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学者姓名：许章华
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Abstract ：
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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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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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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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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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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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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Hyperspectral images have continuous spectral information of features and have great potential for shadow detection. but bigh band redundancy requires band preference, Normalized Shaded Vegetation Index (NSVI) expand the spectral difference, and the application of NSVI in hyperspectral images will identify shadows more effectively, ZY1 02D satellite is the first hyperspectral operational satellite independently developed and successfully operated in China, with a large data signal-to- noise ratio and strong coverage capability, and it is important to perform accurate shadow detection on this hyperspectral image. In this paper, ZY1-0213 AHSI images were used as experimental data to extract and analyze the spectral reflectance of vegetation in bright areas, vegetation in shaded areas and water bodies, and Combining Competitive Adaptive Reweighted Sampling (CARS) and Successive Projection Algorithms (SPA) to filter the main wavelands that can effectively distinguish typical features, the characteristics of the algorithms are considered to select the characteristic wavebands further to construct NSVL The optimal threshold value is determined by the step method to classify the images, and the best band for constructing NSVI is compared in terms of image element value distribution, classification accuracy and spectral enhancement effect. A comprehensive evaluation is made by combining different shadow indices, bands and images to verify the significance and universality of the method in this paper. The results show that band 32 and band 73 are the best bands for NSVI construction, corresponding to the Red band and NIR band, respectively, the classification accuracy of NSVI constructed by different bands is generally higher than 90%, and the classification accuracy of NSVI constructed by the best band is 94.33% with a Kappa coefficient of 0.832 8. which is the best classification effect: NSVI can enhance the spectral difference between typical features and alleviate the "easy saturation" phenomenon of Normalized Difference Vegetation Index, and the small peaks generated by the accumulation of water bodies in this image is helpful to extract water bodies, The classification of NSVI in ZY1-02D AHSI image is better than Normalized Different Umbra Indes and Shadow Indes, and the classification accuracy in another scene image also reaches 93.55% with a kappa coefficient of 0.816 7, Therefore, the wavebands filtered by the algorithm are representative, and the NSVI constructed by the best waveband has better shadow detection ability in ZY1-02D AHSI images, which has a certain reference and significance for hyperspectral image shadow detection and construction of vegetation index.
Keyword ：
Competitive adaptive reweighted sampling (CARS) Competitive adaptive reweighted sampling (CARS) Normalized shaded vegetation index (NSVI) Normalized shaded vegetation index (NSVI) Shadow detection Shadow detection Successive projection algorithm (SPA) Successive projection algorithm (SPA) ZY1-02D AHSI image ZY1-02D AHSI image
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| GB/T 7714 | Xu, Zhang-hua , Chen, Ling-yan , Xiang, Song-yang et al. Band Selection and Its Construction for the Normalized Shadow Vegetation Index (NSVI) of ZY1-02D AHSI Image [J]. | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (9) : 2626-2637 . |
| MLA | Xu, Zhang-hua et al. "Band Selection and Its Construction for the Normalized Shadow Vegetation Index (NSVI) of ZY1-02D AHSI Image" . | SPECTROSCOPY AND SPECTRAL ANALYSIS 44 . 9 (2024) : 2626-2637 . |
| APA | Xu, Zhang-hua , Chen, Ling-yan , Xiang, Song-yang , Deng, Xi-peng , Li, Yi-fan , Yu, Hui et al. Band Selection and Its Construction for the Normalized Shadow Vegetation Index (NSVI) of ZY1-02D AHSI Image . | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (9) , 2626-2637 . |
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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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Pantana phyllostachysae Chao is a leaf-eating pest that poses a significant threat to bamboo forest health. Current research mainly focuses on statically identifying damage using remote sensing images. However, the mechanism behind the damage's traceability remains unclear, making it difficult to pinpoint early infestation sources accurately. Additionally, our understanding of the pest's spreading laws is limited. This study leverages Sentinel2A/B images from February to November 2021 to investigate P. phyllostachysae infestation traceability through the dynamic age algorithm and indicator analysis method. The results shed light on the distribution of early pest sources over the study period. By analyzing both the overall pest infestation "cluster" and its center of gravity, we dissect P. phyllostachysae infestation characteristics and paths monthly throughout the study period. Our findings reveal three zones with strong spreading momentum, three with slow spreading momentum, and two transitional zones during the February-November period, aligning with P. phyllostachysae occurrence patterns. However, the direction of P. phyllostachysae spreading varies, likely due to a combination of meteorological, topographical, vegetative biochemical, and human activity factors. This study introduces innovative approaches for identifying early pest source points and understand their spreading laws, contributing to more effective pest prevention and control in forest ecosystems.
Keyword ：
Moso bamboo forests Moso bamboo forests Pantana phyllostachysae Chao Pantana phyllostachysae Chao Sentinel-2A/B images Sentinel-2A/B images Spreading characteristics Spreading characteristics Spreading paths Spreading paths Traceability Traceability
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| GB/T 7714 | He, Anqi , Xu, Zhanghua , Li, Bin et al. Revealing early pest source points and spreading laws of Pantana phyllostachysae Chao in Moso bamboo ( Phyllostachys pubescens ) forests from Sentinel-2A/B images [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 129 . |
| MLA | He, Anqi et al. "Revealing early pest source points and spreading laws of Pantana phyllostachysae Chao in Moso bamboo ( Phyllostachys pubescens ) forests from Sentinel-2A/B images" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 129 (2024) . |
| APA | He, Anqi , Xu, Zhanghua , Li, Bin , Li, Yifan , Zhang, Huafeng , Li, Guantong et al. Revealing early pest source points and spreading laws of Pantana phyllostachysae Chao in Moso bamboo ( Phyllostachys pubescens ) forests from Sentinel-2A/B images . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 129 . |
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