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Forests, Volume 15, Issue 9 (September 2024) – 8 articles

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16 pages, 10234 KiB  
Article
Temporal Variations in Methane Emissions from a Restored Mangrove Ecosystem in Southern China
by Pengpeng Tian, Xianglan Li, Zhe Xu, Liangxu Wu, Yuting Huang, Zhao Zhang, Mengna Chen, Shumin Zhang, Houcai Cai, Minghai Xu and Wei Chen
Forests 2024, 15(9), 1487; https://doi.org/10.3390/f15091487 (registering DOI) - 24 Aug 2024
Abstract
The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide has been increasingly investigated in recent years. While studies have shown that mangroves are weak sources of methane (CH4) emissions, measurements of CH4 fluxes from these ecosystems remain scarce. [...] Read more.
The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide has been increasingly investigated in recent years. While studies have shown that mangroves are weak sources of methane (CH4) emissions, measurements of CH4 fluxes from these ecosystems remain scarce. In this study, we examined the temporal variation and biophysical drivers of ecosystem-scale CH4 fluxes in China’s northernmost mangrove ecosystem based on eddy covariance measurements obtained over a 3-year period. In this mangrove, the annual CH4 emissions ranged from 6.15 to 9.07 g C m−2 year−1. The daily CH4 flux reached a peak of over 0.07 g C m−2 day−1 during the summer, while the winter CH4 flux was negligible. Latent heat, soil temperature, photosynthetically active radiation, and tide water level were the primary factors controlling CH4 emissions. This study not only elucidates the mechanisms influencing CH4 emissions from mangroves, strengthening the understanding of these processes but also provides a valuable benchmark dataset to validate the model-derived carbon budget estimates for these ecosystems. Full article
(This article belongs to the Section Forest Ecology and Management)
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24 pages, 5362 KiB  
Article
GLU-YOLOv8: An Improved Pest and Disease Target Detection Algorithm Based on YOLOv8
by Guangbo Yue, Yaqiu Liu, Tong Niu, Lina Liu, Limin An, Zhengyuan Wang and Mingyu Duan
Forests 2024, 15(9), 1486; https://doi.org/10.3390/f15091486 (registering DOI) - 24 Aug 2024
Abstract
In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest [...] Read more.
In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model’s perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in [email protected] for small-target detection compared to the YOLOv8 model, with a resulting [email protected] score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in [email protected] and a 5% increase in [email protected]:0.95, reaching 58.7% for [email protected]. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
17 pages, 3964 KiB  
Article
Characterization of the Expansin Gene Promoters in Populus trichocarpa
by Junkang Zhang, Xiaoyu Li, Lei Wang, Longfeng Gong, Mengtian Li and Jichen Xu
Forests 2024, 15(9), 1485; https://doi.org/10.3390/f15091485 (registering DOI) - 24 Aug 2024
Abstract
The expansin genes are commonly expressed in plant cells, and the encoded proteins influence plant growth and stress resistance by loosening the structure and increasing the flexibility of the cell wall. The objective of this study was to characterize expansin gene promoters in [...] Read more.
