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学者姓名：徐伟铭
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Abstract ：
CONTEXT: Long-term historical information on national -scale grain production is critical for ensuring food security but often limited by the lack of geospatial data. OBJECTIVE: This study aims to conduct the first systematic investigation of grain Cropping Patterns (CP) in China over the past two decades, shedding light on the roles of grain expansion and intensification in sustainable agriculture. METHODS: This study proposes a framework to fully characterize grain production patterns considering crop types, cropping intensity and patterns based on spatiotemporal continuous ChinaCP datasets (2005-2020). Four indicators were developed for measuring the Reality to Capability Ratio (RCR) of grain production regarding the total yield and sow area, the cropland extent and cropping intensity. The capability of grain production was derived based on grain cultivation history. RESULTS AND CONCLUSION: There was a huge gap between the reality and capability of grain production in China, which varied with grain crop types and cropping patterns. At national level, a vast majority (96%) of cropland was capable of grain production, and two fifths of cropland quantified for double grain cropping. However, only 46.65% and 24.89% of the capability was implemented for grain or double -grain cropping in 2020. Maize, rice, and wheat was ever cultivated in 76.88%, 57.05%, and 25.18% of national cropland, respectively. Winter wheat plays an important role in stabilizing grain production by double grain cropping, accounting for 7/8 continuously grain -cultivated areas. However, the RCR of double rice was only 7% in 2020. Bridging these gaps could potentially triple grain production, however, achieving this increase poses challenges due to a series of constraints related to cropland fraction, topographic conditions and lack of agricultural labors along with rapid urbanization. This study found that there was a continuous Northeastward movement & countryside shift in grain production. Continuous support for long-term active agricultural systems is crucial to ensure sustainable grain production in China, with a special emphasis on key grain productive regions, considering targeted cropping patterns and regional disparities. SIGNIFICANCE: This study enhances our understanding of grain production systems in China based on long-term cultivation histories. Findings can inform the development of more geographic -targeted policies concerning grain cropping intensifications to ensure food security and environmental sustainability in developing countries. The long term spatiotemporal continuous CPChina datasets during 2005-2020 was are publicly accessed at: https ://doi.org/10.6084/m9.figshare.25106948.
Keyword ：
China China Cropping patterns Cropping patterns Grain security Grain security Non-grain production Non-grain production Spatiotemporal process Spatiotemporal process
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| GB/T 7714 | Qiu, Bingwen , Jian, Zeyu , Yang, Peng et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification [J]. | AGRICULTURAL SYSTEMS , 2024 , 216 . |
| MLA | Qiu, Bingwen et al. "Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification" . | AGRICULTURAL SYSTEMS 216 (2024) . |
| APA | Qiu, Bingwen , Jian, Zeyu , Yang, Peng , Tang, Zhenghong , Zhu, Xiaolin , Duan, Mingjie et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification . | AGRICULTURAL SYSTEMS , 2024 , 216 . |
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为探究南平市不同发展导向下土地利用模拟对生态系统碳储量的影响,揭示碳储量时空变化特征和未来演变趋势,通过耦合InVEST模型和PLUS模型,分析了 2005-2020年南平市土地利用及碳储量时空演化特征,并从自然发展、耕地保护和生态优先3种情景预测了 2035年土地利用及碳储量变化.结果表明:2005-2020年南平市林地、草地和耕地面积总体上呈下降趋势,水域面积小幅度上升,建设用地面积则增长迅速,是由于建设用地的快速扩张侵占和挤压了大量的城市生态用地所致;南平市碳储量在15年间整体上呈现下降态势,累计损失了 1.61 × 106t;在自然发展和耕地保护情景下,2035年南平市的碳储量较2020年预计将分别损失1.50 × 106t和3.62 × 106t;而在生态优先情景下,2035年的区域碳储量较2020年将上升38 825.37 t;造成3种情景碳储量发生变化的主要因素是土地利用类型的改变,而林地和草地等生态用地向建设用地和耕地更多地转移,是导致碳储量下降的核心因素.开展科学有效的生态环境治理,可有效缓解碳储量下降问题,提升区域碳储量水平,加快落实双碳目标.
