检索结果-南通市图书馆

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)

在线全文

学校读者我要写书评

暂无评论

Ecological Processes 2022年第1期11卷 222-237页

作者： Cuijuan Liao Yizhao Chen Jingmeng Wang Yishuang Liang Yansong Huang Zhongyi Lin Xingjie Lu Yuanyuan Huang Feng Tao Danica Lombardozzi Almut Arneth Daniel SGoll Atul Jain Stephen Sitch Yanluan Lin Wei Xue Xiaomeng Huang Yiqi Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijingChina Joint Innovation Center for Modern Forestry Studies College of Biology and the EnvironmentNanjing Forestry UniversityNanjingChina Department of Geography University of CambridgeCambridgeUK School of Life Science Nanjing UniversityNanjingChina Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Guangdong Province Key Laboratory for Climate Change and Natural Disaster StudiesSchool of Atmospheric SciencesSun Yat-Sen UniversityGuangzhouChina csiro Oceans and Atmosphere Aspendale 3195Australia Terrestrial Sciences Section Climate and Global DynamicsNational Center for Atmospheric ResearchBoulderCO 80305USA Karlsruhe Institute of Technology Institute of Meteorology and Climate Research/Atmospheric Environmental ResearchGarmisch-PartenkirchenGermany UniversitéParis Saclay CEA-CNRS-UVSQLSCE/IPSLGif sur YvetteFrance Department of Atmospheric Sciences University of IllinoisUrbanaILUSA College of Life and Environmental Sciences University of ExeterExeterUK Department of Computer Science and Technology Tsinghua UniversityBeijingChina Center for Ecosystem Science and Society Department of Biological SciencesNorthern Arizona UniversityFlagstaffAZUSA

Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C *** the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)*** the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)*** comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly ***:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer *** analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth ***:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process *** analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model *** stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the en

关键词： Soil organic carbon Inter-model comparison Uncertainty analysis Carbon-nitrogen coupling Vertical resolved soil biogeochemistry structure

Healthy soils for sustainable food production and environmental quality

在线全文

学校读者我要写书评

暂无评论

Frontiers of Agricultural Science and Engineering 2020年第3期7卷 347-355页

作者： Xia LIANG Jizheng HE Fusuo ZHANG Qirong SHEN Jinshui WU lain MYOUNG Anthony GO'DONNELL Ligang WANG Enli WANG Julian HILL Deli CHEN School of Agriculture and Food The University of MelbourneVictoria 3010Australia College of Resources and Environmental Sciences National Academy of Agriculture Green DevelopmentKey Laboratory of Plant Soil Interactions(Ministry of Education)China Agricultural UniversityBeijjng 100193China Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization National Engineering Research Center for Organic-Based FertilizersJiangsu Collaborative Innovation Center for Solid Organic Waste Resource UtilizationCollege of Resources and Environmental ScienceNanjing Agricultural UniversityNanjing 210095China Key Laboratory of Agro-Ecological Processes in Subtropical Regions Institute of Subtropical AgricultureChinese Academy of SciencesChangsha 410125China School of Life and Environmental Sciences and Sydney Institute of Agriculture Faculty of ScienceThe University of SydneyCamperdownNSW 2006Australia University of Western Australia CrawleyWA 6009Australia Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081China csiro Agriculture and Food CanberraACT 2601Australia Ternes Scientific UpweyVictoria 3158Australia

Soil is the foundation for sustainable foodproduction and environmental protection. Created byunsustainable land management practices and a range ofsocial, economic and environmental drivers, soil degrada-tion and pollution have been an ongoing threat tointernational food security and environmental *** degradation and pollution assessments are, however,often focused on the soil itself with little scope to devisenew soil management approaches that match foodproduction systems and/or *** study draws lessons from an Australia-China JointResearch Center Program, Healthy Soils for SustainableFood Production and Environmental Quality: a researchplatform that has brought together multi-disciplinaryapproaches fromworld-renowned universitiesandresearch organizations in Australia and China. To thisend, a framework is presented for future soil managementin a new way that combines excellence in research,industry and policymakers in a partnership that will ensurenot only the right focus of the research but also that high-quality outputs will be transferable to industry and end-users.

