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Keywords = Pinus yunnanensis forest

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14 pages, 2556 KiB  
Article
Differences in Growth Responses to Climate of Three Conifer Species in Lugu Lake of Northwestern Yunnan, Southwestern China
by Tao Yan, Yaoyao Kang, Siyu Xie, Chun Tao, Lianxiang Li, Xuefen Li, Qiong Wang and Yun Zhang
Plants 2025, 14(16), 2508; https://doi.org/10.3390/plants14162508 - 12 Aug 2025
Viewed by 250
Abstract
Responses of tree radial growth to climate are usually species-specific. Northwestern Yunnan has become a hotspot for the study of dendrochronology due to its sensitivity to climate change and the relative integrity of vegetation preservation. In this paper, we take three dominant conifers— [...] Read more.
Responses of tree radial growth to climate are usually species-specific. Northwestern Yunnan has become a hotspot for the study of dendrochronology due to its sensitivity to climate change and the relative integrity of vegetation preservation. In this paper, we take three dominant conifers—Pinus armandii, Pinus yunnanensis and Picea likiangensis—as the research objects and analyze their tree-ring width chronologies in order to reveal the main climate factors affecting tree growth in northwestern Yunnan and to evaluate species-specific variation in climate response. The results showed that the radial growth of the three tree species was co-regulated by temperature and precipitation but that the growth response patterns were varied. Specifically: (1) The radial growth of the three species of conifers was significantly and negatively correlated with the July average maximum temperature (Tmax) and the October Palmer Drought Severity Index (PDSI) in the current year. (2) Current May precipitation significantly promoted P. armandii growth and inhibited P. likiangensis growth, and a wet July was beneficial for both P. yunnanensis and P. likiangensis growth, while the radial growth of P. yunnanensis and P. armandii showed a significant and positive correlation with the August Tmax in the current year. (3) The sliding analysis supported the results of the response function by showing stable relationships with climate factors which significantly affected tree growth. Results from redundancy analysis (RDA) and response function analysis were basically consistent, demonstrating that these two methods could complement each other in the understanding of relationships between tree radial growth and climatic factors. This study elucidates the climate–growth relationship of the main tree species in the study area and provides theoretical guidance and scientific evidence for regional forest management. Full article
(This article belongs to the Special Issue Biological Signaling in Plant Development)
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26 pages, 9324 KiB  
Article
Effects of Prescribed Burning on Species Diversity of Understory in Pinus yunnanensis Forests of Southwestern China
by Xiaona Li, Yinxixue Pan, Huiping Pan, Han Yang, Ailing Yang, Jin Wang, Yuanjie Xu and Qiuhua Wang
Forests 2025, 16(8), 1312; https://doi.org/10.3390/f16081312 - 12 Aug 2025
Viewed by 279
Abstract
The Pinus yunnanensis forest of southwestern China represents a unique and ecologically critical vegetation type, historically shaped by fire disturbances. To mitigate catastrophic wildfire risks, prescribed burning has been widely implemented as a management tool in these ecosystems. However, its effects on plant [...] Read more.
The Pinus yunnanensis forest of southwestern China represents a unique and ecologically critical vegetation type, historically shaped by fire disturbances. To mitigate catastrophic wildfire risks, prescribed burning has been widely implemented as a management tool in these ecosystems. However, its effects on plant community structure and biodiversity remain insufficiently quantified. To investigate the specific changes in plant community characteristics caused by prescribed burning, this study was conducted in the Pinus yunnanensis forest in Zhaobi Hill, Xinping county. Our results revealed that prescribed burning induced differential effects on understory communities while exerting negligible effects on canopy tree composition. In the shrub layer, the number of shrub species decreased from 26 to 20, accompanied by a complete extirpation of arboreal saplings. Dominance hierarchies shifted markedly, transitioning from Lithocarpus mairei and Pinus yunnanensis regeneration cohorts in unburned plots to fire-adapted species Duhaldea cappa and Craibiodendron stellatum. Concomitantly, the average height of shrubs had a significant reduction in burning plots. Contrastingly, the number of herb species increased from 30 to 37 in burning plots, with non-significant alterations in abundance, height, and importance values. Prescribed burning significantly decreases the α species diversity of shrubs, but only has minimal effects on the α species diversity indices of herbs. Overall, prescribed burning appears to be the primary factor affecting the species diversity index of shrubs, while altitude, forest structure, and soil nutrient content exert greater influences on the species diversity index of the herbaceous layer. Prescribed burning was the dominant factor shaping the community structure and species diversity of the shrub layer, and the missing saplings of trees in the shrub layer might influence future forest succession in the long term. Full article
(This article belongs to the Section Forest Ecology and Management)
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17 pages, 2163 KiB  
Article
Allometric Growth of Annual Pinus yunnanensis After Decapitation Under Different Shading Levels
by Pengrui Wang, Chiyu Zhou, Boning Yang, Jiangfei Li, Yulan Xu and Nianhui Cai
Plants 2025, 14(15), 2251; https://doi.org/10.3390/plants14152251 - 22 Jul 2025
Viewed by 291
Abstract
Pinus yunnanensis, a native tree species in southwest China, is shading-tolerant and ecologically significant. Light has a critical impact on plant physiology, and decapitation improves canopy light penetration and utilization efficiency. The study of allometric relationships is well-known in forestry, forest ecology, [...] Read more.
