Forests

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17 pages, 2711 KiB

Open AccessArticle

Study on the Natural Regeneration Characteristics and Influencing Factors of Typical Quercus Forests in Northern China

by Xuefan Hu, Guangshuang Duan, Yingshan Jin, Yuxin Cheng, Fang Liang, Zhenghua Lian, Fang Li, Yuerong Wang and Hongfei Chen

Forests 2025, 16(2), 250; https://doi.org/10.3390/f16020250 - 28 Jan 2025

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Abstract

This study aims to analyze the natural regeneration characteristics and the key factors of Quercus forests, providing a theoretical foundation for maintaining the ecological stability of Quercus forests in northern China. In June and July 2023, 17 square plots of five Quercus species [...] Read more.

This study aims to analyze the natural regeneration characteristics and the key factors of Quercus forests, providing a theoretical foundation for maintaining the ecological stability of Quercus forests in northern China. In June and July 2023, 17 square plots of five Quercus species in Beijing were surveyed, and seedling regeneration and environmental factors (site, stand and soil factors) were measured. Pearson correlation and random forest algorithms were used to identify the relevant and key environmental factors affecting seedling regeneration density (Seedling 1, Seedling 2, Seedling 3). The natural regeneration capabilities of the five Quercus species in the Beijing area vary, with Quercus aliena and Quercus variabilis being stronger, while Quercus mongolica, Quercus acutissima and Quercus dentata are relatively weaker. Correlation analysis showed that Seedling 1 has no significant correlation with environmental factors; Seedling 2 is significantly negatively correlated with Pielou’s evenness (J) and exchangeable calcium (ECa) (p < 0.05); Seedling 3 is significantly positively correlated with species richness (S), Shannon–Wiener index (H), stand volume (M), and litter layer thickness (LT) (p < 0.05), and significantly negatively correlated with Pielou’s evenness (J) (p < 0.01). The random forest algorithm indicated that the regeneration of Seedling 1 is mainly affected by stand factors, while the regeneration of Seedling 2 and Seedling 3 is more influenced by soil and site factors. The Quercus forests in the Beijing region exhibit a rich species composition and demonstrate a certain capacity for natural regeneration. However, seedling growth is more constrained by soil and site factors in the later stages. Therefore, in the management of Quercus forests, environmental factors can be regulated during the seedling growth stage to create more suitable conditions for regeneration. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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17 pages, 1000 KiB

Open AccessArticle

Quantifying the Effects of Carbon Growth Grade and Structural Diversity on Carbon Sinks of Natural Coniferous–Broadleaved Mixed Forests Across the Jilin Province of China

by Xiao He, Hong Guo, Xiangdong Lei, Wenqiang Gao and Yutang Li

Forests 2025, 16(2), 227; https://doi.org/10.3390/f16020227 - 24 Jan 2025

Viewed by 427

Abstract

Natural mixed forests’ carbon sequestration capacity is crucial for mitigating climate change and maintaining ecological balance. However, most of the current studies only consider the role of forest age, ignoring the influence of carbon growth grade and stand structural diversity, which leads to [...] Read more.

Natural mixed forests’ carbon sequestration capacity is crucial for mitigating climate change and maintaining ecological balance. However, most of the current studies only consider the role of forest age, ignoring the influence of carbon growth grade and stand structural diversity, which leads to an increase in uncertainty in large-scale forest carbon sink assessment. The aim of this study was to quantify the effects of carbon growth grade and stand structure diversity on the carbon sink of natural mixed forests and to establish a more accurate stand carbon growth model. Based on sample data from the National Forest Inventory (NFI) of China, the stand carbon growth model was established based on Gompertz and Logistic theoretical growth models, and the forest carbon sink at the regional scale was predicted. It was found that the stand carbon growth model considering only the stand age as a single variable often had poor results, with R² less than 0.36, while R² values of the optimal model introducing carbon growth grade and stand structural diversity were 0.87 and 0.48, respectively, which significantly improved the prediction accuracy of the model, and both had significant effects on stand carbon stocks. By predicting the future forest carbon sink, it was found that the forest carbon sink of the natural coniferous–broadleaved mixed forests in Jilin Province would reach 791 (781–801) t c/a and 843 (833–852) t c/a in 2030 and 2060, respectively, which were 17% lower and 51% higher than that of the forest carbon sink estimated by considering only the age. Moreover, the model considering structural diversity predicted a more positive carbon sink trend, indicating that forest carbon stocks could be more effectively maintained and carbon sinks increased by increasing the complexity of stand diameter at breast height structure, which has important guiding significance for future forest carbon sink management. This study provides scientific support for achieving the goal of “carbon neutrality” proposed by China. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

