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学者姓名：陈崇成
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Abstract ：
Tourism knowledge graphs lack cultural content, limiting their usefulness for cultural tourists. This paper presents the development of a cultural perspective-based knowledge graph (CuPe-KG). We evaluated fine-tuning ERNIE 3.0 (FT-ERNIE) and ChatGPT for cultural type recognition to strengthen the relationship between tourism resources and cultures. Our investigation used an annotated cultural tourism resource dataset containing 2,745 items across 16 cultural types. The results showed accuracy scores for FT-ERNIE and ChatGPT of 0.81 and 0.12, respectively, with FT-ERNIE achieving a micro-F1 score of 0.93, a 26 percentage point lead over ChatGPT's score of 0.67. These underscore FT-ERNIE's superior performance (the shortcoming is the need to annotate data) while highlighting ChatGPT's limitations because of insufficient Chinese training data and lower identification accuracy in professional knowledge. A novel ontology was designed to facilitate the construction of CuPe-KG, including elements such as cultural types, historical figures, events, and intangible cultural heritage. CuPe-KG effectively addresses cultural tourism visitors' information retrieval needs.
Keyword ：
ChatGPT ChatGPT Cultural tourism Cultural tourism Cultural type Cultural type Knowledge graph Knowledge graph Pretrained language models Pretrained language models Travel intelligence Travel intelligence
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| GB/T 7714 | Fan, Zhanling , Chen, Chongcheng . CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (3) . |
| MLA | Fan, Zhanling 等. "CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models" . | INFORMATION PROCESSING & MANAGEMENT 61 . 3 (2024) . |
| APA | Fan, Zhanling , Chen, Chongcheng . CuPe-KG: Cultural perspective-based knowledge graph construction of tourism resources via pretrained language models . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (3) . |
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Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree dense reconstruction and the exported point cloud models are compared with point clouds derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has a higher successful reconstruction rate, better reconstruction in the canopy area and requires less images as input. Compared with the photogrammetric dense reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tend to be noisy and of low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those derived from the NeRF point cloud. The results of this study illustrate the great potential of the NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes.
Keyword ：
3D reconstruction 3D reconstruction 3D tree modeling 3D tree modeling deep learning deep learning individual tree individual tree lidar lidar neural radiance field (NeRF) neural radiance field (NeRF) photogrammetry photogrammetry terrestrial laser scanning terrestrial laser scanning
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| GB/T 7714 | Huang, Hongyu , Tian, Guoji , Chen, Chongcheng . Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method [J]. | REMOTE SENSING , 2024 , 16 (6) . |
| MLA | Huang, Hongyu 等. "Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method" . | REMOTE SENSING 16 . 6 (2024) . |
| APA | Huang, Hongyu , Tian, Guoji , Chen, Chongcheng . Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method . | REMOTE SENSING , 2024 , 16 (6) . |
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元宇宙快速发展正在影响着文旅行业的变革。文旅行业强调创意与场景体验，文旅产品具有重内容、重体验、重参与及重个性化的特点，这与元宇宙的发展高度契合。现阶段，文旅元宇宙的研究还处于襁褓阶段，对文旅元宇宙的概念、核心技术和应用场景还处于探索中。首先，本文回顾了元宇宙的演化进程，并基于文旅行业特征，提出了文旅元宇宙的基本定义、概念模型及主要特征，认为文旅元宇宙是元宇宙的一个子系统，是现有信息技术在文旅行业深度融合形成的文旅互联网形态，是文旅活动在三维数字世界的一种重构和虚拟共生。其基于扩展现实技术提供文旅场景的沉浸式体验，基于数字孪生技术生成现实世界文旅场景的镜像，并依托元宇宙统一架构下的政治、经济、文化等体系，实现文旅行业虚拟世界和现实世界的全方位融合；然后，对文旅元宇宙相关关键技术在文旅行业应用的研究进展和应用进行了详细的描述；最后，展望了文旅元宇宙在文化遗产数字化与保护、景区（酒店）开发与管理、导游导览服务、文旅营销、行业监管与市场治理等方面可能的应用场景，提出了文旅元宇宙未来重点的研究方向。虽然文旅元宇宙的发展还面临着诸多的挑战，技术的发展还要不断地经历螺旋式的上升，但是元宇宙终会重塑未来的社会形态和人类的生活方式。
Keyword ：
元宇宙 元宇宙 扩展现实 扩展现实 文化旅游 文化旅游 虚实融合 虚实融合 虚拟世界 虚拟世界 虚拟地理环境 虚拟地理环境 遥感 遥感
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| GB/T 7714 | 范占领 , 陈崇成 . 文旅元宇宙：概念、关键技术及应用场景 [J]. | 遥感学报 , 2024 , 28 (05) : 1161-1176 . |
| MLA | 范占领 等. "文旅元宇宙：概念、关键技术及应用场景" . | 遥感学报 28 . 05 (2024) : 1161-1176 . |
| APA | 范占领 , 陈崇成 . 文旅元宇宙：概念、关键技术及应用场景 . | 遥感学报 , 2024 , 28 (05) , 1161-1176 . |
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Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R-2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km(2) (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the "Sickle Bend" region of China (the "Sickle Bend" policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.
