Forests

17 pages, 19313 KiB

Open AccessArticle

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

by Zennure Uçar

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

Viewed by 207

Abstract

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

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

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

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13 pages, 4454 KiB

Open AccessArticle

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

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

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

Viewed by 121

Abstract

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

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

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

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25 pages, 21982 KiB

Open AccessArticle

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

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

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

Viewed by 181

Abstract

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

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

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

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18 pages, 5286 KiB

Open AccessArticle

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

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

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

Viewed by 166

Abstract

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

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

(This article belongs to the Section Forest Soil)

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16 pages, 6825 KiB

Open AccessArticle

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

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

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

Viewed by 84

Abstract

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

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

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

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45 pages, 2074 KiB

Open AccessReview

Advancements in Artificial Intelligence Applications for Forest Fire Prediction

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

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

Viewed by 462

Abstract

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

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

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

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17 pages, 12380 KiB

Open AccessArticle

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

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

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

Viewed by 124

Abstract

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

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

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

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20 pages, 1898 KiB

Open AccessReview

Physicochemical Properties of Forest Wood Biomass for Bioenergy Application: A Review

by Leonardo Bianchini, Andrea Colantoni, Rachele Venanzi, Luca Cozzolino and Rodolfo Picchio

Forests 2025, 16(4), 702; https://doi.org/10.3390/f16040702 - 18 Apr 2025

Viewed by 262

Abstract

Forest wood biomass is a key renewable resource for advancing energy transition and mitigating climate change. This review analyzes the physicochemical properties of forest biomass from major European tree species to assess their suitability for bioenergy applications. This study encompasses key parameters, such [...] Read more.

Forest wood biomass is a key renewable resource for advancing energy transition and mitigating climate change. This review analyzes the physicochemical properties of forest biomass from major European tree species to assess their suitability for bioenergy applications. This study encompasses key parameters, such as moisture content, ash content, volatile matter, fixed carbon, elemental composition, bulk density, and energy content (HHV and LHV). This review analyzed data from 43 publications and extracted 140 records concerning the physicochemical properties of the most common European forest species used for bioenergy. The most commonly represented species were Quercus robur, Eucalyptus spp., and Fagus sylvatica. Moisture content, referring to fresh matter, ranged from 5% to 65%; ash content, referring to a dry basis, ranged from 0.2% to 3.5%; and higher heating value (HHV), referring to dry matter, ranged from 17 to 21 MJ kg⁻¹. This study highlights variability among species and underscores the importance of standardizing biomass characterization methods and the scarcity of data on bulk density and other key logistical parameters. These findings emphasize the need for consistent methodologies and species-specific selection strategies to optimize sustainability and efficiency in forest biomass utilization for bioenergy. Full article

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

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17 pages, 2803 KiB

Open AccessArticle

Allometric Models for Estimating Biomass and Carbon Stocks in Natural and Homestead Highland Bamboo Stands in the Sidama Region, Ethiopia

by Dagnew Yebeyen Burru, Jayaraman Durai, Melaku Anteneh Chinke, Gudeta W. Sileshi, Yashwant S. Rawat, Belachew Gizachew, Selim Reza, Fikremariam Haile Desalegne and Kassa Toshe Worassa

Forests 2025, 16(4), 701; https://doi.org/10.3390/f16040701 - 18 Apr 2025

Viewed by 213

Abstract

Highland bamboo (Oldeania alpina) plays a vital role in supporting local livelihoods, fostering biodiversity conservation and sustainable land management. Despite these benefits, its significant potential for carbon sequestration remains underutilized within Ethiopia’s climate mitigation strategies. In this study, we developed site-specific [...] Read more.

Highland bamboo (Oldeania alpina) plays a vital role in supporting local livelihoods, fostering biodiversity conservation and sustainable land management. Despite these benefits, its significant potential for carbon sequestration remains underutilized within Ethiopia’s climate mitigation strategies. In this study, we developed site-specific allometric equations to assess the biomass and carbon storage potential of highland bamboo. Data were collected from the Garamba natural bamboo forest and Hula homestead bamboo stands in the Sidama Regional State, Southern Ethiopia. Data on stand density and structure were gathered using systematically laid transects and sample plots, while plant samples were analyzed in the laboratory to determine the dry-to-fresh weight ratios. We developed allometric models to estimate the aboveground biomass (AGB) and carbon stock. The study results indicated that homestead bamboo stands exhibited higher biomass accumulation than natural bamboo stands. The AGB was estimated at 92.3 Mg ha⁻¹ in the natural forest and 118.3 Mg ha⁻¹ in homestead bamboo stands, with total biomass carbon storage of 52.1 Mg ha⁻¹ and 66.7 Mg ha⁻¹, respectively. The findings highlight the significant potential of highland bamboo for carbon sequestration in both natural stands and homesteads. Sustainable management of natural highland bamboo stands and integrating bamboo into farms can contribute to climate change mitigation, support ecosystem restoration, and enhance the socio-economic development of communities. Full article

(This article belongs to the Special Issue Biomass Estimation and Carbon Stocks in Forest Ecosystems: 3rd Edition)

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21 pages, 2924 KiB

Open AccessReview

Green Belts in Africa: A Diagnostic Review of Urban Forestry and Sustainable Management Strategies

by Komna Balagou, Kossi Adjonou, Kossi Novigno Segla, Kossi Komi, Jean-Bosco Benewinde Zoungrana, Coffi Aholou and Kouami Kokou

Forests 2025, 16(4), 700; https://doi.org/10.3390/f16040700 - 18 Apr 2025

Viewed by 303

Abstract

Green belts, consisting mainly of natural forests, woodlands, and agricultural areas surrounding major cities, play an essential role in regulating urban development and controlling the expansion of metropolitan areas. Although this concept has been extensively studied in the world’s major metropolitan areas, it [...] Read more.

