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学者姓名：汪小钦
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Abstract ：
Building type information indicates the functional properties of buildings and plays a crucial role in smart city development and urban socioeconomic activities. Existing methods for classifying building types often face challenges in accurately distinguishing buildings between types while maintaining well-delineated boundaries, especially in complex urban environments. This study introduces a novel framework, i.e., CNN-Transformer cross-attention feature fusion network (CTCFNet), for building type classification from very high resolution remote sensing images. CTCFNet integrates convolutional neural networks (CNNs) and Transformers using an interactive cross-encoder fusion module that enhances semantic feature learning and improves classification accuracy in complex scenarios. We develop an adaptive collaboration optimization module that applies human visual attention mechanisms to enhance the feature representation of building types and boundaries simultaneously. To address the scarcity of datasets in building type classification, we create two new datasets, i.e., the urban building type (UBT) dataset and the town building type (TBT) dataset, for model evaluation. Extensive experiments on these datasets demonstrate that CTCFNet outperforms popular CNNs, Transformers, and dual-encoder methods in identifying building types across various regions, achieving the highest mean intersection over union of 78.20% and 77.11%, F1 scores of 86.83% and 88.22%, and overall accuracy of 95.07% and 95.73% on the UBT and TBT datasets, respectively. We conclude that CTCFNet effectively addresses the challenges of high interclass similarity and intraclass inconsistency in complex scenes, yielding results with well-delineated building boundaries and accurate building types.
Keyword ：
Accuracy Accuracy Architecture Architecture Buildings Buildings Building type classification Building type classification CNN-transformer networks CNN-transformer networks cross-encoder cross-encoder Earth Earth Feature extraction Feature extraction feature interaction feature interaction Optimization Optimization Remote sensing Remote sensing Semantics Semantics Transformers Transformers very high resolution remote sensing very high resolution remote sensing Visualization Visualization
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| GB/T 7714 | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan et al. Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 976-994 . |
| MLA | Zhang, Shaofeng et al. "Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 976-994 . |
| APA | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan , Wang, Xiaoqin , Wu, Qunyong . Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 976-994 . |
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Building change detection is essential to many applications, such as monitoring of urban areas, land use management, and illegal building detection. It has been seen as an effective means to detect building changes from remote-sensing images. This paper proposes an object-based Siamese neural network, labeled as Obj-SiamNet, to detect building changes from high-resolution remote-sensing images. We combine the advantages of object-based image analysis methods and Siamese neural networks to improve the geometric accuracies of detected boundaries. Moreover, we implement the Obj-SiamNet at multiple segmentation levels and automatically construct a set of fuzzy measures to fuse the obtained results at multi-levels. Furthermore, we use generative adversarial methods to generate target-like training samples from publicly available datasets and construct a relatively sufficient training dataset for the Obj-SiamNet model. Finally, we apply the proposed method into three high-resolution remote-sensing datasets, i.e., a GF-2 image-pair in Fuzhou City, and a GF2 image pair in Pucheng County, and a GF-2—GF-7 image pair in Quanzhou City. We also compare the proposed method with three other existing ones, namely, STANet, ChangeNet, and Siam-NestedUNet. Experimental results show that the proposed method performs better than the other three in terms of detection accuracy. (1) Compared with the detection results from single-scale segmentation, the detection results from multi-scale increases the recall rate by up to 32%, the F1-Score increases by up to 25%, and the Global Total Classification error (GTC) decreases by up to 7%. (2) When the number of available samples is limited, the adopted Generative Adversarial Network (GAN) is able to generate effective target-like samples for diverting samples. Compared with the detection without using GAN-generated samples, the proposed detection increases the recall rate by up to 16%, increases the F1-Score by up to 14%, and decreases GTC by 9%. (3) Compared with other change-detection methods, the proposed method improves the detection accuracies significantly, i.e., the F1-Score increases by up to 23%, and GTC decreases by up to 9%. Moreover, the boundaries of the detected changes by the proposed method have a high consistency with that of ground truth. We conclude that the proposed Obj-SiamNet method has a high potential for building change detection from high-resolution remote-sensing images. © 2024 Science Press. All rights reserved.