16 pages, 6301 KiB

Open AccessArticle

Stand Age Affects Biomass Allocation and Allometric Models for Biomass Estimation: A Case Study of Two Eucalypts Hybrids

by Runxia Huang, Wankuan Zhu, Apeng Du, Yuxing Xu and Zhichao Wang

Forests 2025, 16(2), 193; https://doi.org/10.3390/f16020193 - 21 Jan 2025

Viewed by 459

Abstract

We studied the effects of stand age on the allocation of biomass and allometric relationships among component biomass in five stands ages (1, 3, 5, 7, and 8 years old) of two eucalypts hybrids, including Eucalyptus urophylla × E. grandis and E. urophylla [...] Read more.

We studied the effects of stand age on the allocation of biomass and allometric relationships among component biomass in five stands ages (1, 3, 5, 7, and 8 years old) of two eucalypts hybrids, including Eucalyptus urophylla × E. grandis and E. urophylla × E. tereticornis, in the Leizhou Peninsula, China. The stem, bark, branch, leaf, and root biomass from 60 destructively harvested trees were quantified. Allometric models were applied to examine the relationship between the tree component biomass and predictor variable (diameter at breast height, D, and height, H). Stand age was introduced into the allometric models to explore the effect of stand age on biomass estimation. The results showed the following: (1) Stand age significantly affected the distribution of biomass in each component. The proportion of stem biomass to total tree biomass increased with stand age, the proportions of bark, branch, and leaf biomass to total tree biomass decreased with stand age, and the proportion of root biomass to total tree biomass first decreased and then increased with stand age. (2) There were close allometric relationships between biomass (i.e., the components biomass, aboveground biomass, and total biomass per tree) and diameter at breast height (D), height (H), the product of diameter at breast height and tree height (DH), and the product of the square of the diameter at breast height and tree height (D²H). The allometric relationship between biomass and measurement parameters (D, H, DH, D²H) could be applied to the biomass assessment of eucalypts plantation. (3) Allometric equations that included stand age as a complementary variable significantly improved the fit and enhanced the accuracy of biomass estimates. The optimal independent variable for the biomass prediction model varied according to each organ. These results indicate that stand age has an important influence on biomass allocation. Allometric equations considering stand age could improve the accuracy of carbon sequestration estimates in plantations. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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12 pages, 4466 KiB

Open AccessArticle

An Algorithm for Determining Pith Position Based on Crown Width Size

by Jianfeng Yao, Xiaowei Shang, Xuefan Hu, Yingshan Jin, Liming Cai, Zhuofan Li, Fang Li and Fang Liang

Forests 2024, 15(12), 2172; https://doi.org/10.3390/f15122172 - 10 Dec 2024

Viewed by 627

Abstract

To accurately estimate the pith position, a method was proposed for estimating the pith position by the crown width. The crown widths of 120 trees and radiuses of each disc extracted at the height of 1.3 m from these trees were measured in [...] Read more.

To accurately estimate the pith position, a method was proposed for estimating the pith position by the crown width. The crown widths of 120 trees and radiuses of each disc extracted at the height of 1.3 m from these trees were measured in four directions. The crown and radius ratios of the length of each direction to the total length in that direction and the opposite direction were calculated. Using the crown ratio as an independent variable, as well as the radius ratio as a dependent variable, the linear, logarithmic, exponential, and polynomial models were built. The model with the highest R² was selected as the radius ratio model. The geometric center method and the crown width method were applied to estimate the pith position, and the estimation errors were calculated, respectively. The R² of the linear, logarithmic, exponential, and polynomial models were 0.405, 0.379, 0.403, 0.404, respectively, and the linear model was chosen as the radius ratio model. The prediction error based on the crown width was 7.6%, and that of the geometric center method was 10.1%. The findings indicate that the crown width method can improve the accuracy of estimating the pith position. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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17 pages, 2835 KiB

Open AccessArticle

A Study on the Growth Model of Natural Forests in Southern China Under Climate Change: Application of Transition Matrix Model

by Xiangjiang Meng, Zhengrui Ma, Ying Xia, Jinghui Meng, Yuhan Bai and Yuan Gao

Forests 2024, 15(11), 1947; https://doi.org/10.3390/f15111947 - 5 Nov 2024

Viewed by 814

Abstract

This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in [...] Read more.