Green belts, consisting mainly of natural forests, woodlands, and agricultural areas surrounding major cities, play an essential role in regulating urban development and controlling the expansion of metropolitan areas. Although this concept has been extensively studied in the world’s major metropolitan areas, it remains relatively unknown in many countries, particularly in Africa. There is a great need for research to better understand urban vegetation cover on the continent. This article proposes a systematic review of African publications on green cover for the period 2010 to 2024. A descriptive and thematic analysis of the selected scientific papers was carried out using a database established to examine the state of existing research and understanding of the management of these plant formations in Africa. The results of these analyses highlight several major challenges facing urban forestry, including increasing anthropogenic pressures, lack of urban planning that integrates urban forestry, and shortcomings in the management of existing forest landscapes. The thematic analysis has also helped to identify the topics addressed by African researchers, identify gaps in research, and suggest directions for future studies. Three priority areas emerge from this analysis: the conservation of natural or artificial green belts around cities, the impact of these forest landscapes on urban heat islands (climate impact), and the sustainability of ecosystem management in the context of sustainable urbanization. These guidelines will enable a better understanding and valorization of green belts in Africa, thus contributing to the construction of more sustainable cities and the efficient management of forest landscapes. Full article

(This article belongs to the Special Issue Ecosystem Services in Urban and Peri-Urban Landscapes)

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18 pages, 10232 KiB

Open AccessArticle

Evaluation of Landscape Soil Quality in Different Types of Pisha Sandstone Areas on Loess Plateau

by Lei Huang and Liangyi Rao

Forests 2025, 16(4), 699; https://doi.org/10.3390/f16040699 - 18 Apr 2025

Viewed by 241

Abstract

Severe soil erosion and land productivity degradation caused by inadequate vegetation cover pose significant challenges to regional ecological protection and sustainable development. To assess changes and variations in soil quality, three sample areas with different distinct texture characteristics were selected from the Pisha [...] Read more.

Severe soil erosion and land productivity degradation caused by inadequate vegetation cover pose significant challenges to regional ecological protection and sustainable development. To assess changes and variations in soil quality, three sample areas with different distinct texture characteristics were selected from the Pisha sandstone region located northeastern of the Loess Plateau. The total data set (TDS) was determined through sampling experiments, and the minimum data set (MDS) was established using principal component analysis. A Random Forest (RF) machine learning model was applied to predict soil quality distribution. The prediction indices were derived from soil analysis dimensions, mean weight diameter measured via wet sieving, and soil enrichment ratio obtained from slope erosion experiments conducted at the corresponding sampling points. During the RF modeling process, 80% of the total soil quality index (SQI), calculated using TDS and MDS evaluation methods, was allocated for model training. The results indicated that pH, ammonia nitrogen, bulk density, silt content, clay content, soil water content, hygroscopic water content, total phosphorus, soluble calcium, and actinomycetes were identified as the optimal predictors for SQI. Furthermore, the RF model demonstrated superior performance in predicting the regional distribution of SQI, with evaluation metrics including (R² = 0.76–0.78, RMSE = 0.03–0.06, MAE = 0.04–0.09). This study confirms the reliability of RF in simulating SQI within the study area and highlights that, in regions undergoing extensive vegetation restoration and with limited sampling conditions, experimental measurements of soil particles and sediment parameters provide an effective approach for evaluating SQI. Full article

(This article belongs to the Section Forest Soil)

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27 pages, 6389 KiB

Open AccessArticle

FPGA-Accelerated Lightweight CNN in Forest Fire Recognition

by Youming Zha and Xiang Cai

Forests 2025, 16(4), 698; https://doi.org/10.3390/f16040698 - 18 Apr 2025

Viewed by 145

Abstract

Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics of CNN algorithms, it is difficult to implement them at [...] Read more.

Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics of CNN algorithms, it is difficult to implement them at edge terminals with limited memory and computing resources. This paper uses a FPGA (Field-Programmable Gate Array) to accelerate CNNs to realize forest fire recognition in the field environment and solves the problem of the difficulty in giving consideration to the accuracy and speed of a forest fire recognition network in the implementation of edge terminal equipment. First, a simple seven-layer lightweight network, LightFireNet, is designed. The network is compressed using a knowledge distillation method and the classical network ResNet50 is used as the teacher network to supervise the learning of LightFireNet so that its accuracy rate reaches 97.60%. Compared with ResNet50, the scale of LightFireNet is significantly reduced. Its model parameter amount is 24 K and its calculation amount is 9.11 M, which are 0.1% and 1.2% of ResNet50, respectively. Secondly, the hardware acceleration circuit of LightFireNet is designed and implemented based on the FPGA development board ZYNQ Z7-Lite 7020. In order to further compress the network and speed up the forest fire recognition circuit, the following three methods are used to optimize the circuit: (1) the network convolution layer adopts a depthwise separable convolution structure; (2) the BN (batch normalization) layer is fused with the upper layer (or full connection layer); (3) half float or ap_fixed<16,6>-type data is used to express feature data and model parameters. After the circuit function is realized, the LightFireNet terminal circuit is obtained through the circuit parallel optimization method of loop tiling, ping-pong operation, and multi-channel data transmission. Finally, it is verified on the test dataset that the accuracy of the forest fire recognition of the FPGA edge terminal of the LightFireNet model is 96.70%, the recognition speed is 64 ms per frame, and the power consumption is 2.23 W. The results show that this paper has realized a low-power-consumption, high-accuracy, and fast forest fire recognition terminal, which can thus be better applied to forest fire monitoring. Full article

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

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10 pages, 1553 KiB

Open AccessArticle

Genus-Specific Molecular Markers for In Vitro Detection of Corinectria Forest Pathogens

by Tania Vásquez, Cristian González and Cristian Montalva

Forests 2025, 16(4), 697; https://doi.org/10.3390/f16040697 - 18 Apr 2025

Viewed by 166

Abstract

Canker disease caused by Corinectria constricta has resulted in significant losses of Pinus radiata for the Chilean forestry industry in recent years. Accurate and prompt detection and identification of the pathogen is essential in this context. In this study, a set of molecular [...] Read more.

