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学者姓名：路春燕
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Abstract ：
As a unique land-sea transitional wetland, mangroves possess high ecosystem service value. Monitoring mangroves in cross-border areas is crucial for shaping bilateral protection policies and guiding effective management decisions. This study examines the spatio-temporal dynamics of mangroves in the Beibu Gulf, situated in the China-Vietnam cross-border region, utilizing long-term Landsat imagery and an object-oriented deep learning classification technique from 1991 to 2021. Combining land cover change, landscape indices, and spatial analysis, the spatio-temporal dynamics of mangroves were quantitatively evaluated. Results reveal that the object-oriented deep learning classification method significantly enhances mapping accuracy and integrality of the classification results. During 1991-2021, the mangrove area in the Beibu Gulf coastal zone (BGCZ) increased by 92.11 km2, with annual increases of 0.82 km2 in the China part and 2.25 km2 in the Vietnam part. Mangroves connectivity improved in both the China and Vietnam parts, though the degree of disturbance was more pronounced in the Vietnam part compared to the China part. Mangrove dynamics are influenced by numerous factors, with aquaculture being the primary cause of mangrove fragmentation in the BGCZ. Varied management mechanisms and coastal zone development models in China and Vietnam have exerted influence on the dynamic evolution of mangroves. This study offers a significant scientific foundation for future cross-border mangrove conservation and management.
Keyword ：
Beibu Gulf Beibu Gulf China-Vietnam cross-border China-Vietnam cross-border Long-term remote sensing observation Long-term remote sensing observation Mangrove dynamics Mangrove dynamics Object-oriented deep learning classification Object-oriented deep learning classification
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| GB/T 7714 | Gao, Wenna , Lu, Chunyan , Yang, Nuocheng et al. Cross-border mangrove dynamics and management in the Beibu Gulf: Long-term remote sensing observation using object-oriented deep learning [J]. | ECOLOGICAL INDICATORS , 2025 , 170 . |
| MLA | Gao, Wenna et al. "Cross-border mangrove dynamics and management in the Beibu Gulf: Long-term remote sensing observation using object-oriented deep learning" . | ECOLOGICAL INDICATORS 170 (2025) . |
| APA | Gao, Wenna , Lu, Chunyan , Yang, Nuocheng , Wu, Yuqi , Wu, Kexin , Chen, Zhangjuan . Cross-border mangrove dynamics and management in the Beibu Gulf: Long-term remote sensing observation using object-oriented deep learning . | ECOLOGICAL INDICATORS , 2025 , 170 . |
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Investigating the urban heat island (UHI) effect across different seasons can reveal the impact of seasonal temperature changes on the urban environment, improve the living standard, and develop feasible measures to mitigate UHI effects. In the study, the land surface temperature (LST) of summer and winter was quantitatively retrieved in a megacity (Shenzhen city) during 2013-2023. Through the standard deviation ellipse, profile analysis and the GeoDetector model, this study systematically analyzed the spatial-temporal evolution characteristics and driving factors of the urban thermal environment in summer and winter. The results showed that summer LST initially decreased and then increased from 2013 to 2023, while winter LST consistently increased. During the study, the summer UHI area decreased, whereas the winter UHI area increased. The LST of green land, wetland, and natural open water were lower than that of cropland, unvegetated land and artificial surface, demonstrating that water and vegetation had a mitigating effect on UHI. Land cover type interactions with normalized difference built-up index (summer) and PM2.5 (winter) have the most significant influence on UHI. This study could provide scientific references for rational urban planning and sustainable urban development.
