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学者姓名：陈楠
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Abstract ：
The Chinese Loess Plateau, the world's largest loess deposit, is characterized by distinctive landforms and severe soil erosion. Limited long-term observational data has hindered a comprehensive understanding of watershed evolution in this region. This study uses laboratory-based artificial rainfall simulations to investigate the long-term geomorphological evolution of watersheds on the Chinese Loess Plateau. Digital elevation models and terrain analysis techniques were employed to analyze geomorphological processes. Through correlation analysis and Sheffield entropy calculations, an optimal combination of terrain factors-including slope, aspect, plan curvature, profile curvature, and elevation-was identified to accurately describe slope characteristics. Multi-resolution image segmentation, Moran's I, and weighted variance were used to optimize segmentation parameters, achieving precise slope unit segmentation. Based on these parameters, we classified slope shapes, constructed slope shape spectra, and analyzed their changes over time. Results indicate that as the watershed evolves, the entropy of slope spectra increases while skewness and kurtosis decrease, indicating greater geomorphological complexity and diversity. The watershed's evolution progresses through three stages: slope shape development, slope shape growth, and slope shape maturity, each marked by distinct geomorphological features. By analyzing landscape indices at both class and landscape levels, we observed significant variation during the early development stage, followed by stabilization during the maturation phase. These findings demonstrate that slope shape spectra effectively capture the dynamic evolution of slopes in the Chinese Loess Plateau, offering new insights for understanding its geomorphological evolution and providing a novel method for quantitative geomorphology.
Keyword ：
Evolution Evolution Loess landform Loess landform Segmentation Segmentation Simulated watershed Simulated watershed Slope shape spectrum Slope shape spectrum
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| GB/T 7714 | Jiang, Hongtao , Chen, Nan , Wang, Chen et al. Geomorphological evolution of small watershed on the Chinese Loess Plateau based on slope shape spectra [J]. | EARTH SCIENCE INFORMATICS , 2025 , 18 (2) . |
| MLA | Jiang, Hongtao et al. "Geomorphological evolution of small watershed on the Chinese Loess Plateau based on slope shape spectra" . | EARTH SCIENCE INFORMATICS 18 . 2 (2025) . |
| APA | Jiang, Hongtao , Chen, Nan , Wang, Chen , Li, Sijia , Ou, Mengyao . Geomorphological evolution of small watershed on the Chinese Loess Plateau based on slope shape spectra . | EARTH SCIENCE INFORMATICS , 2025 , 18 (2) . |
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[目的]揭示在地势起伏影响下植被GPP时空格局特征,进而深入分析地形地貌与植被GPP之间的相互作用机制,为植被碳通量模拟以及空间分异性研究提供新的视角.[方法]采用机器学习模型,基于宏观地形因子构建植被GPP模拟模型.通过谱模型提取6个典型地貌样区的植被GPP空间谱,并运用定性和定量分析方法研究了其空间异质性.[结果]XGBoost模型的模拟精度较好,且引入宏观地形因子特征组模型的决定系数(R2)相较于经典特征组提升11.26％,与微观地形因子特征组相比提高了 0.94％,同时均方根误差(RMSE)分别降低了 21.27％和2.27％.2003-2023年,黄土高原植被GPP整体上升了 19.12％,呈现出东南高西北低的空间分布特征.区域内6种典型样区的GPP在不同地形条件下表现出明显的地形分异性,且普遍随着地形崎岖度的增加,呈现先降后升的波动变化趋势.[结论]地形因子在植被GPP的模拟中起到了关键作用,且宏观地形因子比微观地形因子更能揭示地形起伏对GPP的影响.