Canker disease caused by Corinectria constricta has resulted in significant losses of Pinus radiata for the Chilean forestry industry in recent years. Accurate and prompt detection and identification of the pathogen is essential in this context. In this study, a set of molecular markers was developed using multiple alignments of the actin-1 (ACT) and β-tubulin (Btub) gene regions to detect the genus Corinectria in vitro. The designed molecular markers were evaluated for specificity and sensitivity using conventional PCR assays, which successfully differentiated Corinectria species from other closely related species and fungal pathogens of P. radiata. The results showed that the molecular markers were able to detect Corinectria genus with high specificity and sensitivity, with Act-31F/Act-543R detecting 0.1 ng/µL of template DNA and Btub/BtubR detecting 0.05 ng/µL. This study represents the first report of specific molecular markers being developed to detect/identify the genus Corinectria in vitro, and the use of these markers is suggested for the timely detection of the pathogen in P. radiata plantations. Full article

(This article belongs to the Special Issue Advances in Detection and Identification of Insect Pests and Pathogens)

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14 pages, 3761 KiB

Open AccessArticle

Different Influences of Soil and Climatic Factors on Shrubs and Herbaceous Plants in the Shrub-Encroached Grasslands of the Mongolian Plateau

by Yue Liu, Lei Dong, Jinrong Li, Shuaizhi Lu, Liqing Yi, Huimin Li, Shaoqi Chai and Jian Wang

Forests 2025, 16(4), 696; https://doi.org/10.3390/f16040696 - 17 Apr 2025

Viewed by 221

Abstract

Factors such as climate change, fire, and overgrazing have been commonly considered the main causes of the global expansion of shrub invasion in grasslands over the past 160 years. Nevertheless, the influence of soil substrates on the progression of shrub encroachment has been [...] Read more.

Factors such as climate change, fire, and overgrazing have been commonly considered the main causes of the global expansion of shrub invasion in grasslands over the past 160 years. Nevertheless, the influence of soil substrates on the progression of shrub encroachment has been insufficiently examined. This study examines the fundamental characteristics of the shrub-encroached desert steppe communities of Caragana tibetica in the Mongolian Plateau. Combining field surveys (field surveys and drone aerial photography) and laboratory experiments, using Spearman’s rank correlation analysis and structural equation modeling (SEM), this research systematically explores the impact of varying degrees of soil sandification on the survival of shrubs and herbaceous plants within these grassland communities. The findings indicate the following: (1) In the eight shrub-encroached grassland plots, the soil exhibited a significantly higher sand content compared to silt and clay, with the sand content generally exceeding 64%. (2) The coverage of shrub species is predominantly influenced by soil factors, particularly the soil sand content. (The path coefficient is 0.56, with p < 0.01). In contrast, herbaceous plants are more strongly influenced by climatic factors. (The path coefficient is 0.83, with p < 0.001). This study examines the response patterns of Caragana tibetica communities to edaphic and climatic factors, highlighting the pivotal role of soil sandification in the initiation and succession of shrub encroachment. The findings furnish a theoretical framework for forecasting future trends in grassland shrub encroachment and provide empirical evidence for the conservation and sustainable management of shrub-encroached grasslands. Full article

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

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23 pages, 8466 KiB

Open AccessArticle

Physiological and Flavonoid Metabolic Responses of Black Locust Leaves to Drought Stress in the Loess Plateau of China

by Yan Wang, Ning Peng, Binbin Liu, Yingbin Yang, Chao Yue, Wenfang Hao and Junhao He

Forests 2025, 16(4), 695; https://doi.org/10.3390/f16040695 - 17 Apr 2025

Viewed by 219

Abstract

Drought threatens the stability of artificial black locust forests on the Loess Plateau, yet there is limited research on the physiological and metabolic responses of mature black locust to drought stress. This study employed a throughfall exclusion system—i.e., moderate drought (40% throughfall reduction), [...] Read more.

Drought threatens the stability of artificial black locust forests on the Loess Plateau, yet there is limited research on the physiological and metabolic responses of mature black locust to drought stress. This study employed a throughfall exclusion system—i.e., moderate drought (40% throughfall reduction), extreme drought (80% throughfall reduction), and 0% throughfall reduction for control—to analyze leaf microstructure, relative water content (RWC), osmotic adjustment substances, hormone levels, and flavonoid metabolites in black locust under controlled drought stress. The results demonstrated that as drought stress intensified, stomatal aperture and density decreased, while trichome density and length exhibited significant increases. MDA, proline, IAA, and osmotic adjustment substances (soluble protein, reducing sugar, and total sugar) first increased and then decreased as drought stress intensified. A total of 245 flavonoid compounds were identified through metabolomic analysis, among which 91 exhibited differential expression under drought treatments. Notably, 37 flavonoids, including flavonols and glycosylated derivatives, were consistently upregulated. These findings suggest that drought stress can lead to the accumulation of flavonoids. This study explored the physiological and metabolic responses of mature black locust trees to drought stress, offering insights for selecting drought-resistant species in vegetation restoration and informing ecological management practices in arid regions. Full article

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

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13 pages, 4318 KiB

Open AccessArticle

Physical and Mechanical Properties and Microstructure Characterization of Thermally Modified Flattened Bamboo (Phyllostachys edulis) Material

by Yixuan Zheng, Lina Liu, Minzhen Bao, Feng Lin, Xujun Wu, Yanjun Li, Yan Gong, Weijie Gu and Weigang Zhang

Forests 2025, 16(4), 694; https://doi.org/10.3390/f16040694 - 17 Apr 2025

Viewed by 179

Abstract

This study investigated the effects of thermal modification treatment on flattened bamboo lumber by using temperature (180 °C, 190 °C, 200 °C) and duration (2, 3, 4 h) as experimental variables. The physicochemical properties, crystallinity, bending deformation, chemical composition, and microstructural evolution of [...] Read more.