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| GB/T 7714 | Yang, Nuocheng , Lu, Chunyan , Ouyang, Ling et al. Exploring spatial-temporal evolution patterns of urban heat islands in summer and winter: evidence from a megacity of China [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Yang, Nuocheng et al. "Exploring spatial-temporal evolution patterns of urban heat islands in summer and winter: evidence from a megacity of China" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Yang, Nuocheng , Lu, Chunyan , Ouyang, Ling , Chen, Riqing , Man, Weidong , Wang, Zili et al. Exploring spatial-temporal evolution patterns of urban heat islands in summer and winter: evidence from a megacity of China . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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Mapping and monitoring of mangrove species based on remote sensing technology play a crucial role in biodiversity conservation and management. This paper employs CiteSpace to visualize the literature and presents a comprehensive review of the researches conducted in this domain, focusing primarily on bibliometric characteristics, diverse sensors, and classification algorithms. Since the publication of the first remote sensing-based study on mangrove species classification in 2004, the number of publications in this field has exhibited a general upward trend up to 2023. China, the United States, and India lead in publishing research on mangrove species mapping, with researchers in the United States being particularly active in international collaborations. Mapping of mangrove species is predominantly concentrated on single time points and across 53 small regions, with the majority of research sites located in India and China. Existing studies have utilized various remote sensing image for mangrove species classification, including airborne hyperspectral, spaceborne visible, infrared, multispectral, hyperspectral, synthetic aperture radar (SAR), and drone-borne visible, infrared, multispectral, hyperspectral, light detection and ranging (LiDAR) data. Classification algorithm development has evolved four stages, from pixel-based methods to object-oriented approaches, progressing to approaches incorporating machine learning algorithms, and currently advancing towards ensemble learning and deep learning. Research in this field still faces several challenges in data fusion, classification algorithm enhancement, increased number of classification species, and large-scale long-term mapping. The studys findings would provide valuable guidance to researchers and practitioners in advancing and enhancing the management and conservation of mangroves.
Keyword ：
Bibliometric analysis Bibliometric analysis CiteSpace CiteSpace Mangroves Mangroves Remote sensing Remote sensing Species mapping Species mapping
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| GB/T 7714 | Wu, Yuqi , Lu, Chunyan , Wu, Kexin et al. Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization [J]. | GLOBAL ECOLOGY AND CONSERVATION , 2025 , 57 . |
| MLA | Wu, Yuqi et al. "Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization" . | GLOBAL ECOLOGY AND CONSERVATION 57 (2025) . |
| APA | Wu, Yuqi , Lu, Chunyan , Wu, Kexin , Gao, Wenna , Yang, Nuocheng , Lin, Jingwen . Advancements and trends in mangrove species mapping based on remote sensing: A comprehensive review and knowledge visualization . | GLOBAL ECOLOGY AND CONSERVATION , 2025 , 57 . |
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Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become in-creasingly prominent.In order to promote urban sustainable development and improve the quality of human settlements,it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces.Taking the Landsat series im-ages as the basic data sources,the winter land surface temperature(LST)of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021.Combing comprehensively the standard deviation ellipse model,profile analysis and GeoDe-tector model,the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were system-atically analyzed.The results showed that the winter LST presented an increasing trend in the study area during 2001-2021,and the winter LST of the central urban regions was significantly higher than the suburbs.There was a strong UHI effect from 2001 to 2021 with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale.The LST of green lands and wet-lands are significantly lower than croplands,artificial surface and unvegetated lands.Vegetation and water bodies had a significant mit-igation effect on UHI,especially in the micro-scale.The winter UHI had been jointly driven by the underlying surface and socio-eco-nomic factors in a nonlinear or two-factor interactive enhancement mode,and socio-economic factors had played a leading role.This re-search could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.