Keyword ：
InVEST模型 InVEST模型 PLUS模型 PLUS模型 土地利用模拟 土地利用模拟 多情景模拟 多情景模拟 碳储量 碳储量
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| GB/T 7714 | 邵尔辉 , 徐伟铭 , 杨慧 et al. 耦合PLUS-lnVEST模型的南平市土地利用模拟与碳储量评估 [J]. | 海南大学学报（自然科学版） , 2024 , 42 (2) : 186-196 . |
| MLA | 邵尔辉 et al. "耦合PLUS-lnVEST模型的南平市土地利用模拟与碳储量评估" . | 海南大学学报（自然科学版） 42 . 2 (2024) : 186-196 . |
| APA | 邵尔辉 , 徐伟铭 , 杨慧 , 林馨 , 廖云婷 . 耦合PLUS-lnVEST模型的南平市土地利用模拟与碳储量评估 . | 海南大学学报（自然科学版） , 2024 , 42 (2) , 186-196 . |
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Abstract ：
为探究南平市不同发展导向下土地利用模拟对生态系统碳储量的影响，揭示碳储量时空变化特征和未来演变趋势，通过耦合InVEST模型和PLUS模型，分析了2005—2020年南平市土地利用及碳储量时空演化特征，并从自然发展、耕地保护和生态优先3种情景预测了2035年土地利用及碳储量变化.结果表明：2005—2020年南平市林地、草地和耕地面积总体上呈下降趋势，水域面积小幅度上升，建设用地面积则增长迅速，是由于建设用地的快速扩张侵占和挤压了大量的城市生态用地所致；南平市碳储量在15年间整体上呈现下降态势，累计损失了1.61×10~6t；在自然发展和耕地保护情景下，2035年南平市的碳储量较2020年预计将分别损失1.50×106t和3.62×10~6t；而在生态优先情景下，2035年的区域碳储量较2020年将上升38 825.37 t；造成3种情景碳储量发生变化的主要因素是土地利用类型的改变，而林地和草地等生态用地向建设用地和耕地更多地转移，是导致碳储量下降的核心因素.开展科学有效的生态环境治理，可有效缓解碳储量下降问题，提升区域碳储量水平，加快落实双碳目标.
Keyword ：
InVEST模型 InVEST模型 PLUS模型 PLUS模型 土地利用模拟 土地利用模拟 多情景模拟 多情景模拟 碳储量 碳储量
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| GB/T 7714 | 邵尔辉 , 徐伟铭 , 杨慧 et al. 耦合PLUS-InVEST模型的南平市土地利用模拟与碳储量评估 [J]. | 海南大学学报(自然科学版) , 2024 , 42 (02) : 186-196 . |
| MLA | 邵尔辉 et al. "耦合PLUS-InVEST模型的南平市土地利用模拟与碳储量评估" . | 海南大学学报(自然科学版) 42 . 02 (2024) : 186-196 . |
| APA | 邵尔辉 , 徐伟铭 , 杨慧 , 林馨 , 廖云婷 . 耦合PLUS-InVEST模型的南平市土地利用模拟与碳储量评估 . | 海南大学学报(自然科学版) , 2024 , 42 (02) , 186-196 . |
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Remote sensing scene classification (RSSC) is essential in Earth observation, with applications in land use, environmental status, urban development, and disaster risk assessment. However, redundant background interference, varying feature scales, and high interclass similarity in remote sensing images present significant challenges for RSSC. To address these challenges, this article proposes a novel hierarchical graph-enhanced transformer network (HGTNet) for RSSC. Initially, we introduce a dual attention (DA) module, which extracts key feature information from both the channel and spatial domains, effectively suppressing background noise. Subsequently, we meticulously design a three-stage hierarchical transformer extractor, incorporating a DA module at the bottleneck of each stage to facilitate information exchange between different stages, in conjunction with the Swin transformer block to capture multiscale global visual information. Moreover, we develop a fine-grained graph neural network extractor that constructs the spatial topological relationships of pixel-level scene images, thereby aiding in the discrimination of similar complex scene categories. Finally, the visual features and spatial structural features are fully integrated and input into the classifier by employing skip connections. HGTNet achieves classification accuracies of 98.47%, 95.75%, and 96.33% on the aerial image, NWPU-RESISC45, and OPTIMAL-31 datasets, respectively, demonstrating superior performance compared to other state-of-the-art models. Extensive experimental results indicate that our proposed method effectively learns critical multiscale visual features and distinguishes between similar complex scenes, thereby significantly enhancing the accuracy of RSSC.