关键词： Australia China environmental quality food production healthy soils

Annual Maps of Forests in Australia from Analyses of Microwave and Optical Images with FAO Forest Definition

在线全文

学校读者我要写书评

暂无评论

Journal of Remote Sensing 2021年第1期2021卷 134-144页

作者： Yuanwei Qin Xiangming Xiao Jean-Pierre Wigneron Philippe Ciais Josep G.Canadell Martin Brandt Xiaojun Li Lei Fan Xiaocui Wu Hao Tang Ralph Dubayah Russell Doughty Qing Chang Sean Crowell Bo Zheng Kevin Neal Jorge A.Celis Berrien MooreⅢ Department of Microbiology and Plant Biology Center for Earth Observation and ModelingUniversity of OklahomaNormanOK 73019USA ISPA UMR 1391INRA Nouvelle-AquitaineBordeaux Villenave d’OrnonFrance Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSLCEA-CNRS-UVSQUniversitéParis-Saclay91191 Gif-sur-YvetteFrance csiro Oceans and Atmosphere CanberraACTAustralia Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China Department of Geography National University of Singapore1 Arts LinkKent RidgeSingapore 117570 Department of Geographical Sciences University of MarylandCollege ParkMDUSA Division of Geological and Planetary Sciences California Institute of TechnologyPasadenaCA 91125USA O’Neill School of Public and Environmental Affairs Indiana University Bloomington702 N Walnut Grove AveBloomingtonIN 47405USA College of Atmospheric and Geographic Sciences University of OklahomaNormanOK 73019USA Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

The Australian governmental agencies reported a total of 149 million ha forest in the Food and Agriculture Organization of the United Nations(FAO)in 2010,ranking sixth in the world,which is based on a forest definition with tree height>2 ***,we report a new forest cover data product that used the FAO forest definition(tree cover>10%and tree height>5 meters at observation time or mature)and was derived from microwave(Phased Array type L-band Synthetic Aperture Radar,PALSAR)and optical(Moderate Resolution Imaging Spectroradiometer,MODIS)images and validated with very high spatial resolution images,Light Detection and Ranging(LiDAR)data from the Ice,Cloud,and land Elevation Satellite(ICESat),and in situ field survey *** new PALSAR/MODIS forest map estimates 32 million ha of forest in 2010 over ***/MODIS forest map has an overall accuracy of~95%based on the reference data derived from visual interpretation of very high spatial resolution images for forest and nonforest cover *** with the canopy height and canopy coverage data derived from ICESat LiDAR strips,PALSAR/MODIS forest map has 73%of forest pixels meeting the FAO forest definition,much higher than the other four widely used forest maps(ranging from 36%to 52%).PALSAR/MODIS forest map also has a reasonable spatial consistency with the forest map from the National Vegetation Information *** new annual map of forests in Australia could support cross-country comparison when using data from the FAO Forest Resource Assessment Reports.

关键词： forest Forest PALSAR

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

资源三号影像用于西澳大利亚州木本植被监测

卫星应用 2020年第10期28卷 40-45页

作者： Suzanne Furby Xiaoliang Wu Tony Traylen Drew Deveeurx Peter Caccetta 澳大利亚联邦科学与工业研究组织大数据研究所(csiro Data61)

澳大利亚西澳大利亚州的土地监测项目(land Monitor)是一个在州政府各部门之间长期运行的利用陆地卫星(landsat)进行土地监测的业务化项目。该项目覆盖了36万平方千米的农业林业用地。自1998年起,借助于该项目每年提供的详细遥感分析信... 详细信息

澳大利亚西澳大利亚州的土地监测项目(land Monitor)是一个在州政府各部门之间长期运行的利用陆地卫星(landsat)进行土地监测的业务化项目。该项目覆盖了36万平方千米的农业林业用地。自1998年起,借助于该项目每年提供的详细遥感分析信息,以及结合其他行业信息,政府各部门的业务能力得到了显著提高。随着高分辨率光学遥感卫星的逐渐普及,该项目于2014年利用大量资源三号卫星(ZY-3)影像试图监测landsat无法监测到的稀疏植被信息,例如农田间及城镇中零星孤单、窄条形分布的木本植被。虽然只尝试了木本植被和非木本植被的分类,但由于资源三号数据具有较高空间分辨率和较大辐射动态范围等特点,将有助于分析提取较为细腻的地表信息。

关键词：土地监测西澳大利亚州林业用地高空间分辨率资源三号卫星植被监测动态范围陆地卫星

Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

在线全文

学校读者我要写书评

暂无评论

Plant Phenomics 2021年第1期3卷 277-285页

作者： Etienne David Mario Serouart Daniel Smith Simon Madec Kaaviya Velumani Shouyang Liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng Norbert Kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis Pozniak David Shaner LeBauer Morten Lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du Végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Hiphen SAS 120 Rue Jean DaussetAgroparcBâtiment Technicité84140 AvignonFrance Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Wheat Genetics Resource Center Dep.of Plant PathologyKansas State Univ.4024 Throckmorton Plant Sciences CenterManhattanKansasUSA Global Wheat Program International Maize and Wheat Improvement Centre(CIMMYT)MexicoD.F.Mexico Faculty of Biosciences Norwegian University of Life SciencesP.O.Box 5003NO-1432ÅsNorway Agricultural Research Corporation Wheat Research ProgramP.O.Box 126Wad MedaniSudan Arid land Research Center Tottori UniversityTottori 680-0001Japan Laboratories of Plant Genetics and Plant Breeding Graduate School of AgricultureKyoto UniversityJapan csiro Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Biosystems Dynamics and Exchanges TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada College of Agriculture and Life Sciences University of ArizonaTucsonArizona

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/*** an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science *** this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label *** address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat *** now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.

