Sanjiangyuan National Park

Brief Introduction: Sanjiangyuan National Park

Number of Datasets: 53

  • MODIS vegetation index dataset in Sanjiangyuan (2000-2018)

    MODIS vegetation index dataset in Sanjiangyuan (2000-2018)

    The data set is MODIS vegetation index data (MOD13Q1). The source areas of the three rivers are extracted to carry out the research and analysis of the source areas of the three rivers separately. MOD13Q1 is a 16-day composite vegetation index, including normalized vegetation index (NDVI) and enhanced vegetation index (EVI). The spatial scope of Sanjiang Source covers two MODIS files (h25v05 and h26v05). Data storage format is hdf. Each file contains 12 bands: Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Data Quality (VI Quality), Red Reflectance, Near Infrared Reflectance (NIR Reflectance), Blue Reflectance, Mid Infrared Reflectance, Observation. Viewzenith angle, sun zenith angle, relative azimuth angle, composite day of the year and pixel reliability. The data format of this data set is hdf, spatial resolution is 250m, temporal resolution is 16 days, time range: February 2000 to October 2018.

    2019-09-13 741 14 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.

    2019-09-13 781 24 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.

    2019-09-13 663 8 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.

    2019-09-12 731 22 View Details

  • Automatic weather station dataset from Guoluo station (2017)

    Automatic weather station dataset from Guoluo station (2017)

    The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.

    2019-09-12 583 17 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.

    2019-09-12 643 17 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.

    2019-09-11 605 15 View Details

  • Monthly standard weather station dataset in Sanjiangyuan (1957-2015)

    Monthly standard weather station dataset in Sanjiangyuan (1957-2015)

    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. site_id lat lon elv name_cn 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Wulan 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Qiapuqia 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wudaoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Togton He 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maduo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98.10 9200.00 Shiqu 56043 34.47 100.25 3719.00 Guoluo 56046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Maqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suo County 56116 31.42 95.60 3873.10 Dingqing 56125 32.20 96.48 3643.70 Nangqian 56128 31.22 96.60 3810.00 Leiwuqi 56137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Seda

    2019-05-28 769 27 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

    2019-05-28 542 11 View Details

  • Dataset of growing season average NDVI changing trends in Three River Source National Park (2000-2018)

    Dataset of growing season average NDVI changing trends in Three River Source National Park (2000-2018)

    Based on the average NDVI (spatial resolution 250m) of MODIS during the growing season from 2000 to 2018, the trend of NDVI was calculated by using Mann-Kendall trend detection method. Three parks of Three River Source National Park are calculated (CJYQ: Yangtze River Park; HHYYQ: Yellow River Park; LCJYQ: Lancang River Park). CJYQ_NDVI_trend_2000_2018_ok.tif: Changjiang Source Park NDVI trend. CJYQ_NDVI_trend_2000_2018_ok_significant.tif: Changjiang Source Park NDVI change trend, excluding the area that is not significant (p > 0.05). CJYYQ_gs_avg_NDVI_2000.tif: The average NDVI of the Yangtze River Source Park in 2000 growing season. Unit NDVI changes every year.

    2019-05-28 745 35 View Details

  • Glacier distribution map in the Sanjiangyuan based on the second glacier inventory (2008)

    Glacier distribution map in the Sanjiangyuan based on the second glacier inventory (2008)

    This data set is extracted from the second Glacier Inventory Data Set of China for Three River Source area. The file is SHP format. The attribute data are as follows: Glc_Name (glacier name), Drng_Code (basin code), FCGI_ID (first glacier catalogue code), GLIMS_ID (GLIMS glacier code), Mtn_Name (mountain system name), Pref_Name (administrative division), Glc_Long (glacier longitude), Glc_Lati (glacier latitude), Glc_Area (glacier area), Abs_Accu (absolute area accuracy), Rel_Accu (relative area accuracy), Deb_Area (surface Moraine Area), Deb_A_Accu (absolute accuracy of surface moraine Area), Deb_R_Accu (relative accuracy of surface moraine area)、Glc_Vol_A (estimation of glacier volume 1)、Glc_Vol_B (estimation of glacier volume 2)、Max_Elev (maximum glacier elevation)、Min_Elev (minimum glacier elevation)、Mean_Elev (average glacier elevation)、MA_Elev (median area height of glacier)、Mean_Slp (average glacier slope)、Mean_Asp (average glacier slope direction)、Prm_Image (major remote sensing data)、Aux_Image (auxiliary remote sensing data)、Rep_Date (glacier catalogue represents date)、Elev_Src (elevation data source)、Elev_Date (elevation represents date)、Compiler (glacier cataloguing editor)、Verifier (glacier cataloguing verifier).

    2019-05-28 723 11 View Details

  • Dataset of wild animal distribution investigation in Three River Source National Park (2017)

    Dataset of wild animal distribution investigation in Three River Source National Park (2017)

    The data set was obtained from the background survey of wildlife diversity in Three River Source National Park by Northwest Institute of Plateau Biology, Chinese Academy of Sciences. The time range of the data set is 2017, and the survey area is Three River Source National Park. The survey species include a variety of rare wildlife such as Equus kiang, Canis lupus, Vulpes vulpes, Cervus elaphus, Accipiter nisus, Phoenicurus erythrogastrus, Prionailurus bengalensis, Buteo hemilasius, Procapra picticaudata, Tetraogallus tibetanus, Perdix hodgsoniae, Falco cherrug, etc.

