Cropland Heterogeneity Changes on the Northeast China Plain in the Last Three Decades (1980S–2010S)
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Crop Systems on a County-Scale
Supporting information Chinese cropping systems are a net source of greenhouse gases despite soil carbon sequestration Bing Gaoa,b, c, Tao Huangc,d, Xiaotang Juc*, Baojing Gue,f, Wei Huanga,b, Lilai Xua,b, Robert M. Reesg, David S. Powlsonh, Pete Smithi, Shenghui Cuia,b* a Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China b Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China c College of Resources and Environmental Sciences, Key Laboratory of Plant-soil Interactions of MOE, China Agricultural University, Beijing 100193, China d College of Geography Science, Nanjing Normal University, Nanjing 210046, China e Department of Land Management, Zhejiang University, Hangzhou, 310058, PR China f School of Agriculture and Food, The University of Melbourne, Victoria, 3010 Australia g SRUC, West Mains Rd. Edinburgh, EH9 3JG, Scotland, UK h Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, AL5 2JQ. UK i Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK Bing Gao & Tao Huang contributed equally to this work. Corresponding author: Xiaotang Ju and Shenghui Cui College of Resources and Environmental Sciences, Key Laboratory of Plant-soil Interactions of MOE, China Agricultural University, Beijing 100193, China. Phone: +86-10-62732006; Fax: +86-10-62731016. E-mail: [email protected] Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen 361021, China. Phone: +86-592-6190777; Fax: +86-592-6190977. E-mail: [email protected] S1. The proportions of the different cropping systems to national crop yields and sowing area Maize was mainly distributed in the “Corn Belt” from Northeastern to Southwestern China (Liu et al., 2016a). -
Estimating Frost During Growing Season and Its Impact on the Velocity of Vegetation Greenup and Withering in Northeast China
remote sensing Article Estimating Frost during Growing Season and Its Impact on the Velocity of Vegetation Greenup and Withering in Northeast China Guorong Deng 1,2 , Hongyan Zhang 1,2,*, Lingbin Yang 1,2, Jianjun Zhao 1,2 , Xiaoyi Guo 1,2 , Hong Ying 1,2, Wu Rihan 1,2 and Dan Guo 3 1 Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China; [email protected] (G.D.); [email protected] (L.Y.); [email protected] (J.Z.); [email protected] (X.G.); [email protected] (H.Y.); [email protected] (W.R.) 2 Urban Remote Sensing Application Innovation Center, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China 3 College of Resources and Environment, Jilin Agricultural University, Changchun 130024, China; [email protected] * Correspondence: [email protected]; Tel.: +86-431-8509-9550 Received: 31 March 2020; Accepted: 23 April 2020; Published: 25 April 2020 Abstract: Vegetationphenology and photosynthetic primary production have changed simultaneously over the past three decades, thus impacting the velocity of vegetation greenup (Vgreenup) and withering (Vwithering). Although climate warming reduces the frequency of frost events, vegetation is exposed more frequently to frost due to the extension of the growing season. Currently, little is known about the effect of frost during the growing season on Vgreenup and Vwithering. This study analyzed spatiotemporal variations in Vgreenup and Vwithering in Northeast China between 1982 to 2015 using Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (GIMMS 3g NDVI) data. -
Surface Modelling of Human Population Distribution in China