Keyword ：
数字高程模型 数字高程模型 机器学习 机器学习 植被总初级生产力 植被总初级生产力 谱模型 谱模型 黄土高原 黄土高原
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| GB/T 7714 | 李文戈 , 陈楠 , 孙阵阵 . 地形地貌视角下黄土高原植被GPP模拟及空间分异研究 [J]. | 水土保持研究 , 2025 , 32 (2) : 331-339 . |
| MLA | 李文戈 et al. "地形地貌视角下黄土高原植被GPP模拟及空间分异研究" . | 水土保持研究 32 . 2 (2025) : 331-339 . |
| APA | 李文戈 , 陈楠 , 孙阵阵 . 地形地貌视角下黄土高原植被GPP模拟及空间分异研究 . | 水土保持研究 , 2025 , 32 (2) , 331-339 . |
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Genetic landform recognition is critical for understanding the composition of the Earth surface and its dynamic processes. The graph-driven approach is a significant paradigm in data-driven Earth science, whereas never been reported in landform recognition. The construction of a meaningful graph structure for simulating entire landforms and the developed landform recognition framework based on it are two major challenges. In this context, we first develop a novel graph-driven deep learning (DL) method for genetic landform recognition. Specifically, inspired by the positive negative terrain concept in Earth science, we introduced a terrain structure-based directed graph model (DPN) to model overall landforms as graph data with geo-meaning. We then develop a graph DL technique flow based on a graph attention network (GAT) to leverage DPN to achieve graph-driven landform recognition. We construct a multi-scale landform genesis dataset with seven genesis landforms and 756 000 samples. Based on this dataset and four test regions in China, a series of carefully designed experiments demonstrate that the proposed method is accurate, transferable, and scalable in genetic landform recognition. As a corollary, this reveals that the overall terrain structure is closely related to the genesis of the landforms. The proposed graph-driven method shows superior or comparable performance compared to different image-driven methods with an overall accuracy of 91.67%. This is one of the first extensions of graph-driven methods to landform recognition with pioneer results. Besides, the strategy of using DPN to simulate overall landform outperforms any regional terrain element-based strategy in terms of recognition performance, which confirms the importance of the proposed terrain structure modeling technique. Our study highlights that the conflation of physical geomorphic models and new AI techniques presents a highly promising avenue for geographical inquiry.
Keyword ：
Deep learning (DL) Deep learning (DL) graph attention network (GAT) graph attention network (GAT) landform recognition landform recognition remote sensing remote sensing terrain modeling terrain modeling
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| GB/T 7714 | Lin, Siwei , Wang, Xianyan , Chen, Nan et al. Directed Positive Negative Terrain Structure Graph Attention Network for Genetic Landform Recognition [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Lin, Siwei et al. "Directed Positive Negative Terrain Structure Graph Attention Network for Genetic Landform Recognition" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Lin, Siwei , Wang, Xianyan , Chen, Nan , Shen, Rui . Directed Positive Negative Terrain Structure Graph Attention Network for Genetic Landform Recognition . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Music has long served as a bridge between science and nature, allowing for the artistic expression of natural and human-made phenomena. While music has been used in geomorphology as an engaging teaching strategy, its application in specific scientific inquiries within geomorphology remains relatively unexplored. Drawing on the morphological similarities between music and terrain relief, this work introduced a novel music-based method for modelling and expressing terrain relief based on the drainage basin profile (DBP). It converts terrain relief into pitches and time values according to mapping rules and then describes terrain relief in an audible form. Based on 5 sample areas and 360 drainage basins on the Loess Plateau, we developed the application of the proposed method on four core geomorphic tasks, including landform interpretation, analysis, recognition and classification. Experimental results show that (1) terrain music can interpret the terrain relief and landform evolution processes through its musical structure and rhythmic variations; (2) music derivatives are related to different terrain features, such as terrain relief, terrain variation intensity, landform evolution degree and terrain complexity, and have a well-functioning relationship with a series of conventional terrain derivatives; (3) leveraging machine learning techniques, the terrain music method is effective for landform recognition, achieving an overall accuracy of 88.85% and a mean accuracy of 88.85%; and (4) via a case study in Northern Shaanxi, music modelling successfully divided it into 12 distinct landform regions and 8 landform types. Different landform regions exhibit clear regional boundaries and gradual transition zones, while specific landform regions share prominent terrain, spatial clustering and landform