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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2018

Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2].However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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Erratum to: Vol. 35 No. 1 Publisher’s Erratum

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Chinese Journal of Oceanology and Limnology 2017年第2期35卷 466-467页

作者： Shuangyan He Mingxia He Jürgen Fischer Dongliang Yuan Peng Xu Tengfei Xu Xianping Yang Leonid Sokoletsky Xiaodao Wei Fang Shen Juhong Zou Maohua Guo Songxue Cui Wu Zhou Dalu Gao Guangzhen Jin Xianqing Lü Fuwen Qiu Wendong Fang Aijun Pan Jing Cha Shanwu Zhang Jiang Huang Tao Wang Yongzhou Cheng Xiaoyan Chen Zhaopu Liu Xiaohua Long Zhishuai Hou Haishen Wen Jifang Li Feng He Qun Liu Jinhuan Wang Biao Guan Qinglong Wang Md. Shahjahan Md. Farajul Kabir Kizar Ahmed Sumon Lipi Rani Bhowmik Harunur Rashid Shu Li Kefu Yu Jianxin Zhao Yuexing Feng Tianran Chen Shun Zhou Yichao Ren Christopher M. Pearce Shuanglin Dong Xiangli Tian Qinfeng Gao Fang Wang Liming Liu Rongbin Du Xiaoling Zhang Shichun Sun Song Feng Jianing Lin Song Sun Fang Zhang Zhipeng Zhang Xuexi Tang Haitian Tang Jingjing Song Jian Zhou Hongjun Liu Qixiang Wang Kuimei Qian Xia Liu Yuwei Chen Chengjun Sun Fenghua Jiang Wei Gao Xiaoyun Li Yanzhen Yu Xiaofei Yin Yong Wang Haibing Ding Zhongmin Sun Yongqiang Wang Pengcheng Yan Hui Guo Jianting Yao Jiro Tanaka Hiroshi Kawai Na Song Muyan Chen Tianxiang Gao Takashi Yanagimoto Xia Lu Sheng Luan Jie Kong Longyang Hu Yong Mao Shengping Zhong Yan Liu Weihong Zhao Caiyan Li Hui Miao Ocean Remote Sensing Institute Ocean University of China Qingdao China Institute for Space Sciences Freie Universität Berlin Berlin Germany Ocean College Zhejiang University Hangzhou China Key Laboratory of Ocean Circulation and Waves (KLOCAW) Institute of Oceanology Chinese Academy of Sciences Qingdao China Qingdao Collaborative Innovation Center of Marine Science and Technology Qingdao China University of Chinese Academy of Sciences Beijing China First Institute of Oceanography State Oceanic Administration Qingdao China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai China National Satellite Ocean Application Service Beijing China Key Laboratory of Space Ocean Remote Sensing and Application SOA Beijing China Key Laboratory of Physical Oceanography (Ocean University of China) Ministry of Education Qingdao China Key Lab of Marine Science and Numerical Modeling First Institute of Oceanography SOA Qingdao China Third Institute of Oceanography State Oceanic Administration Xiamen China State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China Jiangsu Provincial Key Laboratory of Marine Biology College of Resources and Environmental Sciences Nanjing Agricultural University Nanjing China Fisheries College Ocean University of China Qingdao China Key Laboratory of Mariculture Ministry of Education Ocean University of China Qingdao China Department of Fisheries Management Bangladesh Agricultural University Mymensingh Bangladesh Sado Marine Biological Station Faculty of Science Niigata University Sado Niigata Japan Department of Aquatic Ecology and Water Quality Management Wageningen University Wageningen University and Research Centre Amsterdam The Netherlands Science and Math Program Asian University for Women Chittagong Bangladesh Key laboratory of Marginal Sea Geology South China Sea Institute of Oceanology Chinese Academy of Sciences

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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Near surface camera informed agricultural land monitoring for climate smart agriculture

Climate Smart Agriculture

引用

Climate Smart Agriculture 2024年第1期1卷

作者： Le Yu Zhenrong Du Xiyu Li Qiang Zhao Hui Wu Duoji weise Xinqun Yuan Yuanzheng Yang Wenhua Cai Weimin Song Pei Wang Zhicong Zhao Ying Long Yongguang Zhang Jinbang Peng Xiaoping Xin Fei Xu Miaogen Shen Hui Wang Yuanmei Jiao Yong Luo Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing China Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing China Tsinghua University (Department of Earth System Science)- Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing China School of Information and Communication Engineering Dalian University of Technology Dalian China Geothermal Geological Brigade Tibet Autonomous Region Geological and Mineral Exploration and Development Bureau Lhasa China International Department Beijing No. 35 High School Beijing China Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education Nanning Normal University Nanning China Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation Nanning Normal University Nanning China Key Laboratory of Coastal Environmental Processes and Ecological Restoration Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China Yellow River Delta Field Observation and Research Station of Coastal Wetland Ecosystem Chinese Academy of Sciences Dongying China Department of Landscape Architecture School of Architecture Tsinghua University Beijing China Institute for National Parks Tsinghua University Beijing China School of Architecture and Hang Lung Center for Real Estate Key Laboratory of Eco Planning & Green Building Ministry of Education Tsinghua University Beijing China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Nanjing China 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 Univer

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

关键词： Near surface camera (NSCam) Climate-smart agriculture (CSA) Agricultural monitoring Data fusion Sentinel-2

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STATE OF THE CLIMATE IN 2021

引用

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2022年第8期103卷 S1-S1页

作者： Blunden, Jessica NOAA/NESDIS National Centers for Environmental Information Asheville NC United States NOAA NESDIS National Centers for Environmental Information Silver Spring MD United States Met Office Hadley Centre Exeter United Kingdom Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville NC United States Communications and Outreach NOAA/NESDIS National Centers for Environmental Information Asheville NC United States Innovative Consulting and Management Services LLC NOAA/NESDIS National Centers for Environmental Information Asheville NC United States European Centre for Medium-Range Weather Forecasts Reading United Kingdom CMNS-Earth System Science Interdisciplinary Center University of Maryland College Park MD United States Department of Meteorology National Centre for Earth Observation University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton VA United States National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Life and Environmental Sciences Osaka Prefecture University Osaka Sakai Japan University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder CO United States Centro de Investigaciones sobre Desertificación Spanish National Research Council (CIDE CSIC-UV-Generalitat Valenciana) Climate Atmosphere and Ocean Laboratory (Climatoc-Lab) Moncada Valencia Spain Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich United Kingdom Joint Research Centre of the European Commission Ispra Italy GloH2O Almere Netherlands University of Reading Reading United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Cité Institut de physique du globe de Paris CNRS IGN Paris

Editors note: For easy download the posted pdf of the State of the Climate in 2021 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few mi... 详细信息

关键词： Greenhouse gases

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