Ecological Modelling 181 (2005) 461–478 Surface modelling of human population distribution in China Tian Xiang Yuea,∗, Ying An Wanga, Ji Yuan Liua, Shu Peng Chena, Dong Sheng Qiua, Xiang Zheng Denga, Ming Liang Liua, Yong Zhong Tiana, Bian Ping Sub a Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 917 Building, Datun, Anwai, Beijing 100101, China b College of Science, Xi’an University of Architecture and Technology, Xi’an 710055, China Received 24 March 2003; received in revised form 23 April 2004; accepted 4 June 2004 Abstract On the basis of introducing major data layers corresponding to net primary productivity (NPP), elevation, city distribution and transport infrastructure distribution of China, surface modelling of population distribution (SMPD) is conducted by means of grid generation method. A search radius of 200 km is defined in the process of generating each grid cell. SMPD not only pays attention to the situation of relative elements at the site of generating grid cell itself but also calculates contributions of other grid cells by searching the surrounding environment of the generating grid cell. Human population distribution trend since 1930 in China is analysed. The results show that human population distribution in China has a slanting trend from the eastern region to the western and middle regions of China during the period from 1930 to 2000. Two scenarios in 2015 are developed under two kinds of assumptions. Both scenarios show that the trends of population floating from the western and middle regions to the eastern region of China are very outstanding with urbanization and transport development. -
R Graphics Output
China China LEGEND Previously sampled Malaise trap site Ecoregion Alashan Plateau semi−desert North Tibetan Plateau−Kunlun Mountains alpine desert Altai alpine meadow and tundra Northeast China Plain deciduous forests Altai montane forest and forest steppe Northeast Himalayan subalpine conifer forests Altai steppe and semi−desert Northern Indochina subtropical forests Amur meadow steppe Northern Triangle subtropical forests Bohai Sea saline meadow Northwestern Himalayan alpine shrub and meadows Central China Loess Plateau mixed forests Nujiang Langcang Gorge alpine conifer and mixed forests Central Tibetan Plateau alpine steppe Ordos Plateau steppe Changbai Mountains mixed forests Pamir alpine desert and tundra Changjiang Plain evergreen forests Qaidam Basin semi−desert Da Hinggan−Dzhagdy Mountains conifer forests Qilian Mountains conifer forests Daba Mountains evergreen forests Qilian Mountains subalpine meadows Daurian forest steppe Qin Ling Mountains deciduous forests East Siberian taiga Qionglai−Minshan conifer forests Eastern Gobi desert steppe Rock and Ice Eastern Himalayan alpine shrub and meadows Sichuan Basin evergreen broadleaf forests Eastern Himalayan broadleaf forests South China−Vietnam subtropical evergreen forests Eastern Himalayan subalpine conifer forests Southeast Tibet shrublands and meadows Emin Valley steppe Southern Annamites montane rain forests Guizhou Plateau broadleaf and mixed forests Suiphun−Khanka meadows and forest meadows Hainan Island monsoon rain forests Taklimakan desert Helanshan montane conifer forests -
World Bank Document
Document of The World Bank Public Disclosure Authorized Report No: 21860 Public Disclosure Authorized PROJECT APPRAISAL DOCUMENT ON A PROPOSED LOAN IN THE AMOUNT OF USS100 MILLION TO THE PEOPLE'S REPUBLIC OF CHINA Public Disclosure Authorized FORA LIAO RIVER BASIN PROJECT May 21, 2001 Urban Development Sector Unit East Asia and Pacific Region Public Disclosure Authorized CURRENCY EQUIVALENTS (Exchange R.ate Effective May 1, 2001) Currency Unit = Yuan (Y) Y 1.00 = US$0.12 USSLOO = Y8.3 FISCAL YEAR January 1 -- Decernber 31 ABBREVIATIONS AND ACRONYMS AIC Average IncrementalCost LFD LiaoningProvincial Finance Department CAS Country AssistanceStrategy LIEP Liaoning IntegratedEnvironment Program CITC China InternationalTendering I.P LiaoningProvince Company LPG LiaoningProvincial Government CNAO China National Audit Office LRB Liao River Basin COD ChemicalOxygen Demand LRBP Liao River Basin Project DRA DesignReview & Advisory LUCRPOLiaoning Urban Construction& Renewal Project (Consultancy) Office EA EnvironmentalAssessment MOC Ministryof Construction EMP EnvironmentalManagement Plan MOF Ministry of Finance EPB EnvironmentalProtection Bureau i NCB National CompetitiveBidding ERSF Environment Revolving Subloan NGO NongovernmentalOrganization Facility OED Operations EvaluationDepartment ES EnvironmentSubloans PAP Project-AffectedPersons EU EuropeanUnion PDMC Panjin MunicipalDrainage ManagementCompany FMS Financial ManagementSystem PMO Project ManagementOffice GPN GeneralProcurement Notice PRC People's Republicof China ICB InternationalCompetitive -