Pinus yunnanensis, a native tree species in southwest China, is shading-tolerant and ecologically significant. Light has a critical impact on plant physiology, and decapitation improves canopy light penetration and utilization efficiency. The study of allometric relationships is well-known in forestry, forest ecology, and related fields. Under control (full daylight exposure, 0% shading), L1 (partial shading, 25% shading), L2 (medium shading, 50% shading), and L3 (serious shading, 75% shading) levels, this study used the decapitation method. The results confirmed the effectiveness of decapitation in annual P. yunnanensis and showed that the main stem maintained isometric growth in all shading treatments, accounting for 26.8% of the individual plant biomass, and exhibited dominance in biomass allocation and high shading sensitivity. These results also showed that lateral roots exhibited a substantial biomass proportion of 12.8% and maintained more than 0.5 of higher plasticity indices across most treatments. Moreover, the lateral root exhibited both the lowest slope in 0.5817 and the highest significance (p = 0.023), transitioning from isometric to allometric growth under L1 shading treatment. Importantly, there was a positive correlation between the biomass allocation of an individual plant and that of all components of annual P. yunnanensis. In addition, the synchronized allocation between main roots and lateral branches, as well as between main stems and lateral roots, suggested functional integration between corresponding belowground and aboveground structures to maintain balanced resource acquisition and architectural stability. At the same time, it has been proved that the growth of lateral roots can be accelerated through decapitation. Important scientific implications for annual P. yunnanensis management were derived from these shading experiments on allometric growth. Full article
(This article belongs to the Special Issue Development of Woody Plants)
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24 pages, 2109 KiB  
Article
Individual Tree Mortality Prediction of Pinus yunnanensis Franch.—Based on Stacking Ensemble Learning and Threshold Optimization
by Longfeng Deng, Jianming Wang, Jiting Yin, Yuling Chen and Baoguo Wu
Forests 2025, 16(6), 938; https://doi.org/10.3390/f16060938 - 3 Jun 2025
Viewed by 473
Abstract
Accurate prediction of individual tree mortality in Pinus yunnanensis Franch. is essential for sustainable forest management and ecological monitoring in southwest China. The aim of this study is to develop a tree mortality prediction model for Pinus yunnanensis based on resurvey data from [...] Read more.
Accurate prediction of individual tree mortality in Pinus yunnanensis Franch. is essential for sustainable forest management and ecological monitoring in southwest China. The aim of this study is to develop a tree mortality prediction model for Pinus yunnanensis based on resurvey data from the Cangshan area in Dali, Yunnan Province, using a stacked ensemble learning algorithm. After an initial evaluation of model performance, the classification thresholds were optimized using the Minimum Classification Error method, the Maximum Sensitivity and Specificity method, the Kappa coefficient method, and the Precision-Recall (PR) curve method to enhance classification results. The findings show that, compared to traditional statistical methods and individual machine learning models, the stacked ensemble learning model (Stacked-RSX) outperforms others in tree mortality classification tasks, which achieved an accuracy of 0.8947, recall of 0.9431, true negative rate of 0.9490, misclassification rate of 0.2289, and an area under the curve of 0.953. Through an exhaustive search for the best classification thresholds, the PR curve method demonstrated good adaptability across all models. All optimal thresholds, relative to the default threshold, significantly improved overall classification performance. Furthermore, feature importance analysis revealed that tree height, diameter at breast height (DBH), Hegyi competition index, and the ratio of DBH to stand basal area are key variables influencing mortality risk. These results indicate that the stacking ensemble learning algorithm effectively analyzes the complex relationships among different factors, significantly improving the prediction accuracy of tree mortality, and providing scientific insights for the management and health monitoring of Pinus yunnanensis forests. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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20 pages, 3964 KiB  
Article
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 405
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
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18 pages, 8730 KiB  
Article
How Prescribed Burning Affects Surface Fine Fuel and Potential Fire Behavior in Pinus yunnanensis in China
by Xilong Zhu, Shiying Xu, Ruicheng Hong, Hao Yang, Hongsheng Wang, Xiangyang Fang, Xiangxiang Yan, Xiaona Li, Weili Kou, Leiguang Wang and Qiuhua Wang
Forests 2025, 16(3), 548; https://doi.org/10.3390/f16030548 - 20 Mar 2025
Viewed by 476
Abstract
Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of [...] Read more.
