Table of Content

    26 December 2014, Volume 69 Issue s1 Previous Issue    Next Issue
    Global change research data publishing and repository
    LIU Chuang, SHI Ruixiang, CHEN Wenbo
    2014, 69 (s1):  3-11.  doi: 10.11821/dlxb2014S000
    Abstract ( )   PDF (4335KB) ( )   Save
    The region of the Roof of the World is the highest region in the world, which covers the Qinghai-Tibet Plateau, the Hengduan Mountains, the Himalaya Mountains, the Hindu Kush, and the Pamir Plateau of nine countries, including China, Myanmar, Nepal, Bhutan, India, Pakistan, Afghanistan, Tajikistan, and Kyrgyzstan. We have produced eco-regional boundary data both in Google Earth .kmz and Arc/Info .shp formats, using scenarios of elevation more than 4000 m above sea level, land slopes more than 7 degrees, and integrated with the remote sensing images. Our boundary around the region is 22,089 km, with the total area of the region being 4,000,947 km2. Each of the 40 geographical parts of the boundary is identified and coded.
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    Eco-regional boundary data of the Roof of the World
    LIU Chuang, SHI Ruixiang, LV Tingting, CHEN Wenbo, ZHOU Xiang, WANG Zhengxing
    2014, 69 (s1):  12-19.  doi: 10.11821/dlxb2014S001
    Abstract ( )   PDF (11235KB) ( )   Save
    This dataset, titled as elevation cluster dataset covering the eco-region of the Roof of the World (ROTWEVC for short), is 30-m raster data on land elevation classification in the eco-region of the Roof of the World. 17 classes of land elevation levels are identified from less than 500 meters above sea level to more than 8000 meters. The dataset is developed based on ASTER GDEM 2 and integrated partly with SRTM where the former data is not available. By statistics, the land located between 4500-5000 meters above sea level is 910,860 km2, which occupies 22.77% of the region; while the land above 4000 meters is 2,150,236 km2, 53.75% of the region is located in this high area. The area with elevation less than 4000 meters is mainly distributed around area of plateau, where is the transition zone from low altitude plain to high altitude plateau. 475 datasets with 1 × 1 degree latitude and longitude, the total volume of the data is 249 MB, compressed 194 MB.
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    Elevation cluster dataset covering the eco-region of the Roof of the World
    LIU Chuang, SHI Ruixiang, LV Tingting, CHEN Wenbo, WANG Zhengxing, ZHOU Xiang
    2014, 69 (s1):  20-24.  doi: 10.11821/dlxb2014S002
    Abstract ( )   PDF (7169KB) ( )   Save
    The land slope cluster data of the eco-region of the Roof of the World (ROTWSLC) is the basic for ecosystem analysis of the region. It was derived from ASTER GDEM 2 with the cluster models in slope degree and slope percentage. 7 clusters of the land slope according to the degree and 6 clusters according the percentage were identified. The statistics of each cluster had been calculated. The data indicates that the total area of land which slope was equal and higher than 7º was 72.02% of the region and the area of land which slope was equal and higher than 10% was 77.05% of the region. The data in raster were break down into 475 tiles of each of two groups of clusters by 1º x 1º.
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    Land slope cluster dataset of the eco-region of the Roof of the World
    JIANG Dong, FU Jingying, HUANG Yaohuan
    2014, 69 (s1):  25-29.  doi: 10.11821/dlxb2014S003
    Abstract ( )   PDF (6165KB) ( )   Save
    Biodiesel from bio-energy crops is expected to play an increase role in solving energy crisis. Thus, large-scale cultivation of bio-energy crops causes worldwide attention. Asia is the most populous continent and has a really fast speed of economic growth which causes a massive increase in energy demand. This dataset from the suitability assessment of marginal land resources for cultivating bio-energy crops without affecting food security. A multi-factor analysis method was applied to identify marginal land available for bioenergy development and multiple datasets were used as background including the natural characteristics of bio-energy crops and geographic data. The results indicate that the marginal land resources in Asia are about 16.99 million km2. The validations were conducted in Wuhan Botanical Garden of the Chinese Academy of Sciences and South China Botanical Garden with the accuracy higher than 85%. This dataset provides important support to the large-scale cultivation of energy plants and sustainable development of bioenergy.
