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Drainage pattern recognition method considering local basin shape based on graph neural network

International Journal of Digital Earth 2023年第1期16卷 593-619页

作者： Wenning Wang Haowen Yan Xiaomin Lu Yi He Tao Liu Wende Li Pengbo Li Fang Xu Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhouPeople’s Republic of China Lanzhou Ji Er Ai Si Spatial information technology Company Limited LanzhouPeople’s Republic of China National-Local Joint Engineering Research Center of Technologies and Applications for National geographic State Monitoring LanzhouPeople’s Republic of China Gansu provincial Engineering Laboratory for National geographic State Monitoring LanzhouPeople’s Republic of China People’s Republic of China Navigation Publication Press TianjinPeople’s Republic of China

Drainage pattern recognition is crucial for geospatial understanding and hydrologic modelling.Currently,drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features,so that potential correlations between river segments are usually ignored,resulting in poor drainage pattern recognition results.In order to overcome this problem,this paper proposes a supervised graph neural network method that considers the local basin unit shape of river networks.First,based on the overall hierarchy of the river networks,the confluence angle of river segments and the shape of river basin units,multiple drainage pattern classification features are extracted.Then,typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training,validation and testing steps.Experimental results show that the drainage pattern indexes proposed can describe the characteristics of different drainage patterns.The method can effectively sample the adjacent river segments,flexibly transfer the associated pattern features among river segment neighbours,and aggregate the deeper characteristics of the river networks,thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

关键词： River drainage pattern recognition Basin unit shape supervised learning graph neural networks

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The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere

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Environmental science and Ecotechnology 2022年第4期12卷 3-7页

作者： Mengwei Jia Fei Li Yuzhong Zhang Mousong Wu Yingsong Li Shuzhuang Feng Hengmao Wang Huilin Chen Weimin Ju Jun Lin Jianwei Cai Yongguang Zhang Fei Jiang jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System ScienceNanjing UniversityNanjingJiangsu Province210023China key Laboratory of Coastal Environment and Resources of Zhejiang Province School of EngineeringWestlake UniversityHangzhouZhejiang Province310030China Joint International Research Laboratory of Atmospheric and Earth System sciences School of Atmospheric SciencesNanjing UniversityNanjingJiangsu Province210023China China Centre for Resources Satellite Data and Application Beijing100094China International Joint Carbon Neutrality Laboratory NanjingJiangsu Province210023China Nantong Academy of Intelligent Sensing NantongJiangsu Province226000China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application NanjingJiangsu Province210023China Frontiers science Center for Critical Earth Material Cycling Nanjing UniversityNanjingJiangsu Province210023China

Sudden mega natural gas leaks of two Nord Stream pipelines in the Baltic Sea(Denmark)occurred from late September to early October 2022,releasing large amounts of methane into the atmosphere.We inferred the methane emissions of this event based on surface in situ observations using two inversion methods and two meteorological reanalysis datasets,supplemented with satellite-based observations.We conclude that approximately 220±30 Gg of methane was released from September 26 to October 1,2022.

关键词： methane approximate conclude

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A landslide extraction method of channel attention mechanismU-Net network based on Sentinel-2A remote sensing images

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International Journal of Digital Earth 2023年第1期16卷 552-577页

作者： Hesheng Chen Yi He Lifeng Zhang Sheng Yao Wang Yang Yumin Fang Yaoxiang Liu Binghai Gao Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhouPeople’s Republic of China National-Local Joint Engineering Research Center of Technologies and Applications for National geographic State Monitoring LanzhouPeople’s Republic of China Gansu provincial Engineering Laboratory for National geographic State Monitoring LanzhouPeople’s Republic of China

Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.

关键词： Sentinel-2A remote sensing image landslide extraction U-Net attention mechanism deep learning

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Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

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Acta Oceanologica Sinica 2021年第11期40卷 104-118页

作者： Bao Wang Shichao Liu Bin Wang Wenzhou Wu Jiechen Wang Dingtao Shen jiangsu provincial key Laboratory of geographic information science and technology/key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources/School of Geography and Ocean science Nanjing UniversityNanjing 210023China key Laboratory of Marine Hazards Forecasting National Marine Environmental Forecasting CenterMinistry of Natural ResourcesBeijing 100081China State key Laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing UniversityNanjing 210023China Changjiang River Scientific Research Institute Changjiang Water Resources CommissionWuhan 430010China

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

关键词： storm surge prediction CNN LSTM combination

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2022年夏季长江流域破纪录的高温干旱事件导致了植被光合作用显著下降

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科学通报:英文版 2023年第19期68卷 2160-2163,M0003页