Keyword ：
Crop mapping Crop mapping Cross -region Cross -region Maize index Maize index National -scale National -scale Spatiotemporal variations Spatiotemporal variations
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| GB/T 7714 | Huang, Yingze , Qiu, Bingwen , Yang, Peng et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
| MLA | Huang, Yingze et al. "National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 221 (2024) . |
| APA | Huang, Yingze , Qiu, Bingwen , Yang, Peng , Wu, Wenbin , Chen, Xuehong , Zhu, Xiaolin et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
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Given the ecological, cultural and psychological functions of urban vegetation, a growing number of studies have focused on daily accessible greenery visibility. Mobile laser scanning (MLS) can rapidly obtain dense point clouds that can be used to extract vegetation. To address this issue, we propose a novel method for calculating the green view index (GVI) based on MLS three-dimensional (3D) point clouds. In our study, the GVI was specified as the ratio of greenery viewing angles to the total number of viewing angles in view. The GVI calculation procedure was as follows. First, the vegetation points were extracted using the density-based spatial clustering of appli-cations with noise (DBSCAN) algorithm and the PointNet++ deep learning algorithm. Second, based on the GVI specification, a virtual camera was constructed in a 3D point scenario to estimate greenery viewing angles in view and generate depth images, and then, the GVI value was calculated. This method is flexible and can be used to calculate the GVI at any site with any direction where 3D point scene data are available, and thus, it is suitable for evaluating various types of urban greenery. We conducted a case human-centered assessment of road greenery in a partial area of Jinshan District in Fuzhou, China, based on MLS point clouds, evaluating visible greenery and analyzing the relations among the GVI, greening pattern, and road green belt mode. The results showed that the overall visible greenery in the study area was good and that the GVI value of most road sections was more than 15 %. The method has potential for urban green space planning and management.
Keyword ：
Green view index Green view index Point clouds Point clouds Three-dimensional scene Three-dimensional scene Urban greenery Urban greenery Visual perception Visual perception
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| GB/T 7714 | Tang, Liyu , He, Jianguo , Peng, Wei et al. Assessing the visibility of urban greenery using MLS LiDAR data [J]. | LANDSCAPE AND URBAN PLANNING , 2023 , 232 . |
| MLA | Tang, Liyu et al. "Assessing the visibility of urban greenery using MLS LiDAR data" . | LANDSCAPE AND URBAN PLANNING 232 (2023) . |
| APA | Tang, Liyu , He, Jianguo , Peng, Wei , Huang, Hongyu , Chen, Chongcheng , Yu, Can . Assessing the visibility of urban greenery using MLS LiDAR data . | LANDSCAPE AND URBAN PLANNING , 2023 , 232 . |
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Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R-2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R-2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R-2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.