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    Suitability assessment data on marginal land resources for cultivating bio-energy crops of Asia
    LIU Chuang
    2014, 69 (s1):  30-33.  doi: 10.11821/dlxb2014S004
    Abstract ( )   PDF (7225KB) ( )   Save
    The launching of rocket and satellite was one of the major tasks of International Geophysical Year (IGY). The first of Russia satellite named Sputnik 1 was successful launched on 4 October 1957, which is recognized as a milestone for a new era - a space age. Besides the satellites of Sputnik 1, 2 and 3, Luna 1, 2 and 3 from Russia, the Explorer 1, 3, 4, 6, 7, Vanguard 1, 2, 3, Pioneer 1,2,3, 4, Discoverer 1, 2, 5, 6, 7, 8 and Score from USA were successful launched from 1957-1959 in IGY (The Rocket and Satellite task was extended for implementation for one more year in 1959 than that of tasks else from 1957-1958). For celebrating and commemorating the great achievements in human history, 23 countries in the world issued post stamps during IGY, the first three years of the space era. The collection of post stamps consisted of 349 pieces from 23 countries are archived in LIN Chao Geomuseum (www.geomuseum.cn), The dataset consisted of 349 .jpg files for all of archived stamps and one table file which is the list of the collections. The code, image, date issued, country issued, contributor and descriptions are listed at the table items.
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    Dataset of post stamps on rocket and satellite(1957-1959)
    FU Jingying, JIANG Dong, HUANG Yaohuan
    2014, 69 (s1):  34-40.  doi: 10.11821/dlxb2014S005
    Abstract ( )   PDF (9540KB) ( )   Save
    Census data with the spatial scale of administrative district cannot reveal the spatial differences sufficiently. Meanwhile, this kind of statistical population data cannot be integrated and analyzed with most of other gridded geographic datasets. People in pixels provide an effective way to solve this problem. We established multivariate statistical models for population in 1 KM pixels based on the correlation relationships between population and land use types. The urban population density, traffic conditions, DEM and total amount control were used for model correction. Forty counties with township population data from east, west and middle of China were chose for precision verification. The errors of the spatial population data in these counties are between 4.5% and 13.6% and most of them are less than 10%.
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    1 km grid population dataset of China (2005, 2010)
    HUANG Yaohuan, JIANG Dong, FU Jingying
    2014, 69 (s1):  41-44.  doi: 10.11821/dlxb2014S006
    Abstract ( )   PDF (2396KB) ( )   Save
    The GDP data in China are available in the format of statistical tables with the spatial scale of administrative district in GIS. Sometimes they are not instable, imprecise and not unified with most geographic datasets. GDP in pixels instead of administrative polygons will facilitate the integration and comprehensive analysis between GDP and remote sensing products, such as land use and land cover changes. GDP datasets of China are composed of primary industry GDP, secondary industry GDP, and tertiary industry GDP. The correlative relationships between the land use and three industries were analyzed, and the spatial correlation model was implemented. The model was used to calculate the statistical GDP of every county for each pixel. The 1 km grid GDP datasets of China in 2005 and 2010 were derived. Forty counties including township GDP data were selected for precision verification. The results showed that the errors of the spatial GDP data in these counties were between 6% and 17%.
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    1 km grid GDP data of China (2005, 2010)
    XU Xinliang, LIU Luo
    2014, 69 (s1):  45-48.  doi: 10.11821/dlxb2014S007
    Abstract ( )   PDF (2725KB) ( )   Save
    The cropping rotation is one of essential factors for agriculture in China, because it has been shown to significantly increase the grain yield and promote agricultural development. Cropping Rotation System Data in China (CropSysChina) is the research results of multiple-cropping system in China under climate change. The CropSysChina is spatial-temporal data of multiple-cropping system under the rain-fed and irrigated scenarios at intervals of 10 years from the 1960s to the 2000s with a 1 km spatial resolution with the model support from the Global Agro-Ecological Zones Model (AEZ, FAO, IIASA). The calculated potential multiple-cropping system in 2000 was compared with the actual result from remote sensing monitoring based on MODIS data, and it was consistent with the actual system overall.
