Sanjiangyuan National Park

Brief Introduction: Sanjiangyuan National Park

Number of Datasets: 53

  • MODIS daily cloud-free snow cover area product for Sanjiangyuan from 2000 to 2018

    MODIS daily cloud-free snow cover area product for Sanjiangyuan from 2000 to 2018

    The dataset was produced based on MODIS data. Parameters and algorithm were revised to be suitable for the land cover type in the Three-River-Source Regions. By using the Markov de-cloud algorithm, SSM/I snow water equivalent data was fused to the result. Finally, high accuracy daily de-cloud snow cover data was produced. The data value is 0(no snow) or 1(snow). The spatial resolution is 500m, the time period is from 2000-2-24 to 2018-12-31. Data format is geotiff, Arcmap or python+GDAL were recommended to open and process the data.

    2019-12-17 1291 19 View Details

  • The Boundary Dataset of The Three-River-Source National Park

    The Boundary Dataset of The Three-River-Source National Park

    The Three-River-Source National Park with an area of 123,100 km2 and include three sub regions, they are source region of the Yangtze River in the national park, source region of Yellow River in the national park and source region of Lancang River in the national park. The national park is located between longitude 89°50'57" -- 99°14'57", latitude 32°22'36" -- 36°47'53". It accounts for 31.16% of the total area of Three-River-Source region. This data set is generated by digitizing the location map of Three-River-Source national park in the comprehensive planning of Three-River-Source national park. The data include the boundary for the national park. Data format is Shapefile. Arcmap is recommended to open the data.

    2019-12-17 3124 164 View Details

  • The Boundary Dataset of The Three-River-Source National Park

    The Boundary Dataset of The Three-River-Source National Park

    The Three-River-Source National Park with an area of 123,100 km2 and include three sub regions, they are source region of the Yangtze River in the national park, source region of Yellow River in the national park and source region of Lancang River in the national park. The national park is located between longitude 89°50'57" -- 99°14'57", latitude 32°22'36" -- 36°47'53". It accounts for 31.16% of the total area of Three-River-Source region. This data set is generated by digitizing the location map of Three-River-Source national park in the comprehensive planning of Three-River-Source national park. The data include the boundary for the national park. Data format is Shapefile. Arcmap is recommended to open the data.

    2019-12-17 3124 164 View Details

  • MODIS NDVI based phenology for the Three-River-Source National Park from 2001 to 2018

    MODIS NDVI based phenology for the Three-River-Source National Park from 2001 to 2018

    This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2018. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.

    2019-12-17 1987 45 View Details

  • Permafrost stability type map for Sanjiangyuan in 2010s

    Permafrost stability type map for Sanjiangyuan in 2010s

    The permafrost stability map was created based on the classification system proposed by Guodong Cheng (1984), which mainly depended on the inter-annual variation of deep soil temperature. By using the geographical weighted regression method, many auxiliary data was fusion in the map, such as average soil temperature, snow cover days, GLASS LAI, soil texture and organic from SoilGrids250, soil moisture products from CLDAS of CMA, and FY2/EMSIP precipitation products. The permafrost stability data spatial resolution is 1km and represents the status around 2010. The following table is the permafrost stability classification system. The data format is Arcgis Raster.

    2019-12-17 1123 27 View Details

  • Natural places names dataset at 1:250,000 in Sanjiangyuan Region (2015)

    Natural places names dataset at 1:250,000 in Sanjiangyuan Region (2015)

    This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, water system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example NAME Name Ramsay Laboniwa PINYIN Chinese Pinyin Lamusailabaoniwa CLASS Toponymic Classification Code HB

    2019-09-15 1711 23 View Details

  • Vegetation quadrat survey dataset in Maduo County (2016)

    Vegetation quadrat survey dataset in Maduo County (2016)

    These are the vegetation quadrat survey data of the alpine grassland and alpine meadow in Maduo County in September 2016. The dimensions of the square quadrat are 50 cm x 50 cm. The main contents of the survey include coverage, species name, vegetation height, biomass (dry weight and fresh weight), the latitude and longitude coordinates of the quadrat, slope, aspect, slope position, soil type, vegetation type, surface features (litter, gravel, wind erosion, water erosion, saline-alkaline spots, etc.), use patterns, utilization intensity and others.

    2019-09-15 1205 29 View Details

  • The places names dataset at 1:250,000 in Sanjiangyuan region (2015)

    The places names dataset at 1:250,000 in Sanjiangyuan region (2015)

    This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 residential place names (AANP) in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AANP): Attribute Item Description Fill in Example NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun CLASS Geographical Name Classification Code AK GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township

    2019-09-15 1417 17 View Details

  • GIMMS3g NDVI-based phenology for Sanjiangyuan (1982-2015)

    GIMMS3g NDVI-based phenology for Sanjiangyuan (1982-2015)

    The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.