processes. Our delineation provides reasonable and effective landform differentiation but captures additional bed-rock mountain features compared with a traditional method. This study highlights that music is not only an artistic expression but also a valuable research paradigm for a wide range of geomorphic tasks, offering fresh perspectives and enhancing our understanding of loess landforms. The results show a promising effort to integrate music theory into practical geomorphic tasks, demonstrating the potential of using music as a medium for conveying and analysing spatial information in geomorphology. This study presents a novel music modelling method for terrains, encompassing extraction techniques, quantitative indices and specific applications in geomorphology. By utilizing DBP as a key starting point for investigation, a series of experiments were conducted to demonstrate the feasibility and potential of the proposed method in landform quantitative analysis. By adopting music theory in various geomorphic tasks, this study aims to assess the potential of the approach and uncover new implications for geomorphic study from an auditory perspective. image
Keyword ：
drainage basin profile drainage basin profile geomorphology geomorphology music modelling music modelling terrain terrain
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| GB/T 7714 | Lin, Siwei , Yu, Yang , Chen, Nan et al. Using music and music-based analysis to model and to classify terrain in geomorphology [J]. | EARTH SURFACE PROCESSES AND LANDFORMS , 2024 , 49 (5) : 1544-1559 . |
| MLA | Lin, Siwei et al. "Using music and music-based analysis to model and to classify terrain in geomorphology" . | EARTH SURFACE PROCESSES AND LANDFORMS 49 . 5 (2024) : 1544-1559 . |
| APA | Lin, Siwei , Yu, Yang , Chen, Nan , Shen, Rui , Wang, Xianyan . Using music and music-based analysis to model and to classify terrain in geomorphology . | EARTH SURFACE PROCESSES AND LANDFORMS , 2024 , 49 (5) , 1544-1559 . |
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Traffic flow prediction in a given area is often influenced by the interactions with complex dependencies among multiple areas. By far, it remains unexplored to obtain interactive information. To address the issue, MSTMN was proposed, a multi-task learning framework that jointly learns interactive information and spatiotemporal dependencies across tasks. MSTMN consists of a node network, an edge network, and a prediction network. The node network and edge network were trained using the proposed meta-fully convolutional blocks to extract interactive features and generalizable features. The prediction network employed the meta-gated fusion and the recalibration block to both integrate these learned features and external factors. This ensures that the features capture optimal interaction information during the training phase. The proposed model was validated on two real-world movement-on-demand traffic datasets collected in Xiamen, China. Experimental results showed that MSTMN improved performance by 38.42% and 31.77% for one-step and multi-step prediction compared to the state-of-the-art baseline. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Multi-task learning Multi-task learning Prediction models Prediction models Traffic control Traffic control
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| GB/T 7714 | Zhou, Qianqian , Chen, Nan . MSTMN: a novel meta-attention-based multi-task spatiotemporal network for traffic flow prediction [J]. | Neural Computing and Applications , 2024 , 36 (36) : 23195-23222 . |
| MLA | Zhou, Qianqian et al. "MSTMN: a novel meta-attention-based multi-task spatiotemporal network for traffic flow prediction" . | Neural Computing and Applications 36 . 36 (2024) : 23195-23222 . |
| APA | Zhou, Qianqian , Chen, Nan . MSTMN: a novel meta-attention-based multi-task spatiotemporal network for traffic flow prediction . | Neural Computing and Applications , 2024 , 36 (36) , 23195-23222 . |
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The current research focus on visualizing terrain features emphasizes quantification and detailed simulation, without adequately considering the impact of spatial-temporal variations in the terrain on human cognition. However, advancements in visualization technology, such as efficient and rapid construction of large-scale three-dimensional (3D) terrain scenes, real-time dynamic display, and free-roaming from any viewpoint, currently provide ample technical support for visualizing spatial-temporal information. Therefore, this article proposes a 3D terrain viewing model that considers the spatial-temporal changes in light intensity and incident direction in a terrain scene, based on the principles of radiometry and computer graphics theory and supported by the physically based rendering techniques. This model aims to accurately represent the subtle variations in real-world terrain surfaces and highlight the key elements of hill terrain. Theoretically, this model provides a foundation for the virtual reconstruction of real-world terrain.