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    Cropping rotation system data of China
    GE Quansheng, DAI Junhu, WANG Huanjiong
    2014, 69 (s1):  49-53.  doi: 10.11821/dlxb2014S008
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    The gridded dataset of spring phenology of Fraxinus chinensis in China from 1952 to 2007 includes 56 annual files in this time period. This dataset represent the first leaf date of Fraxinus chinensis stored as ARCGIS standard format with a spatial resolution of 1°. This dataset is the result of phenological research in China, and could reflect the spatial-temporal change of spring phenology in China over the past half century. The research article based on this dataset was cited by the fifth IPCC assess report.
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    The gridded dataset of spring phenology of Fraxinus chinensis in China from 1952 to 2007
    ZHOU Tianmo, ZHU Yunqiang, FU Qiang, HU Zhuowei, YANG Fei
    2014, 69 (s1):  54-56.  doi: 10.11821/dlxb2014S009
    Abstract ( )   PDF (2258KB) ( )   Save
    Based on a series of measures, such as unit transformation, livestock and poultry equivalent normalization, and errors correction etc., we produce pollutant generation coefficients of livestock industry dataset at provincial level in the mainland of China. This dataset includes six main species of livestock and poultry, which are dairy cattle, beef, pig, broiler, laying hen and draft cattle. In addition, this dataset can support more accurate computing and comparing researches on livestock and poultry pollutant production at provincial level.
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    Pollutant coefficients data of livestock industry at provincial level in China
    WANG Liang, XU Xinliang, LIU Luo
    2014, 69 (s1):  57-60.  doi: 10.11821/dlxb2014S010
    Abstract ( )   PDF (522KB) ( )   Save
    Remotely sensed dataset of grassland degradation in the Qinghai-Tibet Plateau (GLD_Tibet) is a production based on a related research about spatiotemporal changes of grassland degradation on the Qinghai-Tibet Plateau (QTP) over the periods of 1991-2000 and 2001-2012 using Sense's slope and Mann-Kendall trend test. During the 1990s, more than half (53.41%) of the grassland on the QTP was in some degree of degradation, but after 2000, more than three-fourths (78.62%) of the grassland had improved. The dataset provide scientific evidence for monitoring, assessment, and restoration management of alpine grasslands on the QTP, as well as the sustainable development and management of other grassland ecosystems.
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    Remotely sensed dataset of grassland degradation on the Qinghai-Tibet Plateau
    ZHANG Yili, LI Bingyu, ZHENG Du
    2014, 69 (s1):  61-64.  doi: 10.11821/dlxb2014S011
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    The Tibetan Plateau (TP) is an unique geomorphic region composed of specific geomorphic types, such as extreme high mountains, hills, plains, and plateaus of high altitude or sub-high altitude. There are different opinions as to the exact scope of the TP. According to the latest results of long-term fieldwork, questions related to the area and boundaries of the TP were reviewed geographically. The geomorphologic features were used to define the boundary. A 1?1,000,000 geomorphologic map was compiled based on a 1?100,000 aerial photographic map, a 1?500,000 topographic map, and the interpretation of satellite images. The boundary of the TP was delineated by referring to a relief map in 1?3,000,000 scale. The position of the boundary was quantitatively determined with GIS, and an electronic version of the map of the TP was compiled. The main conclusion is that: the TP starts from the southern edge of the Himalayan Range (not including the low Himalayas Mountains); abuts India, Nepal and Bhutan; connects to the northern edge of Kunlun, Altun and Qilian Mountains; and joins the Tarim Basin and Hexi Corridor in Central Asia. To the west of the TP are the Pamir and Karakorum Mountains, bordering on Kyrgyzstan, Tajikistan, Afghanistan, Pakistan, and Kashmir. To the east of it are the Yulong Jokul, Daxueshan, Jiajin, and Qionglai Mountains as well as the south or east piedmont of the Mt. Minshan. The TP joins the Qinling Mountains and the Loess Plateau in its eastern and northeastern part. It ranges from 25°59′37″N to 39°49′33″N, and from 73°29′56″E to 104°40′20″E, covering an area of 2542.30×103 km2, with a total boundary length of about 11,745.96 km.