Keyword ：
Attention mechanism Attention mechanism Attention mechanisms Attention mechanisms Data mining Data mining Earth Earth Feature extraction Feature extraction graph neural network (GNN) graph neural network (GNN) Graph neural networks Graph neural networks Remote sensing Remote sensing remote sensing scene classification (RSSC) remote sensing scene classification (RSSC) Scene classification Scene classification Sensors Sensors spatial structural feature spatial structural feature transformer transformer Transformers Transformers Visualization Visualization
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| GB/T 7714 | Li, Ziwei , Xu, Weiming , Yang, Shiyu et al. A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 : 20315-20330 . |
| MLA | Li, Ziwei et al. "A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17 (2024) : 20315-20330 . |
| APA | Li, Ziwei , Xu, Weiming , Yang, Shiyu , Wang, Juan , Su, Hua , Huang, Zhanchao et al. A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 , 20315-20330 . |
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The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder-decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model's effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction.
Keyword ：
agricultural parcel extraction agricultural parcel extraction boundary-aware weighted loss boundary-aware weighted loss dual-stream feature extraction (DSFE) dual-stream feature extraction (DSFE) feature compensation restoration (FCR) feature compensation restoration (FCR) global feature fusion module (GFFM) global feature fusion module (GFFM)
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| GB/T 7714 | Xu, Weiming , Wang, Juan , Wang, Chengjun et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images [J]. | REMOTE SENSING , 2024 , 16 (14) . |
| MLA | Xu, Weiming et al. "Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images" . | REMOTE SENSING 16 . 14 (2024) . |
| APA | Xu, Weiming , Wang, Juan , Wang, Chengjun , Li, Ziwei , Zhang, Jianchang , Su, Hua et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images . | REMOTE SENSING , 2024 , 16 (14) . |
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The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R-2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.
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| GB/T 7714 | Qiu, Bingwen , Liu, Baoli , Tang, Zhenghong et al. National-scale 10-m maps of cropland use intensity in China during 2018-2023 [J]. | SCIENTIFIC DATA , 2024 , 11 (1) . |
| MLA | Qiu, Bingwen et al. "National-scale 10-m maps of cropland use intensity in China during 2018-2023" . | SCIENTIFIC DATA 11 . 1 (2024) . |
| APA | Qiu, Bingwen , Liu, Baoli , Tang, Zhenghong , Dong, Jinwei , Xu, Weiming , Liang, Juanzhu et al. National-scale 10-m maps of cropland use intensity in China during 2018-2023 . | SCIENTIFIC DATA , 2024 , 11 (1) . |
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Tea trees (Camellia sinensis), a quintessential homestead agroforestry crop cultivated in over 60 countries, hold significant economic and social importance as a vital specialty cash crop. Accurate nationwide crop data is imperative for effective agricultural management and resource regulation. However, many regions grapple with a lack of agroforestry cash crop data, impeding sustainable development and poverty eradication, especially in economically underdeveloped countries. The large-scale mapping of tea plantations faces substantial limitations and challenges due to their sparse distribution compared to field crops, unfamiliar characteristics, and spectral confusion among various land cover types (e.g., forests, orchards, and farmlands). To address these challenges, we developed the Manual management And Phenolics substance-based Tea mapping (MAP-Tea) framework by harnessing Sentinel-1/2 time series images for automated tea plantation mapping. Tea trees, exhibiting higher phenolic content, evergreen characteristics, and multiple shoot sprouting, result in extensive canopy coverage, stable soil exposure, and radar backscatter signal interference from frequent picking activities. We developed three phenology-based indicators focusing on phenolic content, vegetation coverage, and canopy texture leveraging the temporal features of vegetation, pigments, soil, and radar backscattering. Characteristics of biochemical substance content and manual management measures were applied to tea mapping for the first time. The MAP-Tea framework successfully generated China's first updated 10 m resolution tea plantation map in 2022. It achieved an overall accuracy of 94.87% based on 16,712 reference samples, with a kappa coefficient of 0.83 and an F1 score of 85.63%. The tea trees are typically cultivated in mountainous and hilly areas with a relatively low planting density (averaging about 10%). Alpine tea trees exhibited a notably dense concentration and dominance, mainly found in regions with elevations ranging from 700 m to 2000 m and slopes between 2 degrees to 18 degrees. The areas with low altitudes and slopes hold the largest tea plantation area and output. As the slope increased, there was a gradual decline in the dominance of tea areas. The results suggest a good potential for the knowledge-based approaches, combining biochemical substance content and human activities, for national-scale tea plantation mapping in complex environment conditions and challenging landscapes, providing important reference significance for mapping other agroforestry crops. This study contributes significantly to advancing the achievement of the Sustainable Development Goals (SDGs) considering the crucial role that agroforestry crops play in fostering economic growth and alleviating poverty. The first 10m national Tea tree data products in China with good accuracy (ChinaTea10m) are publicly accessed at https://doi.org/10.6084/m9.figshare .25047308.
