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学者姓名：徐伟铭
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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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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 × 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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It is challenging to directly obtain global information of existing deep learning-based remote sensing intelligent interpretation methods, resulting in blurred object edges and low classification accuracy between similar classes. This study proposes a semantic segmentation model called SRAU-Net based on Swin Transformer and convolutional neural network. SRAU-Net adopts a Swin Transformer encoder-decoder framework with a U-Net shape and introduces several improvements to address the limitations of previous methods. First, Swin Transformer and convolutional neural network are used to construct a dual-branch encoder, which effectively captures spatial details with different scales and complements the context features, resulting in higher classification accuracy and sharper object edges. Second, a feature fusion module is designed as a bridge for the dual-branch encoder. This module efficiently fuses global and local features in channel and spatial dimensions, improving the segmentation accuracy for small target objects. Moreover, the proposed SRAU-Net model incorporates a feature enhancement module that utilizes attention mechanisms to adaptively fuse features from the encoder and decoder and enhances the aggregation of spatial and semantic features, further improving the ability of the model to extract features from remote sensing images. The effectiveness of the proposed SRAU-Net model is demonstrated using the ISPRS Vaihingen dataset for land cover classification. The results show that SRAU-Net outperforms other models in terms of overall accuracy and F1 score, achieving 92. 06% and 86. 90%, respectively. Notably, the SRAU-Net model excels in extracting object edge information and accurately classifying small-scale regions, with an improvement of 2. 57 percentage points in the overall classification accuracy compared with the original model. Furthermore, it effectively distinguishes remote sensing objects with similar characteristics, such as trees and low vegetation. © 2024 Universitat zu Koln. All rights reserved.
Keyword ：
convolutional neural network convolutional neural network feature fusion feature fusion high-resolution remote sensing image high-resolution remote sensing image semantic segmentation semantic segmentation Swin Transformer Swin Transformer
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| GB/T 7714 | Xiaoying, H. , Weiming, X. , Kaixiang, P. et al. Classification of High-Resolution Remote Sensing Image Based on Swin Transformer and Convolutional Neural Network; [基于 Swin Transformer 与卷积神经网络的高分遥感影像分类] [J]. | Laser and Optoelectronics Progress , 2024 , 61 (14) . |
| MLA | Xiaoying, H. et al. "Classification of High-Resolution Remote Sensing Image Based on Swin Transformer and Convolutional Neural Network; [基于 Swin Transformer 与卷积神经网络的高分遥感影像分类]" . | Laser and Optoelectronics Progress 61 . 14 (2024) . |
| APA | Xiaoying, H. , Weiming, X. , Kaixiang, P. , Juan, W. , Ziwei, L. . Classification of High-Resolution Remote Sensing Image Based on Swin Transformer and Convolutional Neural Network; [基于 Swin Transformer 与卷积神经网络的高分遥感影像分类] . | Laser and Optoelectronics Progress , 2024 , 61 (14) . |
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针对现有基于深度学习的遥感智能解译方法直接获取全局信息具有挑战性，造成地物边缘模糊、相似类间分类精度低等问题，提出基于Swin Transformer和卷积神经网络的高分遥感图像语义分割模型（SRAU-Net）。SRAU-Net以Swin Transformer编码器-解码器框架为基础，采用U-Net形状，提出了以下改进：构造基于Swin Transformer和基于卷积神经网络的双分支编码器，用不同尺度空间细节特征补充具有全局信息的上下文特征，以获得更高的地物分类精度和更清晰的地物边缘；设计特征融合模块，作为双分支编码器的桥梁从通道和空间维度对全局和局部特征进行有效融合，提升对小目标地物的分割精度；添加特征增强模块，利用注意力机制自适应融合来自编码器和解码器的特征，进一步有效聚合空间和语义特征，提升模型对特征的提取效果。结果表明，SRAU-Net能够更好地提取地物的边缘信息，总体分类精度较原始模型提升了2.57百分点，提高了对小尺度地物的分类精度，有效区分如树木和低矮植被等类间相似的遥感地物，总体精度和F1分数分别为92.60%和86.90%，总体效果优于对比模型。
Keyword ：
Swin Transformer Swin Transformer 卷积神经网络 卷积神经网络 特征融合 特征融合 语义分割 语义分割 高分辨率遥感影像 高分辨率遥感影像
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| GB/T 7714 | 何小英 , 徐伟铭 , 潘凯祥 et al. 基于Swin Transformer与卷积神经网络的高分遥感影像分类 [J]. | 激光与光电子学进展 , 2024 , 61 (14) : 255-266 . |
| MLA | 何小英 et al. "基于Swin Transformer与卷积神经网络的高分遥感影像分类" . | 激光与光电子学进展 61 . 14 (2024) : 255-266 . |
| APA | 何小英 , 徐伟铭 , 潘凯祥 , 王娟 , 李紫微 . 基于Swin Transformer与卷积神经网络的高分遥感影像分类 . | 激光与光电子学进展 , 2024 , 61 (14) , 255-266 . |
