Forests

17 pages, 2162 KiB

Open AccessReview

The Transcriptional Regulatory Mechanisms Exploration of Jujube Biological Traits through Multi-Omics Analysis

by Shulin Zhang, Zhuo Chen, Luying Feng, Zhaokun Zhi, Yiteng Liu, Mengmeng Zhang, Huafeng Yue, Gao-Pu Zhu and Fuling Gao

Forests 2024, 15(2), 395; https://doi.org/10.3390/f15020395 - 19 Feb 2024

Viewed by 821

Abstract

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological research on jujube. This review began by summarizing advancements in jujube genomics. Subsequently, we provided a [...] Read more.

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological research on jujube. This review began by summarizing advancements in jujube genomics. Subsequently, we provided a comprehensive overview of the integrated application of genomics, transcriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress and discussed the transcriptional regulatory mechanisms underlying these traits. Furthermore, challenges in multi-omics research on jujube biological traits were outlined, and we proposed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic enhancement of jujube. Full article

(This article belongs to the Special Issue Non-timber Forestry Breeding, Cultivation and Processing Technology—Volume II)

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28 pages, 18824 KiB

Open AccessArticle

Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

by Dongyang Han, Jialong Zhang, Dongfan Xu, Yi Liao, Rui Bao, Shuxian Wang and Shaozhi Chen

Forests 2024, 15(2), 394; https://doi.org/10.3390/f15020394 - 19 Feb 2024

Viewed by 762

Abstract

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks [...] Read more.

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm⁻² to 25.313 t·hm⁻². We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models. Full article

(This article belongs to the Special Issue Economy and Sustainability of Forest Natural Resources)

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21 pages, 6380 KiB

Open AccessArticle

Empirical Study on the Impact of Different Types of Forest Environments in Wuyishan National Park on Public Physiological and Psychological Health

by Yuxi Weng, Yujie Zhu, Yabing Huang, Qimei Chen and Jianwen Dong

Forests 2024, 15(2), 393; https://doi.org/10.3390/f15020393 - 19 Feb 2024

Viewed by 860

Abstract

Amidst the challenges of global environmental change and urbanization, the salutary effects of natural environments on public health are increasingly being recognized. This study investigates the specific effects of varied forest environments in China’s Wuyishan National Park on physiological and psychological health. Eight [...] Read more.

Amidst the challenges of global environmental change and urbanization, the salutary effects of natural environments on public health are increasingly being recognized. This study investigates the specific effects of varied forest environments in China’s Wuyishan National Park on physiological and psychological health. Eight distinct forest environments were carefully selected, and a repeated-measures ANOVA approach was used to evaluate 41 participants over three days. Physiological assessments included Heart Rate Variability, Skin Conductance Level, and surface Electromyography, complemented by psychological evaluations using the Profile of Mood States. The key findings include the following: (1) Notable variations in physiological indicators were observed among different forest types. In valley tea gardens and broadleaf forest streamside, significant changes in heart rate indicators highlighted the influence of these settings on autonomic nervous activities. Skin Conductance Level and surface Electromyography also indicated varying emotional arousal and pleasure across the forests. The mixed broadleaf and coniferous forest valley, along with the rock-bedded streamscape, elicited emotions of low arousal but high pleasure, inducing feelings of calmness and pleasure. The valley’s tea gardens were associated with low arousal and pleasure, suggesting tranquility without positive emotional induction, while the broadleaf ridge forest induced high arousal and pleasure, reflecting an exciting and joyful environment. (2) The study found that different forest environments had a notable impact on participants’ mood states, indicating reductions in tension, anger, fatigue, and depression, along with an increase in vigor levels. In summary, forest environments offer unique psychological and physiological health benefits compared to urban settings. These findings underscore the importance of integrating forest environments into urban development and public health frameworks, and the need to further explore their impact on the health of diverse populations. Full article

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

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18 pages, 5057 KiB

Open AccessArticle

Contrasting Altitudinal Patterns and Composition of Soil Bacterial Communities along Stand Types in Larix principis-rupprechtii Forests in Northern China

by Yajie Niu, Xin Li, Chuanxu Wang, Youzhi Han, Zhuo Wang and Jing Yang

Forests 2024, 15(2), 392; https://doi.org/10.3390/f15020392 - 19 Feb 2024

Viewed by 712

Abstract

Bacterial communities inhabiting the soil of mountain ecosystems perform critical ecological functions. Although several studies have reported the altitudinal distribution patterns of bacterial communities in warm-temperate mountain forests, our understanding of typical zonal vegetation dominated by Larix principis-rupprechtii Mayr (abbreviated as larch hereafter) [...] Read more.

Bacterial communities inhabiting the soil of mountain ecosystems perform critical ecological functions. Although several studies have reported the altitudinal distribution patterns of bacterial communities in warm-temperate mountain forests, our understanding of typical zonal vegetation dominated by Larix principis-rupprechtii Mayr (abbreviated as larch hereafter) and the understory elevation distribution patterns of soil bacterial communities is still limited. In this study, the Illumina NovaSeq 6000 sequencing platform was used to investigate the changes of surface and subsurface soil bacterial communities along an altitudinal gradient (from 1720 m to 2250 m) in larch forests in northern China. Altitude significantly affected the relative abundance of Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi (bacterial dominant phylum) and Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria (bacterial dominant classes). The diversity of bacterial communities showed a concomitant increase with altitude. The variations in available nitrogen and soil temperature content at different altitudes were the main factors explaining the bacterial community structures in pure stands and mixed stands, respectively. Altitude and the contents of soil organic carbon and soil organic matter were the main factors explaining the dominant phylum (taxonomy). Our results suggest that stand type has a greater effect on the structure and composition of soil bacterial communities than elevation and soil depth, and bacterial communities show divergent patterns along the altitudes, stand types, and soil profiles. Full article

(This article belongs to the Special Issue Soil Microbial Ecology in Forest Ecosystems)

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15 pages, 4134 KiB

Open AccessArticle

An Assessment Framework for Mapping the Air Purification Service of Vegetation at the Regional Scale

by Yu Liu, Wudong Zhao, Liwei Zhang, Xupu Li, Lixian Peng, Zhuangzhuang Wang, Yongyong Song, Lei Jiao and Hao Wang

Forests 2024, 15(2), 391; https://doi.org/10.3390/f15020391 - 19 Feb 2024

Viewed by 740

Abstract

Efficiently mitigating the severe air pollution resulting from rapid progress is crucial for the sustainable development of the socio-ecological system. Recently, concerns about nature-based solutions have emerged in the research on the treatment of air pollution. Studies on the purification of PM_2.5 [...] Read more.