Keyword ：
Agroforestry crop mapping Agroforestry crop mapping Phenology-based algorithm Phenology-based algorithm Sentinel-1/2 Sentinel-1/2 Special cash crop Special cash crop Tea plantation Tea plantation
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| GB/T 7714 | Peng, Yufeng , Qiu, Bingwen , Tang, Zhenghong et al. Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images [J]. | REMOTE SENSING OF ENVIRONMENT , 2024 , 303 . |
| MLA | Peng, Yufeng et al. "Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images" . | REMOTE SENSING OF ENVIRONMENT 303 (2024) . |
| APA | Peng, Yufeng , Qiu, Bingwen , Tang, Zhenghong , Xu, Weiming , Yang, Peng , Wu, Wenbin et al. Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images . | REMOTE SENSING OF ENVIRONMENT , 2024 , 303 . |
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探索耕地占补的时空分异特征及其对生态系统服务价值(ESV)的影响,有助于摸清耕地变化趋势,对保障粮食安全和生态文明建设具有重要意义.基于2000、2005、2010、2015和2020 年5期土地利用遥感解译数据,运用空间分析和数字地形分析等方法揭示福建省耕地占补过程的时空分异特征,并结合修正的ESV当量对福建省ESV评估,最后利用冷热点分析等方法进一步揭示耕地占补对ESV的影响.结果表明:1)2000-2020年福建耕地面积大幅度减少,耕地净变化率为-22.3%,存在"占多补少"现象.耕地占用主要以林地和建设用地为主,耕地补充类型以林地居多,且耕地占补空间分布不均匀,耕地占用主要发生在东南沿海,耕地补充主要发生在中部和西北部;2)20年间占用耕地平均坡度/海拔均小于补充耕地.耕地占补坡度分布优势区分别为 0°-11°、0°-13°坡度区段,海拔分布优势区分别为 0-320、0-340 m海拔区段,且占用耕地优势区向低坡度/海拔移动、补充耕地优势区向高坡度/海拔移动,说明福建省耕地资源存在"占缓补陡"、"占低补高"现象;3)20年间福建省ESV总体上表现为持续降低,耕地占补导致ESV减少了15.6亿元,主要是林地、水域补偿耕地和建设占用耕地导致ESV减少.其中 2015-2020 年,耕地占补导致ESV减少达到峰值,占整体变化的 49.3%.2000-2020 年福建省耕地占补导致ESV变化值存在明显的空间聚集特征,热点区主要分布在漳州市南部,冷点区主要分布在厦门市和泉州市沿海地区.研究结果可为福建省完善耕地占补政策及生态文明建设提供科学参考和决策依据.
