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Spatial matching and flow in supply and demand of water provision services: A case study in Xiangjiang River Basin

Journal of Mountain Science 2022年第1期19卷 228-240页

作者： DENG Chu-xiong ZHU Da-mei LIU Yao-jun LI Zhong-wu College of Resources and Environmental Sciences Hunan Normal UniversityChangsha 410081China Hunan Key Laboratory of Geospatial Big Data Mining and Application Changsha 410081China College of Environmental Science and Engineering Hunan UniversityChangsha 410082China

Global climate change and increased human consumption have aggravated the uneven spatiotemporal distribution of watershed water resources, affecting the water provision supply and demand state. However, this problem has often been ignored. The present study used the Xiangjiang River basin(XRB) as the study area, and the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST) model, demand quantification model,supply–demand ratio, and water flow formula were applied to explore the spatial heterogeneity, flow, and equilibrium between water supply and demand. The results demonstrated significant spatial heterogeneity in the upstream, midstream, and downstream *** areas of water shortage were mainly located the downstream of the Changsha–Zhuzhou–Xiangtan urban agglomeration, and the Hengyang basin was the most scarcity area. Affected by terrain gradients and human needs, water flow varied from-16.33 × 10^(8) m^(3) to 13.69 × 10^(8) m^(3)from the upstream to the downstream area, which provided a possibility to reduce spatial heterogeneity. In the future, measures such as strengthening water resource system control,sponge city construction, and dynamic monitoring technology should be taken to balance the supply and demand of water in different river sections of the basin. This study can provide references for regulating water resources allocation in different reaches of the basin.

关键词： Water provision services Supply and demand Spatiotemporal dislocation Water flow Water management and saving policy Xiangjiang River basin

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Social inequity of park accessibility in Taiyuan:highlighting the unfair layout of parks in second-tier cities of China and the relative role of contributors

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地球空间信息科学学报（英文版） 2024年第4期27卷 1118-1140页

作者： Jing He Fu Ren Jiaxin Dong Huihui Zhang Wenhao Yan Jiping Liu School of Resource and Environmental Sciences Wuhan UniversityWuhanChina School of Resource and Environmental Sciences Wuhan UniversityWuhanChina Key Laboratory of Geographic Information Systems Ministry of EducationWuhan UniversityWuhanChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Guangdong Provincial Department of Natural Resources Land and Resources Technology CenterGuangzhouChina Research Center of Geospatial Big Data Application Chinese Academy of Surveying and MappingBeijingChina

The scarcity of public resources and environmental pollution caused by rapid urbanization highlight the practical significance of parks in ensuring the sustainable development of a ***,the social equity of parks warrants further *** paper proposes a fine-grained comprehensive evaluation framework that combines geographic accessibility models,geo-statistical analysis,and machine learning algorithms to explore social inequity in Taiyuan,*** this framework,gini coefficient and lorentz curve express spatial equality,accessibility shows spatial equity,and ridge regression model handles the interdependence of variables with different dimensions to quantify the relative effects of local participants on changes in park *** this basis,the imbalance between vulnerable groups and park supply is analyzed to further understand the core concept of social *** highlight serious spatial inequality in all three types of parks allocation of six urban areas,especially in commu-nity *** actual access level of people to parks is also stratified by their demographic and socioeconomic characteristics,revealing the social inequity in access to *** distribution is indeed not conducive to some social vulnerable groups,whose contradiction between supply and demand is highly prominent in urban-rural junctions and new urban *** paper also confirms the unfair layout of public facilities can be observed in second-tier cities of China by highlighting the social inequity of parks in *** findings of this work have profound implications for urban planning and sustainable development.