Efficiently mitigating the severe air pollution resulting from rapid progress is crucial for the sustainable development of the socio-ecological system. Recently, concerns about nature-based solutions have emerged in the research on the treatment of air pollution. Studies on the purification of PM_2.5 using vegetation currently concentrate on the individual scale of tree species or urban vegetation, ignoring the regional scale, which could better assist ecological governance. Therefore, taking the Fenwei Plain of China as the study area, an assessment framework of the air purification service’s spatial distribution reflecting regional vegetation was constructed. The dry deposition model and GeoDetector were used to quantify the spatial-temporal pattern and explore natural driving factors on the removal of PM_2.5. The results showed that (1) the PM_2.5 purification services offered by various types of vegetation exhibit notable variations. The average removal rates of PM_2.5 by vegetation were 0.186%, 0.243%, and 0.435% in 2000, 2010, and 2021, respectively. (2) Meanwhile, a wide range of spatial mismatch exists between the PM_2.5 concentration and PM_2.5 removal. Insufficient supply regions of PM_2.5 purification services account for 50% of the Fenwei Plain. (3) PM_2.5 removal was strongly influenced by the types of vegetation and the Normalized Vegetation Index (NDVI), followed by the Digital Elevation Model (DEM), and less affected by meteorological factors; a strong joint effect was shown among the factors. The findings in this research provide a new perspective on regional air pollution management at the regional scale. Full article

(This article belongs to the Special Issue Urban Forests and Human Health)

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22 pages, 43774 KiB

Open AccessArticle

Fine Classification of Urban Tree Species Based on UAV-Based RGB Imagery and LiDAR Data

by Jingru Wu, Qixia Man, Xinming Yang, Pinliang Dong, Xiaotong Ma, Chunhui Liu and Changyin Han

Forests 2024, 15(2), 390; https://doi.org/10.3390/f15020390 - 19 Feb 2024

Viewed by 896

Abstract

Rapid and accurate classification of urban tree species is crucial for the protection and management of urban ecology. However, tree species classification remains a great challenge because of the high spatial heterogeneity and biodiversity. Addressing this challenge, in this study, unmanned aerial vehicle [...] Read more.

Rapid and accurate classification of urban tree species is crucial for the protection and management of urban ecology. However, tree species classification remains a great challenge because of the high spatial heterogeneity and biodiversity. Addressing this challenge, in this study, unmanned aerial vehicle (UAV)-based high-resolution RGB imagery and LiDAR data were utilized to extract seven types of features, including RGB spectral features, texture features, vegetation indexes, HSV spectral features, HSV texture features, height feature, and intensity feature. Seven experiments involving different feature combinations were conducted to classify 10 dominant tree species in urban areas with a Random Forest classifier. Additionally, Plurality Filling was applied to further enhance the accuracy of the results as a post-processing method. The aim was to explore the potential of UAV-based RGB imagery and LiDAR data for tree species classification in urban areas, as well as evaluate the effectiveness of the post-processing method. The results indicated that, compared to using RGB imagery alone, the integrated LiDAR and RGB data could improve the overall accuracy and the Kappa coefficient by 18.49% and 0.22, respectively. Notably, among the features based on RGB, the HSV and its texture features contribute most to the improvement of accuracy. The overall accuracy and Kappa coefficient of the optimal feature combination could achieve 73.74% and 0.70 with the Random Forest classifier, respectively. Additionally, the Plurality Filling method could increase the overall accuracy by 11.76%, which could reach 85.5%. The results of this study confirm the effectiveness of RGB imagery and LiDAR data for urban tree species classification. Consequently, these results could provide a valuable reference for the precise classification of tree species using UAV remote sensing data in urban areas. Full article

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

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17 pages, 4587 KiB

Open AccessArticle

Effects of Soil Fauna on the Home-Field Advantage of Litter Total Phenol and Condensed Tannin Decomposition

by Lingyuan Lei, Jing Zeng, Quanwei Liu, Lijuan Luo, Zhiliang Ma, Yamei Chen and Yang Liu

Forests 2024, 15(2), 389; https://doi.org/10.3390/f15020389 - 19 Feb 2024

Cited by 1 | Viewed by 855

Abstract

Soil fauna play a vital role in contributing to the home-field advantage (HFA: litter decomposes faster in its natural habitat than elsewhere) during litter decomposition. Whether the presence of soil fauna affects the HFA of the decomposition of total phenols and condensed tannins, [...] Read more.

Soil fauna play a vital role in contributing to the home-field advantage (HFA: litter decomposes faster in its natural habitat than elsewhere) during litter decomposition. Whether the presence of soil fauna affects the HFA of the decomposition of total phenols and condensed tannins, which are important components of litter, has rarely been investigated. In this study, litterbags with different mesh sizes were transplanted reciprocally, 0.04 mm (basically excluding soil fauna) and 3 mm (basically allowing all soil fauna to enter), in Lindera megaphylla and Cryptomeria fortunei forests. The results illustrated that the loss rates of total phenols and condensed tannins reached 64.07% to 84.49% and 69.67% to 88.37%, respectively, after 2 months of decomposition. Moreover, soil fauna positively contributed to the decomposition of condensed tannins in high-quality litter. After 2 months of decomposition, a significantly positive HFA (HFA index: 10.32) was found for total phenol decomposition in the coarse mesh, while a significantly negative HFA (HFA index: −1.81) was observed for condensed tannin decomposition in the fine mesh after 10 months of decomposition. Polyphenol oxidase (PPO) and peroxidase (POD) activities were significantly influenced by litter types. The loss rates of total phenols and condensed tannins were significantly negatively correlated with the initial N content, P content, N/P ratio, and POD activity and were positively related to the initial C content, total phenol content, condensed tannin content, C/P ratio, and C/N ratio. Only the loss of condensed tannins was negatively correlated with PPO activity (after 2 months’ decomposition). However, none of these correlations were observed after 10 months of decomposition. Our study illustrated that (1) soil fauna contributed to the decomposition of total phenols and condensed tannins but were influenced by litter type for condensed tannins. (2) The soil fauna had inconsistent effects on the HFA of total phenols and condensed tannins, possibly due to the combined regulatory effects of environmental context, litter quality, and rapid decomposition rates. In sum, the results indicated that soil fauna played an important role in the decomposition of condensed tannins and total phenols in litter, and additional studies on the effects of soil faunal abundance and class on HFA of condensed tannins and total phenols are needed. Full article

(This article belongs to the Special Issue Forest Litter Decomposition and Biogeochemistry)

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11 pages, 1868 KiB

Open AccessArticle

Evaluating Dendrolimus superans (Lepidoptera: Lasiocampidae) Occurrence and Density Modeling with Habitat Conditions

by Daxiao Han, Shuo Wang, Jili Zhang, Rong Cui and Qianxue Wang

Forests 2024, 15(2), 388; https://doi.org/10.3390/f15020388 - 19 Feb 2024

Viewed by 706

Abstract

Dendrolimus superans, a prominent forest pest in northeast China, exerts detrimental effects on tree growth and development, disrupts the ecological functioning of forests, and even alters the trajectory of succession. The objective of this study was to investigate the influence of habitat [...] Read more.