Keyword ：
地形梯度 地形梯度 时空分异 时空分异 生态系统服务价值 生态系统服务价值 福建省 福建省 耕地占补 耕地占补
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| GB/T 7714 | 林馨 , 徐伟铭 , 廖云婷 et al. 福建省耕地占补时空分异及其对生态系统服务价值的影响研究 [J]. | 生态环境学报 , 2024 , 33 (12) : 1837-1848 . |
| MLA | 林馨 et al. "福建省耕地占补时空分异及其对生态系统服务价值的影响研究" . | 生态环境学报 33 . 12 (2024) : 1837-1848 . |
| APA | 林馨 , 徐伟铭 , 廖云婷 , 邵尔辉 . 福建省耕地占补时空分异及其对生态系统服务价值的影响研究 . | 生态环境学报 , 2024 , 33 (12) , 1837-1848 . |
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This paper addresses the challenges of high model complexity and low classification accuracy in remote sensing image classification using convolutional neural networks. To overcome these challenges, a modified DeeplabV3+ network is proposed, which replaces the deep feature extractor in the encoder with lightweight networks MobilenetV2 and Xception_65. The decoder structure is also modified to feature fusion layer by layer in order to refine the up -sampling process in the decoding region. In addition, a channel attention module is introduced to strengthen the information association between codecs, and multiscale supervision is used to adapt the receptive field. Four networks with different encoding and decoding structures are constructed and verified on the CCF dataset. The experimental results show that the MS-XDeeplabV3+ network , which uses Xception_65 in the encoder and layer by layer connection, channel attention module, and multiscale supervision in the decoder, has reduced number of model parameters, faster training speed, refined edge information for ground objects, and improved classification accuracy for grassland and linear ground objects such as roads and water bodies. The overall pixel accuracy and Kappa coefficient of the MS-XDeeplabV3+ network reach 0.9122 and 0.8646, respectively, which show the best performance among all networks in remote sensing image classification.
Keyword ：
channel attention module channel attention module convolutional neural network convolutional neural network encode and decode structure encode and decode structure layer by layer feature fusion layer by layer feature fusion multiscale supervision multiscale supervision remote sensing image classification remote sensing image classification
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| GB/T 7714 | Huang, Dongqing , Xu, Weiming , Xu, Wendi et al. High-Resolution Remote Sensing Image Classification Based on DeeplabV3+ Network [J]. | LASER & OPTOELECTRONICS PROGRESS , 2023 , 60 (16) . |
| MLA | Huang, Dongqing et al. "High-Resolution Remote Sensing Image Classification Based on DeeplabV3+ Network" . | LASER & OPTOELECTRONICS PROGRESS 60 . 16 (2023) . |
| APA | Huang, Dongqing , Xu, Weiming , Xu, Wendi , He, Xiaoying , Pan, Kaixiang . High-Resolution Remote Sensing Image Classification Based on DeeplabV3+ Network . | LASER & OPTOELECTRONICS PROGRESS , 2023 , 60 (16) . |
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针对卷积神经网络在遥感影像分类时遇到的模型参数量过大和分类精度低等问题,在DeeplabV3+网络的基础上,将编码器中的深层特征提取器替换为轻量化网络MobilenetV2和Xception_65,将解码器结构改为逐层特征融合实现解码区上采样的细化,引入通道注意力模块加强编解码器之间的信息关联,引入多尺度监督实现感受野自适应.构建4种具有不同编解码结构的网络,在CCF数据集上对网络进行验证测试.实验结果表明,编码器采用Xception_65,解码器同时引入逐层连接、通道注意力模块和多尺度监督的MS-XDeeplabV3+网络在减少模型参数量、加快模型训练速度的同时能更细化地物的边缘信息,提高对道路、水体等线状地物和草地的分类精度,像素总体精度和Kappa系数分别达0.9122和0.8646,在遥感影像分类中效果最佳.
Keyword ：
卷积神经网络 卷积神经网络 多尺度监督 多尺度监督 编解码结构 编解码结构 逐层特征融合 逐层特征融合 通道注意力模块 通道注意力模块 遥感影像分类 遥感影像分类
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| GB/T 7714 | 黄冬青 , 徐伟铭 , 许文迪 et al. 基于DeeplabV3+网络的高分遥感影像分类 [J]. | 激光与光电子学进展 , 2023 , 60 (16) : 346-355 . |
| MLA | 黄冬青 et al. "基于DeeplabV3+网络的高分遥感影像分类" . | 激光与光电子学进展 60 . 16 (2023) : 346-355 . |
| APA | 黄冬青 , 徐伟铭 , 许文迪 , 何小英 , 潘凯祥 . 基于DeeplabV3+网络的高分遥感影像分类 . | 激光与光电子学进展 , 2023 , 60 (16) , 346-355 . |
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