This study investigated the effects of thermal modification treatment on flattened bamboo lumber by using temperature (180 °C, 190 °C, 200 °C) and duration (2, 3, 4 h) as experimental variables. The physicochemical properties, crystallinity, bending deformation, chemical composition, and microstructural evolution of the material before and after treatment were systematically analyzed using universal mechanical testing, scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and nanoindentation. This comprehensive approach aimed to achieve high-performance flattened bamboo lumber. The results revealed that thermal modification significantly reduced the flexural modulus of elasticity and hardness of the flattened bamboo lumber, which reached their minimum values of 4479 MPa and 786.71 N, under the treatment at 190 °C/3 h. Conversely, it enhanced the longitudinal compressive strength of flattened bamboo lumber, achieving a maximum value of 57.28 MPa at 180 °C/2 h. At the microscale, the nanomechanical strength decreased under 190–200 °C treatments, accompanied by a tighter cell arrangement and evident shrinkage and deformation of the parenchyma cells. Dimensional stability tests combined with FTIR and crystallinity analyses demonstrated a reduction in the number of hydrophilic groups and improved dimensional stability after thermal modification. Notably, the material treated at 200 °C for 4 h retained its dimensional stability and exhibited no deformation. Full article

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

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23 pages, 2802 KiB

Open AccessArticle

Research on the Impact of Climate Change Perceptions on the Carbon Offset Behavior of Visitors to Wuyi Mountain Forestry Heritage Site

by Sunbowen Zhang, Cuifei Liu, Youcheng Chen, Jingxuan Liang and Yongqiang Ma

Forests 2025, 16(4), 693; https://doi.org/10.3390/f16040693 - 17 Apr 2025

Viewed by 215

Abstract

Forestry heritage tourism can spread the ecological concept of harmonious coexistence between humans and nature, and it is a nature-based solution to climate change. However, how tourists are guided to form an emotional identity and how their attention to climate change issues can [...] Read more.

Forestry heritage tourism can spread the ecological concept of harmonious coexistence between humans and nature, and it is a nature-based solution to climate change. However, how tourists are guided to form an emotional identity and how their attention to climate change issues can be stimulated continuously remain unclear. Therefore, in this study, we selected the Wuyi Mountain Forestry Heritage Site as our study site and employed PLS-SEM to analyze the responses of 384 tourists, thereby examining the underlying mechanism linking their perceptions of climate change to carbon offset behaviors within forestry heritage sites. The results showed the following: Perceptions of climate change had a positive and significant impact on carbon offset behavior (β = 0.310, p < 0.001), ecological identity had a positive and significant impact on carbon offset behavior (β = 0.375, p < 0.001), and the sense of environmental responsibility had a positive and significant impact on carbon offset behavior (β = 0.226, p < 0.01). At the same time, ecological identity and environmental responsibility play an intermediary role, and the impact of climate change perception on the carbon offset behavior of tourists at forestry heritage sites is moderated by tourists’ health attitudes. In addition, gender, age, and educational background have an impact on the process of carbon-offsetting behavior development at forestry heritage sites. This research further clarifies the internal logic of tourists’ carbon offset behavior in the context of heritage tourism, helps to enrich the theoretical system of Nbs and heritage tourism research, and provides a feasible reference for the realization of the SDGs. Full article

(This article belongs to the Special Issue Forests and Nature Tourism: Navigating Conservation, Recreation, and Change in the Anthropocene)

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23 pages, 25076 KiB

Open AccessArticle

Integrating DEM and Deep Learning for Forested Terrain Analysis: Enhancing Fire Risk Assessment Through Mountain Peak and Water System Extraction in Chongli District

by Yihui Wu, Xueying Sun, Liang Qi, Jiang Xu, Demin Gao and Zhengli Zhu

Forests 2025, 16(4), 692; https://doi.org/10.3390/f16040692 - 16 Apr 2025

Viewed by 254

Abstract

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework [...] Read more.

Accurate fire risk assessment in forested terrain is crucial for effective disaster management and ecological conservation. This study innovatively proposes a novel framework that integrates Digital Elevation Models (DEMs) with deep learning techniques to enhance fire risk assessment in Chongli District. Our framework innovatively combines DEM data with Faster Regions with Convolutional Neural Networks (Faster R-CNN) and CNN-based methods, breaking through the limitations of traditional approaches that rely on manual feature extraction. It is capable of automatically identifying critical terrain features, such as mountain peaks and water systems, with higher accuracy and efficiency. DEMs provide high-resolution topographical information, which deep learning models leverage to accurately identify and delineate key geographical features. Our results show that the integration of DEMs and deep learning significantly improves the accuracy of fire risk assessment by offering detailed and precise terrain analysis, thereby providing more reliable inputs for fire behavior prediction. The extracted mountain peaks and water systems, as fundamental inputs for fire behavior prediction, enable more accurate predictions of fire spread and potential impact areas. This study not only highlights the great potential of combining geospatial data with advanced machine learning techniques but also offers a scalable and efficient solution for forest fire risk management in mountainous regions. Future work will focus on expanding the dataset to include more environmental variables and validating the model in different geographical areas to further enhance its robustness and applicability. Full article

(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)

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14 pages, 5210 KiB

Open AccessArticle

Integrated Metabolome and Transcriptome Analysis Reveals New Insights into the Walnut Seed Coat Coloration

by Ruiqi Wang, Xin Huang, Xueqin Wan, Shuaiying Zhang, Xiandan Luo, Jianghong Qian, Fang He, Lianghua Chen, Fan Zhang and Hanbo Yang

Forests 2025, 16(4), 691; https://doi.org/10.3390/f16040691 - 16 Apr 2025

Viewed by 157

Abstract

The color of the walnut seed coat is a critical determinant of its market value; however, research into the mechanisms responsible for seed coat color formation is yet to be determined. Using two walnut clones with contrasting pale-yellow and light purple seed coats, [...] Read more.