    2019-09-15 1410 19 View Details

  • Dataset of net primary productivity in Sanjiangyuan region (2000-2015)

    Dataset of net primary productivity in Sanjiangyuan region (2000-2015)

    Monthly meteorological data of Sanjiangyuan includes 32 national standard meteorological stations. There are 26 variables: average local pressure, extreme maximum local pressure, date of extreme maximum local pressure, extreme minimum local pressure, date of extreme minimum local pressure, average temperature, extreme maximum temperature, date of extreme maximum temperature, extreme minimum temperature and date of extreme minimum temperature, average temperature anomaly, average maximum temperature, average minimum temperature, sunshine hours, percentage of sunshine, average relative humidity, minimum relative humidity, date of occurrence of minimum relative humidity, precipitation, days of daily precipitation >=0.1mm, maximum daily precipitation, date of maximum daily precipitation, percentage of precipitation anomaly, average wind speed, maximum wind speed, date of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed and occurrence date of maximum wind speed. The data format is txt, named by the site ID, and each file has 26 columns. The names and units of each column are explained in the SURF_CLI_CHN_MUL_MON_readme.txt file. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters

    2019-09-15 1384 54 View Details

  • The boundaries of the source regions in Sanjiangyuan region (2018)

    The boundaries of the source regions in Sanjiangyuan region (2018)

    The data set contains the boundaries of the three source regions of the Yellow River, the Yangtze River and the Lancang River, the boundary of the whole Sanjiangyuan region and the boundaries of the counties within the basin. The observation projects include the boundaries of the three source regions of the Yellow River, the Yangtze River and the Lancang River, the boundary of the whole Sanjiangyuan region and the boundaries of the counties within the basin.

    2019-09-15 1991 106 View Details

  • MODIS NDVI based phpenology for Sanjiangyuan (2001-2014)

    MODIS NDVI based phpenology for Sanjiangyuan (2001-2014)

    The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.

    2019-09-14 1660 22 View Details

  • The meteorological data of Mt. Qomolangma, Namco, and Linzhi Stations on the Tibetan Plateau (2006-2008)

    The meteorological data of Mt. Qomolangma, Namco, and Linzhi Stations on the Tibetan Plateau (2006-2008)

    The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.

    2019-09-14 1381 34 View Details

  • GF-1 NDVI dataset in Maduo County (2016)

    GF-1 NDVI dataset in Maduo County (2016)

    This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.

    2019-09-14 1297 19 View Details

  • 300-m ESA climate change initiative land cover (CCI-LC) in Sanjiangyuan (1992-2015)

    300-m ESA climate change initiative land cover (CCI-LC) in Sanjiangyuan (1992-2015)

    The data set contains land cover data sets from the Yellow River Source, the Yangtze River Source, and the Lancang River from 1992 to 2015. A total of 22 land cover classifications based on the UN Land Cover Classification System were included. NOAA AVHRR, SPOT, ENVISAT, PROBA-V and other vegetation classification products were integrated. In China, (1) first, combined with the 1:100,000 vegetation classification (2007) of China, quality correction and control were performed, and (2) the vegetation classification of China emphasized the combination with climate zones, when correcting CCI-LC, climate divisions and the corresponding vegetation types were combined, and the data label was comprehensively revised.

    2019-09-13 1792 53 View Details

  • SPOT Vegetation NDVI-based phenology for Sanjiangyuan (1999-2013)

    SPOT Vegetation NDVI-based phenology for Sanjiangyuan (1999-2013)

    The data set includes the estimated data of the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on 10-day synthetic NDVI products from the SPOT satellite. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage is from 1999 to 2013, and the spatial resolution is 1 km.

    2019-09-13 1394 15 View Details

  • Source region of the Yangtze River - land cover and vegetation type ground verification point dataset

    Source region of the Yangtze River - land cover and vegetation type ground verification point dataset

    The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of the Yangtze River (in the south of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

    2019-09-12 1830 37 View Details

  • Source region of Yellow River - land cover and vegetation type ground verification point dataset

    Source region of Yellow River - land cover and vegetation type ground verification point dataset

    The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

    2019-09-12 1642 35 View Details

  • Hoh Xil - land cover and vegetation type ground verification point dataset

    Hoh Xil - land cover and vegetation type ground verification point dataset

    The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

    2019-09-11 1590 24 View Details

  • Primary road network dataset at 1:1000 000 in the Sanjiangyuan region (2017)

    Primary road network dataset at 1:1000 000 in the Sanjiangyuan region (2017)

    This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2017. The data set is 1:1 million traffic data in Sanjiangyuan area, including road (LRDL) and railway (LRRL) layers. Highway (LRDL) includes national, provincial, county, Township and other highways; Railway (LRRL) includes standard rail, narrow rail, subway and light rail. Highway (LRDL) Attribute Item Name and Definition: Attribute Item Description Fill in Example GB National Standard Classification Code 420301 RN Road Number X828 NAME Road Name RTEG Road Grade IV TYPE Road Type Viaduct Meaning of Highway (LRDL) Attribute Item: Attribute Item Code Description GB 420101 National Highway 420102 Building China Road 420201 Provincial Highway 420102 Provincial Highway in Architecture 420301 County Road 420302 Jianzhong County Road 420400 Rural Road 420800 Tractor ploughing Road 440100 Simple Highway 440200 Rural Road 440300 Trail Name and definition of railway (LRRL) attribute item: Attribute Item Description Fill in Example GB National Standard Classification Code 410101 RN Railway No. 0907 NAME Railway Name Qinghai-Tibet Railway TYPE Railway Type Elevated

    2019-05-28 1192 17 View Details