Harbin Information Pack Harbin, Also Known As the 'Paris of the East'
Harbin Information Pack Harbin, also known as the ‘Paris of the East’. Content Page About Harbin- History Local amenities and facilities – Health, leisure and shopping. Expat – What it is and groups. Climate and lifestyle. Cost of living. Local attractions. Tourist attractions- Harbin and other cities Public transport. About Harbin Harbin is the capital of Heilongjiang Province and located in the northeast of the northeast China Plain. Harbin is famous as a historical and cultural city and renowned for its snow and ice culture. Harbin is also well known for its large number of European-style buildings. Harbin is also known as the ice city. Through the winter Harbin displays thousands of ice sculptures and has hundreds of ice-related activities. Harbin’s History Harbin’s history isn’t as long as some cities. The city is around 110 years old and has become the biggest city in the north-eastern section of China with currently over 10 million people. Harbin was originally a fishing village until the Russians started to build a railroad into the area in1897. Local amenities and facilities Harbin has many shops and leisure facilities available. There are many different places to go shopping but the most famous shopping streets are: Zhongyang Dajie (Central street)- Full of new shopping malls such as Euro Plaza, Parksons, and Lane Crawford that carry international brands and are expensive. There are Nike stores, KFC and interesting Russian thrift stores. The streets are lined with beer gardens during the summer as Harbin is the 3rd biggest city for beer consumption. Guogeli Dajie- The area around here is dotted with Russian buildings and large shopping complexes. -
Monitoring Population Evolution in China Using Time-Series DMSP/OLS Nightlight Imagery
remote sensing Article Monitoring Population Evolution in China Using Time-Series DMSP/OLS Nightlight Imagery Sisi Yu 1,2, Zengxiang Zhang 1 and Fang Liu 1,* 1 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [email protected] (S.Y.); [email protected] (Z.Z.) 2 University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected]; Tel.: +86-10-6488-9205; Fax: +86-10-6488-9203 Received: 16 November 2017; Accepted: 26 January 2018; Published: 28 January 2018 Abstract: Accurate and detailed monitoring of population distribution and evolution is of great significance in formulating a population planning strategy in China. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime lights time-series (NLT) image products offer a good opportunity for detecting the population distribution owing to its high correlation to human activities. However, their detection capability is greatly limited owing to a lack of in-flight calibration. At present, the synergistic use of systematically-corrected NLT products and population spatialization is rarely applied. This work proposed a methodology to improve the application precision and versatility of NLT products, explored a feasible approach to quantitatively spatialize the population to grid units of 1 km × 1 km, and revealed the spatio-temporal characteristics of population distribution from 2000 to 2010. Results indicated that, (1) after inter-calibration, geometric, incompatibility and discontinuity corrections, and adjustment based on vegetation information, the incompatibility and discontinuity of NTL products were successfully solved. Accordingly, detailed actual residential areas and luminance differences between the urban core and the peripheral regions could be obtained. -
Monitoring Spatio-Temporal Changes of Terrestrial Ecosystem Soil Water Use Efficiency in Northeast China Using Time Series Remote Sensing Data