Dendrolimus superans, a prominent forest pest in northeast China, exerts detrimental effects on tree growth and development, disrupts the ecological functioning of forests, and even alters the trajectory of succession. The objective of this study was to investigate the influence of habitat conditions on the occurrence probability and density of overwintering D. superans, aiming to provide scientific insights for the effective prevention of and control measures against this pest infestation. The investigation encompassed 142 plots (20 m × 20 m) in various forest types within the primary distribution area of D. superans in the Great Xing’ an Mountains, focusing on factors such as topography, forest vegetation, and larval density. Binary logistic regression was employed to establish models for predicting the occurrence probability of D. superans, while generalized linear models (GLMs) and categorical regression (CATREG) were utilized to develop models for estimating its population size. Subsequently, an evaluation was conducted to assess the performance of these models. The occurrence probability model showed high accuracy (AUC = 0.826) in predicting infestation. The slope aspect and herb cover were the key factors affecting the occurrence of D. superans. The occurrence probability was the lowest on shady slopes and the highest on sunny slopes. The occurrence probability of D. superans increased with the increase in herb cover. The model of quantification showed that the density of D. superans was the least on shady slopes and the highest on sunny slopes. As the slope gradient increased, the density decreased. D. superans occurred most frequently on ridges. Similarly, with the increase in canopy cover or the decrease in diameter at breast height (DBH) and stand density, the density of D. superans increased. The influence of the topography factors surpassed that of the forest vegetation factors in shaping the population dynamics of D. superans, despite both being significant contributors. The study revealed that D. superans is prone to occur on sunny slopes, flat slopes, and ridges, which should be the focus of prevention and control in forest management practices, such as replanting, thinning, and regular weeding, to help restrain the growth of the population of this pest. Full article

(This article belongs to the Section Forest Health)

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20 pages, 5305 KiB

Open AccessArticle

Design and Implementation of an Intelligent Log Diameter Grading and Sorting Line Based on Machine Vision

by Zhigang Ding, Yangyang Gong, Linghua Kong and Jishi Zheng

Forests 2024, 15(2), 387; https://doi.org/10.3390/f15020387 - 19 Feb 2024

Viewed by 837

Abstract

In order to address the challenges posed by elevated manual labor costs and limited automation in traditional log diameter grading and sorting processes, this paper centers on the design and research of an intelligent log diameter grading and sorting line utilizing machine vision. [...] Read more.

In order to address the challenges posed by elevated manual labor costs and limited automation in traditional log diameter grading and sorting processes, this paper centers on the design and research of an intelligent log diameter grading and sorting line utilizing machine vision. The study focuses on logs with smaller diameters located in Fujian province, China. By analyzing production requirements, the study formulates the structure of the feeding, alignment, detection, and sorting zones to fulfill sorting functions. Using the YOLOv5 model, the system achieves accurate log end face positioning, and the diameter is computed through a designated algorithm. The operational process of the system is examined, and the control logic governing the production line is elucidated. Evaluating the practical performance of the production line, the study assesses the accuracy of diameter recognition, precision in grading, and operational efficiency. The results reveal that the absolute error in diameter detection for the sorting line averages 1.12 mm, with sorting accuracy exceeding 95%. The sorting line can automatically categorize logs with diameters ranging from 60 mm to 300 mm and lengths ranging from 2 m to 6 m, achieving an annual sorting capacity of 120,000 to 130,000 cubic meters. The research findings illustrate that the system fulfills the industry’s demands for log diameter grading and sorting, thereby enhancing economic efficiency for enterprises. Full article

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

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25 pages, 8875 KiB

Open AccessArticle

Utilizing Comprehensive Criteria and Indicators for Post-Fire Forest Restoration in Spatial Decision Support Systems (SDSS)

by Rahaf Alayan and Zoltán Lakner

Forests 2024, 15(2), 386; https://doi.org/10.3390/f15020386 - 19 Feb 2024

Viewed by 762

Abstract

Amidst the increasing frequency and severity of forest fires globally, the imperative of effective post-fire forest restoration has gained unprecedented significance. This study outlines a comprehensive approach to post-fire forest restoration and discusses its implementation through spatial decision-making systems. The methodology involves utilizing [...] Read more.

Amidst the increasing frequency and severity of forest fires globally, the imperative of effective post-fire forest restoration has gained unprecedented significance. This study outlines a comprehensive approach to post-fire forest restoration and discusses its implementation through spatial decision-making systems. The methodology involves utilizing multi-criteria analysis (MCA) to identify and prioritize criteria based on their relative importance. This allows for the creation of easily assessable alternatives and their application to spatial maps, providing local officials with valuable information. To achieve optimal decision-making, the study utilized the Analytic Hierarchy Process (AHP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods along with Spatial Decision Support Systems (SDSS) to generate a suitability map. The results highlight that 28% of the study area is well-suited for post-fire forest restoration, with 44% moderately appropriate, while 3% is deemed unsuitable for restoration until the end of 2023 due to severe soil loss or inherent geographical challenges. Full article

(This article belongs to the Topic Forest Ecosystem Restoration)

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14 pages, 2018 KiB

Open AccessArticle

Contrasting Effects of Nitrogen Deposition and Phosphorus Addition on Soil Organic Carbon in a Subtropical Forest: Physical Protection versus Chemical Stability

by Xiaodong Wang, Anqi Wu, Fu-Sheng Chen, Xiangmin Fang, Huimin Wang and Fangchao Wang

Forests 2024, 15(2), 385; https://doi.org/10.3390/f15020385 - 19 Feb 2024

Viewed by 839

Abstract

Soil organic carbon (SOC) not only contributes to maintain soil health, but is also important in regulating global climate change. How atmospheric nitrogen (N) deposition and phosphorus (P) addition affects SOC dynamics remains unclear, especially in subtropical forests. The response of SOC in [...] Read more.

