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学者姓名：许章华
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Abstract ：
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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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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针对新时代背景下“双一流”高校人才培养的发展需求，在“三创五育”理念指导下，解析基于产学研合作理论引入的DIE模式的内涵，系统阐述了DIE理念贯彻下的教学系统设计并付诸实践，旨在打破教学系、研究所、企业之间的壁垒，为学生学习、实践提供更多样化的渠道。问卷调查结果表明DIE模式在本科生团队协作、学术创造、自信心树立等方面有切实作用，为培养全面发展的高素质本科人才提供了参考。
Keyword ：
DIE模式 DIE模式 “三创五育” “三创五育” “双一流”高校 “双一流”高校 本科人才培养 本科人才培养
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| GB/T 7714 | 许章华 , 张超飞 , 罗敏 et al. “三创五育”背景下“双一流”高校本科人才培养的DIE模式初探及评价 [J]. | 高等理科教育 , 2024 , 9 (02) : 53-61 . |
| MLA | 许章华 et al. "“三创五育”背景下“双一流”高校本科人才培养的DIE模式初探及评价" . | 高等理科教育 9 . 02 (2024) : 53-61 . |
| APA | 许章华 , 张超飞 , 罗敏 , 杨远垚 , 刘智才 . “三创五育”背景下“双一流”高校本科人才培养的DIE模式初探及评价 . | 高等理科教育 , 2024 , 9 (02) , 53-61 . |
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Carrying out remote sensing refinement identification of forest land in complex environment is of great significance for timely mapping of forest distribution. Aiming at the problem that remote sensing images have bias in the extraction of forest land information data, based on the semantic segmentation algorithm Unet, combining the ResNet50 deep learning network, the attention mechanism module and the feature pyramid structure, we construct RAF-Unet (ResNet+Attention+FPN+Unet) to improve the extraction of forest land information data. The ResNet50 classification network is used as the encoder of the Unet network to extract the feature maps at five different scales; then, the attention mechanism module is introduced in the decoder stage of the Unet network to extract the key task goal information by learning the weight values of the features; finally, the feature pyramid structure is used in the output stage of the encoder to fuse the information from the shallow network and the deep network to extract the remote sensing forest land information in the image. The results show that the RAF-Unet algorithm outperforms the Unet algorithm in all the indexes, with a precision of 95.24%, a recall of 91.80%, an F1-score value of 93.49%, an intersection over union of 87.63%, and an accuracy of 93.68%; the validity of the modules is verified by the ablation experiments, and the ResNet network, the attention mechanism, and the feature pyramid structure are all effective in improve the classification effect. It helps the forestry department to better manage and dynamically monitor forestry information, which is of great significance to the scientific development, utilization and protection of forest land resources. © Published under licence by IOP Publishing Ltd.
Keyword ：
deep learning deep learning forest land information forest land information RAF-Unet (ResNet+Attention+FPN+Unet) RAF-Unet (ResNet+Attention+FPN+Unet) remote sensing remote sensing
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| GB/T 7714 | Wang, Z. , Chen, L. , Shen, W. et al. RAF-Unet: A Remote Sensing Identification Method for Forest Land Information with Modified Unet [未知]. |
| MLA | Wang, Z. et al. "RAF-Unet: A Remote Sensing Identification Method for Forest Land Information with Modified Unet" [未知]. |
| APA | Wang, Z. , Chen, L. , Shen, W. , Xiao, J. , Xu, Z. , Liu, J. . RAF-Unet: A Remote Sensing Identification Method for Forest Land Information with Modified Unet [未知]. |
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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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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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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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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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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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