Keyword ：
Change detection Change detection Fuzzy sets Fuzzy sets Generative adversarial networks Generative adversarial networks Image enhancement Image enhancement Land use Land use Object detection Object detection Remote sensing Remote sensing
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| GB/T 7714 | Liu, Xuanguang , Li, Mengmeng , Wang, Xiaoqin et al. Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images [J]. | National Remote Sensing Bulletin , 2024 , 28 (2) : 437-454 . |
| MLA | Liu, Xuanguang et al. "Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images" . | National Remote Sensing Bulletin 28 . 2 (2024) : 437-454 . |
| APA | Liu, Xuanguang , Li, Mengmeng , Wang, Xiaoqin , Zhang, Zhenchao . Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images . | National Remote Sensing Bulletin , 2024 , 28 (2) , 437-454 . |
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Evaluating the ecological health of Fujian Province, the first pilot demonstration zone and experimental area for ecological civilization in China, would contribute to the advancement of the ecological civilization process. The critical challenges in ecological health assessment are how to construct a rational indicator system and objective⁃ ly select crucial evaluation indicators that align with the research area. Grounded in the pressure⁃state⁃response (PSR) framework and ecological hierarchical network (EHN) framework, coupled with a quantitative screening model of network indicator system based on goal optimization theory, we calculated the ecological health composite index (EHCI) for Fujian Province from 2001 to 2021, and analyzed its spatial and temporal dynamics. The results showed that: (1) The final indicators obtained through the indicator system and screening model effectively cap⁃ tured ecosystem characteristics of the study area. The spatial variation of calculated ecological health status aligned with existing literature and real⁃world conditions, affirming the rationality of the constructed indicator system and screening model for the effective construction of the indicator system and objective selection of key indicators. (2) Over the two decades, Fujian Province had maintained a favorable ecological health status, with over 82% of the area being classified as excellent. The overall spatial distribution of ecological health status exhibited a characteristic pattern of high values in inland and low values in coastal areas, showing significant correlations with both human and natural factors. (3) During the 21 years of implementing the ecological province construction in Fujian, the overall ecological health had been improved. The area of ecological improvement surpassed degraded areas, with Zhangzhou, Longyan, and Quanzhou exhibiting the most significant improvements. Ecological improvement was pri⁃ marily attributed to the effectiveness of soil erosion control and the progress of policies and projects related to the ecological environment. © 2024 Ecological Society of China. All rights reserved.
Keyword ：
ecological health assessment ecological health assessment ecological hierarchy network (EHN) ecological hierarchy network (EHN) quantitative screening model of network indicator system quantitative screening model of network indicator system weight assignment weight assignment
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| GB/T 7714 | Chen, J. , Wang, X. , Lin, M. et al. Analysis and evaluation of changes in ecological health status in Fujian Province from 2001 to 2021; [2001-2021 年福建省生态健康状况变化分析与评估] [J]. | Chinese Journal of Ecology , 2024 , 43 (11) : 3443-3455 . |