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The POI data and Place2vec model within the third ring of Fuzhou City were used to identify the functional zones in the city, on the basis of which we were able to analyze the characteristics of the spatial differentiation of the thermal field levels in each functional zone. We subsequently constructed the functional zone warming sensitivity index using a boosting regression tree algorithm to quantitatively measure the sensitivity difference of different functional zones in the process of surface warming and then explored the spatial differentiation mechanism of the urban thermal environment on a regional scale. The results showed the following. First, the identification framework of urban functional zones was constructed based on POI data and the Place2vec model; and the identification of the five types of functional zones—the living service and residential zone, public management and service zone, commercial zone, industrial zone, green space and square zone—was highly accuratecy. Second, the internal thermal field grades of the green space and square zone are mainly low-temperature and sub-low-temperature, while the rest of the four types of internal surface heat field grades of functional zones are dominated by medium-temperature, sub-high-temperature, high-temperature, and very-high-temperature zones. Additionally, the distribution of sub-low-temperature, low-temperature, and very-low-temperature zones is not significant. Except for the green space and square zone, all four types of functional zones exhibit high-temperature phenomena, of which the extremely high-temperature zone accounts for the most in the industrial zone (as high as 20.68%) and the least in the green space and square zone(1.90%). Third, the overall sensitivity to temperature increase of each type of functional zone, in descending order, is as follows: industrial zone, living service and residential zone, commercial zone, public management and service zone, green space and square zone. The sensitivity to temperature increase of each functional zone in different warming stages differs significantly under the high-temperature gradient. The sensitivity to temperature increase of the ground cover of different functional zones has the distinctive characteristics of the functional zones. Specifically, the higher the degree of development of the zone, the higher the sensitivity to temperature increase of the built land. The higher the degree of regional construction development, the greater the difference between the sensitivity of construction land and the sensitivity of vegetation and water, and the sensitivity of construction land is always higher than the sensitivity of vegetation and water in each functional zone. The results of this study will help overcome the limitations of existing urban thermal environmental research and provide a scientific decision-making basis for promoting the rational layout of urban functional areas, alleviating the urban heat island effect, preventing the risk of high temperatures under extreme weather conditions, and realizing sustainable urbanization. © 2024 Editorial Committee of Tropical Geography. All rights reserved.
Keyword ：
BRT BRT Fuzhou Fuzhou Place2vec Place2vec sensitivity to temperature increase sensitivity to temperature increase surface warming surface warming urban functional areas urban functional areas urban thermal environment urban thermal environment
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| GB/T 7714 | Yang, H. , Xu, W. , Shao, E. et al. Spatial Differentiation of Warming Effects in Urban Functional Areas Based on Warming Sensitivity Indices; [基于增温敏感性指数的城市功能区升温效应空间分异研究] [J]. | Tropical Geography , 2024 , 44 (3) : 557-568 . |
| MLA | Yang, H. et al. "Spatial Differentiation of Warming Effects in Urban Functional Areas Based on Warming Sensitivity Indices; [基于增温敏感性指数的城市功能区升温效应空间分异研究]" . | Tropical Geography 44 . 3 (2024) : 557-568 . |
| APA | Yang, H. , Xu, W. , Shao, E. , Liao, Y. , Lin, X. . Spatial Differentiation of Warming Effects in Urban Functional Areas Based on Warming Sensitivity Indices; [基于增温敏感性指数的城市功能区升温效应空间分异研究] . | Tropical Geography , 2024 , 44 (3) , 557-568 . |
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利用福州市三环内的POI数据和Place2vec模型识别城市功能区，并在此基础上分析各功能区的热场等级空间分异特征，借助增强回归树算法（Boosting Regression Tree, BRT）构建功能区增温敏感性指数，对不同功能区在地表升温过程中的敏感性差异进行定量测度，进而对区域尺度上的城市热环境空间分异机制进行深入探讨。结果表明：1）基于Place2vec模型的城市功能区识别结果具有较高的精度；2）除绿地与广场区外的4类功能区均具有高温现象，其中产业区的热场强度最高；3）各类功能区整体增温敏感性由高至低依次是：产业区、生活服务与住宅区、商业区、公共管理与服务区、绿地与广场区；不同升温阶段的功能区增温敏感性存在差异，在高温梯度下差异显著；地表覆被增温敏感性具有明显的功能区域分异特征。
Keyword ：
BRT算法 BRT算法 Place2vec模型 Place2vec模型 地表升温 地表升温 城市功能区 城市功能区 城市热环境 城市热环境 增温敏感性 增温敏感性 福州市 福州市
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| GB/T 7714 | 杨慧 , 徐伟铭 , 邵尔辉 et al. 基于增温敏感性指数的城市功能区升温效应空间分异研究 [J]. | 热带地理 , 2024 , 44 (03) : 557-568 . |
| MLA | 杨慧 et al. "基于增温敏感性指数的城市功能区升温效应空间分异研究" . | 热带地理 44 . 03 (2024) : 557-568 . |
| APA | 杨慧 , 徐伟铭 , 邵尔辉 , 廖云婷 , 林馨 . 基于增温敏感性指数的城市功能区升温效应空间分异研究 . | 热带地理 , 2024 , 44 (03) , 557-568 . |
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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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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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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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