The expansin genes are commonly expressed in plant cells, and the encoded proteins influence plant growth and stress resistance by loosening the structure and increasing the flexibility of the cell wall. The objective of this study was to characterize expansin gene promoters in Populus trichocarpa to clarify the regulatory mechanisms underlying gene expression and evolution. Sequence alignments revealed that the similarity among 36 poplar expansin genes was greater for the coding sequences than for the promoter sequences, which suggested these promoter sequences evolved asynchronously. The bases flanking the start codon exhibited a usage bias, with sites +3, +4, and +5 biased toward GC, whereas the other sites were biased toward AT. The flanking sites were significantly correlated with gene expression, especially sites −10 and −17, in which C and G are the bases positively associated with gene expression. A total of 435 regulatory elements (61 types) were identified on the promoters of the poplar expansin genes; Skn-1 was the most common element in 23 promoters. Some expansin genes had more regulatory elements on their promoters (e.g., PtrEXPA4, PtrEXPA3, PtrEXPB3, and PtrEXPB1), whereas some others had less (e.g., PtrEXLA2, PtrEXLB1, and PtrEXPA23). Furthermore, 26 types of elements were involved in expansin gene expression, 25 of which positively affected expression in all analyzed samples. The exception was the endosperm expression-related element Skn-1, which negatively regulated expression in four tissues or treatments. Expression analysis showed that the expansin genes in Populus trichocarpa performed much differently under regular and abiotic stress conditions, which well matched the diversity of their promoter sequences. The results show that expansin genes play an important role in plant growth and development and stress resistance through expression adjustment. Full article
(This article belongs to the Special Issue Latest Progress in Research on Forest Tree Genomics)
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21 pages, 13034 KiB  
Article
Variations of Terrestrial Net Ecosystem Productivity in China Driven by Climate Change and Human Activity from 2010 to 2020
by Mei Xu, Bing Guo and Rui Zhang
Forests 2024, 15(9), 1484; https://doi.org/10.3390/f15091484 (registering DOI) - 24 Aug 2024
Abstract
Net ecosystem productivity (NEP) plays an important role in the quantitative evaluation of carbon source/sinks in terrestrial ecosystems. This study used Theil–Sen median trend analysis, the Mann–Kendall method, and the Geodetector model to analyze the variation patterns and its dominant factors of NEP [...] Read more.
Net ecosystem productivity (NEP) plays an important role in the quantitative evaluation of carbon source/sinks in terrestrial ecosystems. This study used Theil–Sen median trend analysis, the Mann–Kendall method, and the Geodetector model to analyze the variation patterns and its dominant factors of NEP from 2010 to 2020. The results showed the following: (1) During 2010–2020, the spatial distribution of carbon sinks in China’s terrestrial ecosystems showed a pattern of high in the southeast and low in the northwest. The area with NEP < 0 accounted for 44.74% of the total area, while the area with NEP > 0 accounted for 55.26%. (2) The northwest region belonged to the significant carbon source, while the other regions belonged to significant carbon sinks. (3) The annual average NEP in different sub-regions showed an increasing trend. During 2010–2020, the overall NEP in China showed a trend in shifting from low-level to high-level, indicating that the NEP of terrestrial ecosystems in China increased during the past 11 years. (4) The NEP gravity center in Northeast China showed a trend in moving southward and then northward, while that of the NEP gravity center in East China showed a circular migration trend of ‘northwest-southwest–northeast–southeast’. The gravity center of NEP in Northwest China was moving northeastward. The migration trajectory of the NEP gravity center in Southwest China presented a “Z” shape. The change in the gravity center of NEP in the central and southern regions had a strong circuitous nature, and the overall trend was to migrate southward. (5) The combined actions of climate change and human activities were the main reason for the change in NEP in China’s terrestrial ecosystem from 2010 to 2020. (6) There were obvious differences in the dominant driving factors of NEP evolution in different regions and different periods in the past 40 years. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)
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17 pages, 2569 KiB  
Article
Impact of Nitrogen Fertilizer Application on Soil Organic Carbon and Its Active Fractions in Moso Bamboo Forests
by Haoyu Chu, Wenhui Su, Shaohui Fan, Xianxian He and Zhoubin Huang
Forests 2024, 15(9), 1483; https://doi.org/10.3390/f15091483 (registering DOI) - 24 Aug 2024
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Abstract
Soil organic carbon (SOC) is a crucial indicator of soil quality and fertility. However, excessive nitrogen (N) application, while increasing Moso bamboo yield, may reduce SOC content, potentially leading to soil quality issues. The impact of N on SOC and its active fraction [...] Read more.