关键词： Wheat adding release

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

黄土高原地区土地资源分区研究

农业灾害研究 2015年第5期5卷 38-47,53页

作者：蔡艳蓉李永红高照良四川省电力设计院四川成都610000 西北农林科技大学水土保持研究所陕西杨凌712100 中国科学院水利部水土保持研究所生态工程技术研究中心陕西杨凌712100 澳大利亚联邦科工组织(csiro)水土资源研究所

以《黄土高原地区综合治理规划大纲(2010—2030年)》为基本框架,在详尽分析黄土高原自然、社会、经济状况前提下,确定了土地资源分区的依据、原则、评价指标体系,采用聚类分析法,结合傅伯杰、唐克丽、宋桂琴等对各个不同时期黄土高原土... 详细信息

以《黄土高原地区综合治理规划大纲(2010—2030年)》为基本框架,在详尽分析黄土高原自然、社会、经济状况前提下,确定了土地资源分区的依据、原则、评价指标体系,采用聚类分析法,结合傅伯杰、唐克丽、宋桂琴等对各个不同时期黄土高原土地分区的深入研究,将黄土高原地区划分为高原沟壑区、黄土丘陵沟壑区、土石山区、沙地和沙漠区、河谷平原区、农灌区。

关键词：土地资源分区水土流失分区原则生产潜力黄土高原地区

The Global Soil Mycobiome consortium dataset for boosting fungal diversity research

在线全文

学校读者我要写书评

暂无评论

Fungal Diversity 2021年第6期111卷 573-588页

作者： Leho Tedersoo Vladimir Mikryukov Sten Anslan Mohammad Bahram Abdul Nasir Khalid Adriana Corrales Ahto Agan Aída-M.Vasco-Palacios Alessandro Saitta Alexandre Antonelli Andrea C.Rinaldi Annemieke Verbeken Bobby P.Sulistyo Boris Tamgnoue Brendan Furneaux Camila Duarte Ritter Casper Nyamukondiwa Cathy Sharp César Marín D.Q.Dai Daniyal Gohar Dipon Sharmah Elisabeth Machteld Biersma Erin K.Cameron Eske De Crop Eveli Otsing Evgeny A.Davydov Felipe E.Albornoz Francis Q.Brearley Franz Buegger Genevieve Gates Geoffrey Zahn Gregory Bonito Indrek Hiiesalu Inga Hiiesalu Irma Zettur Isabel C.Barrio Jaan Pärn Jacob Heilmann-Clausen Jelena Ankuda John Y.Kupagme Joosep Sarapuu Jose G.Maciá-Vicente Joseph Djeugap Fovo József Geml Juha M.Alatalo Julieta Alvarez-Manjarrez Jutamart Monkai Kadri Põldmaa Kadri Runnel Kalev Adamson Kari A.Bråthen Karin Pritsch Kassim I.Tchan Kęstutis Armolaitis Kevin D.Hyde Kevin K.Newsham Kristel Panksep Lateef A.Adebola Louis J.Lamit Malka Saba Marcela Eda Silva Cáceres Maria Tuomi Marieka Gryzenhout Marijn Bauters Miklós Bálint Nalin Wijayawardene Niloufar Hagh-Doust Nourou S.Yorou Olavi Kurina Peter E.Mortimer Peter Meidl RHenrik Nilsson Rasmus Puusepp Rebeca Casique-Valdés Rein Drenkhan Roberto Garibay-Orijel Roberto Godoy Saleh Alfarraj Saleh Rahimlou Sergei Põlme Sergey V.Dudov Sunil Mundra Talaat Ahmed Tarquin Netherway Terry W.Henkel Tomas Roslin Vladimir E.Fedosov Vladimir G.Onipchenko WAErandi Yasanthika Young Woon Lim Meike Piepenbring Darta Klavina Urmas Kõljalg Kessy Abarenkov Mycology and Microbiology Center University of TartuTartuEstonia Institute of Ecology and Earth Sciences TartuEstonia Department of Ecology Swedish University of Agricultural SciencesUppsalaSweden Department of Botany University of the PunjabQuaid-e-Azam CampusLahorePakistan Centro de Investigaciones en Microbiología y Biotecnología-UR(CIMBIUR) Facultad de Ciencias NaturalesUniversidad del RosarioBogotáColombia Grupo de Microbiología Ambiental y BioMicro Escuela de MicrobiologíaUniversidad de Antioquia UdeAMedellínColombia Department of Agricultural Food and Forest SciencesUniversity of PalermoPalermoItaly Royal Botanic Gardens SurreyUK Department of Biomedical Sciences University of CagliariCagliariItaly Research Group Mycology Department of BiologyGhent UniversityGhentBelgium Department of Biomedicine Indonesia International Institute for Life SciencesJakartaIndonesia Plant Pathology and Agricultural Zoology Research Unit Department of Plant ProtectionFaculty of Agronomy and Agricultural SciencesUniversity of DschangDschangCameroon Department of Ecology and Genetics Uppsala UniversityUppsalaSweden Eukaryotic Microbiology University of Duisburg-EssenEssenGermany Department of Biological Sciences and Biotechnology Botswana International University of Science and TechnologyPalapyeBotswana Natural History Museum of Zimbabwe BulawayoZimbabwe Institute of Botany Czech Academy of SciencesPrůhoniceCzech Republic Center for Yunnan Plateau Biological Resources Protection and Utilization College of Biological Resource and Food EngineeringQujing Normal UniversityQujingPeople’s Republic of China Department of Botany Jawaharlal Nehru Rajkeeya MahavidyalayaPondicherry UniversityPort BlairIndia British Antarctic Survey NERCCambridgeUK Natural History Museum of Denmark University of CopenhagenCopenhagenDenmark Department of Environmental Science Saint Mary’s UniversityHalifaxNSCanada Altai State University BarnaulRussia csiro land and water WembleyWAAustralia Department of N