| MLA | Chen, J. et al. "Analysis and evaluation of changes in ecological health status in Fujian Province from 2001 to 2021; [2001-2021 年福建省生态健康状况变化分析与评估]" . | Chinese Journal of Ecology 43 . 11 (2024) : 3443-3455 . |
| APA | Chen, J. , Wang, X. , Lin, M. , Sun, W. . Analysis and evaluation of changes in ecological health status in Fujian Province from 2001 to 2021; [2001-2021 年福建省生态健康状况变化分析与评估] . | Chinese Journal of Ecology , 2024 , 43 (11) , 3443-3455 . |
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[目的]模拟未来土地利用和气候影响下的流域水沙变化有利于制定适合的流域管理计划。[方法]基于土地利用和气象数据，结合CMIP6气候模式数据、PLUS模型和SWAT模型，定量模拟2030年土地利用及不同气候情景下径流和泥沙的时空变化。[结果](1)SWAT模型在闽江流域月尺度模拟精度较好，其中径流模拟的R~2范围为0.80～0.95,NSE范围为0.75～0.91;泥沙模拟的R~2范围为0.75～0.98,NSE范围为0.64～0.94。(2)利用2020年土地利用数据对PLUS模型进行精度评估的Kappa系数为0.77,模拟2030年闽江流域建设用地和耕地将分别增加325.64,1 157.51 km~2。(3)SSP2-4.5和SSP5-8.5情景下，2025—2035年平均降水量分别增加0.15%和2.18%,年平均气温分别增加0.23,0.62℃。(4)低碳情景和高碳情景下，仅土地利用变化导致年平均径流量相较于基准期分别增加0.08%和0.07%,年平均输沙量分别增加0.24%和减少0.05%;仅气候变化导致年平均径流量相较基准期分别减少4.76%和4.11%,年平均输沙量分别增加18.12%和0.13%;土地利用和气候综合影响导致年平均径流量相较于基准期分别减少4.57%和3.93%,年平均输沙量分别增加18.28%和0.33%。(5)未来气候和土地利用综合变化情景下，地表径流和产沙量较高且增幅较大的区域集中在以南平邵武市为中心的流域西北部和以三明将乐县为中心的流域西南部。[结论]研究结果为未来闽江流域的合理开发建设提供一定参考依据。
Keyword ：
土地利用变化 土地利用变化 径流 径流 模拟 模拟 气候情景 气候情景 输沙量 输沙量 闽江流域 闽江流域
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| GB/T 7714 | 余文广 , 陈芸芝 , 唐丽芳 et al. 气候和土地利用变化情景下闽江流域水沙变化模拟 [J]. | 水土保持学报 , 2024 , 38 (02) : 216-233,245 . |
| MLA | 余文广 et al. "气候和土地利用变化情景下闽江流域水沙变化模拟" . | 水土保持学报 38 . 02 (2024) : 216-233,245 . |
| APA | 余文广 , 陈芸芝 , 唐丽芳 , 汪小钦 . 气候和土地利用变化情景下闽江流域水沙变化模拟 . | 水土保持学报 , 2024 , 38 (02) , 216-233,245 . |
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生态环境健康评价对于促进生态保护、引导区域经济发展战略、调整和衡量生态文明建设结果具有重要意义。综合指标体系模型是现今国内外主流的评价方法，然而，如何构建不同地区通用、普适性强的指标体系，如何从众多繁杂的指标通过客观、科学的方法自动筛选出能表征研究区特点的重要指标是目前所面临的难点。本文集成压力-状态-响应模型（PressureState-Response,PSR）和生态层次网络模型（Ecological Hierarchy Network,EHN），并考虑部分指标所存在的信息重叠，建立了目标层-准则层-要素层-指标层-同类指标层的5层网状指标体系，提出了基于优劣解距离法（Technique for Order Preference by Similarity to Ideal Solution,TOPSIS）的同类指标层约简和基于目标优化理论的指标层约简相结合的两段式自适应指标约简模式。结合两者完成面向生态环境健康评价的自适应指标约简模型的构建，并在地理大数据的支持下，应用于云南、福建、京津冀、陕西、湖北、新疆和吉林7个生态环境迥异区域的2001—2021年生态环境健康评价。研究结果表明：(1)利用两段式自适应指标约简模型所筛选出的中选指标可以较好地体现不同地区生态系统特点，中选指标中权重靠前的指标被较多文献应用于各地区指标体系构建，说明所构建的指标体系和两段式自适应指标约简模型具有较好的普适性和合理性，有效避免了人为指标体系构建的主观性；(2) 7个地区生态环境健康状况的空间分布和时间变化趋势符合实际情况，并且能与现有的文献、资料进行互相印证，从侧面证实了本文所提出模型的有效性。本文所提出的模型可为其他领域指标体系构建和筛选提供参考，也为大范围不同区域的生态环境健康评价提供方法支撑。
Keyword ：
CRITIC法 CRITIC法 优劣解距离法 优劣解距离法 压力-状态-响应模型 压力-状态-响应模型 指标约简 指标约简 最大化偏差模型 最大化偏差模型 熵权法 熵权法 生态层次网络模型 生态层次网络模型 目标优化模型 目标优化模型 网状指标体系 网状指标体系
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| GB/T 7714 | 陈建辉 , 汪小钦 , 孔令凤 . 面向生态环境健康评价的自适应指标约简模型构建 [J]. | 地球信息科学学报 , 2024 , 26 (05) : 1193-1211 . |
| MLA | 陈建辉 et al. "面向生态环境健康评价的自适应指标约简模型构建" . | 地球信息科学学报 26 . 05 (2024) : 1193-1211 . |
| APA | 陈建辉 , 汪小钦 , 孔令凤 . 面向生态环境健康评价的自适应指标约简模型构建 . | 地球信息科学学报 , 2024 , 26 (05) , 1193-1211 . |
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福建省作为首个生态文明先行示范区和生态文明试验区,对其进行生态健康评价有助于生态文明进程的推进.目前,在生态健康评价领域中如何构建合理的指标体系及如何客观筛选出符合研究区的重要评价指标,是决定生态健康评价结果的关键问题.本文以压力-状态-响应(PSR)框架和生态等级网络框架(EHN)为基础,结合基于目标优化理论的网状指标定量化筛选模型,计算了福建省2001-2021年的生态健康综合指标(EHCI),分析了自生态省战略实施21年来其空间分布及时间变化趋势.结果表明:(1)利用指标体系和指标筛选模型获得的最终指标能够较好体现研究区的生态系统特点,且所计算的生态健康状况的空间分布与现有文献和实际情况相符,说明所构建的指标体系和指标筛选模型是合理的;(2)福建省多年来生态健康状况良好,优良面积超过82％;空间分布总体呈现出内陆高、沿海低的特征,全省生态健康状况的空间分布与人文和自然都有着显著的相关性;(3)福建省实施生态省建设21年来,全省生态健康状况趋于改善,生态改善区域面积远高于退化区域,其中漳州市、龙岩市与泉州市改善最为明显,生态改善主要得益于水土流失治理成效及生态环境相关政策和项目的推进.