The color of the walnut seed coat is a critical determinant of its market value; however, research into the mechanisms responsible for seed coat color formation is yet to be determined. Using two walnut clones with contrasting pale-yellow and light purple seed coats, we characterized pigmentation, particularly anthocyanin content, using spectrophotometry. We then conducted integrated transcriptomic and metabolomic analyses to identify the molecular mechanisms and pathways underlying their formation. The anthocyanin content in the light purple seed coat clone was significantly greater than that in the clone with a white seed coat. The results of comparative metabolomics indicated that four anthocyanins (delphinidin, cyanidin-3-(caffeoylglucoside), pelargonidin-3-(6″-caffeoylglucoside), and delphinidin-3-O-sophoroside) were significantly more abundant in the light purple seed coat clone. These anthocyanins were the key pigments responsible for the light purple coloration of the walnut seed coat. Furthermore, comparative transcriptomics revealed that structural genes in the anthocyanin biosynthesis pathway (e.g., phenylalanine ammonia-lyase, 4-coumarate-CoA ligase, chalcone isomerase, and bronze-1) were significantly upregulated in the purple seed coat clone. Coexpression network analysis revealed that several transcription factors (e.g., ARF, bHLH, and MYB-related) were significantly correlated with the upregulation of these structural genes and the accumulation of four key anthocyanins. These transcription factors may serve as critical regulators influencing seed coat color formation. In conclusion, these findings establish a strong theoretical foundation for walnut breeding aimed at developing diverse seed coat colors. Full article

(This article belongs to the Special Issue Genetic Diversity and Gene Analysis in Forest Tree Breeding)

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18 pages, 5147 KiB

Open AccessArticle

Improvement of 3D Green Volume Estimation Method for Individual Street Trees Based on TLS Data

by Yanghong Zhu, Jianrong Li and Yannan Xu

Forests 2025, 16(4), 690; https://doi.org/10.3390/f16040690 - 16 Apr 2025

Viewed by 124

Abstract

Vertical structure monitoring of urban vegetation provides data support for urban green space planning and ecological management, playing a significant role in promoting sustainable urban ecological development. Three-dimensional green volume (3DGV) is a comprehensive index used to characterize the ecological benefit of urban [...] Read more.

Vertical structure monitoring of urban vegetation provides data support for urban green space planning and ecological management, playing a significant role in promoting sustainable urban ecological development. Three-dimensional green volume (3DGV) is a comprehensive index used to characterize the ecological benefit of urban vegetation. As a critical component of urban vegetation, street trees play a key role in urban ecological benefits evaluation, and the quantitative estimation of their 3DGV serves as the foundation for this assessment. However, current methods for measuring 3DGV based on point cloud data often suffer from issues of overestimation or underestimation. To improve the accuracy of the 3DGV for urban street trees, this study proposed a novel approach that used convex hull coupling k-means clustering convex hulls. A new method based on terrestrial laser scanning (TLS) data was proposed, referred to as the Convex Hull Coupling Method (CHCM). This method divides the tree crown into two parts in the vertical direction according to the point cloud density, which better adapts to the lower density of the upper layer of TLS data and obtains a more accurate 3DGV of individual trees. To validate the effectiveness of the CHCM method, 30 sycamore (Platanus × acerifolia (Aiton) Willd.) plants were used as research objects. We used the CHCM and five traditional 3DGV calculation methods (frustum method, convex hull method, k-means clustering convex hulls, alpha-shape algorithm, and voxel-based method) to calculate the 3DGV of individual trees. Additionally, the 3DGV was predicted and analyzed using five fitting models. The results show the following: (1) Compared with the traditional methods, the CHCM improves the estimation accuracy of the 3DGV of individual trees and shows a high consistency in the data verification, which indicates that the CHCM method is stable and reliable, and (2) the fitting results R² of the five models were all above 0.75, with the exponential function model showing the best fitting accuracy (R² = 0.89, RMSE = 74.85 m³). These results indicate that for TLS data, the CHCM can achieve more accurate 3DGV estimates for individual trees, outperforming traditional methods in both applicability and accuracy. The research results not only offer a novel technical approach for 3DGV calculation using TLS data but also establish a reliable quantitative foundation for the scientific assessment of the ecological benefits of urban street trees and green space planning. Full article

(This article belongs to the Section Urban Forestry)

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27 pages, 6455 KiB

Open AccessArticle

Tackling the Wildfire Prediction Challenge: An Explainable Artificial Intelligence (XAI) Model Combining Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) for Enhanced Interpretability and Accuracy

by Bin Liao, Tao Zhou, Yanping Liu, Min Li and Tao Zhang

Forests 2025, 16(4), 689; https://doi.org/10.3390/f16040689 - 16 Apr 2025

Viewed by 266

Abstract

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. [...] Read more.

The intensification of global climate change, combined with increasing human activities, has significantly increased wildfire frequency and severity, posing a major global environmental challenge. As an illustration, Guizhou Province in China encountered a total of 221 wildfires over a span of 12 days. Despite significant advancements in wildfire prediction models, challenges related to data imbalance and model interpretability persist, undermining their overall reliability. In response to these challenges, this study proposes an explainable wildfire risk prediction model (EWXS) leveraging Extreme Gradient Boosting (XGBoost), with a focus on Guizhou Province. The methodology involved converting raster and vector data into structured tabular formats, merging, normalizing, and encoding them using the Weight of Evidence (WOE) technique to enhance feature representation. Subsequently, the cleaned data were balanced to establish a robust foundation for the EWXS model. The performance of the EWXS model was evaluated in comparison to established models, such as CatBoost, using a range of performance metrics. The results indicated that the EWXS model achieved an accuracy of 99.22%, precision of 98.48%, recall of 96.82%, an F1 score of 97.64%, and an AUC of 0.983, thereby demonstrating its strong performance. Moreover, the SHAP framework was employed to enhance model interpretability, unveiling key factors influencing wildfire risk, including proximity to villages, meteorological conditions, air humidity, and variations in vegetation temperature. This analysis provides valuable support for decision-making bodies by offering clear, explanatory insights into the factors contributing to wildfire risk. Full article

(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)

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21 pages, 7705 KiB

Open AccessArticle

Quantifying Missed Opportunities for Cumulative Forest Road Carbon Storage over the Past 50 Years in the Boreal Forest of Eastern Canada

by Alejandro Vega Escobar, François Girard and Osvaldo Valeria

Forests 2025, 16(4), 688; https://doi.org/10.3390/f16040688 - 16 Apr 2025

Viewed by 272

Abstract

Forest road networks are essential for forest operations but significantly contribute to carbon loss and landscape fragmentation in boreal ecosystems. This study evaluates the potential of reforesting unused forest roads to enhance carbon storage (CS) in Quebec’s boreal forests. Four reforestation scenarios were [...] Read more.