Forest fine fuels are a crucial component of surface fuels and play a key role in igniting forest fires. However, despite nearly 20 years of long-term prescribed burning management on Zhaobi Mountain in Xinping County, Yunnan Province, China, there remains a lack of specific quantification regarding the effectiveness of fine fuel management in Pinus yunnanensis forests. In this study, 10 m × 10 m sample plots were established on Zhaobi Mountain following one year of growth after prescribed burning. The plots were placed in a prescribed burning (PB) area and an unburned control (UB) area. We utilized indicators such as forest stand characteristics, fine fuel physicochemical properties, and potential fire behavior parameters for evaluation. The results indicate that prescribed burning at one-year intervals significantly affects stand characteristics, particularly in metrics such as crown base height, diameter breast height, and fuel load (p < 0.05). However, the physical and chemical properties of fine fuels did not show significant differences. Notably, the mean range of spread (RS) of PB fuels downhill was 43.3% lower than that of UB fuels, and the mean flaming height (FH) was 35.2% lower. The fire line intensity was <750 kW/m, categorizing it as a low-intensity fire. These findings provide data on the composition of fine fuels and the variables of fire behavior affected by prescribed burning, demonstrating that low-intensity prescribed burns can regulate fine fuels in the understory and maintain a stable regional fire risk level. Full article
(This article belongs to the Special Issue Fire Ecology and Management in Forest—2nd Edition)
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16 pages, 2509 KiB  
Article
Adaptations of Pinus yunnanensis Seedlings to Simulated Light Patches: Growth Dynamics and C:N:P Stoichiometry
by Weisong Zhu, Yuanxi Liu, Junwen Wu and Chaojun Li
Forests 2025, 16(3), 517; https://doi.org/10.3390/f16030517 - 15 Mar 2025
Cited by 3 | Viewed by 504
Abstract
Many seedlings and a few young trees have recently been observed in Pinus yunnanensis forests, reducing the natural regeneration ability and succession. Shade treatments were applied to potted 1-year-old P. yunnanensis seedlings, and the shade net was opened at noon to simulate light [...] Read more.
Many seedlings and a few young trees have recently been observed in Pinus yunnanensis forests, reducing the natural regeneration ability and succession. Shade treatments were applied to potted 1-year-old P. yunnanensis seedlings, and the shade net was opened at noon to simulate light patches. We used four treatments, i.e., 80% shade with 1 h light at noon (T80-1), 80% shade all the time (T80), 95% shade with 1 h light at noon (T95-1), and 95% shade all the time (T95), and a control (natural light). We analyzed the effects of light patches on the growth and C:N:P stoichiometry of P. yunnanensis seedlings. (1) Shading significantly inhibited seedling growth, with height increments reduced by 29.59% and 47.40% under T80 and T95, respectively, and basal diameter increments decreased by 10.97% and 14.41%. (2) Shading reduced biomass across organs, with total biomass under T95 being only 39.02% of CK, but midday light patches alleviated this inhibition (T80-1 total biomass increased by 137.90% compared to T80). (3) Under high shading (T95), seedlings prioritized photosynthetic product allocation to aboveground parts (needle biomass proportion reached 58.01%), while light patches (T80-1) enhanced coarse root biomass (137.90% higher than T80). (4) Shading significantly increased needle C:N and C:P ratios (T95 increased by 69.01% and 129.93% compared to CK, respectively), with N:P > 16 indicating phosphorus limitation; light patches (T80-1) reduced N:P to 14–16, mitigating co-limitation by N and P. The study demonstrates that P. yunnanensis seedlings adopt conservative strategies under shading by adjusting biomass allocation and stoichiometry to adapt to low-light conditions, while midday light patches enhance photosynthetic efficiency and nutrient utilization. We recommend forest thinning to increase understory light patches, thereby improving natural regeneration and promoting sustainable forest management of P. yunnanensis forests. These findings highlight the importance of light management in P. yunnanensis forests to enhance regeneration by regulating understory light patches. Full article
(This article belongs to the Section Forest Ecology and Management)
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14 pages, 2263 KiB  
Article
Five Years of Natural Vegetation Recovery in Three Forests of Karst Graben Area and Its Effects on Plant Diversity and Soil Properties
by Xiaorong Yang, Rouzi-Guli Turmuhan, Lina Wang, Jiali Li and Long Wan
Forests 2025, 16(1), 91; https://doi.org/10.3390/f16010091 - 8 Jan 2025
Cited by 1 | Viewed by 814
Abstract
In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways to control rocky desertification. In this study, vegetation surveys were conducted on three typical plantations in Jianshui County, Yunnan [...] Read more.