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| GB/T 7714 | WANG Zili , LU Chunyan , SU Yanlin et al. Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island:A Case Study of Rapid Urbanization Area of Fuzhou City,China [J]. | 中国地理科学（英文版） , 2024 , 34 (1) : 135-148 . |
| MLA | WANG Zili et al. "Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island:A Case Study of Rapid Urbanization Area of Fuzhou City,China" . | 中国地理科学（英文版） 34 . 1 (2024) : 135-148 . |
| APA | WANG Zili , LU Chunyan , SU Yanlin , SU Yue , YU Qianru , LI Wenzhe et al. Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island:A Case Study of Rapid Urbanization Area of Fuzhou City,China . | 中国地理科学（英文版） , 2024 , 34 (1) , 135-148 . |
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Accurately quantifying functional traits across large scales is considered fundamental for the management and conservation of existing mangrove ecosystems. In recent years, hybrid models, which combine radiative transfer model simulations with machine learning regression algorithms (MLRA), have been effectively employed in satellite-based estimations of plant functional traits across diverse ecosystems. Nevertheless, the inevitable data redundancy stemming from heavy-parameterization radiative transfer models restricts the application of the hybrid model. Previous studies have indicated that active learning (AL) strategies can mitigate this redundancy through smart sampling selection criteria. While many studies have attempted to investigate mangrove functional traits using various models, there is limited understanding of the performance of hybrid models coupled with active learning strategies in retrieving the traits. In recent years, Sentinel-2 has become mainstream for retrieving detailed and reliable information across diverse ecosystems. The aim of this study is to utilize a retrieval scheme to extract four mangrove functional traits from Sentinel-2 imagery: leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter content (Cm), and leaf equivalent water thickness (Cw). In order to achieve this goal, we systematically evaluated 36 different MLRA-AL models, which were combinations of six MLRAs and six AL strategies. Retrieval results showed that GPR (Gaussian processes regression)-ABD (anglebased diversity) and GPR-PAL (variance-based pool of regressors) yielded the highest accuracies for LAI (R 2 = 0.68, NRMSE = 10.488 %) and Cw (R 2 = 0.47, NRMSE =13.868 %), respectively. GPR-EBD (Euclidean distancebased diversity) had the highest accuracies of Cm (R 2 = 0.54, NRMSE = 11.695 %) and Cab (R 2 = 0.71, NRMSE = 13.764 %). The retrieval models were subsequently applied to produce distribution pattern maps of four mangrove functional traits within a Ramsar site. This study represents the first attempt to utilize AL strategies to enhance the efficiency of traditional hybrid models and map multiple functional traits of mangrove forests. The retrieval scheme and mapping results could significantly contribute to the management of mangrove ecosystems and provide a fundamental data source for future research on the ecological services of mangroves.
Keyword ：
Active learning Active learning Machine learning regression algorithm Machine learning regression algorithm Mangrove functional traits Mangrove functional traits Radiative transfer model Radiative transfer model Sentinel-2 Sentinel-2
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| GB/T 7714 | Jia, Mingming , Guo, Xianxian , Zhang, Lin et al. Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 130 . |
| MLA | Jia, Mingming et al. "Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 130 (2024) . |
| APA | Jia, Mingming , Guo, Xianxian , Zhang, Lin , Wang, Mao , Wang, Wenqing , Lu, Chunyan et al. Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 130 . |
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Under the influence of anthropogenic and climate change, the problems caused by urban heat island (UHI) has become increasingly prominent. In order to promote urban sustainable development and improve the quality of human settlements, it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces. Taking the Landsat series images as the basic data sources, the winter land surface temperature (LST) of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021. Combing comprehensively the standard deviation ellipse model, profile analysis and GeoDetector model, the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed. The results showed that the winter LST presented an increasing trend in the study area during 2001-2021, and the winter LST of the central urban regions was significantly higher than the suburbs. There was a strong UHI effect from 2001 to 2021 with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale. The LST of green lands and wetlands are significantly lower than croplands, artificial surface and unvegetated lands. Vegetation and water bodies had a significant mitigation effect on UHI, especially in the micro-scale. The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode, and socio-economic factors had played a leading role. This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.