关键词： Parks social inequity spatial equality accessibility social vulnerable groups Taiyuan

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Late Mesozoic Tectonic Transfer in the Southern Great Xing'An Range, Ne China: Evidence from Geochronology, Geochemistry and Lu-Hf Isotopes of Magmatic Intrusions in Inner Mongolia

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SSRN 2023年

作者： Zhao, Zhixiong Dong, Guochen Santosh, M. Bao, Xiangping Ren, Yingwei Xie, Zhuolin School of Earth Science and Mineral Resources China University of Geosciences Beijing100083 China Ulanqab Key Laboratory of Geospatial Big Data Application and Environmental Monitoring Jining Normal University Ulanqab012000 China Department of Earth Science University of Adelaide Adelaide5005 Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

The geodynamic background of the Mesozoic magmatism in the Jarud region of Inner Mongolia remains debated. Here we present zircon U-Pb geochronology, geochemistry and Hf isotope data on the Late Jurassic diorite and Early Cretaceous quartz monzonite porphyry and granite porphyry from the Jarud area to constrain the petrogenesis and tectonic setting. Zircon U-Pb data suggest that the multiphase magmatism extended from the Late Jurassic to Early Cretaceous, i.e., 161.0 ± 1.0 Ma (diorite), 129.9 ± 1.4 Ma (quartz monzonite porphyry), and 125.8 ± 1.1 Ma and 126.8 ± 1.1 Ma (granite porphyry). These rocks are characterized by enrichment of light rare earth and large ion lithophile elements. Based on the geochemistry and Hf isotope data, the diorite with elevated crystallization temperatures (1061 to 1098 ℃), and moderate logfO2 (-22.80 to -7.53) is inferred to have formed by low degree partial melting of depleted mantle wedge metasomatized by fluids at a relatively shallow depth. The quartz monzonite porphyry with moderate crystallization temperatures (755 to 839 °C) and high logfO2 (-18.35 to -9.53) was generated by partial melting of lower crust triggered by depleted mantle-derived basaltic magma underplating, subsequent experienced the fractional crystallization of olivine, pyroxene, Fe-Ti oxides and apatite. The granite porphyry with low crystallization temperatures (643 to 821 °C) and logfO2 (-27.59 to -12.40), is classified as S-type granites, and was sourced from greywacke. The results presented in this study indicate that the Late Jurassic magmatism was related to the southward subduction of the Mongol- Okhotsk Ocean, while the Early Cretaceous magmatic rocks were formed in a post-collision extensional setting. © 2023, The Authors. All rights reserved.

关键词： Quartz

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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Gaining water bodies by climate change benefits water crisis mitigation in central Asia

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Science Bulletin 2025年

作者： Su, Yanan Chen, Shengqian Sui, Yijie Li, Xin Xu, Jinhao Che, Xianghong Xie, Tingting Chen, Jianhui Sheng, Yongwei Feng, Min Chen, Fahu State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Research Center of Geospatial Big Data Application Chinese Academy of Surveying and Mapping Beijing100830 China College of Water Resources and Architectural Engineering Tarim University Alar843300 China Key Laboratory of Western China's Environmental Systems Ministry of Education College of Earth and Environmental Sciences Lanzhou University Lanzhou730000 China Department of Geography University of California Los AngelesCA90095 United States Academy of Plateau Science and Sustainability Qinghai Normal University Xining810016 China

Central Asia (CA) faces a severe water crisis exemplified by the shrinking Aral Sea. However, little is known about the entire region, particularly the numerous small water bodies that are vulnerable to climate change yet vital for regional sustainability. We examined water bodies as small as 0.0045 km2 across CA from 1992 to 2020, identifying 66,215 water bodies in 2020, 82.2% of which were previously unstudied. In contrast to the well-documented decline of the Aral Sea, other water bodies have expanded by 10.7% (8714.3 km2), with a net gain of 15,831 lakes since 1992. These findings challenge the perception of a drying CA and provide evidence of a warm-wet climate trend, which is redistributing water resources and creating opportunities for transforming water management to address the long-standing water crisis in CA. © 2025 Science China Press

关键词： Central Asia Climate Change Small Water Bodies Water Crisis Water Resource Management

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Roof greening in major Chinese cities possibly afford a large potential carbon sink

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Science Bulletin 2024年第20期69卷 3216-3220页