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| GB/T 7714 | Song, Ci , Chen, Nan , Xu, YueXue et al. Field-of-view modeling of hilly terrain based on physically based rendering of spatial-temporal variations within optical radiation [J]. | TRANSACTIONS IN GIS , 2024 , 28 (7) : 2005-2024 . |
| MLA | Song, Ci et al. "Field-of-view modeling of hilly terrain based on physically based rendering of spatial-temporal variations within optical radiation" . | TRANSACTIONS IN GIS 28 . 7 (2024) : 2005-2024 . |
| APA | Song, Ci , Chen, Nan , Xu, YueXue , Zhang, YiNing , Zhu, HongChun . Field-of-view modeling of hilly terrain based on physically based rendering of spatial-temporal variations within optical radiation . | TRANSACTIONS IN GIS , 2024 , 28 (7) , 2005-2024 . |
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基于九期人工降雨模拟流域DEM数据,运用MATLAB进行了演化条件中的流域天文辐射计算,并模拟了流域内各阶段年天文辐射量空间分布.采用统计学中均值、变异系数、峰度及偏度量化了天文辐射的数量特征,运用景观生态学指标量化了其空间结构特征.结果表明:流域演化各阶段年天文辐射的数值变化在2～12 596 MJ∙m-2之间.辐射量景观格局的变化特征方面得出:斑块层次上各景观指数在小流域不同演化阶段的变化规律基本一致,而景观层次上的各指数则呈现波动性变化特征.此外,活跃演化阶段(实验阶段Ⅳ～Ⅵ)天文辐射量的数量特征及空间结构特征的变化程度最为明显.
Keyword ：
天文辐射 天文辐射 数字高程模型 数字高程模型 景观指数 景观指数 演化条件 演化条件 空间分布 空间分布
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| GB/T 7714 | 万佳旭 , 陈楠 . 流域演化过程中天文辐射空间分布 [J]. | 海南大学学报（自然科学版） , 2024 , 42 (3) : 268-277 . |
| MLA | 万佳旭 et al. "流域演化过程中天文辐射空间分布" . | 海南大学学报（自然科学版） 42 . 3 (2024) : 268-277 . |
| APA | 万佳旭 , 陈楠 . 流域演化过程中天文辐射空间分布 . | 海南大学学报（自然科学版） , 2024 , 42 (3) , 268-277 . |
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Fozi Mountain National Geopark is located in Zhenghe County in the northern region of Fujian Province, where the volcanic rocks of the Zhaixia Formation of the Shimaoshan Group are exposed. Zircon U-Pb dating and geochemical analysis were carried out to constrain its age and tectonic environment. The results show that three zircon U-Pb dating samples have attained ages of 99.2 +/- 1.0 Ma, 99.6 +/- 0.8 Ma, and 99.7 +/- 2.0 Ma. Volcanic rocks in the core scenic area of Fozi Mountain were formed during the Late Cretaceous period. Elemental analysis showed that these volcanic rocks were dominated by the shoshonite series. They include gray dacite porphyry, grayish-white breccia tuff, volcanic agglomerate, and gray tuffaceous sandstone. These rocks were characterized by high silicon, high alkali content, and rich potassium levels. Lu-Hf isotope analysis of zircons revealed that their epsilon Hf(t) values varied from -8.7 to -6.8. The corresponding TDM2 values were primarily distributed in the range of 1.71 Ga to 1.59 Ga. These findings indicated that the magma primarily originated from the partial melting of the Mesoproterozoic crystalline basement, accompanied by a small number of mantle-derived materials. Tectonic environment analysis indicated that these rocks were formed in the post-orogenic intraplate extensional environment, which was associated with the back-arc extension or lithospheric thinning caused by the subduction of the paleo-Pacific plate beneath the Eurasian plate. The formation of these volcanic rocks was attributed to post-orogenic magmatism.