Forest road networks are essential for forest operations but significantly contribute to carbon loss and landscape fragmentation in boreal ecosystems. This study evaluates the potential of reforesting unused forest roads to enhance carbon storage (CS) in Quebec’s boreal forests. Four reforestation scenarios were simulated using spatial data from AQréseau+ and the Ecoforestry Map of Quebec, combined with the CBM-CFS3 carbon model. These scenarios varied in site preparation conditions and species selection, including the use of fast-growing local species. Random forest (RF) models were applied to analyze the influence of key variables on CS dynamics, focusing on the road area and years to harvest. The study area covered approximately 294,000 km², and the temporal dimension was incorporated by estimating the construction dates of forest roads. Results show that scenarios integrating soil preparation and fast-growing species (S1I1) achieved the highest CS potential, with up to 6.8 million tons (Mt) of additional carbon stored over a 40–100 year period for medium-category roads, compared to 1.15 million tons in scenarios without intervention (S0I0). These findings underscore the role of reforestation in enhancing CS within managed forests. Future work should prioritize road segments for reforestation, considering ecological benefits, operational feasibility, and climate resilience. Full article

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

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18 pages, 2005 KiB

Open AccessArticle

Comparison of Growth Strategies and Biomass Allocation in Chinese Fir Provenances from the Subtropical Region of China

by Zhibing Wan, Ning Liu, Chenggong Liu, Meiman Zhang, Chengcheng Gao, Lingyu Yang, Liangjin Yao and Xueli Zhang

Forests 2025, 16(4), 687; https://doi.org/10.3390/f16040687 - 16 Apr 2025

Viewed by 249

Abstract

This study aims to evaluate the growth characteristics of six Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) provenances (S1–S6) from different climatic regions in subtropical China in order to select superior provenances with strong adaptability, fast growth, and reasonable biomass allocation. These results [...] Read more.

This study aims to evaluate the growth characteristics of six Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) provenances (S1–S6) from different climatic regions in subtropical China in order to select superior provenances with strong adaptability, fast growth, and reasonable biomass allocation. These results will provide references for genetic improvement and resource utilization of Chinese fir plantations. A total of 385 trees, aged 26 to 48 years, were selected from the Chinese fir gene bank in Anhui. Wood core sampling was used to obtain tree ring width and early/latewood width data. Growth rate, fast-growth period, and biomass allocation of each provenance were analyzed using methods such as the logistic growth equation, BAI (basal area increment), latewood percentage, and biomass estimation. The fast-growth period of Chinese fir starts from the 2nd to the 4th year, with significant growth occurring around the 14th year and growth stabilizing between 30 and 50 years. Provenance S2 showed clear advantages in growth rate and biomass, while S6 was relatively weak. BAI analysis revealed that the provenances reached their growth peak around 10 years of age, with a gradual decline afterward, but S2 maintained higher growth levels for a longer period. Root-shoot ratio analysis showed that S2 had the most balanced ratio, promoting stable growth and efficient water and nutrient absorption, while S6 had a higher root-shoot ratio, indicating growth limitations. Furthermore, S2 demonstrated continuous biomass increase after 30 years, indicating excellent growth potential. This study provides quantitative analysis of the growth characteristics and adaptability of different Chinese fir provenances, offering scientific support for the construction and breeding of Chinese fir plantations, and contributing to enhancing the productivity and ecological adaptability of Chinese fir plantations for sustainable resource utilization. Full article

(This article belongs to the Special Issue Adaptive Mechanisms of Tree Seedlings to Adapt to Stress—Second Edition)

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15 pages, 2645 KiB

Open AccessArticle

Establishing Models for Predicting Above-Ground Carbon Stock Based on Sentinel-2 Imagery for Evergreen Broadleaf Forests in South Central Coastal Ecoregion, Vietnam

by Nguyen Huu Tam, Nguyen Van Loi and Hoang Huy Tuan

Forests 2025, 16(4), 686; https://doi.org/10.3390/f16040686 - 15 Apr 2025

Viewed by 918

Abstract

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this [...] Read more.

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this context, the present study aimed to develop correlation equations between Total Above-Ground Carbon (TAGC) and vegetation indices derived from Sentinel-2 imagery to enable direct estimation of carbon stock for assessing emissions and removals. In this study, the remote sensing indices most strongly associated with TAGC were identified using principal component analysis (PCA). TAGC values were calculated based on forest inventory data from 115 sample plots. Regression models were developed using Ordinary Least Squares and Maximum Likelihood methods and were validated through Monte Carlo cross-validation. The results revealed that Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Near Infrared Reflectance (NIR), as well as three variable combinations—(NDVI, ARVI), (SAVI, SIPI), and (NIR, EVI — Enhanced Vegetation Index)—had strong influences on TAGC. A total of 36 weighted linear and non-linear models were constructed using these selected variables. Among them, the quadratic models incorporating NIR and the (NIR, EVI) combination were identified as optimal, with AIC values of 756.924 and 752.493, R² values of 0.86 and 0.87, and Mean Percentage Standard Errors (MPSEs) of 22.04% and 21.63%, respectively. Consequently, these two models are recommended for predicting carbon stocks in Evergreen Broadleaf (EBL) forests within Vietnam’s South Central Coastal Ecoregion. Full article

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

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18 pages, 3894 KiB

Open AccessArticle

Carbon in Woody Debris and Charcoal Layer in Cold Temperate Coniferous Forest 13 Years After a Severe Wildfire

by Yuanchun Peng, Lina Shi, Xingyu Hou and Yun Zhang

Forests 2025, 16(4), 685; https://doi.org/10.3390/f16040685 - 15 Apr 2025

Viewed by 128

Abstract

Pyrogenic carbon (PyC) is generated from the incomplete combustion of biomass and fossil fuels. Pyrogenic carbon is highly stable and is often referred to as a missing carbon sink. It plays a crucial role in global carbon cycling and climate change research. We [...] Read more.

