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学者姓名：许章华
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Abstract ：
Moso bamboo forests (MBFs) are unique subtropical ecosystems characterized by distinct leaf phenology, bamboo shoots, rapid growth, and carbon sequestration capability. Leaf area index (LAI) is an essential metric for evaluating the productivity and ecological quality of MBFs. However, accurate and large-scale methods for remote-sensing-based LAI monitoring during the winter growth stage remain underdeveloped. This study introduces a novel method integrating hyperspectral indices from Zhuhai-1 Orbit Hyperspectral Satellites (OHS) imagery with the particle swarm optimization-support vector machine (PSO-SVM) coupling model to estimate LAI in winter MBFs. Five traditional vegetation indices (VIRs) and their red-edge variants (VIREs) were optimized to build empirical models. Machine learning algorithms, including SVM, Random Forest, extreme gradient boosting, and partial linear regression, were also applied. The PSO-SVM model, integrating three VIRs and three VIREs, achieved the highest accuracy (R-2 = 0.721, RMSE = 0.490), outperforming traditional approaches. LAI was strongly correlated with indices, such as NDVIR, RVIR, EVIRE, and SAVI(R) (R > 0.77). LAI values of MBFs primarily ranged from 2.1 to 5.5 during winter, with values exceeding 4.5 indicating high winter bamboo shoot harvesting. These findings demonstrate the potential of OHS data to improve LAI retrieval models for large-scale LAI mapping, offering new insights into MBFs monitoring and contributing to sustainable forest management practices.
Keyword ：
hyperspectral remote sensing hyperspectral remote sensing leaf area index leaf area index machine learning machine learning multi-purpose bamboo plant multi-purpose bamboo plant particle swarm algorithm particle swarm algorithm red edge vegetation index red edge vegetation index
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| GB/T 7714 | Guo, Xiaoyu , Wang, Weisen , Meng, Fangyu et al. LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model [J]. | FORESTS , 2025 , 16 (3) . |
| MLA | Guo, Xiaoyu et al. "LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model" . | FORESTS 16 . 3 (2025) . |
| APA | Guo, Xiaoyu , Wang, Weisen , Meng, Fangyu , Li, Mingjing , Xu, Zhanghua , Zheng, Xiaoman . LAI Mapping of Winter Moso Bamboo Forests Using Zhuhai-1 Hyperspectral Images and a PSO-SVM Model . | FORESTS , 2025 , 16 (3) . |
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针对当前难以在多种因素干扰下准确测出不同状态植物叶片面积的问题,研制一种可测算植物健康与病态叶片面积的装置.该装置由叶片面积信息采集系统和叶片面积计算终端系统组成.通过获取健康与病态植物叶照片,利用叠加分析、亮度检验等技术实时测算叶片面积.以毛竹叶片为测算对象,并设计对照实验.结果表明,该装置适用于室内外多种复杂光照拍摄环境下植物健康与病态叶片面积的测算,可在植物叶生理参数的相关教学实验中推广应用.
Keyword ：
健康与病态叶 健康与病态叶 叶片面积测算 叶片面积测算 图像采集与处理 图像采集与处理 教学实验设计 教学实验设计 装置研制 装置研制
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| GB/T 7714 | 孙磊 , 蓝文静 , 许章华 et al. 健康与病态叶片面积测算装置研制及教学实验设计 [J]. | 实验室研究与探索 , 2025 , 44 (2) : 15-18,41 . |
| MLA | 孙磊 et al. "健康与病态叶片面积测算装置研制及教学实验设计" . | 实验室研究与探索 44 . 2 (2025) : 15-18,41 . |
| APA | 孙磊 , 蓝文静 , 许章华 , 袁现茂 , 詹俊杰 , 朱苡萱 . 健康与病态叶片面积测算装置研制及教学实验设计 . | 实验室研究与探索 , 2025 , 44 (2) , 15-18,41 . |
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To address gaps in understanding how external stresses influence remote-sensing inversion of vegetation biochemical components, a P-PROSAIL model incorporating stress factors was developed, with Shunchang County and Yanping District in Fujian Province as the study areas. The models' effectiveness was assessed, yielding R2 values of 0.7133, 0.7066, 0.6441, 0.6392, 0.6057, 0.7038, 0.5323, and 0.5149 for leaf area index (LAI), canopy dry matter content (CDMC), canopy cellulose content (CCC), canopy lignin content (CLC), canopy protein content (CPC), canopy nitrogen content (CNC), canopy tannin content (CTC), and canopy flavonoid content (CFC), respectively. While CDMC and most other components showed stable inversions, CTC and CFC exhibited uncertainties due to pest stress. This study clarified the internal and external change characteristics and mechanisms of Moso bamboo forests under Pantana phyllostachysae stress, providing empirical support for the ecological health of bamboo forests.