Soil organic carbon (SOC) not only contributes to maintain soil health, but is also important in regulating global climate change. How atmospheric nitrogen (N) deposition and phosphorus (P) addition affects SOC dynamics remains unclear, especially in subtropical forests. The response of SOC in three layers to N deposition and P addition in this study is estimated by analyzing the soil aggregates and C chemical stability composition fertilized with N (100 kg N hm⁻² a⁻¹) and/or P (50 kg P hm⁻² a⁻¹) over 9 years in a Chinese fir (Cunninghamia lanceolata) plantation. Treatments involving N deposition increased the SOC concentration, while P addition alone decreased the SOC concentration in soil layers above 10 cm. The addition of N significantly increased the mean diameter of topsoil aggregates, macroaggregates SOC concentration, and the contribution of N to total SOC. P addition decreased the relative abundances of aromatic and aliphatic functional groups while decreasing the chemical stability of SOC in the topsoil. A structural equation model indicated that N deposition promoted SOC concentration by mainly improving the physical protection of soil aggregates, while P addition reduced SOC sequestration by decreasing the chemical stability of SOC. Our research suggested that elevated N deposition might promote the soil C sink, while P fertilization would not be recommended under increased N deposition to protect soil C storage in subtropical forests. Full article

(This article belongs to the Section Forest Soil)

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Graphical abstract

12 pages, 8112 KiB

Open AccessArticle

Preparation and Properties of Soft-/Hard-Switchable Transparent Wood with 0 °C as a Boundary

by Yang Liu, Yi Zhang, Jianhui Guo, Gaiping Guo and Cheng Li

Forests 2024, 15(2), 384; https://doi.org/10.3390/f15020384 - 19 Feb 2024

Viewed by 867

Abstract

Transparent wood has excellent optical and thermal properties and has great potential utilization value in energy-saving building materials, optoelectronic devices, and decorative materials. In this work, transparent wood with soft-/hard-switchable and shape recovery capabilities was prepared by introducing an epoxy-based polymer with a [...] Read more.

Transparent wood has excellent optical and thermal properties and has great potential utilization value in energy-saving building materials, optoelectronic devices, and decorative materials. In this work, transparent wood with soft-/hard-switchable and shape recovery capabilities was prepared by introducing an epoxy-based polymer with a glass transition temperature of about 0 °C into the delignified wood template. The epoxy resin was well filled in the pore structure of the delignified wood, and the as-prepared wood exhibited excellent transparency; the optical transmittance and haze of the transparent wood with a thickness of 2.0 mm were approximately 70% and 95%, respectively. Because the glass transition temperature of the epoxy-based polymer was about 0 °C, the prepared transparent wood was rigid below 0 °C and flexible above °C; meanwhile, the transparent wood exhibited shape change and shape recovery properties. Incorporating optical transparency and soft-/hard-switchable ability into the transparent wood opens a new avenue for developing advanced functional wood-based materials. Full article

(This article belongs to the Special Issue Sustainable Materials in the Forest Products Industry)

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17 pages, 2344 KiB

Open AccessArticle

Analysis of Basidiomycete Fungal Communities in Soil and Wood from Contrasting Zones of the AWPA Biodeterioration Hazard Map across the United States

by Grant T. Kirker, Amy B. Bishell, Jed Cappellazzi, Samuel V. Glass, Jonathan A. Palmer, Nathan J. Bechle and William J. Hickey

Forests 2024, 15(2), 383; https://doi.org/10.3390/f15020383 - 18 Feb 2024

Viewed by 766

Abstract

Wood deterioration due to basidiomycetous decay fungi shortens the useful life span of wood and wood-based materials. Prescriptive preservative treatment is the most effective way to reduce the detrimental effects of these microorganisms, particularly in soil contact and areas of critical use (difficult [...] Read more.

Wood deterioration due to basidiomycetous decay fungi shortens the useful life span of wood and wood-based materials. Prescriptive preservative treatment is the most effective way to reduce the detrimental effects of these microorganisms, particularly in soil contact and areas of critical use (difficult to replace or vital to structure). Current American Wood Protection Association (AWPA) guidelines in the standardized use category system specify 3 zones of severity regarding wood decay fungal hazards but contain very little information on the diversity and abundance of these fungi colonizing soil and wood. In this study, amplicon based sequencing was utilized to compare fungal communities in wood and adjacent soil to provide baseline data on the fungi involved in the process. A thorough understanding of decay hazards is critical for the proper selection and use of wood in soil contact. The goal of this work is to provide baseline data on basidiomycete fungal diversity and species composition in different zones of the existing 3-zone AWPA hazard map as compared to the previous 5-zone hazard map and Scheffer decay indices and discuss the ecological implications for wood decay. Full article

(This article belongs to the Special Issue Improving the Service Life of Wood: Durability and Preservation)

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20 pages, 11531 KiB

Open AccessFeature PaperArticle

Urban Vegetation Classification for Unmanned Aerial Vehicle Remote Sensing Combining Feature Engineering and Improved DeepLabV3+

by Qianyang Cao, Man Li, Guangbin Yang, Qian Tao, Yaopei Luo, Renru Wang and Panfang Chen

Forests 2024, 15(2), 382; https://doi.org/10.3390/f15020382 - 18 Feb 2024

Viewed by 952

Abstract

Addressing the problems of misclassification and omissions in urban vegetation fine classification from current remote sensing classification methods, this research proposes an intelligent urban vegetation classification method that combines feature engineering and improved DeepLabV3+ based on unmanned aerial vehicle visible spectrum images. The [...] Read more.

Addressing the problems of misclassification and omissions in urban vegetation fine classification from current remote sensing classification methods, this research proposes an intelligent urban vegetation classification method that combines feature engineering and improved DeepLabV3+ based on unmanned aerial vehicle visible spectrum images. The method constructs feature engineering under the ReliefF algorithm to increase the number of features in the samples, enabling the deep learning model to learn more detailed information about the vegetation. Moreover, the method improves the classical DeepLabV3+ network structure based on (1) replacing the backbone network using MoblieNetV2; (2) adjusting the atrous spatial pyramid pooling null rate; and (3) adding the attention mechanism and the convolutional block attention module. Experiments were conducted with self-constructed sample datasets, where the method was compared and analyzed with a fully convolutional network (FCN) and U-Net and ShuffleNetV2 networks; the migration of the method was tested as well. The results show that the method in this paper is better than FCN, U-Net, and ShuffleNetV2, and reaches 92.27%, 91.48%, and 85.63% on the accuracy evaluation indices of overall accuracy, MarcoF1, and mean intersection over union, respectively. Furthermore, the segmentation results are accurate and complete, which effectively alleviates misclassifications and omissions of urban vegetation; moreover, it has a certain migration ability that can quickly and accurately classify the vegetation. Full article

(This article belongs to the Topic Karst Environment and Global Change)

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24 pages, 15701 KiB

Open AccessArticle

Robots for Forest Maintenance

by Tiago Gameiro, Tiago Pereira, Carlos Viegas, Francesco Di Giorgio and NM Fonseca Ferreira

Forests 2024, 15(2), 381; https://doi.org/10.3390/f15020381 - 18 Feb 2024

Viewed by 1045

Abstract

Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire [...] Read more.

Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system’s robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers. Full article

(This article belongs to the Special Issue Forest Fires: Latest Advances and Perspectives)

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17 pages, 7178 KiB

Open AccessArticle

CsAFS2 Gene from the Tea Plant Intercropped with Chinese Chestnut Plays an Important Role in Insect Resistance and Cold Resistance

by Jianzhao Wang, Mei Dao, Ziyun Yang, Yan Bai, Ying Qin and Tian Wu

Forests 2024, 15(2), 380; https://doi.org/10.3390/f15020380 - 18 Feb 2024

Viewed by 649

Abstract

α-Farnesene, a crucial secondary metabolite in sesquiterpenes, is crucial for plant biotic and abiotic stress resistance. In this study, we screened an AFS gene from transcriptome data of tea plants (Camellia sinensis) intercropped with Chinese chestnut (Castanea mollissima), resulting [...] Read more.

α-Farnesene, a crucial secondary metabolite in sesquiterpenes, is crucial for plant biotic and abiotic stress resistance. In this study, we screened an AFS gene from transcriptome data of tea plants (Camellia sinensis) intercropped with Chinese chestnut (Castanea mollissima), resulting in the cloning of CsAFS2. CsAFS2 expression increased following treatment with MJ (Methyl jasmonate), SA (Salicylic acid), GA₃ (Gibberellin A3), and various plant growth regulators, as well as under high-salt, drought, and low-temperature conditions. The heterologous genetic transformation of tobacco with CsAFS2 led to an enhanced resistance to low-temperature stress and aphid feeding, evident from elevated levels of osmotic regulatory substances, increased protective enzyme activity, and the upregulation of cold and insect resistance-related genes. Trichomes, crucial in cold and insect resistance, exhibited significantly greater length and density in transgenic tobacco as compared to control plants. These results confirm the vital role of CsAFS2 in enhancing cold and insect resistance, providing comprehensive insights into stress regulation mechanisms in tea plants and advancing stress-resistant tea plant breeding. Full article

(This article belongs to the Special Issue Latest Progress in Research on Forest Tree Genomics)

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16 pages, 4162 KiB

Open AccessEditor’s ChoiceArticle

Potential Distribution Projections for Senegalia senegal (L.) Britton under Climate Change Scenarios

by Jiaqi Fang, Jianfei Shi, Ping Zhang, Minghao Shao, Na Zhou, Yongdong Wang and Xinwen Xu

Forests 2024, 15(2), 379; https://doi.org/10.3390/f15020379 - 18 Feb 2024

Viewed by 972

Abstract

The gum acacia Senegalia senegal (L.) Britton (Fabales: Fabaceae) is a drought-tolerant plant belonging to the genus Acacia of the Leguminosae family, possessing significant economic and ecological value. Despite its importance, there is a knowledge gap regarding the potential impact of climate change [...] Read more.

The gum acacia Senegalia senegal (L.) Britton (Fabales: Fabaceae) is a drought-tolerant plant belonging to the genus Acacia of the Leguminosae family, possessing significant economic and ecological value. Despite its importance, there is a knowledge gap regarding the potential impact of climate change on the distribution of S. senegal, crucial for the conservation of plant resources and optimizing its use in introductory silviculture. In this study, we selected 23 environmental variables and utilized the optimized maximum entropy (MaxEnt) model to analyze the key environmental factors affecting the distribution of S. senegal worldwide and simulate the current and future distribution range of S. senegal in Pakistan under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios. The results highlight key environmental factors influencing the distribution of S. senegal, including BIO3 (isothermally), BIO4 (coefficient of seasonal variation of temperature), BIO11 (mean temperature of the coldest season), and BIO12 (annual precipitation). Regions with higher and less fluctuating temperatures exhibit a higher potential for S. senegal distribution. Currently, suitable habitats of S. senegal are concentrated in the southern region of Pakistan, covering provinces such as Punjab, Sindh, and Balochistan, with highly suitable habitats accounting for 6.06% of the total area. Under the current climatic conditions, this study identifies the spatial patterns of suitable habitats and their concentration in specific regions. With climate change, a notable expansion of suitable habitats towards higher latitudes is observed, with the most significant expansion under the extremely severe climate change scenario (SSP5-8.5), reaching 223.45% of the current level. The results of this study enhance our understanding of the dynamics of S. senegal distribution under climate change and offer valuable insights into the long-term introduction of S. senegal for afforestation and soil conservation in Pakistan. This study provides theoretical support for the sustainable development of the local ecosystem and socio-economy, emphasizing the importance of proactive measures to adapt to changing climatic conditions. Full article

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

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25 pages, 1729 KiB

Open AccessArticle

The Impact of Aesthetic Expectations and Aesthetic Experiential Qualities on Tourist Satisfaction: A Case Study of the Zhangjiajie National Forest Park

by Ying Wen, Fen Luo and Hao Li

Forests 2024, 15(2), 378; https://doi.org/10.3390/f15020378 - 18 Feb 2024

Viewed by 750

Abstract

Aesthetic expectations often constitute the primary focus in marketing nature-based tourist destinations. However, academic research has insufficiently explored the disparity between tourists’ aesthetic expectations and the actual aesthetic quality maintenance in shaping satisfaction. Employing the Expectation Confirmation Theory, this study utilized structural equation [...] Read more.

Aesthetic expectations often constitute the primary focus in marketing nature-based tourist destinations. However, academic research has insufficiently explored the disparity between tourists’ aesthetic expectations and the actual aesthetic quality maintenance in shaping satisfaction. Employing the Expectation Confirmation Theory, this study utilized structural equation modeling techniques to analyze survey data (n = 446). It proposed and tested an Aesthetic Expectation Confirmation Model to examine the relationship between aesthetic expectations, experiential qualities, and tourist satisfaction in the Zhangjiajie National Forest Park. The empirical findings show that aesthetic expectations have a direct, negative impact on satisfaction, while aesthetic expectation confirmation has a positive direct impact on satisfaction. Moreover, aesthetic expectation confirmation also plays a mediating role in the influence of aesthetic expectations and experiential quality on satisfaction. Specifically, aesthetic expectations indirectly impact satisfaction negatively through aesthetic expectation confirmation, whereas aesthetic experiential qualities have a positive, indirect impact on satisfaction through the same process. These findings offer theoretical contributions to the literature on forest recreation aesthetics and hold practical significance for the planning and management of destination aesthetics. Full article

(This article belongs to the Special Issue Talking about Forest Culture Research from the Environment to Society)

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14 pages, 4761 KiB

Open AccessArticle

Efficacy and Antifungal Mechanism of Rosemary Essential Oil against Colletotrichum gloeosporioides

by Tiantian Yuan, Yang Hua, Dangquan Zhang, Chaochen Yang, Yong Lai, Mingwan Li, Shen Ding, Song Li and Yuanyuan Chen

Forests 2024, 15(2), 377; https://doi.org/10.3390/f15020377 - 18 Feb 2024

Viewed by 1044

Abstract

The antifungal activity and mechanism of rosemary essential oil against Colletotrichum gloeosporioides, the walnut anthracnose pathogen, were investigated using scanning electron microscopy (SEM), index determination and transcriptome technique. The results showed that rosemary essential oil could inhibit the growth of C. gloeosporioides [...] Read more.