Keyword ：
Fuzhou City, China Fuzhou City, China GeoDetector model GeoDetector model land surface temperature (LST) retrieval land surface temperature (LST) retrieval profile analysis profile analysis rapid urbanization area rapid urbanization area winter urban heat island (UHI) winter urban heat island (UHI)
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| GB/T 7714 | Wang, Zili , Lu, Chunyan , Su, Yanlin et al. Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island: A Case Study of Rapid Urbanization Area of Fuzhou City, China [J]. | CHINESE GEOGRAPHICAL SCIENCE , 2024 , 34 (1) : 135-148 . |
| MLA | Wang, Zili et al. "Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island: A Case Study of Rapid Urbanization Area of Fuzhou City, China" . | CHINESE GEOGRAPHICAL SCIENCE 34 . 1 (2024) : 135-148 . |
| APA | Wang, Zili , Lu, Chunyan , Su, Yanlin , Su, Yue , Yu, Qianru , Li, Wenzhe et al. Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island: A Case Study of Rapid Urbanization Area of Fuzhou City, China . | CHINESE GEOGRAPHICAL SCIENCE , 2024 , 34 (1) , 135-148 . |
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Plant functional traits are key variables for assessing vegetation’s dynamic response to changing pressures. Although mangroves are one of the most vulnerable ecosystems, the methodology for retrieving their functional traits is still unclear. In recent years, hybrid retrieval schemes, which combine radiative transfer model simulations with machine learning regression algorithm (MLRA), have been effectively employed in satellite-based estimations of plant functional traits in other ecosystems. However, inevitable data redundancy caused by heavy-parameterization radiative transfer model restricts the application of the hybrid model. Previous studies have indicated that active learning (AL) strategies can mitigate this redundancy through smart sampling selection criteria. Nowadays, Sentinel-2 is becoming mainstream for retrieving detailed and reliable information across diverse ecosystems. This study aims to use hybrid models coupling with AL strategies to retrieve four mangrove functional traits from Sentinel-2 imagery, i.e., leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter content (Cm), and leaf equivalent water thickness (Cw). In order to achieve the goal, we systematically evaluated 36 different MLRA-AL models, i.e., the combinations of six MLRAs and six AL strategies. Retrieval results showed that GPR (Gaussian processes regression)-ABD (angle-based diversity) and GPR-PAL (variance-based pool of regressors) yielded the highest accuracies for LAI (R2 = 0.68, NRMSE = 10.49%) and Cw (R2 = 0.47, NRMSE = 13.87%), respectively; GPR-EBD (Euclidean distance-based diversity) gained the highest accuracies of Cm (R2 = 0.54, NRMSE = 11.70%) and Cab (R2 = 0.71, NRMSE = 13.76%). These retrieval models were then applied to produce distribution pattern maps of four mangrove functional traits in a Ramsar site. This study conducts the first attempt to utilize AL strategies to improve the efficiency of the traditional hybrid model and mapped multiple functional traits of mangrove forests. The retrieval scheme and mapping results could facilitate the management of mangrove ecosystem and provide basic information for comprehending mangrove biodiversity-ecosystem functioning relationship. © 2024, The Authors. All rights reserved.
Keyword ：
Biodiversity Biodiversity Ecosystems Ecosystems Forestry Forestry Learning algorithms Learning algorithms Learning systems Learning systems Machine learning Machine learning Mapping Mapping Radiative transfer Radiative transfer Redundancy Redundancy
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| GB/T 7714 | Jia, Mingming , Guo, Xianxian , Zhang, Lin et al. Mapping Mangrove Functional Traits From Sentinel-2 Imagery Based on Hybrid Models Coupling with Active Learning Strategies [J]. | SSRN , 2024 . |
| MLA | Jia, Mingming et al. "Mapping Mangrove Functional Traits From Sentinel-2 Imagery Based on Hybrid Models Coupling with Active Learning Strategies" . | SSRN (2024) . |
| APA | Jia, Mingming , Guo, Xianxian , Zhang, Lin , Wang, Mao , Wang, Wenqing , Lu, Chunyan et al. Mapping Mangrove Functional Traits From Sentinel-2 Imagery Based on Hybrid Models Coupling with Active Learning Strategies . | SSRN , 2024 . |