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| GB/T 7714 | Xu, Zhanghua , Sun, Lei , Zhang, Yiwei et al. PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao [J]. | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (5) . |
| MLA | Xu, Zhanghua et al. "PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao" . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 91 . 5 (2025) . |
| APA | Xu, Zhanghua , Sun, Lei , Zhang, Yiwei , Zhang, Huafeng , Zhang, Hongbin , Guan, Fengying et al. PROSAIL Modeling Coupled with Environmental Stress: Remote Sensing Retrieval of Multiple Dry Matters in the Canopy of Moso Bamboo Forests under the Stress of Pantana phyllostachysae Chao . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2025 , 91 (5) . |
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Pantana phyllostachysae Chao is a destructive leaf-eating pest that poses a significant threat to the health of bamboo forests and the bamboo industry. However, the spatial and temporal spread mechanisms of this pest are still unclear. To better understand and predict the spread of this pest, we used Sentinel-2 A/B images from the pest detection period of 2018-2021, to identify association factors from five dimensions, including forest stand, meteorology, topography, pest sources, and human environment factors. The association factor sets for the spread of P. phyllostachysae were established under both existence and nonexistence pest control scenarios. The extreme gradient boosting (XGBoost) model was employed to derive conversion rules for the respective spread models, enabling the determination of suitability probabilities for both healthy and damaged bamboo forests. These probabilities were then utilized in conjunction with cellular automata (CA) to simulate the spread of P. phyllostachysae under two scenarios. The results showed that the OA and Kappa reached more than 85% and 0.7% in both scenarios, respectively. Meanwhile, the division of pest control scenarios and the selection of XGBoost both help to improve the spreading simulation accuracy. Our models effectively coupled the research results of leaf hosts of different damage levels, simulated the spread of P. phyllostachysae, and identified the dynamic mechanisms of the pest's spread. These findings provide decision support for interrupting the spread path of the pest and achieving precise control, thus safeguarding forest ecological security.
Keyword ：
Accuracy Accuracy Adaptation models Adaptation models Bamboo Bamboo Biological system modeling Biological system modeling Forests Forests Hazards Hazards Hazard spread simulation Hazard spread simulation Meteorology Meteorology Moso bamboo Moso bamboo Pest control Pest control P. phyllostachysae Chao P. phyllostachysae Chao Predictive models Predictive models Sentinel-2 A/B images Sentinel-2 A/B images Surfaces Surfaces XGBoost-CA XGBoost-CA
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Zhang, Hongbin et al. Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | He, Anqi et al. "Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | He, Anqi , Xu, Zhanghua , Zhang, Hongbin , Zhou, Xin , Li, Guantong , Zhang, Huafeng et al. Simulation of Pantana phyllostachysae Chao Hazard Spread in Moso Bamboo (Phyllostachys pubescens) Forests Based on XGBoost-CA Model . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (Phyllostachys pubescens) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared with the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R-2 values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation.