The antifungal activity and mechanism of rosemary essential oil against Colletotrichum gloeosporioides, the walnut anthracnose pathogen, were investigated using scanning electron microscopy (SEM), index determination and transcriptome technique. The results showed that rosemary essential oil could inhibit the growth of C. gloeosporioides with minimum inhibitory (MIC) and fungicidal (MFC) concentrations of 15.625 μL/mL and 31.25 μL/mL, respectively. Scanning electron microscopy revealed that the mycelium morphology became shriveled, twisted, and severely deformed after being treated with rosemary essential oil. The activity of chitinase, which decomposes fungal cell wall components in C. gloeosporioides, increased. The ergosterol content in the plasma membrane decreased, while the cell contents including nucleic acids, soluble protein and soluble reducing sugar were released resulting in the extracellular electrical conductivity being changed. For metabolic activity, the enzymes succinate dehydrogenase (SDH), malate dehydrogenase (MDH), ATPase and ATP decreased, whereas phosphofructokinase (PFK) increased. Transcriptome sequencing results showed that the antifungal mechanism of rosemary essential oil involves the destruction of the cell wall and membrane, inhibition of genetic material synthesis, and cell division and differentiation. The results are helpful to understand the efficacy and antifungal mechanism of rosemary essential oil against C. gloeosporioides and provide a theoretical basis for the development of rosemary essential oil as a biological control agent. Full article

(This article belongs to the Special Issue Advances in the Cultivation, Protection and High-Value Utilization of Forest Resources)

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16 pages, 6685 KiB

Open AccessArticle

Transcriptional Profiling Reveals Key Regulatory Roles of the WUSCHEL-Related Homeobox Gene Family in Yellowhorn (Xanthoceras sorbifolia Bunge)

by Wentao Zhang, Xinyao Xie, Linlin Le and Fuliang Cao

Forests 2024, 15(2), 376; https://doi.org/10.3390/f15020376 - 18 Feb 2024

Viewed by 691

Abstract

The WUSCHEL-related homeobox (WOX) gene family plays a crucial role in regulating embryonic development, organ formation, and stress resistance. Yellowhorn (Xanthoceras sorbifolia Bunge), a drought-resistant tree known for its oil production, lacks sufficient information regarding the WOX gene family. To understand the [...] Read more.

The WUSCHEL-related homeobox (WOX) gene family plays a crucial role in regulating embryonic development, organ formation, and stress resistance. Yellowhorn (Xanthoceras sorbifolia Bunge), a drought-resistant tree known for its oil production, lacks sufficient information regarding the WOX gene family. To understand the evolutionary mechanisms and potential functions of this gene family in yellowhorn, we conducted a comprehensive investigation on its expression patterns and evolutionary characteristics. Our analysis revealed the presence of nine XsWOX genes in the yellowhorn genome, which could be categorized into three distinct clades through a phylogenetic analysis. A chromosomal localization analysis indicated that these nine XsWOX genes were situated on six out of the fifteen chromosomes. An intra-species collinear analysis revealed only one pair of tandem duplicated genes within the XsWOX family. The promoter regions of the XsWOX family were found to contain responsive cis-acting elements associated with plant growth and development, stress responses, and hormone signaling. Moreover, an analysis of the gene expression profiles in different developmental stages of callus revealed significant expressions of XsWOX1, XsWOX4, and XsWOX5 in embryogenic callus and somatic embryo formation, suggesting that they have special roles in regulating yellowhorn’s somatic embryogenesis. Furthermore, the expression level of XsWOX5 indicated its potential involvement not only in organ formation but also in responding to low temperature, salt, and saline-alkali stresses. Overall, our findings lay a solid foundation for future in-depth studies on the functionality and evolution of XsWOX genes in yellowhorn. Full article

(This article belongs to the Special Issue Forest-Tree Comparative Genomics and Adaptive Evolution)

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11 pages, 1769 KiB

Open AccessArticle

Development of a Real-Time PCR Assay for the Early Detection of the Eucalyptus Pathogen Quambalaria eucalypti

by Roberto Faedda and Gabriela B. Silva

Forests 2024, 15(2), 375; https://doi.org/10.3390/f15020375 - 17 Feb 2024

Viewed by 773

Abstract

Quambalaria eucalypti is a fungal pathogen that causes leaf spot, shoot blight, and stem canker on Eucalyptus spp. Early diagnosis of the disease is difficult, although the symptoms are clear in its advanced phase. To enable a rapid and sensitive screening of asymptomatic [...] Read more.

Quambalaria eucalypti is a fungal pathogen that causes leaf spot, shoot blight, and stem canker on Eucalyptus spp. Early diagnosis of the disease is difficult, although the symptoms are clear in its advanced phase. To enable a rapid and sensitive screening of asymptomatic or latently infected plant material for Q. eucalypti, a SYBR green-based real-time PCR assay targeting the partial histone-H3 region was developed. The assay demonstrated specificity for Q. eucalypti, not showing cross-reactivity with other Quambalaria species or the other eucalyptus fungal pathogens tested. The primers developed in this study ensured high analytical sensitivity, allowing the detection of Q. eucalypti DNA concentrations as low as 10 fg DNA from asymptomatic plants. The robustness and efficacy of the assay was demonstrated by interlaboratory comparisons with similar results. This newly developed quantitative PCR assay can be used for more comprehensive epidemiological investigations, testing the plant material in known Q. eucalypti distribution areas for early management strategies, or collecting data for resistance breeding programs. Full article

(This article belongs to the Special Issue Editorial Board Members' Collection Series: Advances in Forest Health: Invasive Species, Insects and Diseases)

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18 pages, 2638 KiB

Open AccessArticle

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

by Zhaohui Yang, Guangshuang Duan, Ram P. Sharma, Wei Peng, Lai Zhou, Yaru Fan and Mengtao Zhang

Forests 2024, 15(2), 374; https://doi.org/10.3390/f15020374 - 17 Feb 2024

Viewed by 719

Abstract

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques [...] Read more.