Keyword ：
权重分配 权重分配 生态健康评价 生态健康评价 生态等级网络框架(EHN) 生态等级网络框架(EHN) 网状指标定量化筛选模型 网状指标定量化筛选模型
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| GB/T 7714 | 陈建辉 , 汪小钦 , 林梦婧 et al. 2001-2021年福建省生态健康状况变化分析与评估 [J]. | 生态学杂志 , 2024 , 43 (11) : 3443-3455 . |
| MLA | 陈建辉 et al. "2001-2021年福建省生态健康状况变化分析与评估" . | 生态学杂志 43 . 11 (2024) : 3443-3455 . |
| APA | 陈建辉 , 汪小钦 , 林梦婧 , 孙为静 . 2001-2021年福建省生态健康状况变化分析与评估 . | 生态学杂志 , 2024 , 43 (11) , 3443-3455 . |
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Accurately delineating sediment export dynamics using high-quality vegetation factors remains challenging due to the spatio-temporal resolution imbalance of single remote sensing data and persistent cloud contamination. To address these challenges, this study proposed a new framework for estimating and analyzing monthly sediment inflow to rivers in the cloud-prone Minjiang River Basin. We leveraged multi-source remote sensing data and the Continuous Change Detection and Classification model to reconstruct monthly vegetation factors at 30 m resolution. Then, we integrated the Chinese Soil Loss Equation model and the Sediment Delivery Ratio module to estimate monthly sediment inflow to rivers. Lastly, the Optimal Parameters-based Geographical Detector model was harnessed to identify factors affecting sediment export. The results indicated that: (1) The simulated sediment transport modulus showed a strong Coefficient of Determination (R2 = 0.73) and a satisfactory Nash-Sutcliffe Efficiency coefficient (0.53) compared to observed values. (2) The annual sediment inflow to rivers exhibited a spatial distribution characterized by lower levels in the west and higher in the east. The monthly average sediment value from 2016 to 2021 was notably high from March to July, while relatively low from October to January. (3) Erosive rainfall was a decisive factor contributing to increased sediment entering the rivers. Vegetation factors, manifested via the quantity (Fractional Vegetation Cover) and quality (Leaf Area Index and Net Primary Productivity) of vegetation, exert a pivotal influence on diminishing sediment export.
Keyword ：
Chinese soil loss equation Chinese soil loss equation cloud-prone regions cloud-prone regions monthly remote sensing vegetation index monthly remote sensing vegetation index optimal parameters-based geographical detector optimal parameters-based geographical detector sediment delivery ratio sediment delivery ratio sediment inflow to rivers sediment inflow to rivers
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| GB/T 7714 | Wang, Xiaoqin , Yu, Zhichao , Li, Lin et al. Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China [J]. | WATER , 2024 , 16 (22) . |
| MLA | Wang, Xiaoqin et al. "Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China" . | WATER 16 . 22 (2024) . |
| APA | Wang, Xiaoqin , Yu, Zhichao , Li, Lin , Li, Mengmeng , Lin, Jinglan , Tang, Lifang et al. Unveiling the Intra-Annual and Inter-Annual Spatio-Temporal Dynamics of Sediment Inflow to Rivers and Driving Factors in Cloud-Prone Regions: A Case Study in Minjiang River Basin, China . | WATER , 2024 , 16 (22) . |
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The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.