Soil organic carbon (SOC) is a crucial indicator of soil quality and fertility. However, excessive nitrogen (N) application, while increasing Moso bamboo yield, may reduce SOC content, potentially leading to soil quality issues. The impact of N on SOC and its active fraction in Moso bamboo forests remains underexplored. Investigating these effects will elucidate the causes of soil quality decline and inform effective N management strategies. Four N application gradients were set: no nitrogen (0 kg·hm−2·yr−1, N0), low nitrogen (242 kg·hm−2·yr−1, N1), medium nitrogen (484 kg·hm−2·yr−1, N2), and high nitrogen (726 kg·hm−2·yr−1, N3), with no fertilizer application as the control (CK). We analyzed the changes in SOC, active organic carbon components, and the Carbon Pool Management Index (CPMI) under different N treatments. The results showed that SOC and its active organic carbon components in the 0~10 cm soil layer were more susceptible to N treatments. The N0 treatment significantly increased microbial biomass carbon (MBC) content but had no significant effect on SOC, particulate organic carbon (POC), dissolved organic carbon (DOC), and readily oxidizable organic carbon (ROC) contents. The N1, N2, and N3 treatments reduced SOC content by 29.36%, 21.85%, and 8.67%, respectively. Except for POC, N1,N2 and N3 treatments reduced MBC, DOC, and ROC contents by 46.29% to 71.69%, 13.98% to 40.4%, and 18.64% to 48.55%, respectively. The MBC/SOC ratio can reflect the turnover rate of SOC, and N treatments lowered the MBC/SOC ratio, with N1 < N2 < N3, indicating the slowest SOC turnover under the N1 treatment. Changes in the Carbon Pool Management Index (CPMI) illustrate the impact of N treatments on soil quality and SOC sequestration capacity. The N1 treatment increased the CPMI, indicating an improvement in soil quality and SOC sequestration capacity. The comprehensive evaluation index of carbon sequestration capacity showed N3 (−0.69) < N0 (−0.13) < CK (−0.05) < N2 (0.24) < N1 (0.63), with the highest carbon sequestration capacity under the N1 treatment and a gradual decrease with increasing N fertilizer concentration. In summary, although the N1 treatment reduced the SOC content, it increased the soil CPMI and decreased the SOC turnover rate, benefiting soil quality and SOC sequestration capacity. Therefore, the reasonable control of N fertilizer application is key to improving soil quality and organic carbon storage in Moso bamboo forests. Full article
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23 pages, 2578 KiB  
Article
Expansion of Naturally Grown Phyllostachys edulis (Carrière) J. Houzeau Forests into Diverse Habitats: Rates and Driving Factors
by Juan Wei, Yongde Zhong, Dali Li, Jinyang Deng, Zejie Liu, Shuangquan Zhang and Zhao Chen
Forests 2024, 15(9), 1482; https://doi.org/10.3390/f15091482 - 23 Aug 2024
Viewed by 141
Abstract
Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the [...] Read more.
Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the absence of human intervention, it remains poorly understood how indigenous moso bamboo forests naturally expand into surrounding areas over the long term, and whether these patterns vary with environmental changes. Using multi-year forest resource inventory data, we extracted moso bamboo patches that emerged from 2010 to 2020 and proposed a bamboo expansion index to calculate the average rate of patch expansion during this period. Using the first global 30 m land-cover dynamic monitoring product with a fine classification system, we assessed the expansion speeds of moso bamboo into various areas, particularly forests with different canopy closures and categories. Using parameter-optimized geographic detectors, we explored the significance of multi-factors in the expansion process. The results indicate that the average expansion rate of moso bamboo forests in China is 1.36 m/y, with evergreen broadleaved forests being the primary area for invasion. Moso bamboo expands faster into open forest types (0.15 < canopy closure < 0.4), shrublands, and grasslands. The importance of factors influencing the expansion rate is ranked as follows: temperature > chemical properties of soil > light > physical properties of soil > moisture > atmosphere > terrain. When considering interactions, the primary factors contributing to expansion rates include various climate factors and the combined effect of climate factors and soil factors. Our work underscores the importance of improving the quality and density of native vegetation, such as evergreen broadleaved forests. Effective management strategies, including systematic monitoring of environmental variables, as well as targeted interventions like bamboo removal and soil moisture control, are essential for mitigating the invasion of moso bamboo. Full article
22 pages, 5370 KiB  
Article
Soil Erosion Risk Analysis in the Ría de Arosa (Pontevedra, Spain) Using the RUSLE and GIS Techniques
by Carlos E. Nieto, Antonio Miguel Martínez-Graña and Leticia Merchán
Forests 2024, 15(9), 1481; https://doi.org/10.3390/f15091481 - 23 Aug 2024
Viewed by 133
Abstract
Soil erosion in coastal areas, driven by global change and human activity, poses a significant threat to ecological and economic stability. This research investigates water erosion in the southeast of the Ría de Arosa (Pontevedra, Spain), utilizing the Revised Universal Soil Loss Equation [...] Read more.