Fungi are highly important biotic components of terrestrial ecosystems,but we still have a very limited understanding about their diversity and *** data article releases a global soil fungal dataset of the Global Soil Mycobiome consortium(GSMc)to boost further research in fungal diversity,biogeography and *** dataset comprises 722,682 fungal operational taxonomic units(OTUs)derived from PacBio sequencing of full-length ITS and 18S-V9 variable regions from 3200 plots in 108 countries on all *** plots are supplied with geographical and edaphic *** OTUs are taxonomically and functionally assigned to guilds and other functional *** entire dataset has been corrected by excluding chimeras,index-switch artefacts and potential *** dataset is more inclusive in terms of geographical breadth and phylogenetic diversity of fungi than previously published *** GSMc dataset is available over the PlutoF repository.

关键词： Soil fungi Global dataset PacBio sequencing Fungal richness

景观对婆罗岛东加里曼丹Punan-Pelancau人的重要性初解

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

AMBIO－人类环境杂志 2007年第B11期36卷 559-564页

作者： Robert N. Cunliffe Timothy J. P. Lynam Douglas Sheil Meilinda Wan Agus Salim Imam Basuki Hari Priyadi 寒江（译）常驻津巴布韦哈拉雷的自由环境顾问常驻澳大利亚汤斯维尔的联邦科学与工业研究组织（csiro）可持续生态系统部的社会和生态系统科学家印度尼西亚的国际林业研究中心的生态学家印度尼西亚的国际林业研究中心的农艺学家澳大利亚国立大学国家流行病学及人口健康中心工作的生物统计学家印度尼西亚的国际林业研究中心工作的土壤学家印度尼西亚的国际林业研究中心工作的森林管理专家不详

为将当地社区的观点纳入新的发展倡议中,需要清晰地了解当地人的感受,并形成计划制定者和研发人员易于获取的文件形式。本研究意在对生活在东加里曼丹一个森林景观中的Punan Pelancau社区如何赋予周围景观以重要性建立一种先期了解,并... 详细信息

为将当地社区的观点纳入新的发展倡议中,需要清晰地了解当地人的感受,并形成计划制定者和研发人员易于获取的文件形式。本研究意在对生活在东加里曼丹一个森林景观中的Punan Pelancau社区如何赋予周围景观以重要性建立一种先期了解,并将他们的感受以地图的方式加以表现。这种方法的特色是将参与式社区评估法与更正式的建模技术和地理信息系统技术结合体的迭代运用。结果表明,景观重要性很大程度上受潜在利益的制约,如对产出、健康及房屋的投入。土地类型和距离都不是景观重要性的良好指标。本研究的一部分——栅格法,提供了一种获取和表示当地社区知识的简便技术,即使那些与土地的关系极为复杂的社区(如本研究的这一具体社区)。

关键词：加里曼丹地理信息系统技术景观土地类型文件形式建模技术社区结合体