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation. Full article

(This article belongs to the Special Issue Advances in Forest Growth and Site Productivity Modeling—Series II)

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18 pages, 6249 KiB

Open AccessReview

Charting the Research Terrain for Large Old Trees: Findings from a Quantitative Bibliometric Examination in the Twenty-First Century

by Chunping Xie, Chang Liu, Dawei Liu and C. Y. Jim

Forests 2024, 15(2), 373; https://doi.org/10.3390/f15020373 - 17 Feb 2024

Viewed by 704

Abstract

Despite their relatively small numbers, large old trees play disproportionately important roles in global biodiversity and ecosystem functions. There is a lack of systematic reviews and quantitative analyses of the accumulated literature. Understanding the research context and evolution could pump prime research and [...] Read more.

Despite their relatively small numbers, large old trees play disproportionately important roles in global biodiversity and ecosystem functions. There is a lack of systematic reviews and quantitative analyses of the accumulated literature. Understanding the research context and evolution could pump prime research and conservation endeavors. Using the comprehensive Web of Science, we applied VOSviewer (1.6.19) and CiteSpace (6.1R2) bibliometric software to examine the large old tree research field in 2000–2022. The queries of the bibliographic database generated quantitative–visual depictions in the form of knowledge maps. The nodes denote research intensity, and inter-node linkages denote the pathways and frequencies of collaborative activities. The research outputs differed significantly in terms of regions, countries, institutions, high-citation articles, productive researchers, hot topics, and research frontiers. Conspicuous spatial disparities were displayed, with the U.S.A., China, and Australia leading in publication counts and a cluster of European countries making considerable collective contributions. The research collaboration demonstrated a dichotomy: European countries networked more by geographical propinquity, and the top three countries connected by long-distance leap-frog jumps. The entrenched discrepancies between the endowed developed domains vis-à-vis the deprived developing domains were clearly expressed. The research productivity progressed through three stages: initial, growth, and flourishing. The leading institutions, researchers, and highly cited papers were recognized. The keyword analysis pinpointed diverse research hotspots: growth dynamics, conservation and management, ecological functions, and environmental response. This study informs recommendations for future research directions and cooperation on longevity mechanisms, evolutionary adaptation, dynamic monitoring, and temporal–spatial patterns. The integrated application of GIS, machine learning, and big data technologies could strengthen research capability. Full article

(This article belongs to the Special Issue Sustainable Forest Management, Utilization and Biodiversity Conservation)

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15 pages, 2497 KiB

Open AccessArticle

Long-Term Nitrogen Addition Accelerates Litter Decomposition in a Larix gmelinii Forest

by Miao Wang, Guancheng Liu, Yajuan Xing, Guoyong Yan and Qinggui Wang

Forests 2024, 15(2), 372; https://doi.org/10.3390/f15020372 - 16 Feb 2024

Viewed by 664

Abstract

Elevated atmospheric N deposition has the potential to alter litter decomposition patterns, influencing nutrient cycling and soil fertility in boreal forest ecosystems. In order to study the response mechanism of litter decomposition in Larix gmelinii forest to N deposition, we established four N [...] Read more.

Elevated atmospheric N deposition has the potential to alter litter decomposition patterns, influencing nutrient cycling and soil fertility in boreal forest ecosystems. In order to study the response mechanism of litter decomposition in Larix gmelinii forest to N deposition, we established four N addition treatments (0, 25, 50, 75 kg N ha⁻¹ yr⁻¹) in the Greater Khingan Mountains region. The results showed that (1) both needle and mixed leaf litter (Betula platyphylla and Larix gmelinii) exhibited distinct decomposition stages, with N addition accelerating decomposition for both litter types. The decomposition of high-quality (low C/N ratio) mixed leaf litter was faster than that of low-quality needle litter. (2) Mixed leaf litter increased the decomposition coefficients of litter with lower nutrients. (3) All N addition treatments promoted the decomposition of needle litter, while the decomposition rate of mixed leaf litter decreased under high-N treatment. (4) N addition inhibited the release of N and P in needle litter and promoted the release of N in mixed leaf litter, while high-N treatment had no positive effect on the release of C and P in mixed leaf litter. Our research findings suggest that limited nutrients in litter may be a key driving factor in regulating litter decomposition and emphasize the promoting effect of litter mixing and nitrogen addition on litter decomposition. Full article

(This article belongs to the Topic Litter Decompositions: From Individuals to Ecosystems)

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14 pages, 4029 KiB

Open AccessArticle

Mechanical and Antibacterial Properties of Bamboo Charcoal/ZnO-Modified Bamboo Fiber/Polylactic Acid Composites

by Chunlin Liu, Shuai Zhang, Shi Yan, Mingzhu Pan and Hui Huang

Forests 2024, 15(2), 371; https://doi.org/10.3390/f15020371 - 16 Feb 2024

Viewed by 651

Abstract

In this study, biodegradable bamboo fiber/PLA composites (BPCs) modified using bamboo charcoal (BC)/ZnO were prepared. The effects of BC/ZnO addition on the mechanical properties and antibacterial properties of BPCs were investigated. The chemical structure, microscopic morphology, and crystallization of the composites were analyzed [...] Read more.

In this study, biodegradable bamboo fiber/PLA composites (BPCs) modified using bamboo charcoal (BC)/ZnO were prepared. The effects of BC/ZnO addition on the mechanical properties and antibacterial properties of BPCs were investigated. The chemical structure, microscopic morphology, and crystallization of the composites were analyzed using FTIR, SEM, and XRD, respectively. The results showed that in terms of mechanical strength, when the addition of BC was 2%, the tensile impact and flexural strength of the BPCs were most obviously improved, with a tensile strength of 51.6 MPa. However, when the addition of BC was more than 2%, the uneven dispersion of too much BC in the BPCs resulted in a reduction in their mechanical strength. A certain amount of ZnO did not affect the crystallinity of the BPCs. In addition, the uneven distribution of ZnO and its poor compatibility with PLA resulted in a deterioration in the tensile properties of the BPCs. In terms of antibacterial properties, when 2% ZnO was added, the BPCs had better antibacterial properties against Escherichia coli and Staphylococcus aureus, with values of 58.9% and 52.5% against both, respectively. BPC biodegradable materials with both mechanical strength and antimicrobial properties have promising medical uses. Full article

(This article belongs to the Special Issue Advances in Wood- and Bamboo-Based Materials: Modification and Assessment)

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23 pages, 30648 KiB

Open AccessArticle

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

by Qianqian Zhang, Lei Gu, Yongqiang Liu and Yongfu Zhang

Forests 2024, 15(2), 370; https://doi.org/10.3390/f15020370 - 16 Feb 2024

Viewed by 640

Abstract

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, [...] Read more.