Keyword ：
Fozi Mountain Fozi Mountain geochemistry geochemistry volcanic rock volcanic rock zircon Hf isotope zircon Hf isotope zircon U-Pb zircon U-Pb
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| GB/T 7714 | Chen, Nan , Li, Dunpeng , Huang, Yanna et al. Zircon U-Pb Dating, Geochemistry, Lu-Hf Isotope Characteristics, and Geological Significance of Volcanic Rocks in Zhenghe Fozi Mountain National Geopark, Fujian, China [J]. | MINERALS , 2024 , 14 (6) . |
| MLA | Chen, Nan et al. "Zircon U-Pb Dating, Geochemistry, Lu-Hf Isotope Characteristics, and Geological Significance of Volcanic Rocks in Zhenghe Fozi Mountain National Geopark, Fujian, China" . | MINERALS 14 . 6 (2024) . |
| APA | Chen, Nan , Li, Dunpeng , Huang, Yanna , Fu, Yihang , Yang, Xiaomin , Wang, Hanbin . Zircon U-Pb Dating, Geochemistry, Lu-Hf Isotope Characteristics, and Geological Significance of Volcanic Rocks in Zhenghe Fozi Mountain National Geopark, Fujian, China . | MINERALS , 2024 , 14 (6) . |
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Previous traffic flow prediction studies have utilized spatio-temporal neural networks combined with the multi-task learning framework to seek complementary information for enhancing prediction performance. However, the existing methods still face two challenges: they fail to capture global interaction patterns between regions and lack consideration for inter-correlations within interaction patterns. To solve these issues, we propose a novel multi-task spatio-temporal fully convolutional model named MSTFCM. First, the model includes the interaction tensor and raster tensor as task inputs, where the interaction tensor extends the raster tensor by incorporating global interaction patterns between regions. Second, a multi-task framework combined spatio-temporal convolutional block was used to learn generalized features and interaction features. A channel spatio-temporal attention is added to adaptively adjust feature weights and capture inter-correlations. To train the MSTFCM, the uncertainty loss was designed as the learnable loss functions, which capture various flow fluctuations, to facilitate multi-task optimization. The proposed model was validated on two real-world traffic datasets collected in Xiamen, China. Experimental results showed that MSTFCM outperformed nine baselines in one-step and multi-step prediction, with slower performance degradation as predicted time intervals and steps increased. We further validated the model's effectiveness through designed variants and visualization results.
Keyword ：
interaction pattern interaction pattern multi-task learning multi-task learning spatio-temporal dependencies spatio-temporal dependencies Traffic flow prediction Traffic flow prediction
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| GB/T 7714 | Qianqian, Zhou , Tu, Ping , Chen, Nan . A multi-task spatio-temporal fully convolutional model incorporating interaction patterns for traffic flow prediction [J]. | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE , 2024 , 39 (1) : 142-180 . |
| MLA | Qianqian, Zhou et al. "A multi-task spatio-temporal fully convolutional model incorporating interaction patterns for traffic flow prediction" . | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 39 . 1 (2024) : 142-180 . |
| APA | Qianqian, Zhou , Tu, Ping , Chen, Nan . A multi-task spatio-temporal fully convolutional model incorporating interaction patterns for traffic flow prediction . | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE , 2024 , 39 (1) , 142-180 . |
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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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