This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in Chongqing and climate data from Climate AP are utilized. The model is used to predict forest growth and compare the number of trees, basal area, and stock volume under different climate scenarios. The results show that the climate-sensitive transition matrix growth model has high accuracy. The relationships between the variables and forest growth, mortality, and recruitment correspond to natural succession and growth. Although the number of trees, basal area, and stock volume do not differ significantly for different climate scenarios, the forest has sufficient seedling regeneration and large-diameter trees. The growth process aligns with succession, with pioneer species being replaced by climax species. The proposed climate-sensitive transition matrix growth model fills the gap in growth models for natural secondary forests in Chongqing and is an accurate method for predicting forest growth. The model can be used for long-term prediction of forest stands to understand future forest growth trends and provide reliable references for forest management. Forest growth can be predicted for different harvesting intensities to determine the optimal intensity to guide natural forest management in Chongqing City. The results of this study can help formulate targeted forest management policies to deal more effectively with climate change and promote sustainable forest health. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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21 pages, 3277 KiB

Open AccessArticle

LiDAR-Based Modeling of Individual Tree Height to Crown Base in Picea crassifolia Kom. in Northern China: Comparing Bayesian, Gaussian Process, and Random Forest Approaches

by Zhaohui Yang, Hao Yang, Zeyu Zhou, Xiangxing Wan, Huiru Zhang and Guangshuang Duan

Forests 2024, 15(11), 1940; https://doi.org/10.3390/f15111940 - 4 Nov 2024

Viewed by 849

Abstract

This study compared hierarchical Bayesian, mixed-effects Gaussian process regression, and random forest models for predicting height to crown base (HCB) in Qinghai spruce (Picea crassifolia Kom.) forests using LiDAR-derived data. Both modeling approaches were applied to a dataset of 510 [...] Read more.

This study compared hierarchical Bayesian, mixed-effects Gaussian process regression, and random forest models for predicting height to crown base (HCB) in Qinghai spruce (Picea crassifolia Kom.) forests using LiDAR-derived data. Both modeling approaches were applied to a dataset of 510 trees from 16 plots in northern China. The models incorporated tree-level variables (height, diameter at breast height, crown projection area) and plot-level spatial competition indices. Model performance was evaluated using leave-one-plot-out cross-validation. The Gaussian mixed-effects process model (with an RMSE of 1.59 and MAE of 1.25) slightly outperformed the hierarchical Bayesian model and the random forest model. Both models identified LiDAR-derived tree height, DBH, and LiDAR-derived crown projection area as primary factors influencing HCB. The spatial competition index (SCI) emerged as the most effective random effect, with the lowest AIC and BIC values, highlighting the importance of local competition dynamics in HCB formation. Uncertainty analysis revealed consistent patterns across the predicted values, with an average relative uncertainty of 33.89% for the Gaussian process model. These findings provide valuable insights for forest management and suggest that incorporating spatial competition indices can enhance HCB predictions. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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13 pages, 1297 KiB

Open AccessArticle

Tree Height–Diameter Model of Natural Coniferous and Broad-Leaved Mixed Forests Based on Random Forest Method and Nonlinear Mixed-Effects Method in Jilin Province, China

by Qigang Xu, Fan Yang, Sheng Hu, Xiao He and Yifeng Hong

Forests 2024, 15(11), 1922; https://doi.org/10.3390/f15111922 - 31 Oct 2024

Viewed by 861

Abstract

Objective: The purpose of this article was to use the Random Forest method and nonlinear mixed-effects method to develop a model for determining tree height–diameter at breast height (DBH) for a natural coniferous and broad-leaved mixed forest in Jilin Province and to compare [...] Read more.

Objective: The purpose of this article was to use the Random Forest method and nonlinear mixed-effects method to develop a model for determining tree height–diameter at breast height (DBH) for a natural coniferous and broad-leaved mixed forest in Jilin Province and to compare the advantages and disadvantages of the two methods to provide a basis for forest management practice. Method: Based on the Chinese national forest inventory data, the Random Forest method and nonlinear mixed-effects method were used to develop a tree height–DBH model for a natural coniferous and broad-leaved mixed forest in Jilin Province. Results: The Random Forest method performed well on both the fitting set and validation set, with an R² of 0.970, MAE of 0.605, and RMSE of 0.796 for the fitting set and R² of 0.801, MAE of 1.44 m, and RMSE of 1.881 m for the validation set. Compared with the nonlinear mixed-effects method, the Random Forest model improved R² by 33.83%, while the MAE and RMSE decreased by 67.74% and 66.44%, respectively, in the fitting set; the Random Forest model improved R² by 9.88%, while the MAE and RMSE decreased by 14.38% and 12.05%, respectively, in the validation set. Conclusions: The tree height–DBH model constructed based on the Random Forest method had higher prediction accuracy for a natural coniferous and broad-leaved mixed forest in Jilin Province and had stronger adaptability for higher-dimensional data, which can be used for tree height prediction in the study area. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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15 pages, 5856 KiB