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)

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18 pages, 3783 KiB

Open AccessArticle

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

by Hongbo Zhu, Bing Zhang, Weidong Song, Qinghua Xie, Xinyue Chang and Ruishan Zhao

Forests 2024, 15(2), 369; https://doi.org/10.3390/f15020369 - 16 Feb 2024

Viewed by 701

Abstract

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have [...] Read more.

As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accurately. Therefore, many studies have aimed to address this issue by proposing machine learning models that accurately invert forest canopy height. However, most of the these approaches feature PolSAR observations from a data-driven viewpoint in the feature selection part of the machine learning model, without taking into account the intrinsic mechanisms of PolSAR polarization observation variables. In this work, we evaluated the correlations between eight polarization observation variables, namely, T11, T22, T33, total backscattered power (SPAN), radar vegetation index (RVI), the surface scattering component (Ps), dihedral angle scattering component (Pd), and body scattering component (Pv) of Freeman-Durden three-component decomposition, and the height of the forest canopy. On this basis, a weighted inversion method for determining forest canopy height under the view of structural equation modeling was proposed. In this study, the direct and indirect contributions of the above eight polarization observation variables to the forest canopy height inversion task were estimated based on structural equation modeling. Among them, the indirect contributions were generated by the interactions between the variables and ultimately had an impact on the forest canopy height inversion. In this study, the covariance matrix between polarization variables and forest canopy height was calculated based on structural equation modeling, the weights of the variables were calculated by combining with the Mahalanobis distance, and the weighted inversion of forest canopy height was carried out using PSO-SVR. In this study, some experiments were carried out using three Gaofen-3 satellite (GF-3) images and ICESat-2 forest canopy height data for some forest areas of Gaofeng Ridge, Baisha Lizu Autonomous County, Hainan Province, China. The results showed that T11, T33, and total backscattered power (SPAN) are highly correlated with forest canopy height. In addition, this study showed that determining the weights of different polarization observation variables contributes positively to the accurate estimation of forest canopy height. The forest canopy height-weighted inversion method proposed in this paper was shown to be superior to the multiple regression model, with a 26% improvement in r and a 0.88 m reduction in the root-mean-square error (RMSE). Full article

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

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20 pages, 7170 KiB

Open AccessArticle

Modeling the Geometry of Tree Trunks Using LiDAR Data

by Fayez Tarsha Kurdi, Zahra Gharineiat, Elżbieta Lewandowicz and Jie Shan

Forests 2024, 15(2), 368; https://doi.org/10.3390/f15020368 - 16 Feb 2024

Viewed by 867

Abstract

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. [...] Read more.

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas. Full article

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

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12 pages, 2913 KiB

Open AccessArticle

Construction of Riboswitches for Screening Antibacterial Agents from Forest Plants

by Zhanjun Liu, Taotao Li, Xingyu Zhang, Shiquan Liu, Zhiyuan Hu, Songlin Yu and Xiaohong Zhou

Forests 2024, 15(2), 367; https://doi.org/10.3390/f15020367 - 15 Feb 2024

Viewed by 711

Abstract

Forest plants contain abundant natural products, providing a valuable resource for obtaining compounds with various functional activities, such as antimicrobial, lipid-lowering, and immunoregulatory activities. The development of efficient tools for rapidly screening functional natural products from forest plants is essential for human health. [...] Read more.

Forest plants contain abundant natural products, providing a valuable resource for obtaining compounds with various functional activities, such as antimicrobial, lipid-lowering, and immunoregulatory activities. The development of efficient tools for rapidly screening functional natural products from forest plants is essential for human health. In this study, we constructed some transgenic strains (Escherichia coli) containing Ahy1-1 riboswitches that respond to cyclic di-guanylate (c-di-GMP), serving as a novel bacteriostatic target. The Ahy1-1 riboswitches contained the LacZ gene (encoding β-galactosidase) and c-di-GMP aptamer in order to monitor β-galactosidase activity due to changes in c-di-GMP. After co-incubating with extracts from fresh orange peel, fresh tea leaves, and Fuzhuan brick tea, the orange peel exhibited a significant inhibition of c-di-GMP generation. The extract of tea leaves had a minor influence on the synthesis of c-di-GMP, whereas Fuzhuan brick tea, which is fermented by various microorganisms, inhibited the production of c-di-GMP. Our constructed transgenic strains could be used to screen for antibacterial agents from forest plants. Beyond antibacterial agents, other functional compounds from forest plants could be selected by designing diverse riboswitches. Full article

(This article belongs to the Special Issue Advances in the Cultivation, Protection and High-Value Utilization of Forest Resources)

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18 pages, 4555 KiB

Open AccessArticle

Feedstock-Induced Changes in the Physicochemical Characteristics of Biochars Produced from Different Types of Pecan Wastes

by Miaomiao Zhang, Fangren Peng, Jinping Yu and Zhuangzhuang Liu

Forests 2024, 15(2), 366; https://doi.org/10.3390/f15020366 - 14 Feb 2024

Viewed by 733

Abstract

Large amounts of residues are generated in pecan cultivation processes. Biochar is an environmentally friendly way to utilize residues but attempts to prepare and apply biochar with pecan residues are rare. In this study, six types of biochars were produced from pecan branches, [...] Read more.

Large amounts of residues are generated in pecan cultivation processes. Biochar is an environmentally friendly way to utilize residues but attempts to prepare and apply biochar with pecan residues are rare. In this study, six types of biochars were produced from pecan branches, trunks, roots, nutshells, husks, and leaves under pyrolysis, and their physicochemical properties were compared to assess their application perspective in environmental and agricultural fields. The yields of six pecan biochars were 32.1%–45.9%, with the highest yield for husk biochar (HB) (45.9%). Among the pecan biochars, trunk biochar (TB) and root biochar (RB) had much larger specific surface areas. Branch biochar (BB), TB, and RB presented tubular structures with elliptical pores, while nutshell biochar (NSB), HB, and leaf biochar (LB) appeared flaky or as clustered structures with relatively rougher outer surfaces and irregular pores. The functional group types of pecan biochars were generally similar, but the intensities of the peak near 2900 cm⁻¹ in BB were obviously higher than those of the other biochars. RB and LB contained significantly more ash and volatile than those of the other pecan biochars, with the highest fixed carbon content being found in NSB (70.1%). All of the pecan biochars were alkaline (7.90–9.87), and HB, LB, and NSB had significantly higher pH values than those of the other biochars. Elemental analysis indicated that RB, NSB, and LB had higher carbon levels (more than 70%) with lower O/C ratios (no more than 0.2). HB possessed a relatively high content of nitrogen, potassium, magnesium; the phosphorus content was highest in NSB; LB had the highest calcium content. The results of principal component analysis showed that BB, LB, and NSB were clustered in the same quadrant with relatively close relationships. The results of this study can guide the utilization of pecan wastes and their application as biochar in different fields. Full article

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

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