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本发明公开了基于厘米级无人机可见光影像的互花米草除治监测方法，本方案基于无人机可见光影像实现已除治区域互花米草信息提取，该方案可有效克服天气情况与潮汐现象对影像成像的影响，较光学卫星影像在数据获取方面时效性和精度大大提高；同时，无人机可见光影像空间分辨率高达厘米级，相对于现有光学卫星遥感影像可细致刻画互花米草的结构与纹理特征；对无人机可见光影像先后进行大尺度棋盘分割与小尺度多尺度分割两次面向对象分割，较直接进行小尺度多尺度面向对象分割更省时，分割效率更高；此外，将分类结果进行网格化处理，利用单位面积覆盖度形式对互花米草除治监测结果进行表达，可对接互花米草除治后期监管工作，为除治工作提供有效辅助。
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| GB/T 7714 | 路春燕 , 赵国帅 , 黄朝法 et al. 基于厘米级无人机可见光影像的互花米草除治监测方法 : CN202311309409.7[P]. | 2023-10-10 . |
| MLA | 路春燕 et al. "基于厘米级无人机可见光影像的互花米草除治监测方法" : CN202311309409.7. | 2023-10-10 . |
| APA | 路春燕 , 赵国帅 , 黄朝法 , 王自立 , 李文哲 , 王梓旭 . 基于厘米级无人机可见光影像的互花米草除治监测方法 : CN202311309409.7. | 2023-10-10 . |
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红树林作为重要的生态系统，监测其动态变化对制定保护政策至关重要。以漳江口红树林—互花米草盐沼交错区为研究对象，基于2005、2011、2015、2017、2019和2021年的Google Earth（GE）影像，采用土地利用动态度、转移矩阵、景观指数、标准差椭圆法及人为干扰度，分析其动态变化，并结合气温、降水等数据探讨驱动因素。研究结果表明：（1）2005—2021年漳江口红树林和互花米草面积分别增加23.3hm
Keyword ：
互花米草 互花米草 时空演变 时空演变 景观格局 景观格局 漳江口 漳江口 红树林 红树林 驱动因素 驱动因素
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| GB/T 7714 | 吴可欣 , 高雯娜 , 赵国帅 et al. 漳江口红树林—互花米草盐沼交错区变化监测与驱动因素分析 [J]. | 赤峰学院学报(自然科学版) , 2024 , 40 (11) : 1-7 . |
| MLA | 吴可欣 et al. "漳江口红树林—互花米草盐沼交错区变化监测与驱动因素分析" . | 赤峰学院学报(自然科学版) 40 . 11 (2024) : 1-7 . |
| APA | 吴可欣 , 高雯娜 , 赵国帅 , 张勇 , 吴玉琪 , 杨诺诚 et al. 漳江口红树林—互花米草盐沼交错区变化监测与驱动因素分析 . | 赤峰学院学报(自然科学版) , 2024 , 40 (11) , 1-7 . |
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A digital economy parallel with green development holds profound significance for achieving sustainability. The primary objective of this study was to explore the synergistic interaction effects between the digital economy and green development in China and forecast their future development. This study analyzed the spatio-temporal characteristics of provincial digital economy and green development in China by integrating a combined assignment method, an unconditional spatial kernel density estimation method, and a standard deviation ellipse model. The interplay between the digital economy and green development was examined using a panel vector autoregression model. Additionally, digital economy and green development levels were forecasted using univariate time series and radial basis function kernel epsilon-support vector regression models. The results indicate that both the digital economy and green development levels in China exhibited an upward trend from 2013 to 2021, with the digital economy increasing at a faster rate. However, both domains demonstrated regional disparities in their development processes. The mutual interaction between the digital economy and green development intensified as the lag period increased. The digital economy contributed 21% to green development, whereas green development contributed 18% to the digital economy. The initial effect of the digital economy on green development was negative, however, this impact gradually diminished over time. Additionally, the influence of green development on the digital economy was shown to follow a consistent trend of transitioning from negative to positive across the eastern, central, and western regions. Therefore, it can be seen that the digital economy exerts a sustainable impact on green development, albeit with a one-phase lag. This research provides a scientific basis for the deep integration of the digital economy and green development, thereby fostering sustainable socioeconomic growth.
Keyword ：
digital economy digital economy green development green development panel vector auto regression panel vector auto regression spatio-temporal characteristics spatio-temporal characteristics support vector regression support vector regression
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| GB/T 7714 | Chen, Tingting , Lu, Chunyan , Lai, Yuting et al. Spatio-Temporal Evolution and Interactive Relationship Between Digital Economy and Green Development in China [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (23) . |
| MLA | Chen, Tingting et al. "Spatio-Temporal Evolution and Interactive Relationship Between Digital Economy and Green Development in China" . | APPLIED SCIENCES-BASEL 14 . 23 (2024) . |
| APA | Chen, Tingting , Lu, Chunyan , Lai, Yuting , Zhou, Mengxing , Hu, Qingping , Wang, Tingyan et al. Spatio-Temporal Evolution and Interactive Relationship Between Digital Economy and Green Development in China . | APPLIED SCIENCES-BASEL , 2024 , 14 (23) . |
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