Open AccessArticle

Carbon Stock Estimation of Poplar Plantations Based on Additive Biomass Models

by Minglong Yin, Ting Gao, Yuhao Zhao, Ruiqiang Ni, Peijin Zheng, Yuyao Zhao, Jinshan Zhang, Kun Li and Chuanrong Li

Forests 2024, 15(10), 1829; https://doi.org/10.3390/f15101829 - 20 Oct 2024

Viewed by 902

Abstract

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent [...] Read more.

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared (

R_{a}^{2}

), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha⁻¹. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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15 pages, 3821 KiB

Open AccessArticle

Optimizing Stand Spatial Structure at Different Development Stages in Mixed Hard Broadleaf Forests

by Qi Sheng, Lingbo Dong and Zhaogang Liu

Forests 2024, 15(9), 1653; https://doi.org/10.3390/f15091653 - 19 Sep 2024

Viewed by 805

Abstract

Thinning plays a key role in regulating the stand spatial structure (SpS) to improve the development of stand quality, and the stand has different characteristics of stand structure (SS) at different growth and development stages (DSs), so it is most important to reasonably [...] Read more.

Thinning plays a key role in regulating the stand spatial structure (SpS) to improve the development of stand quality, and the stand has different characteristics of stand structure (SS) at different growth and development stages (DSs), so it is most important to reasonably determine the stage of growth and development of the stand to optimize the stand structure. We applied the TWINSPAN two-way indicator species analysis method to classify the different development stages of mixed hard broadleaf forests. We provided a comprehensive stand spatial structure optimization model for three selected plots at different development stages, respectively, to optimize the SpS. The results demonstrated the classified DS of 29 mixed hard broadleaf plots for three forest stages: the establishment stage, competitive stage, and quality selection stage. We then applied the SpS optimization model to our three plots; the Q(x) increased by 124.04%, 333.74%, and 116.83% when compared with those with no harvest, in which, upon the removal of 10% of the trees from the three plots, the maximum RIP values were all observed. Our results indicated that the SpS optimization model could regulate the SS for different growth stages and DSs. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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15 pages, 2384 KiB

Open AccessArticle

Soil-Sensitive Weibull Distribution Models of Larix principis-rupprechtii Plantations across Northern China

by Hong Guo, Xianzhao Liu and Dan Liu

Forests 2024, 15(9), 1562; https://doi.org/10.3390/f15091562 - 5 Sep 2024

Viewed by 721

Abstract

Tree diameter distribution models are important tools for forest management decision making. Soil variables affect tree growth and thus diameter distribution. However, few studies have been conducted on diameter distribution models describing the effects of soil. This study developed a soil-sensitive diameter distribution [...] Read more.

Tree diameter distribution models are important tools for forest management decision making. Soil variables affect tree growth and thus diameter distribution. However, few studies have been conducted on diameter distribution models describing the effects of soil. This study developed a soil-sensitive diameter distribution model based on 213 sample plots of Larix principis-rupprechtii plantations in northern China. The Weibull distribution model was modified by a compatible simultaneous system and the percentile method with the inclusion of soil variables. The most significant factors influencing the diameter distribution of L. principis-rupprechtii in terms of both scale and shape were stand characteristics and available K and alkali-hydrolysable N. The adjusted coefficient of determination for parameter γ significantly improved by 16.0%, while the root mean square error for parameter β decreased by 10.4%. The F test indicated a substantial difference between the models with and without soil variables. From the perspective of adjustable R² values, the Akaike information criterion, root mean square error, relative error index, and absolute error index, the inclusion of stand and soil factors in the tree diameter distribution model enhanced its performance compared to the model that did not consider soil factors. The soil-sensitive diameter distribution model is proven to be effective and accurate. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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16 pages, 1939 KiB

Open AccessArticle

A Three-Level Model System of Biomass and Carbon Storage for All Forest Types in China

by Weisheng Zeng, Wentao Zou, Xinyun Chen and Xueyun Yang

Forests 2024, 15(8), 1305; https://doi.org/10.3390/f15081305 - 25 Jul 2024

Cited by 2 | Viewed by 1736

Abstract

Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China’s diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon [...] Read more.