Soil erosion in coastal areas, driven by global change and human activity, poses a significant threat to ecological and economic stability. This research investigates water erosion in the southeast of the Ría de Arosa (Pontevedra, Spain), utilizing the Revised Universal Soil Loss Equation model and Geographic Information System technologies. Key factors analyzed include rainfall erosivity, soil erodibility, topography, land cover, and conservation practices. High-resolution maps (1 × 1 m pixels) identified areas at high risk of erosion. Vulnerable zones, such as coastal cliffs and vineyards, show severe erosion rates exceeding 50 t/ha/year (>5 mm/year), with the most extreme zones reaching up to 200 t/ha/year (>200 mm/year). These results emphasize that intervention could be required or recommended. Suggested measures include reforestation, effective agricultural land management, or the implementation of vegetative barriers to reduce erosion. These areas, characterized by steep slopes and sparse vegetation, are particularly susceptible to soil loss, necessitating specific conservation efforts. The results underscore the need for sustainable coastal management practices and preventive strategies to protect this vulnerable coastal zone. Implementing these measures is crucial to mitigating the impacts of soil erosion, preserving natural resources, and ensuring long-term ecological and economic resilience in the region. Full article
21 pages, 6985 KiB  
Article
An Improved YOLOv5 Algorithm for Bamboo Strip Defect Detection Based on the Ghost Module
by Ru-Xiao Yang, Yan-Ru Lee, Fu-Shin Lee, Zhenying Liang and Yang Liu
Forests 2024, 15(9), 1480; https://doi.org/10.3390/f15091480 - 23 Aug 2024
Viewed by 132
Abstract
Detecting surface defects in bamboo strips is essential for producing Asian bamboo products. Currently, the detection of surface defects in bamboo strips mainly relies on manual labor. The labor intensity is high, and the detection efficiency is low. Improving the speed and accuracy [...] Read more.
Detecting surface defects in bamboo strips is essential for producing Asian bamboo products. Currently, the detection of surface defects in bamboo strips mainly relies on manual labor. The labor intensity is high, and the detection efficiency is low. Improving the speed and accuracy of identifying bamboo strip defects is crucial in enhancing enterprises’ production efficiency. Hence, this research designs a lightweight YOLOv5s neural network algorithm using the Ghost module to identify surface defects of bamboo strips. The research introduces an attention mechanism CA module to improve the recognition ability of the model target; the research also implements a C2f model to enhance the network performance and the surface quality of bamboo strips. The experimental results show that after training with the acquired image dataset, the YOLOv5s model can exert an intelligent detection effect on five common types of defects in bamboo strips, and the Ghost module makes YOLOv5s lightweight, which can effectively reduce model parameters and improve detection speed while maintaining recognition accuracy. Meanwhile, the C2f module and CA module can further leverage the model’s ability to identify specific defects in bamboo strips after lightweight improvement. Full article
(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)
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