Keyword ：
ecological evaluation ecological evaluation fujian fujian GEI (Geospatial Ecological Index) GEI (Geospatial Ecological Index) geometric space geometric space soil line soil line
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| GB/T 7714 | Lin, Mengjing , Zhao, Yang , Shi, Longyu et al. A novel ecological evaluation index based on geospatial principles and remote sensing techniques [J]. | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 , 31 (7) : 809-826 . |
| MLA | Lin, Mengjing et al. "A novel ecological evaluation index based on geospatial principles and remote sensing techniques" . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY 31 . 7 (2024) : 809-826 . |
| APA | Lin, Mengjing , Zhao, Yang , Shi, Longyu , Wang, Xiaoqin . A novel ecological evaluation index based on geospatial principles and remote sensing techniques . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 , 31 (7) , 809-826 . |
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建筑物变化检测在城市环境监测、土地规划管理和违章违规建筑识别等应用中具有重要作用。针对传统孪生神经网络在影像变化检测中存在的检测边界与实际边界吻合度低的问题，本文结合面向对象图像分析技术，提出一种基于面向对象孪生神经网络（Obj-SiamNet）的高分辨率遥感影像变化检测方法，利用模糊集理论自动融合多尺度变化检测结果，并通过生成对抗网络实现训练样本迁移。该方法应用在高分二号和高分七号高分辨率卫星影像中，并与基于时空自注意力的变化检测模型（STANet）、视觉变化检测网络（ChangeNet）和孪生UNet神经网络模型（Siam-NestedUNet）进行比较。结果表明：（1）融合面向对象多尺度分割的检测结果较单一尺度分割的检测结果，召回率最高提升32%,F1指数最高提升25%，全局总体误差（GTC）最高降低7%;(2)在样本数量有限的情况下，通过生成对抗网络进行样本迁移，与未使用样本迁移前的检测结果相比，召回率最高提升16%,F1指数最高提升14%,GTC降低了9%;(3) Obj-SiamNet方法较其他变化检测方法，整体检测精度得到提升，F1指数最高提升23%,GTC最高降低9%。该方法有效提高了建筑物变化检测在几何和属性方面的精度，并能有效利用开放地理数据集，降低了模型训练样本制作成本，提升了检测效率和适用性。
Keyword ：
孪生神经网络 孪生神经网络 模糊集融合 模糊集融合 生成对抗网络 生成对抗网络 遥感变化检测 遥感变化检测 面向对象多尺度分析 面向对象多尺度分析 高分辨率遥感影像 高分辨率遥感影像
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| GB/T 7714 | 刘宣广 , 李蒙蒙 , 汪小钦 et al. 基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测 [J]. | 遥感学报 , 2024 , 28 (02) : 437-454 . |
| MLA | 刘宣广 et al. "基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测" . | 遥感学报 28 . 02 (2024) : 437-454 . |
| APA | 刘宣广 , 李蒙蒙 , 汪小钦 , 张振超 . 基于面向对象孪生神经网络的高分辨率遥感影像建筑物变化检测 . | 遥感学报 , 2024 , 28 (02) , 437-454 . |
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Abstract ：
Soil erosion constitutes a critical environmental issue with far-reaching ramifications. Vegetation cover has been identified as a key factor in mitigating soil erosion. This study utilized remote sensing data and cloud computing resources provided by Google Earth Engine (GEE) to compute the land cover classification and Fractional Vegetation Cover (FVC) of the Minjiang River Basin in 2020. Subsequent to the utilization of the CSLE model incorporating precipitation data, Digital Elevation Model (DEM) data, and other relevant data, an assessment of soil erosion in the Minjiang River basin during 2020 was conducted. Furthermore, the correlation between FVC and soil erosion modulus was quantitatively examined. Our findings demonstrate that the predominant land cover type in the Minjiang River basin is forest and grassland, followed by arable land, with water having the smallest coverage. Over 65%of the study are a exhibits a FVC exceeding 0.8, indicative of a generally high level of vegetation coverage. The soil erosion modulus exhibited a marked decline with increasing FVC within the FVC intervals of 0-0.15 and 0.45-0.8. While the change of soil erosion modulus with FVC under other ranges was relatively flat. As such, erosion control measures may be more effective when implemented in the FVC ranges of 0-0.15 and 0.45-0.80. These findings provide decision-making references for governmental departments to formulate targeted soil and water conservation measures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Keyword ：
Decision making Decision making Erosion Erosion Forestry Forestry Remote sensing Remote sensing Rivers Rivers Soil conservation Soil conservation Soils Soils Surveying Surveying Vegetation Vegetation Water conservation Water conservation Watersheds Watersheds
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| GB/T 7714 | Chen, Miao , Wang, Xiaoqin . Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology [C] . 2024 . |
| MLA | Chen, Miao et al. "Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology" . (2024) . |
| APA | Chen, Miao , Wang, Xiaoqin . Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology . (2024) . |
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