作者：王军阎然吴国雄刘屹岷王美蓉曾宁江飞王恒茂何维吴谋松居为民陈镜明 Frontiers science Center for Critical Earth Material Cycling International Institute for Earth System ScienceNanjing UniversityNanjing 210023China jiangsu provincial key Laboratory of geographic information science and technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China State key Laboratory of Numerical Modelling for Atmospheric sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Joint Center for Data Assimilation Research and Applications Key Laboratory of Meteorological DisasterMinistry of EducationJoint International Research Laboratory of Climate and Environment ChangeCollaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Department of Atmospheric and Oceanic science and Earth System Interdisciplinary Center University of MarylandCollege Park MD 20742USA Department of Geography and Planning University of TorontoToronto ON M5S3G3Canada

2022年夏季,一场破纪录的高温干旱席卷了长江流域,造成了自2000年以来7~9月最严重的高温、水汽压差增强和土壤湿度减少,但此次事件对陆地生态系统及其碳通量的影响尚不清楚.利用最新的多源数据,本研究及时评估了其对植被绿度和光合作用... 详细信息

2022年夏季,一场破纪录的高温干旱席卷了长江流域,造成了自2000年以来7~9月最严重的高温、水汽压差增强和土壤湿度减少,但此次事件对陆地生态系统及其碳通量的影响尚不清楚.利用最新的多源数据,本研究及时评估了其对植被绿度和光合作用的影响.结果表明,这一复合极端事件导致长江流域植被绿度和光合作用普遍减少,特别是重庆和湖南的植被NDVI、总初级生产力(GPP)和近红外反射率(NIRv)分别下降了5%~8%、12%和11%.整个长江流域平均NDVI下降了0.04(5.16%),总GPP下降了84.50 Tg C(7.46%)和NIRv下降了0.01(7.43%),是自2000年以来夏季最大的降幅.将长江流域区分为森林、灌木和农田3种主要植被类型,平均NDVI在农田地区下降最强,而光合作用在森林地区降幅最大.另外,研究发现除了农田NDVI的减少主要是高温胁迫引起,其他植被类型NDVI的减少和所有植被类型光合能力的下降主要是由增加的VPD导致.而随着全球变暖复合极端气候事件的强度和频率增加,其将潜在增加长江流域生态系统的脆弱性,并严重威胁其固碳能力.

关键词：高温干旱近红外反射率极端气候事件流域生态系统全球变暖土壤湿度多源数据极端事件

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Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018

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Advances in Atmospheric sciences 2021年第9期38卷 1474-1485页

作者： Xiaoyi SHEN Chang-Qing KE Bin CHENG Wentao XIA Mengmeng LI Xuening YU Haili LI School of Geography and Ocean science Nanjing UniversityNanjing 210023China jiangsu provincial key Laboratory of geographic information science and technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software technology and Industrialization Nanjing 210023China Finnish Meteorological Institute Helsinki 00101Finland

In August 2018,a remarkable polynya was observed off the north coast of Greenland,a perennial ice zone where thick sea ice cover persists.In order to investigate the formation process of this polynya,satellite observations,a coupled iceocean model,ocean profiling data,and atmosphere reanalysis data were applied.We found that the thinnest sea ice cover in August since 1978(mean value of 1.1 m,compared to the average value of 2.8 m during 1978-2017) and the modest southerly wind caused by a positive North Atlantic Oscillation(mean value of 0.82,compared to the climatological value of-0.02) were responsible for the formation and maintenance of this polynya.The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind.Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

关键词： polynya sea ice thickness wind sea ice drift Greenland

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Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products

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Journal of Remote Sensing 2021年第1期2021卷 145-162页

作者： Zhaoying Zhang Yongguang Zhang Jing M.Chen Weimin Ju Mirco Migliavacca Tarek S.El-Madany International Institute for Earth System sciences Nanjing UniversityNanjingJiangsuChina Yuxiu Postdoctoral Institute Nanjing UniversityNanjingJiangsuChina jiangsu provincial key Laboratory of geographic information science and technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjingJiangsuChina Huangshan Park Ecosystem Observation and Research Station Ministry of EducationChina Department of Geography and Planning University of TorontoTorontoOntarioCanada Max Planck Institute for Biogeochemistry Department Biogeochemical Integration Germany