Pyrogenic carbon (PyC) is generated from the incomplete combustion of biomass and fossil fuels. Pyrogenic carbon is highly stable and is often referred to as a missing carbon sink. It plays a crucial role in global carbon cycling and climate change research. We analyzed the storage of PyC and uncharred biological organic carbon (BOC) within woody debris (WD) and the charcoal layer, as well as the properties of PyC, across four forest types in the cold temperate coniferous forest of the Greater Khingan Mountains. Pyrogenic carbon in WD appears as charred, blackened material, while PyC in the charcoal layer was extracted through chemical oxidation using HF/HCl treatment. Our methodology included particle size separation through dry sieving, followed by the analysis of four size fractions (>2 mm, 2–1 mm, 1–0.5 mm and <0.5 mm) for elemental composition, and the chemical composition was analyzed using DRIFT. With respect to WD, PyC storage ranged from 0.040 to 0.179 Mg·ha⁻¹, whereas BOC storage ranged from 3.1 to 16.8 Mg·ha⁻¹. In the charcoal layer, PyC storage ranged from 7.9 to 44.3 Mg·ha⁻¹, and BOC storage ranged from 3.8 to 11.6 Mg·ha⁻¹. Pyrogenic carbon storage in the charcoal layer dominated (>99%) on the above-ground in each forest type. The DRIFT analysis confirmed that the coarse fraction (>2 mm) contain more polymeric aromatic structures, and most likely indicated the presence of benzene carboxylic compounds (1710 cm⁻¹), which may originate from the charred plant material. Our research aims to enhance the understanding of the retention effects of recalcitrant carbon in WD and charcoal layer of cold temperate coniferous forest, thereby providing new insights into the impact of fire disturbances on carbon cycling within forest ecosystems. Full article

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

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20 pages, 3964 KiB

Open AccessArticle

Response of Litter Decomposition and Nutrient Release Characteristics to Simulated N Deposition in Pinus yunnanensis Franch. Forest in Central Yunnan Plateau

by Yaoping Nian, Wen Chen, Yangyi Zhao, Zheng Hou, Long Zhang, Xiaoling Liang and Yali Song

Forests 2025, 16(4), 684; https://doi.org/10.3390/f16040684 - 15 Apr 2025

Viewed by 179

Abstract

Nitrogen deposition can significantly impact soil biogeochemical cycling; however, its effects on the decomposition processes and nutrient release from leaf and twig litter in subtropical plantations remain inadequately understood. In this study, we focused on the Pinus yunnanensis Franch. forest in the central [...] Read more.

Nitrogen deposition can significantly impact soil biogeochemical cycling; however, its effects on the decomposition processes and nutrient release from leaf and twig litter in subtropical plantations remain inadequately understood. In this study, we focused on the Pinus yunnanensis Franch. forest in the central Yunnan Plateau, southwestern China, and explored how nitrogen addition influences litter decomposition nutrient release over two years, under four levels: control (CK, 0 g·m⁻²·a⁻¹), low nitrogen (LN, 10 g·m⁻²·a⁻¹), medium nitrogen (MN, 20 g·m⁻²·a⁻¹), and high nitrogen (HN, 25 g·m⁻²·a⁻¹). The results indicate that after 24 nitrogen application treatments, the rates of remaining mass in both leaf and twig litters followed the pattern: LN < CK = MN < HN. Under all nitrogen application treatments, the rate of remaining mass in leaf litters was significantly lower than that of twig litters (p < 0.05). Under LN, the mass retention in leaf and twig litters decreased by 3.96% and 8.41%, respectively, compared to CK. In contrast, under HN treatments, the rates of remaining mass in leaf and twig litters increased by 8.57% and 5.35%, respectively. This demonstrates that low nitrogen accelerates decomposition, whereas high nitrogen inhibits it. Significant differences in the remaining amounts of lignin and cellulose in both leaf and twig litters were observed when compared to CK (p < 0.05). Additionally, decomposition time and nitrogen deposition had significant effects on the remaining rates of nutrients (C, N, P) and their C/N, C/P, and N/P in litters (p < 0.05). Following nitrogen application, the C/N of the litters significantly reduced, while the N/P increased. The results suggest that nitrogen addition alleviates the nitrogen limitation on the litters while intensifying the phosphorus limitation. Full article

(This article belongs to the Special Issue Advances in Forest Carbon Sequestration and Emission Reduction Potential)

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14 pages, 1827 KiB

Open AccessArticle

Effectiveness of Silvicultural Options in Renewal of Trembling Aspen–Jack Pine Mixedwood Stands, 21 Years After Treatment

by Rongzhou Man

Forests 2025, 16(4), 683; https://doi.org/10.3390/f16040683 - 15 Apr 2025

Viewed by 179

Abstract

Regenerating conifers after harvest through planting and postharvest broadcast application of herbicide is effective in ensuring the survival and growth of seedlings, but faces challenges in meeting broad social and ecological objectives of forest management. This study reports the effectiveness of alternative options [...] Read more.

Regenerating conifers after harvest through planting and postharvest broadcast application of herbicide is effective in ensuring the survival and growth of seedlings, but faces challenges in meeting broad social and ecological objectives of forest management. This study reports the effectiveness of alternative options in regenerating jack pine (Pinus banksiana Lamb.), 21 years after harvest of trembling aspen (Populus tremuloides Michx.)-dominated boreal mixedwood stands. The treatment options included (i) preharvest spray—aerial broadcast spray prior to harvest, (ii) postharvest partial spray—ground herbicide application in strips, (iii) partial harvest in strips, (iv) postharvest aerial broadcast, and (v) uncut reference. Twenty-one years after treatments, the four harvest treatments were similar in overstory density (4000 stems/ha) and basal area (BA, 20 m²/ha), but differed in composition and structure. The preharvest spray had an intimate mixture of aspen and jack pine (22% and 57% by BA, respectively), compared to spatial mosaics of aspen and pine corridors in the partial spray (36% and 41%), and aspen and maple corridors in the partial cut (21% and 31%). While the postharvest broadcast was pine-dominated (74% by BA) as expected, uncut and partial cut were similar in pine composition (10% by BA), which is inadequate for aspen and pine mixedwood stands. The early positive effects of preharvest spray and partial harvest on understory species abundance and diversity became neutral 21 years postharvest. The implications of these findings are discussed with respect to stand conditions before harvest, postharvest regeneration dynamics, and treatment objectives for the renewal of trembling aspen and jack pine mixedwood stands after harvest. Full article

(This article belongs to the Special Issue Forest Growth and Regeneration Dynamics)

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17 pages, 8791 KiB

Open AccessArticle

The Estimation of Carbon Storage and Volume in Forest Stands: A Model Incorporating Species Composition and Site Quality

by Weiping Hua, Tian Qiu, Xidian Jiang, Junzhong Pan and Baoyin Li

Forests 2025, 16(4), 682; https://doi.org/10.3390/f16040682 - 14 Apr 2025

Viewed by 204

Abstract

We developed a model for estimating the carbon storage and volume of entire forest stands at the provincial level, aiming to improve the accuracy of regional productivity assessments. Based on data from the branches, roots, leaves, and trunks of eight dominant tree species [...] Read more.