Keyword ：
canopy height canopy height forest age forest age Fujian Fujian GEDI GEDI LiDAR LiDAR time-series remote sensing time-series remote sensing
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| GB/T 7714 | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China [J]. | REMOTE SENSING , 2023 , 15 (2) . |
| MLA | Zhou, Xiaocheng et al. "Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China" . | REMOTE SENSING 15 . 2 (2023) . |
| APA | Zhou, Xiaocheng , Hao, Youzhuang , Di, Liping , Wang, Xiaoqin , Chen, Chongcheng , Chen, Yunzhi et al. Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China . | REMOTE SENSING , 2023 , 15 (2) . |
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Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite- 2 (ICESat- 2) products provide reliable global references for the accuracy evaluation and correction of Global Digital Elevation Model (GDEM). However, existing DEM correction methods mainly address the signal of vegetation in DEM errors and mostly use linear regression models. So, we first analyze the relationship between Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM v3 data accuracy and the land cover type, elevation, slope, relief amplitude, and vegetation coverage. Based on this, this paper proposes a Digital Elevation Model (DEM) error correction method that takes into account various influencing factors and combines Extreme Gradient Boosting (XGBoost) machine learning and spatial interpolation to model the errors. The analysis of the results shows that the overall error of the original ASTER GDEM has a normal distribution with a large negative offset (average error of - 3.463 m). The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of original ASTER GDEM are 12.930 m and 16.695 m, respectively, and the elevation accuracy decreases with the increase of elevation, slope, relief amplitude, and vegetation coverage. After correction, the Mean Error (ME) of ASTER GDEM is reduced to -0.233 m, which means the negative deviation is effectively removed and the overall MAE and overall RMSE are reduced by 26.04% and 23.56%, respectively. The MAE and RMSE of DEM for cultivated lands, forests, grasslands, wetlands, water bodies, and man-made surfaces are all reduced by different degrees. The DEM accuracy evaluation and correction method proposed in this paper models the non-linear relationships between multiple feature elements and terrain errors and achieves better correction results in the study area. © 2023 Journal of Geo-Information Science. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Digital instruments Digital instruments Error correction Error correction Forestry Forestry Geomorphology Geomorphology Interpolation Interpolation Landforms Landforms Mean square error Mean square error Normal distribution Normal distribution Regression analysis Regression analysis Surveying Surveying Vegetation Vegetation
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| GB/T 7714 | Jiao, Huaijin , Chen, Chongcheng , Huang, Hongyu . Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data [J]. | Journal of Geo-Information Science , 2023 , 25 (2) : 409-420 . |
| MLA | Jiao, Huaijin et al. "Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data" . | Journal of Geo-Information Science 25 . 2 (2023) : 409-420 . |
| APA | Jiao, Huaijin , Chen, Chongcheng , Huang, Hongyu . Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data . | Journal of Geo-Information Science , 2023 , 25 (2) , 409-420 . |
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星载激光雷达ICESat-2和GEDI可以为数字高程模型产品的精度评价与修正提供全球覆盖的、可靠的高精度参考数据源。然而,现有的DEM修正方法主要是针对DEM误差中的植被高信号且多采用线性回归模型。为此,本文分析了ASTER GDEM v3精度与土地覆盖类型、高程、坡度、起伏度及植被覆盖率的关系。在此基础上,提出了一种考虑上述多种精度影响因素并结合XGBoost和空间插值的DEM误差修正方法。结果分析表明:原始ASTER GDEM的误差整体呈正态分布,平均误差为-3.463 m,存在较大负偏差,高程精度随着高程、坡度、起伏度及植被覆盖率VCF的增大呈降低趋势;经过修正后,ASTER GDEM平均误差降低到了-0.233 m,负偏差得到有效改善,整体平均绝对误差降低了26.04%,整体均方差降低了23.56%,耕地、林地、草地、湿地、水域及人造地表的DEM平均绝对误差和均方差都有不同程度的降低;本文提出的方法对多种特征要素与地形误差间的非线性关系进行拟合建模,在研究区取得了较好的修正效果。
Keyword ：
ASTER ASTER DEM修正 DEM修正 GEDI GEDI ICESat-2 ICESat-2 XGBoost XGBoost 土地覆盖类型 土地覆盖类型 空间插值 空间插值 高程精度评价 高程精度评价
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| GB/T 7714 | 焦怀瑾 , 陈崇成 , 黄洪宇 . 结合ICESat-2和GEDI的中国东南丘陵地区ASTERGDEM高程精度评价与修正 [J]. | 地球信息科学学报 , 2023 , 25 (02) : 409-420 . |