Keyword ：
Canopy chlorophyll content (CCC) Canopy chlorophyll content (CCC) hybrid method hybrid method leaf area index (LAI) leaf area index (LAI) Moso bamboo forests Moso bamboo forests PROSAIL RTM PROSAIL RTM
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| GB/T 7714 | Xu, Zhanghua , Zhang, Chaofei , Xiang, Songyang et al. A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 3125-3143 . |
| MLA | Xu, Zhanghua et al. "A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 3125-3143 . |
| APA | Xu, Zhanghua , Zhang, Chaofei , Xiang, Songyang , Chen, Lingyan , Yu, Xier , Li, Haitao et al. A Hybrid Method of PROSAIL RTM for the Retrieval Canopy LAI and Chlorophyll Content of Moso Bamboo (Phyllostachys pubescens) Forests From Sentinel-2 MSI Data . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 3125-3143 . |
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Human activity is an important factor affecting regional thermal environment change, and it is of great significance to explore the response relationship between human activity intensity (HAI) and land surface temperature (LST) for regional sustainable development. Based on MOD11A1 ground temperature data and combined with multi-source data such as land use (LU),population density (PD),night light (NTL),grassland use intensity (GUI) and existing biomass (EB),this paper proposed a method suitable for measuring the HAI in Three-River Source Region,and analyzed the spatio-temporal variation characteristics of HAI and LST in Three-River Source Region from 2000 to 2020. Comprehensive use of spatial autocorrelation and spatial autoregressive model to explore the spatial relationship between them. The results show that: (1) According to the regional ecological characteristics of Three-River Source Region, the HAI index model constructed in this paper can effectively identify the spatial distribution of HAI in Three-River Source Region,and can better distinguish the spatial differences of HAI in Three-River Source Region;(2) The average value of HAI in Three-River Source Region in the past 20 years was 0.285,and the overall intensity was low.The spatial distribution characteristics of HAI and LST were both high in the east and low in the west,and the two were significantly positively correlated.(3) LU,PD,GUI and EB in HAI index significantly affect the change of LST in Three-River Source Region,while there is a phenomenon of ' warming lag' between NTL and LST,and its response to LST is not obvious.In general,in order to slow down the further rise of LST in this region and alleviate the challenges faced by the regional ecological security system,it is necessary to limit the spread of high human activity areas. © 2025 Editorial Board of Journal of Basic Science and Engineering. All rights reserved.
Keyword ：
Population statistics Population statistics
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| GB/T 7714 | Liu, Zhicai , Zheng, Weiwen , Long, Zihan et al. Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region [J]. | Journal of Basic Science and Engineering , 2025 , 33 (2) : 349-361 . |
| MLA | Liu, Zhicai et al. "Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region" . | Journal of Basic Science and Engineering 33 . 2 (2025) : 349-361 . |
| APA | Liu, Zhicai , Zheng, Weiwen , Long, Zihan , Wang, Lin , Xu, Zhanghua . Modeling of Human Activity Intensity Index and Its Spatial Relationship with Land Surface Temperature in Three-River Source Region . | Journal of Basic Science and Engineering , 2025 , 33 (2) , 349-361 . |
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The on-year and off-year phenomenon is a distinctive phenological characteristic of Moso bamboo, reflecting variations in nutrient dynamics and endogenous hormonal rhythms during the transition from bamboo shoot to the culm. This phenomenon also influences pest resistance between the on-year and off-year cycles of Moso bamboo. Pantana phyllostachysae Chao is a leaf-feeding pest that affects Moso bamboo. However, monitoring P. phyllostachysae damage using remote sensing data is challenging because the off-year Moso bamboo has physiological characteristics similar to on-year Moso bamboo infested with P. phyllostachysae. This study utilizes the Recursive Feature Elimination (RFE) algorithm to investigate hyperspectral remote sensing characteristics of P. phyllostachysae in Moso bamboo forests. We analyzed the impact of on-year and off-year phenological characteristics on the accuracy of hazard extraction and developed detection models for P. phyllostachysae hazard levels in on-year and off-year Moso bamboo using Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and one-dimensional Convolutional Neural Network (1D-CNN). The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. Analyzing the impact of the phenological differences between on-year and off-year Moso bamboo on pest identification accuracy revealed that when four machine learning models accounted for these phenological characteristics, their accuracy in identifying pests was significantly higher than that of a model which did not take into account the bamboo phenology. This finding highlights that considering the phenological characteristics of on-year and off-year Moso bamboo can substantially improve the detection accuracy of UAV hyperspectral remote sensing in monitoring P. phyllostachysae damage. This provides more accurate technical support for the health management and resource protection of bamboo forests and offers a scientific basis for maximizing the ecological and economic benefits of bamboo forests.