作者： Chao Yang Yinghui Zhang Min Chen Song Zhu Yuzhi Tang Zhixin Zhang Wei Ma Huizeng Liu Junyi Chen Bohui Tang Dejin Zhang Zhengdong Huang Xuqing Wang Wei Tu Cuiling Liu Tiezhu Shi Haiying Xu Aihong Cui Fanyi Meng Tianhong Zhao Kai Guo Wenhao Guo Bi Fan Quande Qin Wuyang Hong Haishan Wu Bing Wang Jinrong Zeng Ka Wai Wu Qingquan Li Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Ministry of Natural Resources&Guangdong Key Laboratory of Urban InformaticsShenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Shenzhen Zhiyuan Space Innovation Technology Co. LtdShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China School of Geography and Ocean Science Nanjing UniversityNanjing 210008China School of Geography Nanjing Normal UniversityNanjing 210023China School of Civil Engineering Chongqing Jiaotong UniversityChongqing 400074China Institute for Advanced Study&Shenzhen University Shenzhen 518060China Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Landscape Architecture and Urban Planning Texas A&M UniversityTX 77840USA Department of Geography Hong Kong Baptist UniversityHong Kong 999077China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China College of Management Shenzhen UniversityShenzhen 518060China WeBank(Shenzhen)Co. LtdShenzhen 518060China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong 100872China Esri C

China is experiencing accelerated urbanisation,with a large number of people moving from rural to urban areas[1].It has resulted in large losses in the net primary production(NPP),biodiversity and carbon stocks and an increase in environmental pollution and CO_(2)emissions[2–4].In 2015,196 countries signed the Paris Agreement and committed to setting long-term goals to jointly manage climate change and reduce their individual emissions,aiming to control the increase in global average temperature from the pre-industrial level to below 2℃and to curtail the temperature rise within 1.5℃till the end of the 21st century[5].China is bolstering its efforts to achieve the climate change mitigation goals and has announced a plan for achieving carbon neutrality by 2060[6].The carbon neutrality goal poses a challenge to the current policies promoting rapid urbanisation across China.

关键词： jointly carbon goals

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Identifying stepping-stones within small wetlands in highly urbanized areas and under network construction: A case study of the Guangdong–Hong Kong–Macao greater Bay Area, China

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Ecological Indicators 2025年 176卷

作者： Ye, Ying Chen, Caixia Yu, Chunxue Sun, Caige Geng, Shoubao Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China School of Geography South China Normal University Guangzhou510631 China Research Center for Eco-environmental Engineering Dongguan University of Technology Dongguan523808 China

In highly urbanized areas, small wetlands can provide multiple ecosystem services while benefiting the well-being of citizens. Although existing studies on wetland ecological networks have prioritized large wetland conservation measures, insufficient attention has been paid to the distribution patterns of small wetlands and their role in ecological networks. Utilizing the Guangdong–Hong Kong–Macao Greater Bay Area as a case study, we constructed a wetland ecological network to identify ecological pinch points while identifying small wetlands suitable to function as stepping-stones. This approach aims to establish potential corridors within the network and to assess the connectivity and robustness of the network under various scenarios. Our findings identified 297 ecological pinch points within the wetland ecological network alongside 53 small wetlands designated as stepping-stones, predominantly located in Foshan and Zhongshan cities in the central and western regions of the study area. The stepping-stone network comprised of 241 ecological corridors, with enhancements observed in the α, β, and γ indices, thereby significantly expanding the network's connectivity. Robustness index alterations in the stepping-stone network were moderated in relation to the ecological network, demonstrating that small wetlands can effectively bolster ecological network stability. It is recommended that, considering the distribution of ecological stepping stones of small wetlands, priority be given to the conservation and restoration of small wetlands in Foshan and Zhongshan within the central and western regions of the Guangdong–Hong Kong–Macao Greater Bay Area. This includes delineating conservation zones for small wetlands or establishing clusters of wetland parks centered on small wetlands, thereby maximizing their ecosystem value in supporting the urban agglomeration of the Guangdong–Hong Kong–Macao Greater Bay Area. © 2025 The Author(s)

关键词： Biotic

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