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学者姓名：陈楠
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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 等. "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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植被吸收光合有效辐射(Absorbed Photosynthetically Active Radiation, APAR)是植被进行光合作用中实际吸收的太阳辐射量,是植被净第一性生产力的重要指标,也是生态系统的功能模型、作物生长模型、净初级生产力模型、气候模型等的重要参数。因此高空间分辨率和精确性的植被吸收光合有效辐射对于高精度的区域生产力及光能利用率的研究具有重要意义。对CASA(Carnegie-Ames-Stanford Approach)模型进行了改进,利用30m×30m的数字高程模型(Digital Elevation Model, DEM)数据直接计算太阳辐射,从而将其作为CASA模型的输入参数。结合多源遥感数据、气象数据,研究2015—2020年江汉平原APAR的时空分布及其影响因素。顾及江汉平原的土地利用分布特点,着重分析了江汉平原农田APAR的时空特性,研究结果较好的反映了江汉平原APAR分布。实验结果表明:(1)2015—2020年APAR年总值在3.42×1013MJ—3.73×1013MJ之间,总体空间分布与植被类型的分布情况相符;(2)农田月均APAR值在4月、7月高于其他月份,表现出“双峰”的特征;(3)在空间分布上,水田APAR表现出明显的纬度地带性,而旱地APAR正好相反,这可能源于种植结构重心转移;(4)通过借助地理探测器,着重考虑与植被生长相关的12个因子(包括≧10℃积温、年总日照时数、年均气温、年总降雨量、农田种植结构、年散射辐射、农田施肥、土壤类型、土壤质地(砂土、粉砂土、黏土))进行分析,结果表明这12个因素对APAR空间变异性都具有很明显的影响。对CASA的改进方法可以适用于大范围高空间精度的计算。
Keyword ：
CASA模型 CASA模型 光合有效辐射 光合有效辐射 地理探测器 地理探测器 数字高程模型 数字高程模型 植被吸收光合有效辐射 植被吸收光合有效辐射
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| GB/T 7714 | 林婷敏 , 陈楠 , 林偲蔚 . 2015—2020年植被吸收光合有效辐射的时空特征及影响因素分析 [J]. | 生态科学 , 2024 , 43 (02) : 211-222 . |
| MLA | 林婷敏 等. "2015—2020年植被吸收光合有效辐射的时空特征及影响因素分析" . | 生态科学 43 . 02 (2024) : 211-222 . |
| APA | 林婷敏 , 陈楠 , 林偲蔚 . 2015—2020年植被吸收光合有效辐射的时空特征及影响因素分析 . | 生态科学 , 2024 , 43 (02) , 211-222 . |
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Loess landform attaches importance to surface processes and soil erosion. Taking watersheds as basic landform units, gullies (negative terrain skeletons) were generally quantified for loess landform recognition. However, ridges (positive terrain skeletons) were rarely considered, neglecting their discriminations in telling different loess landform types apart. Considering ridges and gullies in combination, this study proposed Watershed Dual Skeleton Networks (WDSN) for loess landform recognition. Specifically, hydrologic analysis and network theory were applied to extract WDSN. Then, 10 network indices were used to quantify ridges and gullies respectively. With the Light Gradient Boosting Machine (LightGBM), recognition results showed that the WDSN-based approach had a superior performance, achieving an overall accuracy of 93.33% and a Kappa coefficient of 0.90. Compared to single ridge or gully networks and traditional terrain indices, WDSN can express topographic differences more comprehensively among loess tableland, loess ridge and loess hill. © 2024 Copyright held by the owner/author(s).
Keyword ：
Digital elevation model Digital elevation model Landform recognition Landform recognition LightGBM LightGBM Terrain skeleton Terrain skeleton
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| GB/T 7714 | Wang, C. , Chen, N. . Watershed Dual Skeleton Networks for Loess Landform Recognition [未知]. |
| MLA | Wang, C. 等. "Watershed Dual Skeleton Networks for Loess Landform Recognition" [未知]. |
| APA | Wang, C. , Chen, N. . Watershed Dual Skeleton Networks for Loess Landform Recognition [未知]. |
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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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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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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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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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采用光线追踪算法模拟起伏地形下的可照时间时，随着对地形遮蔽状况进行搜索的半径不同将直接影响到可照时间计算的准确性与高效性.本研究基于DEM数据，针对水星独特的轨道运动特征，太阳高度角随水星运动变化缓慢的特点，研究了水星2种典型地貌下不同太阳高度角的搜索半径和基于搜索半径的平均可照时间变化状况.同时构建了以5种影响搜索半径与可照时间的因子作为输入变量，分别以搜索半径与平均可照时间作为输出变量的BP神经网络.模型通过了检验，5种影响因子与搜索半径影响的显著性由高到低：太阳高度角>高程标准差>地形开阔度平均值>地形起伏度>地表粗糙度平均值；与平均可照时间影响的显著性由高到低：太阳高度角>高程标准差>地表粗糙度平均值>地形开阔度平均值>地形起伏度.该模型可为计算水星最搜索半径以及可照时间提供参考.
Keyword ：
BP神经网络 BP神经网络 DEM DEM 可照时长 可照时长 太阳高度角 太阳高度角 搜索半径 搜索半径 水星 水星
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| GB/T 7714 | 范玉贵 , 陈楠 , 林偲蔚 . 水星可照时间与搜索半径影响因素研究 [J]. | 海南大学学报(自然科学版) , 2024 , 42 (01) : 67-77 . |
| MLA | 范玉贵 et al. "水星可照时间与搜索半径影响因素研究" . | 海南大学学报(自然科学版) 42 . 01 (2024) : 67-77 . |
| APA | 范玉贵 , 陈楠 , 林偲蔚 . 水星可照时间与搜索半径影响因素研究 . | 海南大学学报(自然科学版) , 2024 , 42 (01) , 67-77 . |
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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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