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学者姓名：许章华
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Abstract ：
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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为快速准确地检测健康与病态竹叶单叶面积，提出一种基于自制着色底板和光谱特征(CBP-SF)的叶面积检测方法。根据叶片光谱特征设计底板，然后利用波段计算、碎片过滤和自适应阈值方法进行图像分割，再根据竹叶大小进行参照物切割，最后统计叶片区域和参考矩形框的像元数并计算叶面积。与随机森林(RF)、最大类间方差法(OTSU)和叶面积仪法(LAM)的对比结果表明：对于健康竹叶的检测效果，CBP-SF>RF>OTSU=LAM;对于病态竹叶的检测效果，CBP-SF>RF>OTSU>LAM;对于全样本竹叶的检测效果，CBP-SF>RF>OTSU>LAM。CBP-SF具备检测健康与病态竹叶单叶面积的能力。
Keyword ：
光谱特征 光谱特征 叶面积检测 叶面积检测 图像分割 图像分割 着色底板 着色底板 竹叶 竹叶
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| GB/T 7714 | 贺安琪 , 李彬 , 许章华 et al. 自制着色底板和光谱特征检测竹叶面积 [J]. | 实验室研究与探索 , 2024 , 43 (02) : 39-44 . |
| MLA | 贺安琪 et al. "自制着色底板和光谱特征检测竹叶面积" . | 实验室研究与探索 43 . 02 (2024) : 39-44 . |
| APA | 贺安琪 , 李彬 , 许章华 , 杨远垚 , 李增禄 . 自制着色底板和光谱特征检测竹叶面积 . | 实验室研究与探索 , 2024 , 43 (02) , 39-44 . |
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基于生态宜居和以人为本双重理念，选取区域绿度、区域热度、区域蓝度、区域亮度、区域透明度和区域起伏度等6个指标，利用主成分分析法构建基于遥感的区域人居环境指数(RHEI)。以福建省为例，分季度测算其RHEI,揭示区域人居环境的时空差异。结果表明：福建省RHEI随着季节变化逐渐降低，呈现东南低西北高、沿海岸线向内陆上升的空间分布格局。通过比较各指标的回归模型系数，发现区域蓝度对区域人居环境的影响最大，区域透明度的影响最小。由年度均值回归模型预测，未来每增加0.166单位的区域蓝度或减少0.278单位的区域热度，能提升0.1单位的人居环境质量。
Keyword ：
主成分分析 主成分分析 区域人居环境指数(RHEI) 区域人居环境指数(RHEI) 区域治理 区域治理 福建省 福建省 遥感 遥感
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| GB/T 7714 | 陈秋霞 , 杨远垚 , 许章华 et al. 基于遥感的区域人居环境指数构建及其应用 [J]. | 环境监测管理与技术 , 2024 , 36 (02) : 19-24,31 . |
| MLA | 陈秋霞 et al. "基于遥感的区域人居环境指数构建及其应用" . | 环境监测管理与技术 36 . 02 (2024) : 19-24,31 . |
| APA | 陈秋霞 , 杨远垚 , 许章华 , 黄森慰 . 基于遥感的区域人居环境指数构建及其应用 . | 环境监测管理与技术 , 2024 , 36 (02) , 19-24,31 . |
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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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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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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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选取我国大豆主产区黑龙江省为研究区，采用生命周期评价(LCA)法核算该地区2011—2020年大豆生产的碳足迹，分析其时空分布特征，利用灰色关联分析法分析全省各地级市(地区)大豆生产碳足迹的影响因素，确定其碳排放的主要来源，提出大豆生产的生态优化方案。结果表明：2011—2020年黑龙江省大豆生产碳足迹平均值为0.337 kg/kg(以CO_2当量计),整体呈现反复波动、“北多南少”的格局；在所选的9个黑龙江省大豆生产碳足迹影响因素中，农药、种子、柴油3个因素的贡献度最大。
Keyword ：
大豆生产 大豆生产 时空分布 时空分布 生命周期评价 生命周期评价 生态优化 生态优化 碳足迹 碳足迹 黑龙江省 黑龙江省
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| GB/T 7714 | 陈晗奕 , 陈一灵 , 洪志坤 et al. 黑龙江省大豆生产的碳足迹时空分布特征及生态优化研究 [J]. | 环境监测管理与技术 , 2024 , 36 (03) : 21-26 . |
| MLA | 陈晗奕 et al. "黑龙江省大豆生产的碳足迹时空分布特征及生态优化研究" . | 环境监测管理与技术 36 . 03 (2024) : 21-26 . |
| APA | 陈晗奕 , 陈一灵 , 洪志坤 , 游璐萍 , 郑先鑫 , 王琳 et al. 黑龙江省大豆生产的碳足迹时空分布特征及生态优化研究 . | 环境监测管理与技术 , 2024 , 36 (03) , 21-26 . |
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水下图像增强技术能够提升水下图像的质量和可视性，在丰富数字媒体资源、水下探测、水下通信等领域具有重要应用价值。近年来，深度学习方法在水下图像增强方面取得了显著的效果。然而，现有的方法计算复杂度高，限制了它们在计算资源有限的场景中的使用。针对这一问题，提出了一种轻量化的水下图像增强方法，该方法基于跨尺度深度蒸馏特征感知，采用U型网络结构，在保证非线性抽象层级抽取的同时，大幅减少了模型参数量。实验结果表明，所提出方法在视觉效果和客观评价指标上均取得了具有竞争力的结果。
Keyword ：
U型网络 U型网络 水下图像增强 水下图像增强 蒸馏特征 蒸馏特征 跨尺度 跨尺度 轻量化 轻量化
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| GB/T 7714 | 吴晓华 , 李增禄 , 许章华 et al. 跨尺度蒸馏特征感知的轻量化水下图像增强 [J]. | 大气与环境光学学报 , 2024 , 19 (03) : 381-390 . |