sensors Article Monitoring Spatio-Temporal Changes of Terrestrial Ecosystem Soil Water Use Efficiency in Northeast China Using Time Series Remote Sensing Data Hang Qi, Fang Huang * and Huan Zhai School of Geographical Sciences, Northeast Normal University, Renmin Street No. 5268, Changchun 130024, China; [email protected] (H.Q.); [email protected] (H.Z.) * Correspondence: [email protected]; Tel.: +86-431-8509-9550 Received: 19 February 2019; Accepted: 21 March 2019; Published: 26 March 2019 Abstract: Soil water use efficiency (SWUE) was proposed as an effective proxy of ecosystem water use efficiency (WUE), which reflects the coupling of the carbon–water cycle and function of terrestrial ecosystems. The changes of ecosystem SWUE at the regional scale and their relationships with the environmental and biotic factors are yet to be adequately understood. Here, we aim to estimate SWUE over northeast China using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity data and European Space Agency climate change initiative (ESA CCI) soil moisture product during 2007–2015. The spatio-temporal variations in SWUE and their linkages to multiple factors, especially the phenological metrics, were investigated using trend and correlation analysis. The results showed that the spatial heterogeneity of ecosystem SWUE in northeast China was obvious. SWUE distribution varied among vegetation types, soil types, and elevation. Forests might produce higher photosynthetic productivity by utilizing unit soil moisture. The seasonal variations of SWUE were consistent with the vegetation growth cycle. Changes in normalized difference vegetation index (NDVI), land surface temperature, and precipitation exerted positive effects on SWUE variations. The earlier start (SOS) and later end (EOS) of the growing season would contribute to the increase in SWUE. -
Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China
land Article Observed Vegetation Greening and Its Relationships with Cropland Changes and Climate in China Yuzhen Zhang 1,* , Shunlin Liang 2 and Zhiqiang Xiao 3 1 Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China 2 Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA; [email protected] 3 State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; [email protected] * Correspondence: [email protected] Received: 25 July 2020; Accepted: 14 August 2020; Published: 16 August 2020 Abstract: Chinese croplands have changed considerably over the past decades, but their impacts on the environment remain underexplored. Meanwhile, understanding the contributions of human activities to vegetation greenness has been attracting more attention but still needs to be improved. To address both issues, this study explored vegetation greening and its relationships with Chinese cropland changes and climate. Greenness trends were first identified from the normalized difference vegetation index and leaf area index from 1982–2015 using three trend detection algorithms. Boosted regression trees were then performed to explore underlying relationships between vegetation greening and cropland and climate predictors. The results showed the widespread greening in Chinese croplands but large discrepancies in greenness trends characterized by different metrics. Annual greenness trends in most Chinese croplands were more likely nonlinearly associated with climate compared with cropland changes, while cropland percentage only predominantly contributed to vegetation greening in the Sichuan Basin and its surrounding regions with leaf area index data and, in the Northeast China Plain, with vegetation index data. -
Spatial Distribution of Aerosol Microphysical and Optical Properties And
https://doi.org/10.5194/acp-2019-405 Preprint. Discussion started: 28 May 2019 c Author(s) 2019. CC BY 4.0 License. 1 Spatial distribution of aerosol microphysical and optical properties and 2 direct radiative effect from the China Aerosol Remote Sensing Network 3 Huizheng Che1*, Xiangao Xia2,3, Hujia Zhao1,4, Oleg Dubovik5, Brent N. 4 Holben6, Philippe Goloub5, Emilio Cuevas-Agulló7, Victor Estelles8, Yaqiang 5 Wang1, Jun Zhu9 Bing Qi10, Wei Gong 11, Honglong Yang12, Renjian Zhang13, 6 Leiku Yang14, Jing Chen15, Hong Wang1, Yu Zheng1, Ke Gui1,2,Xiaochun 7 Zhang16, Xiaoye Zhang1* 8 1 State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric 9 Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 10 100081, China 11 2 Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), 12 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, 13 China 14 3 University of Chinese Academy of Science, Beijing, 