In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways to control rocky desertification. In this study, vegetation surveys were conducted on three typical plantations in Jianshui County, Yunnan Province, a typical karst fault basin area, in 2016 and 2021. The plantations were Pinus massoniana forest (PM), Pinus yunnanensis forest (PY), and mixed forests of Pinus yunnanensis and Quercus variabilis (MF). Plant diversity and soil nutrients were compared during the five-year period. This paper mainly draws the following results: The plant diversity of PM, PY, and MF increased. With the increase of time, new species appeared in the tree layer, shrub layer, and herb layer of the three forests. Tree species with smaller importance values gradually withdrew from the community. In the tree layer, the Patrick index, Simpson index, and Shannon–Wiener index of the three forests increased significantly. The Pielou index changed from the highest for PM in 2016 to the highest for PY in 2021. In the shrub layer, the Pielou index of the three forests increased. The Patrick index changed from the highest for MF in 2016 to the highest for PY in 2021. There was no significant difference in species diversity index for the herb layer. With the increase of vegetation restoration time, the soil bulk density (BD) of the three forests decreased. There was no significant difference in soil total porosity (TP), soil capillary porosity (CP), and non-capillary porosity (NCP). The pH of PM increased significantly from 5.88~6.24 to 7.24~7.34. The pH of PY decreased significantly (p < 0.05). The contents of total nitrogen (TN) and ammonium nitrogen (NH4+-N) in PY and MF decreased. The content of nitrate nitrogen (NO3-N) in the three forests increased significantly (p < 0.05). Total phosphorus (TP) content decreased in PM and MF. The content of available phosphorus (AP) in PM and PY increased. In general, with the increase of vegetation restoration time, plant diversity and soil physical and chemical properties have also been significantly improved. The results can provide important data support for vegetation restoration in karst areas. Full article
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13 pages, 4281 KiB  
Article
Unique Geoclimatic Factors and Topography-Shaped Pollen Flow of Pinus yunnanensis var. tenuifolia Wild Populations in the Dry–Hot River Basin in China
by Liang-Long Liao, Wei Wei, Yu-Zhuo Wen, Chun-Hui Huang, Tian-Dao Bai and Wei-Xin Jiang
Forests 2024, 15(12), 2215; https://doi.org/10.3390/f15122215 - 16 Dec 2024
Viewed by 1251
Abstract
Exploring the gene flow and its causes in complex habitats of forest trees is valuable for understanding species’ adaptive evolution. Pinus yunnanensis var. tenuifolia (PYT) is mainly distributed in the dry–hot valleys along the Nanpan-Hongshui rivers in southwest China, an ecologically fragile area. [...] Read more.