Keyword ：
machine learning machine learning Moso bamboo forests Moso bamboo forests on-year and off-year phenological characteristics on-year and off-year phenological characteristics Pantana phyllostachysae Chao Pantana phyllostachysae Chao unmanned aerial vehicle (UAV) hyperspectral images unmanned aerial vehicle (UAV) hyperspectral images
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Li, Yifan et al. Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images [J]. | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
| MLA | He, Anqi et al. "Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images" . | GEO-SPATIAL INFORMATION SCIENCE (2025) . |
| APA | He, Anqi , Xu, Zhanghua , Li, Yifan , Li, Bin , Huang, Xuying , Zhang, Huafeng et al. Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV hyperspectral images . | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
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Moso bamboo (Phyllostachys pubescens) stands as a pivotal economic bamboo species globally, holding substantial potential for carbon sequestration. Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is crucial due to its close ties with the ecosystem's carbon cycle. Despite the maturation of monitoring techniques for Pantana phyllostachysae Chao, a significant pest of Moso bamboo, its interplay with AGB in these forests remains enigmatic. This study addressed this gap by categorizing P. phyllostachysae's impact on Moso bamboo forests into four levels: healthy, mild damage, moderate damage, and severe damage. By scrutinizing field data, we delved into the shifts in Moso bamboo leaf biomass under P. phyllostachysae stress. Leveraging Sentinel-2A/B imagery, we extracted diverse correlation factors, including original wave bands, vegetation indices, texture attributes, and vegetation's physical and chemical parameters. Subsequently, machine learning algorithms-namely, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were employed to achieve remote sensing inversion of AGB in Moso bamboo forests, accounting for the presence of insect pests. We analyzed the response of Moso bamboo biomass sensitive factors and to further clarify the changes of AGB of Moso bamboo forests under insect pest stress at the remote sensing level ultimately. The results showed that (1) the degree of Moso bamboo leaf biomass damage was positively related to the damage level, which gradually increased from 15.15 % to 59.42 %; (2) the RF algorithm excelled in estimating Moso bamboo forest AGB, particularly in May, and inclusion of insect pest considerations enhanced AGB estimation accuracy; (3) among the four factor types, Band information and vegetation indices emerged as most impactful, and Band5, Band11, Band12, NDVI68a and MSAVI were selected the most often; (4) at the remote sensing level, AGB in Moso bamboo forests significantly varies under P. phyllostachysae stress. Healthy areas demonstrate an AGB of 66.9037 Mg ha- 1 , while heavily affected regions drop to 52.6591 Mg ha- 1 . It can be seen that combining pest factors for Moso bamboo biomass estimation solves the problem of rough biomass estimation, and this study provides a more promising method for forest growth monitoring.
Keyword ：
Biomass Biomass Machine learning Machine learning Moso bamboo Moso bamboo Pantana phyllostachysae Chao Pantana phyllostachysae Chao Sentinel-2A/B imagery Sentinel-2A/B imagery
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| GB/T 7714 | Chen, Lingyan , He, Anqi , Xu, Zhanghua et al. Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery [J]. | ECOLOGICAL INDICATORS , 2024 , 158 . |
| MLA | Chen, Lingyan et al. "Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery" . | ECOLOGICAL INDICATORS 158 (2024) . |
| APA | Chen, Lingyan , He, Anqi , Xu, Zhanghua , Li, Bin , Zhang, Huafeng , Li, Guantong et al. Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery . | ECOLOGICAL INDICATORS , 2024 , 158 . |
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Precisely delineating the distribution of moso bamboo forests is critical for forestry management and regional carbon cycle research. The unique phonological characteristics (i.e., on- and off-year phenomenon) of bamboo impose difficulties in bamboo identification. This study aims to develop a new algorithm for mapping bamboo distribution using remote sensing data with the consideration of bamboo phenological characteristics. Three optical indices were proposed based on canopy reflectance retrieved from Sentinel-2 and field inventory data, including modified bamboo index (MBI), bamboo phenological characteristic index (BPCI), and BPCI 2 (BPCI-2). The collaboration of these three indices with the recursive feature elimination (RFE) and extreme gradient boosting (XGBoost) methods can precisely map bamboo distribution and its phenological status. The model based on MBI, BPCI, and BPCI-2 outperformed the model driven by the existing bamboo extracting indices, i.e., bamboo index (BI), yearly change bamboo index (YCBI), and monthly change bamboo index (MCBI), increasing in overall accuracy (OA) by about 1.5%. Additionally, the proposed indices were calculated using the data synthesized from Sentinel-1 synthetic aperture radar (SAR) imageries by the cycle-consistent adversarial network (CycleGAN) method under the condition without cloudy-free Sentinel-2 data available to fill the time series data gaps. The performance of the model based on augmented data improved notably in comparison with the model driven only by indices from original optical images, with the identification accuracy for on- and off-year bamboo samples over 96%. The generated moso bamboo distribution map aligns well with forestry inventory data in terms of both area and spatial distribution. The proposed indices are less sensitive to terrain than the existing bamboo extracting indices. This merit is valuable for better mapping bamboo forests, which are mostly distributed in mountainous areas.
