Study with Pinus radiata in Northwest Spain

by Alberto López-Amoedo, Henrique Lorenzo, Carolina Acuña-Alonso and Xana Álvarez

Forests 2025, 16(7), 1140; https://doi.org/10.3390/f16071140 - 10 Jul 2025

Viewed by 124

Abstract

In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. [...] Read more.

In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. This study proposes a cost-effective strategy using open-access tools and data to characterize and estimate wood volume in these plantations. Two stratification approaches—classical and cluster-based—were compared to a modeling method based on Principal Component Analysis (PCA). Data came from open-access national LiDAR point clouds, acquired using manned aerial vehicles under the Spanish National Aerial Orthophoto Plan (PNOA). Moreover, two volume estimation methods were applied: one from the Xunta de Galicia (XdG) and another from Spain’s central administration (4IFN). A Generalized Linear Model (GLM) was also fitted using PCA-derived variables with logarithmic transformation. The results show that although overall volume estimates are similar across methods, cluster-based stratification yielded significantly lower absolute errors per hectare (XdG: 28.04 m³/ha vs. 44.07 m³/ha; 4IFN: 25.64 m³/ha vs. 38.22 m³/ha), improving accuracy by 7% over classical stratification. Moreover, it does not require precise field parcel locations, unlike PCA modeling. Both official volume estimation methods tended to overestimate stock by about 10% compared to PCA. These results confirm that clustering offers a practical, low-cost alternative that improves estimation accuracy by up to 18 m³/ha in fragmented forest landscapes. Full article

(This article belongs to the Special Issue Forest Ecology and Resource Monitoring Based on Sensors, Signal and Image Processing, 2nd Edition)

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12 pages, 2590 KiB

Open AccessArticle

Summer Cafe: In Vitro Case Study of Biological Repellents Against the Large Pine Weevil

by Ilze Matisone, Kristaps Ozoliņš, Roberts Matisons, Mārtiņš Spāde, Uldis Grīnfelds and Rinalds Trukšs

Forests 2025, 16(7), 1139; https://doi.org/10.3390/f16071139 - 10 Jul 2025

Viewed by 112

Abstract

Growing environmental concerns have led to the search for alternative biological repellents against the large pine weevil Hylobius abietis L., Europe’s most important coniferous forest regeneration pest. A laboratory study was carried out to assess the effectiveness (damage intensity) of six combinations of [...] Read more.

Growing environmental concerns have led to the search for alternative biological repellents against the large pine weevil Hylobius abietis L., Europe’s most important coniferous forest regeneration pest. A laboratory study was carried out to assess the effectiveness (damage intensity) of six combinations of a novel biological repellent, consisting of plant-based oils, beeswax, calcium carbonate, vanillin, pine bark extractives, terpentine, abrasive particles, solvent, and a viscosity agent, in comparison with commercially available repellent Norfort LDW 115. The application complexity of the repellents, their persistence on seedlings, and the extent of H. abietis damage were evaluated. The five alternative repellents had higher protection compared to the control repellent, highlighting the potential for new alternative repellents. The base (without additives) repellent provided the highest protection, indicating a redundancy of admixtures. A mixed cumulative link model, employed to estimate differences between the repellents, estimated 85% undamaged and none significantly damaged saplings in the case of the base repellent. However, the consistency and hence persistence of certain repellents on plantlets would benefit from improvements; further field studies are needed to upscale the test of the stability and efficiency of high levels in real environments under different H. abietis population pressures. Full article

(This article belongs to the Section Forest Health)

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14 pages, 2402 KiB

Open AccessArticle

Application of Machine Learning Models in the Estimation of Quercus mongolica Stem Profiles

by Chiung Ko, Jintaek Kang, Chaejun Lim, Donggeun Kim and Minwoo Lee

Forests 2025, 16(7), 1138; https://doi.org/10.3390/f16071138 - 10 Jul 2025

Viewed by 182

Abstract

Accurate estimation of stem profiles is critical for forest management, timber yield prediction, and ecological modeling. However, traditional taper equations often fail to capture species-specific growth variability and exhibit significant biases, particularly in the upper stem regions. Machine learning regression models were applied [...] Read more.

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

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

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