Exploring the gene flow and its causes in complex habitats of forest trees is valuable for understanding species’ adaptive evolution. Pinus yunnanensis var. tenuifolia (PYT) is mainly distributed in the dry–hot valleys along the Nanpan-Hongshui rivers in southwest China, an ecologically fragile area. In this study, we analyzed 1056 seeds from eleven natural populations of PYT across its range using twelve cpSSR markers to explore haplotype polymorphisms and correlations with environmental factors. The results revealed a high genetic diversity (HE = 0.83), with the private haplotypes significantly exceeding the shared haplotypes. A genealogical structure was observed among the populations, with a moderate differentiation (FST = 0.162). The population clustering and haplotype network demonstrated localized areas of pollen exchange, especially in the middle and lower reaches of the river. Redundancy analysis showed that, as the populations were closer to the river, genetic diversity tended to decrease significantly, implying that the pollen dispersal is restricted by the foehn effect in the valley. Variability in genetic differentiation among the offspring populations was primarily influenced by geographic factors, such as mountains and rivers, which, along with local environmental adaptations, shaped the pollen distribution pattern. These findings may facilitate the sustainable management and conservation of PYT and other species under similar habitats. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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23 pages, 4496 KiB  
Article
Optimization of the Stand Structure in Secondary Forests of Pinus yunnanensis Based on Deep Reinforcement Learning
by Jian Zhao, Jianmming Wang, Jiting Yin, Yuling Chen and Baoguo Wu
Forests 2024, 15(12), 2181; https://doi.org/10.3390/f15122181 - 11 Dec 2024
Cited by 1 | Viewed by 1052
Abstract
This study proposes a multi-objective stand structure optimization scheme based on deep reinforcement learning, demonstrating the strengths of deep reinforcement learning in solving multi-objective optimization problems and providing innovative insights for sustainable forest management. Using the Pinus yunnanensis secondary forest in Southwest China [...] Read more.
This study proposes a multi-objective stand structure optimization scheme based on deep reinforcement learning, demonstrating the strengths of deep reinforcement learning in solving multi-objective optimization problems and providing innovative insights for sustainable forest management. Using the Pinus yunnanensis secondary forest in Southwest China as the research subject, we established a stand structure optimization model with stand spatial structure indexes as the optimization objectives and non-spatial structure indexes as the constraints. We optimized the stand structure by combining deep reinforcement learning with three tree-felling decisions: random selection, tree homogeneity index, and spatial competition. Simulated cutting experiments were conducted on circular plots (P1–P5) using deep reinforcement learning and reinforcement learning. The initial objective function values of all plots (0.2950, 0.2954, 0.3445, 0.3010, 0.3168) were effectively improved. The maximum objective function values after optimization by the deep reinforcement learning schemes (0.3815, 0.3701, 0.4301, 0.4599, 0.3689) were significantly better than those achieved by the reinforcement learning schemes (0.3394, 0.3579, 0.3986, 0.4321, 0.3556). Among these, the optimization scheme combining random selection and deep reinforcement learning showed the greatest average improvement across the five plots (29.73%), with its enhancement of the objective function value significantly surpassing that of other optimization schemes. This study applies deep reinforcement learning to stand structure optimization, proposing a new approach to solving multi-objective optimization problems in stand structure and providing a reference for forest health management in Southwest China. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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16 pages, 3462 KiB  
Article
Response of Hydrothermal Conditions to the Saturation Values of Forest Aboveground Biomass Estimation by Remote Sensing in Yunnan Province, China
by Yong Wu, Binbing Guo, Xiaoli Zhang, Hongbin Luo, Zhibo Yu, Huipeng Li, Kaize Shi, Leiguang Wang, Weiheng Xu and Guanglong Ou
Land 2024, 13(9), 1534; https://doi.org/10.3390/land13091534 - 22 Sep 2024
Cited by 1 | Viewed by 1224
Abstract
Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, [...] Read more.
Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research. Full article
(This article belongs to the Special Issue Land-Based Greenhouse Gas Mitigation for Carbon Neutrality)
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12 pages, 4519 KiB  
Article
Determination and Analysis of Endogenous Hormones and Cell Wall Composition between the Straight and Twisted Trunk Types of Pinus yunnanensis Franch
by Hailin Li, Rong Xu, Cai Wang, Xiaolin Zhang, Peiling Li, Zhiyang Wu and Dan Zong
Forests 2024, 15(9), 1626; https://doi.org/10.3390/f15091626 - 14 Sep 2024
Cited by 1 | Viewed by 1192
Abstract
Pinus yunnanensis Franch., one of the pioneer species of wild mountain afforestation in southwest China, plays an essential role in the economy, society and environment of Yunnan Province. Nonetheless, P. yunnanensis’ trunk twisting and bending phenomenon has become more common, which significantly [...] Read more.