Keyword ：
Forest Forest generative adversarial networks (GANs) generative adversarial networks (GANs) machine learning machine learning moso bamboo moso bamboo remote sensing remote sensing spectral spectral
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| GB/T 7714 | Huang, Xuying , Ju, Weimin , Xu, Zhanghua et al. A Novel Method for Mapping Moso Bamboo Forests Using Remote Sensing Data With the Consideration of Phenological Status [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Huang, Xuying et al. "A Novel Method for Mapping Moso Bamboo Forests Using Remote Sensing Data With the Consideration of Phenological Status" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Huang, Xuying , Ju, Weimin , Xu, Zhanghua , Li, Jing . A Novel Method for Mapping Moso Bamboo Forests Using Remote Sensing Data With the Consideration of Phenological Status . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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The objective of this study was to deeply understand the adaptation mechanism of the functional traits of Moso bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) leaves to the environment under different Pantana phyllostachysae Chao damage levels, analyzing the changes in the relationship between specific leaf area (SLA) and leaf dry matter content (LDMC). We combined different machine learning models (decision tree, RF, XGBoost, and CatBoost regression models), and used different canopy heights and different levels of infestation, to analyze the changes in the relationship between the two under different levels of infestation based on the results of the best estimation model. The results showed the following: (1) The SLA of Ph. pubescens showed a decreasing trend with the increase om insect pest degree, and LDMC showed an inverse trend. (2) The SLA of bamboo leaves was negatively correlated with the LDMC under different insect pest degrees; the correlation of the data under the healthy class was higher than that of other insect pest levels, and at the same time better than that of the full sample, which laterally confirmed the effect of insect pest stress on the functional traits of Ph. pubescens leaves. (3) When modeling under different infestation levels, the CatBoost model was used for heavy damage and the RF model was used for the rest of the cases; the decision tree regression model was used when modeling different canopy heights. The findings contribute certain insights into the nuanced responses and adaptive mechanisms of Ph. pubescens forests to environmental fluctuations. Moreover, these results furnish a robust scientific foundation, essential for ensuring the enduring sustainability of Ph. pubescens forest ecosystems.
Keyword ：
correlation correlation leaf dry matter content leaf dry matter content Moso bamboo Phyllostachys pubescens syn. edulis leaves Moso bamboo Phyllostachys pubescens syn. edulis leaves Pantana phyllostachysae (Lepidoptera: Lymantriidae) Pantana phyllostachysae (Lepidoptera: Lymantriidae) pest level pest level specific leaf area specific leaf area
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| GB/T 7714 | Shen, Wanling , Xu, Zhanghua , Qin, Na et al. Changing Relationship between Specific Leaf Area and Leaf Matter Dry Content of Moso Bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) under the Stress of Pantana phyllostachysae (Lepidoptera: Lymantriidae) [J]. | FORESTS , 2024 , 15 (3) . |
| MLA | Shen, Wanling et al. "Changing Relationship between Specific Leaf Area and Leaf Matter Dry Content of Moso Bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) under the Stress of Pantana phyllostachysae (Lepidoptera: Lymantriidae)" . | FORESTS 15 . 3 (2024) . |
| APA | Shen, Wanling , Xu, Zhanghua , Qin, Na , Chen, Lingyan , Yang, Yuanyao , Zhang, Huafeng et al. Changing Relationship between Specific Leaf Area and Leaf Matter Dry Content of Moso Bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) under the Stress of Pantana phyllostachysae (Lepidoptera: Lymantriidae) . | FORESTS , 2024 , 15 (3) . |
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