| MLA | 焦怀瑾 et al. "结合ICESat-2和GEDI的中国东南丘陵地区ASTERGDEM高程精度评价与修正" . | 地球信息科学学报 25 . 02 (2023) : 409-420 . |
| APA | 焦怀瑾 , 陈崇成 , 黄洪宇 . 结合ICESat-2和GEDI的中国东南丘陵地区ASTERGDEM高程精度评价与修正 . | 地球信息科学学报 , 2023 , 25 (02) , 409-420 . |
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为准确估算森林采伐生物量实现森林碳汇的精准计量，针对采用单一时相可见光无人机影像估算高郁闭度森林采伐生物量较困难的问题，基于伐区采伐前后多时相可见光无人机影像，研究森林采伐生物量高精度的估算方法。以福建省闽侯白沙国有林场一个针叶林采伐小班为试验区，采集分辨率优于10 cm的采伐前后多时相可见光无人机影像，采用动态窗口局部最大值法得到高精度的采伐株数与单木树高信息，再基于采伐后无人机影像，运用YOLO v5方法检测并提取伐桩直径信息，根据胸径-伐桩直径模型来估算采伐木胸径信息，再利用树高和胸径二元生物量公式估算采伐生物量，以实测数据进行验证。根据动态窗口局部最大值法获取株数与平均树高精度分别为96.35%、99.01%,运用YOLO v5方法对伐桩目标检测的总体精度为77.05%,根据伐桩直径估算的平均胸径精度为90.14%,最后得到森林采伐生物量精度为83.08%,结果表明这一新方法具备较大的应用潜力。采用采伐前后多时相无人机可见光遥感，可实现森林采伐生物量的有效估算，有助于降低人工调查成本，为政府及有关部门进行碳汇精准计量提供有效的技术支持。
Keyword ：
可见光遥感 可见光遥感 多时相无人机影像 多时相无人机影像 森林 森林 采伐生物量 采伐生物量
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| GB/T 7714 | 周小成 , 王佩 , 谭芳林 et al. 基于多时相无人机影像的高郁闭度森林采伐生物量估算 [J]. | 农业机械学报 , 2023 , 54 (06) : 168-177 . |
| MLA | 周小成 et al. "基于多时相无人机影像的高郁闭度森林采伐生物量估算" . | 农业机械学报 54 . 06 (2023) : 168-177 . |
| APA | 周小成 , 王佩 , 谭芳林 , 陈崇成 , 黄洪宇 , 林宇 . 基于多时相无人机影像的高郁闭度森林采伐生物量估算 . | 农业机械学报 , 2023 , 54 (06) , 168-177 . |
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Forest harvesting is a forest carbon source. Accurate estimation of forest harvesting biomass is helpful for accurate measurement of forest carbon sinks. Aiming at the challenging problem of using single time-phase visible light UAV image to estimate the biomass of high-density forest harvesting, a high-precision estimation method of forest harvesting biomass was studied based on multi-temporal visible light UAV image before and after logging. Taking a coniferous forest in Fuzhou City of Fujian Province Baisha forest cutting small class as the experimental zone, collecting resolution better than 10 cm long before and after cutting, unmanned aerial vehicle ( UAV) visible light image, the local maximum dynamic window method was adopted to get high precision of cutting plants and single tree height information, and then based on the UAV image after cutting, detection and extraction by the method of YOLO v5 cut pile diameter of information, the DBH information of the cut wood was estimated according to the DBH - pile diameter model, and the biomass of the cut wood was estimated by using the binary biomass formula of tree height and DBH, which was verified by the measured data. The precision of tree number and average tree obtained by dynamic window local maximum method was 96. 35% and 99. 01%, respectively. The overall accuracy of pile cutting target detection by YOLO v5 method was 77. 05%, and the accuracy of average DBH estimated by pile cutting diameter was 90. 14% . Finally, the accuracy of forest harvesting biomass was 83. 08% . The results showed that this method had great application potential. Using multitemporal UAV visible light remote sensing before and after harvesting can realize effective estimation of forest harvesting biomass, which can help to reduce the cost of manual investigation, and provide effective technical support for the government and relevant departments to accurately measure carbon sinks. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
Keyword ：
forest forest harvesting biomass harvesting biomass multi-temporal UAV images multi-temporal UAV images visible light remote sensing visible light remote sensing
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| GB/T 7714 | Zhou, X. , Wang, P. , Tan, F. et al. Biomass Estimation of High-density Forest Harvesting Based on Multi-temporal UAV Images; [基于多时相无人机影像的高郁闭度森林采伐生物量估算] [J]. | Transactions of the Chinese Society for Agricultural Machinery , 2023 , 54 (6) : 168-177 . |
| MLA | Zhou, X. et al. "Biomass Estimation of High-density Forest Harvesting Based on Multi-temporal UAV Images; [基于多时相无人机影像的高郁闭度森林采伐生物量估算]" . | Transactions of the Chinese Society for Agricultural Machinery 54 . 6 (2023) : 168-177 . |
| APA | Zhou, X. , Wang, P. , Tan, F. , Chen, C. , Huang, H. , Lin, Y. . Biomass Estimation of High-density Forest Harvesting Based on Multi-temporal UAV Images; [基于多时相无人机影像的高郁闭度森林采伐生物量估算] . | Transactions of the Chinese Society for Agricultural Machinery , 2023 , 54 (6) , 168-177 . |
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