Forest biomass and carbon storage models are crucial for inventorying, monitoring, and assessing forest resources. This study develops models specific to China’s diverse forests, offering a methodological foundation for national carbon storage estimation and a quantitative basis for national, regional, and global carbon sequestration projections. Utilizing data from 52,700 permanent plots obtained during China’s 9th national forest inventory, we calculated biomass and carbon storage per hectare for 35 tree species groups using respective individual tree biomass models and carbon factors. We then constructed a three-level volume-based model system for forest biomass and carbon storage, applying weighted regression, dummy variable modeling, and simultaneous equations with error-in-variables. This system encompasses one population of forests, three forest categories (level I), 20 forest types (level II), and 74 forest sub-types (level III). Finally, the assessment of these models was carried out with six evaluation indices, and comparative analyses with previously established biomass models of three major forest types were conducted. Determination coefficients (R²) for the population average model, and three dummy models on levels I, II, and III, exceed 0.78, 0.85, 0.92, and 0.95, respectively, with corresponding mean prediction errors (MPEs) of 0.42%, 0.34%, 0.24%, and 0.19%, and mean percent standard errors (MPSEs) of approximately 22%, 21%, 15%, and 12%. Models for 20 forest types and 74 sub-types yield R² values above 0.87 and 0.85, with MPE values below 3% and 5%, respectively. Notably, the estimates of previous biomass models of three major forest types demonstrated considerable uncertainty, with TRE ranging from −20% to 74%. However, accuracy has improved with larger sample sizes. In total biomass and carbon storage estimations, the R² values of dummy models for levels I, II, and III progressively increase and MPSE and MPE values decrease, whereas TRE approximates zero. The tiered model system of simultaneous equations developed herein offers a quantitative framework for precise evaluations of biomass and carbon storage on different scales. For enhanced accuracy in such estimations, applying level III models is recommended whenever feasible, especially for national estimation. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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17 pages, 3484 KiB

Open AccessArticle

Spatial Distribution Pattern of Response of Quercus Variabilis Plantation to Forest Restoration Thinning in a Semi-Arid Area in China

by Mengli Zhou, Yuan Wang, Shanshan Jin, Decai Wang and Dongfeng Yan

Forests 2024, 15(8), 1278; https://doi.org/10.3390/f15081278 - 23 Jul 2024

Viewed by 837

Abstract

Plantations are increasing in frequency and extent across the landscape, especially in China, and forest thinning can accelerate the development of late-successional attributes, thereby enhancing plantation stand structural heterogeneity. To quantify the effect of forest restoration thinning on the spatial heterogeneity and the [...] Read more.

Plantations are increasing in frequency and extent across the landscape, especially in China, and forest thinning can accelerate the development of late-successional attributes, thereby enhancing plantation stand structural heterogeneity. To quantify the effect of forest restoration thinning on the spatial heterogeneity and the structure of Quercus variabilis plantations, a restoration thinning experiment in a 40-year-old Quercus variabilis plantation by removing trees from the upper canopy level was conducted; two one-hectare sample plots with thinning and a control (i.e., unlogged) were sampled; and geostatistics methods were used to analyze the spatial distribution pattern of the DBH, height, and density of the stand. We found that restoration forest thinning in the Quercus variabilis plantation had a significant impact on the average DBH and tree height of the stand. Meanwhile, the coefficient of variation and structure ratio of the DBH, tree height, and stand density in the thinning plot were larger than those in the control plot. The range and spatial autocorrelation distance of the DBH and stand density in the thinning plot were smaller than those in the control plot, but the fractal dimension showed the opposite trend. The range and spatial autocorrelation distance of tree height in the thinning plot were higher than those in the control plot. These findings suggested that, compared with the control plot, the stereoscopic distribution of the DBH and stand density in the thinning plot fluctuated less and changed gentler, and its spatial continuity was not high but its variation was significant; meanwhile, the stereoscopic distribution of the tree height in the thinning plot was highly fluctuating and changed more significantly, with a strong spatial dependence and strip gradient distribution. Hence, forest restoration thinning could improve the distribution of the DBH and stand density and adjust the spatial heterogeneity of the DBH, tree height, and stand density of Quercus variabilis plantations. Full article

(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)

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