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    Datasets of the boundary and area of the Tibetan Plateau
    YU Bohua, LV Changhe, LV Tingting, YANG Aqiang, LIU Chuang
    2014, 69 (s1):  65-68.  doi: 10.11821/dlxb2014S012
    Abstract ( )   PDF (1637KB) ( )   Save
    The dataset of regional vegetation changes from 1981-2006 in the Qinghai-Tibet Plateau was developed based on the NOAA AVHRR NDVI from 1981-2006. Seven vegetation change clusters and four vegetation change sub-regions are included in the dataset. The seven vegetation change clusters include the significantly decreased cluster, the decreased cluster, the slightly decreased cluster, the no change cluster, the slightly increased cluster, the increased cluster and the significantly increased cluster. The four vegetation change sub-regions include the western vegetation change increasing sub-region; the western vegetation stable sub-region; the middle vegetation change increasing sub-region; and the eastern vegetation degreasing sub-region.
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    Dataset of regional vegetation changes from 1981 to 2006 in the Qinghai-Tibet Plateau
    YU Xinfang, ZHUANG Dafang, WANG Qiankun
    2014, 69 (s1):  69-71.  doi: 10.11821/dlxb2014S013
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    Phenology is a discipline and comprehensive indicator of climate and environment changes. Forest phenology is a comprehensive biological index to reflect the influence of short-term or long-term climate change on forest growth stages. With the aid of remote sensing technology, we develop a model to calculate the data from phenological observation points into region scale. MODIS provides time-series information both in seasonal and annual changes to study the phenology. Based on MODIS Enhanced Vegetation Index (EVI) from 2000-2010, we extracted forest phenological variables using percentage thresholds method in Northeast China, which include start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS). The phenological data from published papers and field observed data in the same area were used to validate the results. The validation indicates that forest phenophase from MODIS EVI data is feasible.
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    Remote sensing based forest phenology data of Northeast China (2000-2010)
    2014, 69 (s1):  72-76.  doi: 10.11821/dlxb2014S014
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    Time series of snow cover data of Northeast China
    SHI Ruixiang
    2014, 69 (s1):  77-82.  doi: 10.11821/dlxb2014S015
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    Soil sample data for locust plague analysis around Baiyangdian Lake in 2002 (SSDLPA_BaiyangLake_2002) was conducted from 14 soil samples which were collected from June 21-22, 2002 around Baiyangdian Lake, east of China, where locust plague was serious in June 2001. The data includes two parts, part one is the spatial data indicating the geo-location where the samples are located, and part two is the attribute data of samples. The attribute data is consisted of soil texture, pH value, soil water content, soil salinity etc. The data collection was funded by the National Basic Research Program, Ministry of Science and Technology of China, 2000 (MOST 973 program).
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    Soil sample data for locust plague analysis around Baiyangdian Lake in 2002
    XU Xinliang, ZHAI Mengyuan, LIU Luo
    2014, 69 (s1):  83-85.  doi: 10.11821/dlxb2014S016
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    The winter fallow land is significant to agriculture in Yangtze River, because it could be used to increase the grain yield. The information included in the dataset of winter fallow land in middle and lower reaches of Yangtze River (WFL_MLYR) is helpful for understanding agriculture production condition, exploring agriculture production potential and keeping agriculture development sustainable. The dynamic threshold method based on time series MODIS NDVI is an useful practical method to extract spatial information of winter fallow land. The WFL_MLYR are spatial data of winter fallow land from 2007 to 2008 with a 1km spatial resolution. It includes the data on mature date, sowing date and winter fallow fields. The extraction data were compared with the actual result from agricultural meteorological observation station. The accuracy of mature date is more than 91%, and that of sowing date is 86% approximately.