100049, China 15 4 Institute of Atmospheric Environment, CMA, Shenyang, 110016, China 16 5 Laboratoire d’Optique Amosphérique, Université des Sciences et Technologies de 17 Lille, 59655, Villeneuve d’Ascq, France 18 6 NASA Goddard Space Flight Center, Greenbelt, MD, USA 19 7 Centro de Investigación Atmosférica de Izaña, AEMET, 38001 Santa Cruz de 20 Tenerife , Spain 21 8 Dept. Fisica de la Terra i Termodinamica, Universitat de Valencia, C/ Dr. Moliner 50, 22 46100 Burjassot, Spain 23 9 Collaborative Innovation Center on Forecast and Evaluation of Meteorological 24 Disasters, Nanjing University of Information Science & Technology, Nanjing, 210044, 25 China 26 10 Hangzhou Meteorological Bureau, Hangzhou, 310051, China 27 11 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote 28 Sensing, Wuhan University, Wuhan, 430079, China 29 12 Shenzhen Meteorological Bureau, Shenzhen, 518040, China 30 13 Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, 31 Institute of Atmospheric Physics, Beijing, 100029, Chinese Academy of Sciences. -
Spatial Distribution of Aerosol Microphysical and Optical Properties and Direct Radiative Effect from the China Aerosol Remote Sensing Network
Atmos. Chem. Phys., 19, 11843–11864, 2019 https://doi.org/10.5194/acp-19-11843-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Spatial distribution of aerosol microphysical and optical properties and direct radiative effect from the China Aerosol Remote Sensing Network Huizheng Che1, Xiangao Xia2,3, Hujia Zhao1,4, Oleg Dubovik5, Brent N. Holben6, Philippe Goloub5, Emilio Cuevas-Agulló7, Victor Estelles8, Yaqiang Wang1, Jun Zhu9, Bing Qi10, Wei Gong11, Honglong Yang12, Renjian Zhang13, Leiku Yang14, Jing Chen15, Hong Wang1, Yu Zheng1, Ke Gui1, Xiaochun Zhang16, and Xiaoye Zhang1 1State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China 2Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China 3School of the Earth Science, University of Chinese Academy of Science, Beijing, 100049, China 4Environmental and Meteorological Department, Institute of Atmospheric Environment, CMA, Shenyang, 110016, China 5Laboratoire d’Optique Amosphérique, Université des Sciences et Technologies de Lille, 59655, Villeneuve d’Ascq, France 6NASA Goddard Space Flight Center, Greenbelt, MD, USA 7Centro de Investigación Atmosférica de Izaña, AEMET, 38001 Santa Cruz de Tenerife, Spain 8Dept. Fisica de la Terra i Termodinamica, Universitat de València, C/ Dr. Moliner 50, 46100 Burjassot, Spain 9Collaborative -
Two-Way Feedback Mechanism Between Unfavorable 2 Meteorological Conditions and Cumulative Aerosol 3 Pollution Exists in Various Haze Regions of China
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1077 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 5 November 2018 c Author(s) 2018. CC BY 4.0 License. 1 Two-way feedback mechanism between unfavorable 2 meteorological conditions and cumulative aerosol 3 pollution exists in various haze regions of China 4 Junting Zhong1, Xiaoye Zhang1,2*, Yaqiang Wang1, Jizhi Wang1, Xiaojing Shen1, 5 Hongsheng Zhang3, Tijian Wang4, Zhouqing Xie2,5,6, Cheng Liu2,5,6, Hengde Zhang7, 6 Tianliang Zhao8, Junying Sun1, Shaojia Fan9, Zhiqiu Gao8, Yubin Li8, Linlin Wang10 7 1Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, 8 China. 9 2Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, 10 Xiamen, 361021, China. 11 3Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic 12 Sciences, School of Physics, Peking University, Beijing, 100081, China 13 4School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China 14 5Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, 15 Chinese Academy of Sciences, Hefei, 230031, China 16 6School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, 17 China 18 7National Meteorological Center, China Meteorological Administration, Beijing, 100081, China 19 8Nanjing University of Information Science & Technology, Nanjing, 210044, China 20 9Sun Yat-sen University, Guangzhou, 510275, China 21 10State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute 22 of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 23 Correspondence to: Xiaoye Zhang ([email protected]) 24 Abstract.