| MLA | 吴晓华 et al. "跨尺度蒸馏特征感知的轻量化水下图像增强" . | 大气与环境光学学报 19 . 03 (2024) : 381-390 . |
| APA | 吴晓华 , 李增禄 , 许章华 , 周景春 . 跨尺度蒸馏特征感知的轻量化水下图像增强 . | 大气与环境光学学报 , 2024 , 19 (03) , 381-390 . |
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高光谱影像具有连续的地物光谱信息,在阴影检测方面具有巨大的潜力,而波段冗余度高需进行波段优选.归一化阴影植被指数(NSVI)能够扩大光谱差异,在高光谱影像中应用NSVI将更有效地识别阴影.资源一号02D卫星是我国首颗自主研发并成功运行的高光谱业务卫星,数据信噪比大、覆盖能力强,对该高光谱影像进行准确的阴影检测具有重要意义.以ZY1-02DAHSI影像为试验数据,提取并分析明亮区植被、阴影区植被及水体的光谱反射率;结合竞争自适应重加权采样(CARS)和连续投影算法(SPA)筛选能够有效区分典型地物的主要波段,综合考虑算法的特性进一步选出特征波段构建NSVI;通过步长法确定最佳阈值对影像进行分类,从像元值分布情况、分类精度和光谱增强效果等对比出构建NSVI的最佳波段,并结合不同的阴影指数、波段和影像进行综合评价,验证该方法的意义及普适性.结果表明:波段32和波段73是构建NSVI的最佳波段,分别对应红光波段和近红外波段;不同波段构建的NSVI分类精度均高于90％,由最佳波段构建的NSVI分类精度为94.33％,Kappa系数为0.832 8,分类效果最优;NSVI能够增强典型地物间的光谱差异并缓解归一化植被指数的"易饱和"现象,在该影像中因水体累积产生的小波峰有助于提取水体;在ZY1-02DAHSI影像中NSVI的分类效果优于归一化阴影指数和阴影指数,于另一景影像的分类精度也达到93.55％,Kappa系数为0.816 7.由算法筛选出的波段具有一定的代表性,最佳波段构建的NS-VI在ZY1-02D AHSI影像中具有较好的阴影检测能力,对高光谱影像阴影检测及构建植被指数具有一定的借鉴和参考意义.
Keyword ：
ZY1-02D AHSI影像 ZY1-02D AHSI影像 归一化阴影植被指数NSVI 归一化阴影植被指数NSVI 竞争自适应重加权采样(CARS) 竞争自适应重加权采样(CARS) 连续投影算法(SPA) 连续投影算法(SPA) 阴影检测 阴影检测
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| GB/T 7714 | 许章华 , 陈玲燕 , 项颂阳 et al. ZY1-02D AHSI影像归一化阴影植被指数NSVI的波段选择及其构建 [J]. | 光谱学与光谱分析 , 2024 , 44 (9) : 2626-2637 . |
| MLA | 许章华 et al. "ZY1-02D AHSI影像归一化阴影植被指数NSVI的波段选择及其构建" . | 光谱学与光谱分析 44 . 9 (2024) : 2626-2637 . |
| APA | 许章华 , 陈玲燕 , 项颂阳 , 邓西鹏 , 李一帆 , 俞辉 et al. ZY1-02D AHSI影像归一化阴影植被指数NSVI的波段选择及其构建 . | 光谱学与光谱分析 , 2024 , 44 (9) , 2626-2637 . |
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To evaluate the spatiotemporal changes in the ecological environment of eastern Ukraine since the Russia -Ukraine conflict, this study used MODIS images from March to September 2020 and 2022 to calculate the Remote Sensing-Based Ecological Index. In 2022, compared with 2020, conflict zones exhibited reduced improvement and increased slight degradation, whereas nonconflict areas showed marginal enhancement. Through propensity score matching, the research confirmed the causal relationship between conflict and ecological trends. Pathway analysis revealed that the conflict contributed to 0.016 units increase in ecological quality while reducing the improvement rate by 0.042 units. This study provides empirical support for understanding the correlation between conflicts and specific environmental factors, offering technical references for ecological quality assessments in other conflict areas and future evaluations by the Ukrainian government.
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| GB/T 7714 | Zhang, Chaofei , Xu, Zhanghua , Yang, Yuanyao et al. Dynamic Monitoring of Ecological Quality in Eastern Ukraine Amidst the Russia-Ukraine Conflict [J]. | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2024 , 90 (7) . |
| MLA | Zhang, Chaofei et al. "Dynamic Monitoring of Ecological Quality in Eastern Ukraine Amidst the Russia-Ukraine Conflict" . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 90 . 7 (2024) . |
| APA | Zhang, Chaofei , Xu, Zhanghua , Yang, Yuanyao , Sun, Lei , Li, Haitao . Dynamic Monitoring of Ecological Quality in Eastern Ukraine Amidst the Russia-Ukraine Conflict . | PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING , 2024 , 90 (7) . |
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