We developed a model for estimating the carbon storage and volume of entire forest stands at the provincial level, aiming to improve the accuracy of regional productivity assessments. Based on data from the branches, roots, leaves, and trunks of eight dominant tree species (grouped by origin) in Fujian Province, combined with plot-level data, we developed a compatible carbon storage estimation model. This model integrates species composition coefficients and uses stand volume as the independent variable. We estimated the model parameters using a combination of the immune evolutionary algorithm and an improved simplex method, which enhances convergence speed and solution stability compared to the traditional version. The accuracy of the model was validated by cross-model validation and concurrent testing. Applying the model to forest stand data from Wuyishan City, we simulated theoretical logging volumes to demonstrate its practical utility. The results demonstrated that the model exhibited high accuracy in fitting the observed data, with reliable predictions of carbon storage and volume across different forest components. In the case study area, the volume was 21.0521 million cubic meters and the carbon storage was 7.3238 million tons, both of which increased with decreasing interval periods. When logging factors were considered, the increases in carbon storage fluctuated as the interval periods increased and were higher than those when logging factors were not considered. This study confirmed that the developed models were effective for predicting land carbon storage and volume, and the simulation method successfully overcame the challenges associated with model estimation. Full article

(This article belongs to the Special Issue Research Advances in Management and Design of Forest Operations)

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24 pages, 14653 KiB

Open AccessArticle

Heterogeneity and Influencing Factors of Carbon Sequestration Efficiency of Green Space Patterns in Urban Riverfront Residential Blocks

by Yunfang Jiang, Di Xu, Lixian Peng, Xianghua Li, Tao Song and Fangzhi Zhan

Forests 2025, 16(4), 681; https://doi.org/10.3390/f16040681 - 14 Apr 2025

Viewed by 135

Abstract

Green spaces in waterfront residential blocks, where the water landscape and green space intersect, have a special carbon sequestration effect due to the distinct ecological interaction between water bodies and green spaces. Studying the carbon sequestration efficiency of green space patterns is crucial [...] Read more.

Green spaces in waterfront residential blocks, where the water landscape and green space intersect, have a special carbon sequestration effect due to the distinct ecological interaction between water bodies and green spaces. Studying the carbon sequestration efficiency of green space patterns is crucial for enhancing urban ecological quality. Herein, 100 residential blocks adjacent to water bodies in Shanghai were selected as case areas, and green space pattern classification, random forest algorithm and spatial configuration quantitative analysis were used to analyse the impact of spatial morphology factors, surrounding building environment and water–green coupling environment on the CS efficiency of the green space in residential blocks. The results showed that the importance of the green space morphology index influencing CS is significantly greater than that of the building environment index. Among the indices, the fraction vegetation coverage, coverage ratio of evergreen broadleaved trees and canopy coverage of the green space have a more significant effect. Moreover, the different types and compositions of tree species in residential green spaces have different impacts on CS. Residential blocks with higher levels of water surface ratio (Wr) have a slightly higher CS of the internal green space. In residential blocks 500 m from water bodies, Wr has a significant impact on the CS capacity of the green space. The blocks with an external greenway pattern and external greenway–green grid pattern provide an advantageous environment for CS. This study provides a reasonable basis for the development of riverfront green spaces to increase carbon sequestrations. Full article

(This article belongs to the Special Issue The Role of Urban Trees in Ecology Protection)

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21 pages, 28617 KiB

Open AccessArticle

The Influence of Different Moisture Contents on the Acoustic Vibration Characteristics of Wood

by Hongru Qiu, Yunqi Cui, Liangping Zhang, Tao Ding and Nanfeng Zhu

Forests 2025, 16(4), 680; https://doi.org/10.3390/f16040680 - 14 Apr 2025

Viewed by 266

Abstract

This study investigates the vibrational and acoustic properties of Sitka spruce (Picea sitchensis (Bong.) Carr.) and Indian rosewood (Dalbergia latifolia Roxb.), two common musical instrument woods, at moisture contents of 2%, 7%, and 12%. The specimens with dimensions of 400mm (longitudinal) [...] Read more.

This study investigates the vibrational and acoustic properties of Sitka spruce (Picea sitchensis (Bong.) Carr.) and Indian rosewood (Dalbergia latifolia Roxb.), two common musical instrument woods, at moisture contents of 2%, 7%, and 12%. The specimens with dimensions of 400mm (longitudinal) × 25 mm (radial) × 10 mm (tangential) were tested under cantilever beam conditions using non-contact magnetic field excitation to generate sinusoidal and pulse signals. Vibration data were collected via acceleration sensors and FFT analyzers. The test method was based on ASTM D6874-12 standard. Results indicate that increasing moisture content reduces acoustic vibration characteristics, with hardwoods exhibiting higher declines than softwoods. From 2% to 12% moisture content, the first-order sound radiation quality factor of Sitka spruce and Indian rosewood decreased by 15.41% and 15.57%, respectively, while the sound conversion rate declined by 41.91% and 43.21%. Increased moisture content lowers first-order and second-order resonance frequencies, amplitude ratios, dynamic elastic modulus, vibration propagation velocity, acoustic radiation quality factor, and acoustic conversion efficiency, while increasing acoustic impedance and the loss factor. With excitation frequency increases from 100 Hz to 1500 Hz, vibration propagation velocity rises slightly, while the loss factor declines. Full article

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

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Forests, Volume 16, Issue 4 (April 2025) – 155 articles

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