Remote sensing of solar-induced chlorophyll fluorescence(SIF)provides new possibilities to estimate terrestrial gross primary production(GPP).To mitigate the angular and canopy structural effects on original SIF observed by sensors(SIF_(obs)),it is recommended to derive total canopy SIF _(emission)(SIF_(total))of leaves within a canopy using canopy interception(i0)and reflectance of vegetation(R_(V)).However,the effects of the uncertainties in i_(0) and R_(V) on the estimation of SIFtotal have not been well understood.Here,we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance(SCOPE)model.The SCOPE simulations showed that the R^(2) between GPP and SIF_(total) was clearly higher than that between GPP and SIFobs and the differences in R^(2)(ΔR^(2))tend to decrease with the increasing levels of uncertainties in i_(0) and RV.The resultantΔR^(2) decreased to zero when the uncertainty level in i0 and RV was~30%for red band SIF(RSIF,683 nm)and~20%for far-red band SIF(FRSIF,740 nm).In addition,as compared to the TROPOspheric Monitoring Instrument(TROPOMI)SIFobs at both red and far-red bands,SIF_(total) derived using any combination of i_(0)(from MCD15,VNP15,and CGLS LAI products)and RV(from MCD34,MCD19,and VNP43 BRDF products)showed comparable improvements in estimating GPP.With this study,we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets(SIF_(total))using current satellite products.

关键词： BRDF estimation SCOPE

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Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

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Journal of Forestry Research 2020年第5期31卷 1743-1758页

作者： Fangyan Zhu Wenjuan Shen Jiaojiao Diao Mingshi Li Guang Zheng College of Forestry Nanjing Forestry UniversityNanjing 210037People’s Republic of China Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037People’s Republic of China jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023People’s Republic of China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occu

关键词： High spatial resolution satellite images Vegetation change Direct detection method Objectoriented Purple Mountains

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A comparative study on three EOF analysis techniques using decades of Arctic sea-ice concentration data

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Journal of Central South University 2015年第7期22卷 2681-2690页

作者：陈新保刘信陶李松年 Chow Annie National-Local Joint Engineering Laboratory of Geospatial information technology(Hunan University of science and technology) key Lab of Virtual geographic Environment from Ministry of Education(Nanjing Normal University) Department of Civil Engineering Ryerson University Environment Applied science and Management Ryerson University

Change in Arctic sea ice extent is one of the indicators of global climate changes. Spatio-temporal change and change patterns can be identified using various methods to facilitate human understanding global climate changes. Three empirical orthogonal function(EOF) techniques are discussed and applied to decades of sea-ice concentration(SIC) dataset in Arctic area for identifying independent patterns. It was found that: 1) discrepancies exist in magnitude and scope for each EOF pattern, however, the first two leading EOFs of variability possess high similarities in structure and shape; 2) Even though there are somewhat differences in amplitude of each PC mode, the first two leading PC modes maintain consistent in overall trend and periodicity; 3) There are significant discrepancies and inconsistencies in the third and fourth leading EOF and PC modes. The accuracies of three techniques are further validated in representing the physical phenomena of SIC anomaly patterns.

关键词： EOF分析北极海冰技术全球气候变化经验正交函数 PC模式变化规律时空变化

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Molecular signatures of soil-derived dissolved organic matter constrained by mineral weathering

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Fundamental Research 2023年第3期3卷 377-383页

作者： Ying-Hui Wang Peng Zhang Chen He Jian-Chun Yu Quan Shi Randy A.Dahlgren Robert G.M.Spencer Zhi-Bing Yang Jun-Jian Wang State Environmental Protection key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong provincial key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China State key Laboratory of Water Resources and Hydropower Engineering science Wuhan UniversityWuhan 430072China State key Laboratory of Heavy Oil Processing China University of PetroleumBeijing 102249China Shanghai Engineering Research Center of Hadal science and technology College of Marine SciencesShanghai Ocean UniversityShanghai 201306China Department of Land Air and Water ResourcesUniversity of California DavisDavis 95616United States Department of Earth Ocean and Atmospheric ScienceFlorida State UniversityTallahassee 32306United States

Dissolved organic matter(DOM)in soils drives biogeochemical cycling and soil functions in different directions depending on its molecular signature.Notably,there is a distinct paucity of information concerning how the molecular signatures of soil DOM vary with different degrees of weathering across wide geographic scales.Herein,we resolved the DOM molecular signatures from 22 diverse Chinese reference soils and linked them with soil organic matter and weathering-related mineralogical properties.The mixed-effects models revealed that the yields of DOM were determined by soil organic carbon content,whereas the molecular signature of DOM was primarily constrained by the weathering-related dimension.The soil weathering index showed a positive effect on the lability and a negative effect on the aromaticity of DOM.Specifically,DOM in highly weathered acidic soils featured more amino sugars,carbohydrates,and aliphatics,as well as less O-rich polyphenols and condensed aromatics,thereby conferring a higher DOM biolability and lower DOM aromaticity.This study highlights the dominance of the weathering-related dimension in constraining the molecular signatures and potential functions of DOM in soils across a wide geographic scale.

关键词： Soil organic matter Dissolved organic matter Chemical composition Molecular signature Mineral weathering

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