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arXiv 2022年

作者： Rao, Jinmeng Gao, Song Miller, Michelle Morales, Alfonso Geospatial Data Science Lab University of Wisconsin-Madison Madison United States Center for Integrated Agricultural Systems University of Wisconsin-Madison Madison United States Department of Planning and Landscape Architecture University of Wisconsin-Madison Madison United States

Quantifying the resilience in the food system is important for food security issues. In this work, we present a geospatial knowledge graph (GeoKG)-based method for measuring the resilience of a multi-commodity flow network. Specifically, we develop a CFS-GeoKG ontology to describe geospatial semantics of a multi-commodity flow network comprehensively, and design resilience metrics that measure the node-level and network-level dependence of single-sourcing, distant, or non-adjacent suppliers/customers in food supply chains. We conduct a case study of the US state-level agricultural multi-commodity flow network with hierarchical commodity types. The results indicate that, by leveraging GeoKG, our method supports measuring both node-level and network-level resilience across space and over time and also helps discover concentration patterns of agricultural resources in the spatial network at different geographic scales. © 2022, CC BY.

关键词： Semantics

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Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

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International Journal of Digital Earth 2017年第7期10卷 687-700页

作者： Yongxian Su Xiuzhi Chen Hua Su Liyang Liu Jishan Liao Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangzhouPeople’s Republic of China Jiangsu Key Laboratory of Agricultural Meteorology College of Applied MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhouPeople’s Republic of China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National Engineering Research Centre of Geo-spatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital ***,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer *** using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),*** results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture *** corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),*** monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),***,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.

关键词： Surface soil moisture land surface temperature physical-based radiative transfer model AMSR-E brightness temperatures

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Growth Onset Rather Than Photosynthesis Strongly Regulates Autumn Senescence Termination Besides Climate Change

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Global Ecology and Biogeography 2025年第5期34卷

作者： Shuping Ji Shilong Ren Yann Vitasse Constantin M. Zohner Yongshuo H. Fu Xiaoqiu Chen Xiaoyang Zhang Charlotte Grossiord Huiying Liu Matthias Peichl Dailiang Peng Shuai An Yating Li Maihe Li Lei Fang Jinyue Chen Xinfeng Wang Qingzhu Zhang Guoqiang Wang Qiao Wang Big Data Research Center for Ecology and Environment Environment Research Institute Shandong University Qingdao China WSL Swiss Federal Institute for Forest Snow and Landscape Research Birmensdorf Switzerland Institute of Integrative Biology ETH Zurich (Swiss Federal Institute of Technology) Zurich Switzerland College of Water Sciences Beijing Normal University Beijing China College of Urban and Environmental Sciences Laboratory for Earth Surface Processes of the Ministry of Education Peking University Beijing China Geospatial Science Center of Excellence Department of Geography and Geospatial Sciences South Dakota State University Brookings South Dakota USA Plant Ecology Research Laboratory (PERL) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Tiantong National Station for Forest Ecosystem Research Center for Global Change and Ecological Forecasting The Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration School of Ecological and Environmental Sciences East China Normal University Shanghai China Department of Forest Ecology and Management Swedish University of Agricultural Sciences (SLU) Umeå Sweden Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Environmental Sciences University of Basel Basel Switzerland Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Jilin China College of Life Science Hebei University Baoding Hebei China Center for Geodata and Analysis Faculty of Geographical Science Beijing Normal University Beijing China

Aim Plant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how environmental and developmental factors drive autumn senescence and evaluate whether woody and herbaceous plants exhibit divergent responses to these drivers. Location Eurasia. Time Period 1982–2014. Major Taxa Studied Woody and herbaceous species. Methods Using 120,833 long-term ground phenological observations, we employed partial correlation analysis to investigate the influence of environmental and developmental factors on senescence termination. Experimental records from literature and pasture survey observations from China were separately utilised to further validate the influence of developmental factors on senescence termination. Structural equation modelling was applied to analyse the pathways of growth onset affecting senescence termination. Additionally, multiple linear regression was used to examine the tendency of the sensitivity of senescence termination to plant development rate. Results We find that earlier growth onset primarily leads to earlier senescence termination directly in herbaceous plants, but indirectly in woody plants by accelerating early-season development. The sensitivity of senescence termination to plant development rate shows a declining trend, particularly in early-season negative effects on woody plants and late-season positive effects on herbaceous plants, suggesting diminished impacts of future warming on senescence timing. The impact of growing season photosynthetic activity on senescence termination is not pronounced for both woody and herbaceous plants. Main Conclusions The results demonstrate that growth onset may affect woody and herbaceous senescence termination through different pathways, whereas the carry-over effects of growing season photosynthetic activity are not wi

关键词： global warming growth onset herbaceous plants photosynthesis plant senescence woody plants

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A Modified Spectral Remote Sensing Index to Detect Spatiotemporal Changes of Plastic Greenhouses in Fragmented Terrains

A Modified Spectral Remote Sensing Index to Detect Spatiotem...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yijia Chen Shanshan Chen Song Gao Chun Li Ninglv Li Liding Chen School of Ecology and Environmental Science Yunnan University Kunming Yunnan China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing PR China Department of Geography Geospatial Data Science Lab University of Wisconsin-Madison Madison WI Southwest United Graduate School Kunming China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Plastic greenhouse (PG), as a new type of modern agricultural measure, has been used widely due to its significant benefits for agricultural production. However, it also raises concerns about its potential environmental impact. Timely monitoring of PG is necessary for agricultural sustainability. However, extracting PGs in fragmented terrains is difficult due to the variety of types of PGs and high environmental heterogeneity. In this study, a modified plastic greenhouse index (MPGI) was proposed based on the differences in spectral signatures using the Landsat-8 Operational Land Imager (OLI), and it was used to detect PG's spatiotemporal changes. After precision validation (the F1 score is 92.8% and 88.60% in 2015 and 2020, respectively), the results indicate that MPGI is suitable for extracting PGs in fragmented terrains. Its application to PGs mapping in the Southwest Plateau region demonstrates its potential for heterogeneous surface digital mapping.