Pinus yunnanensis Franch., one of the pioneer species of wild mountain afforestation in southwest China, plays an essential role in the economy, society and environment of Yunnan Province. Nonetheless, P. yunnanensis’ trunk twisting and bending phenomenon has become more common, which significantly restricts its use and economic benefits. In order to clarify the compositional differences between the straight and twisted trunk types of P. yunnanensis and to investigate the reasons for the formation of twisted stems, the present study was carried out to dissect the macroscopic and microscopic structure of the straight and twisted trunk types of P. yunnanensis, to determine the content of cell wall components (lignin, cellulose, hemicellulose), determine the content of endogenous hormones, and the expression validation of phytohormone-related differential genes (GA2OX, COI1, COI2) and cell wall-related genes (XTH16, TCH4). The results showed that the annual rings of twisted trunk types were unevenly distributed, eccentric growth, insignificant decomposition of early and late wood, rounding and widening of the tracheid cells, thickening of the cell wall, and reduction of the cavity diameter; the lignin and hemicellulose contents of twisted trunk types were higher; in twisted trunk types, the contents of gibberellin (GA) and jasmonic acid (JA) increased, and the content of auxin (IAA) was reduced; the GA2OX were significantly down-regulated in twisted trunk types, and the expressions of the genes associated with the cell wall, COI1, COI2, TCH4 and XTH16, were significantly up-regulated. In conclusion, the present study found that the uneven distribution of endogenous hormones may be an important factor leading to the formation of twisted trunk type of P. yunnanensis, which adds new discoveries to reveal the mechanism of the genesis of different trunk types in plants, and provides a theoretical basis for the genetic improvement of forest trees. Full article
(This article belongs to the Section Genetics and Molecular Biology)
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20 pages, 2596 KiB  
Article
Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China
by Feng Cheng, Ruijiao Yang and Junen Wu
Forests 2024, 15(9), 1622; https://doi.org/10.3390/f15091622 - 14 Sep 2024
Cited by 1 | Viewed by 1360
Abstract
The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the [...] Read more.
The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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23 pages, 39653 KiB  
Article
Registration of TLS and ULS Point Cloud Data in Natural Forest Based on Similar Distance Search
by Yuncheng Deng, Jinliang Wang, Pinliang Dong, Qianwei Liu, Weifeng Ma, Jianpeng Zhang, Guankun Su and Jie Li
Forests 2024, 15(9), 1569; https://doi.org/10.3390/f15091569 - 6 Sep 2024
Cited by 5 | Viewed by 1444
Abstract
Multiplatform fusion point clouds can effectively compensate for the disadvantages of individual platform point clouds in forest parameter extraction, maximizing the potential of LiDAR technology. However, existing registration algorithms often suffer from insufficient feature extraction and limited registration accuracy. To address these issues, [...] Read more.
Multiplatform fusion point clouds can effectively compensate for the disadvantages of individual platform point clouds in forest parameter extraction, maximizing the potential of LiDAR technology. However, existing registration algorithms often suffer from insufficient feature extraction and limited registration accuracy. To address these issues, we propose a ULS (Unmanned Aerial Vehicle Laser Scanning)-TLS (Terrestrial Laser Scanning) point cloud data registration method based on Similar Distance Search (SDS). This method enhances coarse registration by accurately retrieving points with similar features, leading to high overlap in the rough registration stage and further improving fine registration precision. (1) The proposed method was tested on four natural forest plots, including Pinus densata Mast., Pinus yunnanensis Franch., Pices asperata Mast., Abies fabri (Mast.) Craib, and demonstrated high registration accuracy. Both coarse and fine registration achieved superior results, significantly outperforming existing algorithms, with notable improvements over the TR algorithm. (2) In addition, the study evaluated the accuracy of individual tree parameter extraction from fusion point clouds versus single-platform point clouds. While ULS point clouds performed slightly better in some metrics, the fused point clouds offered more consistent and reliable results across varying conditions. Overall, the proposed SDS method and the resulting fusion point clouds provide strong technical support for efficient and accurate forest resource management, with significant scientific implications. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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28 pages, 5695 KiB  
Article
Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests
by Shuai Xuan, Jianming Wang, Jiting Yin, Yuling Chen and Baoguo Wu
Forests 2024, 15(7), 1143; https://doi.org/10.3390/f15071143 - 30 Jun 2024
Cited by 1 | Viewed by 1386
Abstract
This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective [...] Read more.
This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry)
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