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    Dataset of winter fallow land in middle and lower reaches of Yangtze River (2007-2008)
    SUN Wei, GUO Chunxia, ZHU Yunqiang
    2014, 69 (s1):  86-89.  doi: 10.11821/dlxb2014S017
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    The dataset of soil potential productivity of Hunan province of China is produced at 1km resolution based on progressively calculating models and the data of climate, soil and terrain by using GIS (Geographic Information System) technology. The aim of this study is to get the pixel based soil potential productivity data in 1km resolution from the table based statistical data in administrative regions and polygon based in administrative county boundary GIS data. The dataset includes data about the photosynthetic potential productivity, light and temperature potential productivity, climatic potential productivity and soil potential productivity. The data also covers temperature correction coefficients, moisture correction coefficients and soil correction coefficients. The geographic distribution of predicting soil potential productivity coincides with the one of actual crop productivity.
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    Average soil potential productivity of Hunan Province, China, 1981-2010
    GE Quansheng, DAI Junhu, LIU Haolong, XU Qiongyao, WANG Huanjiong
    2014, 69 (s1):  90-94.  doi: 10.11821/dlxb2014S018
    Abstract ( )   PDF (3354KB) ( )   Save
    Typical Plant Phenological Observation Dataset of Chinese Phenological Observation Network, Beijing (TPPOD_CPON_BJ for short) includes the observational records about three phenophases (first leaf date, first flowering date and full leaf coloring date) of apricot (Prunus armeniaca), black locust (Robinia pseudoacacia), lilac (Syringa oblata), and wild chrysanthemum (Dendranthema indicum). To a certain degree, this dataset could represent the phenological change in North China, and also can provide scientific support for studying the response and adaptation of plants to global climatic changes.
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    Typical plant phenological observation dataset of Chinese phenological observation network, Beijing (1963-2012)
    LIU Chuang, HONG Shirong, LIN Yongxin
    2014, 69 (s1):  95-97.  doi: 10.11821/dlxb2014S019
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    The Geographical Society of China (GSC) celebrated its 100th anniversary in 2009. A series of activities was held in the celebration, including the commemorating LIN Chao's 100th birthday. Professor LIN Chao (1909-1991) of Peking University was the pioneer of Chinese geographers in the International Geographical Union (IGU). The commemorating activities included the followings: (1) A conference as Chinese Geography's New Journey to the World - Remembering Professor LIN Chao's 100th Birthday was held at Peking University in October 2009; (2) Exhibition of historical archives of Chinese geographer in IGU; (3) Opening of the Jingde Hall, the Historical Museum of the Truth School in Jieyang City, Guangdong Province, which was the former residence of LIN's family; (4) LIN Chao's biographical video. The LIN Chao's biographical video was made by Jieyang TV with the help from Geographical Society of China and supported by the Jieyang Government. The Video listed a series of historical records and scenes of LIN Chao's life in geographical research and education. He was the first Ph.D. in Geography at the Liverpool University, UK in 1938, then the head of the Geography Department and Dean of School of Science in Sun Yat-sen University from 1938-1940. He was also one of the founders of Institute of Geography of China and Director of Human Geography Section in 1940. One of his remarkable contributions in Chinese geographical history was his leading role in China joining the International Geographical Union (IGU) in 1949 when he was the Director of Institute of Geography of China. Professor LIN CHAO was the founder of Comprehensive Physical Geography of China and an outstanding professor in geographical education. His unique work on Naming Mt. Qomolangma (Mt. Everest) have made tremendous contribution to geography based on the culture and history of Tibet. He, as the Editor in Chief, led the first encyclopedia of Geography of China published in 1991. The Video was archived in the Digital LIN Chao Geomuseum which was established by IGU, CODATA and GSC in 2011.
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    Professor LIN Chao - pioneer of Chinese geographers in IGU: Remembering LIN Chao in his 100th birthday
    2014, 69 (s1):  98-103.  doi: 10.11821/dlxb2014S020
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