关键词： Earth Green products Production Spatiotemporal phenomena Plastics Indexes Sustainable development

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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

SSRN

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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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Spatio-Temporal Stratified Associations between Urban Human Activities and Crime Patterns: A Case Study in San Francisco Around the Covid-19 Stay-at-Home Mandate

SSRN

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SSRN 2022年

作者： Chen, Tongxin Bowers, Kate Zhu, Di Gao, Xiaowei Cheng, Tao Space Time Lab for Big Data Analytics Department of Civil Environmental and Geomatic Engineering University College London LondonWC1E 6BT United Kingdom Department of Security and Crime Science University College London LondonWC1H 9EZ United Kingdom Geospatial Data Intelligence Lab Department of Geography Environment and Society University of Minnesota Twin Cities Minneapolis55455 United States

Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities in urban areas has not been explored in depth in empirical studies hitherto. Here, we aim to quantitatively measure the spatio-temporal associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the dynamic stratified associations between the spatial distributions of human activities and crimes. The results of a case study in San Francisco, United States reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centered around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights that tailored crime intervention strategies need to consider human activity variables. © 2022, The Authors. All rights reserved.

关键词： Crime

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FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing geospatial Resilience of Multicommodity Food Flows

arXiv

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arXiv 2023年

作者： Qu, Yuxiao Rao, Jinmeng Gao, Song Zhang, Qianheng Chao, Wei-Lun Su, Yu Miller, Michelle Morales, Alfonso Huber, Patrick R. Department of Computer Science Carnegie Mellon University United States Geospatial Data Science lab University of Wisconsin Madison United States Department of Computer Science and Engineering Ohio State University United States Center for Integrated Agricultural Systems University of Wisconsin Madison United States Department of Planning and Landscape Architecture University of Wisconsin Madison United States Institute of the Environment University of California Davis United States

Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant challenges. This paper proposes FLEE-GNN, a novel Federated Learning System for Edge-Enhanced Graph Neural Network, designed to overcome these challenges and enhance the analysis of geospatial resilience of multicommodity food flow network, which is one type of spatial networks. FLEE-GNN addresses the limitations of current methodologies, such as entropy-based methods, in terms of generalizability, scalability, and data privacy. It combines the robustness and adaptability of graph neural networks with the privacy-conscious and decentralized aspects of federated learning on food supply network resilience analysis across geographical regions. This paper also discusses FLEE-GNN’s innovative data generation techniques, experimental designs, and future directions for improvement. The results show the advancements of this approach to quantifying the resilience of multicommodity food flow networks, contributing to efforts towards ensuring global food security using AI methods. The developed FLEE-GNN has the potential to be applied in other spatial networks with spatially heterogeneous sub-network distributions. © 2023, CC BY.

关键词： Geographical regions

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Extracting human emotions at different places based on facial expressions and spatial clustering analysis

arXiv

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arXiv 2019年

作者： Kang, Yuhao Jia, Qingyuan Gao, Song Zeng, Xiaohuan Wang, Yueyao Angsuesser, Stephan Liu, Yu Ye, Xinyue Fei, Teng Geospatial Data Science Lab Department of Geography University of Wisconsin Madison United States School of Resources and Environmental Sciences Wuhan University Wuhan China College of Urban and Environment Sciences Peking University Beijing China Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China Urban Informatics and Spatial Computing Lab Department of Informatics New Jersey Institute of Technology Newark United States

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation

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Earth science Informatics 2016年第4期9卷 497-509页

作者： Wenzao Shi Zhengyuan Mao Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou China

In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segmentation. First, shadows were detected by potential histogram function. Second, the value of neighborhood total variation for each pixel was calculated, and then binarization and annotation were implemented to generate lable regions whose centroids were used as the seeds of the region growing segmentation, candidate buildings were selected from the segmentation result with the constraint of aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respectively, buildings were extracted by computing the adjacency relationship of the processed shadows and candidate buildings, and the building boundaries were fitted with the minimum enclosing rectangle. For verifying the validity of the proposed method, eighteen representative sub-images were chosen from PLEIADES images covering Shenzhen, China. Experimental results show that the average precision and recall of the proposed method are 97.95 % and 79.40 % for the object-based evaluation, and are 98.75 % and 83.16 % for the area-based evaluation respectively, and it has more 10 % and 6 % increase in the overall performance for above two evaluation criterion comparing with two other similar methods.

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Context-aware knowledge graph framework for traffic speed forecasting using graph neural network

arXiv

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arXiv 2024年

作者： Zhang, Yatao Wang, Yi Gao, Song Raubal, Martin Future Resilient Systems Singapore-ETH Centre ETH Zurich Singapore138602 Singapore Institute of Cartography and Geoinformation ETH Zurich Zurich8093 Switzerland Civil and Natural Resources Engineering Department University of Canterbury Christchurch8041 New Zealand Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison MadisonWI53706 United States

Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatiotemporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46±0.01, 14.76±0.09%, and 5.08±0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting. © 2024, CC BY-NC-SA.

关键词： Knowledge graph

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