    2019-05-28 622 15 View Details

  • SeaWiFS NDVI dataset for Sanjiangyuan (1997-2007)

    SeaWiFS NDVI dataset for Sanjiangyuan (1997-2007)

    The data set is NDVI data of long time series acquired by SeaWiFS. The time range of the data set is from September 1997 to 2007. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 15 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 4 km, temporal resolution is 15 days, time range: 256 days in 1997 to 365 days in 2007.

    2019-05-26 516 9 View Details

  • Drone orthophoto image and DSM of Qumalai alpine meadow plot (2018)

    Drone orthophoto image and DSM of Qumalai alpine meadow plot (2018)

    On August 19, 2018, DJI UAV was used to aerial photograph the alpine meadow sample in Qumali County, the source Park of the Yangtze River. The overlap degree of adjacent photographs was not less than 70% according to the set flight route. The Orthophoto Image and DSM were generated using the photographs taken. The Orthophoto Image included three bands of red, green and blue. The ground resolution of the Orthophoto Image was 2.5 cm, and the area of the image was 860 m x 770 m, and the resolution of DSM. It's 4.5cm.

    2019-05-25 463 5 View Details

  • Drone photoes of Qumalai alpine meadow plot (2018)

    Drone photoes of Qumalai alpine meadow plot (2018)

    On August 20, 2018, a DJI Elf 4 UAV camera was used to take aerial photographs of the alpine meadow sample in Qumali County, which is located in the source area of the Yangtze River. A total of 31 routes were set up. The flight altitude was 100 m, and the overlap degree of adjacent photographs was not less than 70%. A total of 664 aerial photographs were obtained and stored in the Drone Photoes of Qumalai (2018) folder.

    2019-05-22 516 8 View Details

  • The resident site distribution data at 1:1000 000 in Sanjiangyuan (2017)

    The resident site distribution data at 1:1000 000 in Sanjiangyuan (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. In order to use the data more conveniently, the source of the three rivers is spliced and cut as a whole, so as to facilitate the use of the source area of the three rivers. The data trend is 2017. This data set is composed of 1:1 million residential areas in Sanjiangyuan area, including residential land (RESA) and residential place (RESP) layers, RESP residential area (point) layers, including ordinary houses, grazing areas and so on.

    2019-05-20 465 15 View Details

  • Natural places names dataset at 1:1000 000 in Sanjiangyuan region (2017)

    Natural places names dataset at 1:1000 000 in 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. This data set is composed of 1:1 million natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, river 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 CLASS Toponymic Classification Code NAME in Chinese words PINYIN in Chinese Pinyin

    2019-05-17 592 8 View Details

  • The NPP products of MODIS in Sanjiangyuan (1985-2015)

    The NPP products of MODIS in Sanjiangyuan (1985-2015)

    The data set contains NPP products data produced by the maximum synthesis method of the three source regions of the Yellow River, the Yangtze River and the Lancang River. The data of remote sensing products MOD13Q1, MOD17A2, and MOD17A2H are available on the NASA website (http://modis.gsfc.nasa.gov/). The MOD13Q1 product is a 16-d synthetic product with a resolution of 250 m. The MOD17A2 and MOD17A2H product data are 8-d synthetic products, the resolution of MOD17A2 is 1 000 m, and the resolution of MOD17A2H is 500 m. The final synthetic NPP product of MODIS has a resolution of 1 km. The downloaded MOD13Q1, MOD17A2, and MOD17A2H remote sensing data products are in HDF format. The data have been processed by atmospheric correction, radiation correction, geometric correction, and cloud removal. 1) MRT projection conversion. Convert the format and projection of the downloaded data product, convert the HDF format to TIFF format, convert the projection to the UTM projection, and output NDVI with a resolution of 250 m, EVI with a resolution 250 m, and PSNnet with resolutions of 1 000 m and 500 m. 2) MVC maximum synthesis. Synthesize NDVI, EVI, and PSNnet synchronized with the ground measured data by the maximum value to obtain values corresponding to the measured data. The maximum synthesis method can effectively reduce the effects of clouds, the atmosphere, and solar elevation angles. 3) NPP annual value generated from the NASA-CASA model.

    2019-05-16 824 23 View Details

  • Population centres data at 1:250 000 in Sanjiangyuan region (2015)

    Population centres data 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 consists of 1:250,000 residential areas in Sanjiangyuan area, including two layers of residential land (RESA) and residential place (RESP). Resident land (RESA) mainly refers to the outline of surface residential area, and residential place (RESP) includes ordinary houses, shacks, caves, Mongolian yurts, grazing places, etc. Names and definitions of RESA attribute items: Attribute Item Description Fill in Example GB National Standard Classification Code 310200 Name and Definition of Residential Place (RESP) Attribute Item: Attribute Item Description Fill in Example GB National Standard Classification Code 310200 ANGLE Angle 67

    2019-05-16 531 14 View Details

  • Drone orthophoto image and DSM of Qinghai Hoh Xil plot (2018)

    Drone orthophoto image and DSM of Qinghai Hoh Xil plot (2018)

    On August 22, 2018, a DJI camera was used in the fixed sample of Lancang River headwaters. The overlap degree of adjacent photos was not less than 70% according to the set flight route. The Orthophoto Image and DSM were generated using the photographs taken. The Orthophoto Image included three bands of red, green and blue, with a ground resolution of 2.5 cm, a shooting area of 1000m x 1000m and a DSM resolution of 4.5 cm. Due to the communication failure, the middle four airstrips were not photographed, so there was a band in the middle of the image missing.

    2019-05-16 508 5 View Details