Sanjiangyuan National Park

Brief Introduction:Sanjiangyuan National Park

Publish Datetime:2018-10-20

Number of Datasets:53

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • Snow depth product for Sanjiangyuan from 1980 to 2018

    This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2018. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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.

    2020-10-13 0 View Details

  • 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).

    2020-07-28 0 View Details

  • 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

    2020-06-24 0 View Details

  • Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

    The files in this data set are named as: 1. Pressure of the station: SURF_CLI_CHN_MUL_DAY-PRS-10004-SITEID.TXT 2. Temperature: SURF_CLI_CHN_MUL_DAY-TEM-12001-SITEID.TXT 3. Relative humidity: SURF_CLI_CHN_MUL_DAY-RHU-13003-SITEID.TXT 4. Precipitation: SURF_CLI_CHN_MUL_DAY-PRE-13011-SITEID.TXT 5. Evaporation: SURF_CLI_CHN_MUL_DAY-EVP-13240-SITEID.TXT 6. Wind direction and wind speed: SURF_CLI_CHN_MUL_DAY-WIN-11002-SITEID.TXT 7. Sunshine: SURF_CLI_CHN_MUL_DAY-SSD-14032-SITEID.TXT 8.0cm Ground Temperature: SURF_CLI_CHN_MUL_DAY-GST-12030-0cm-SITEID.TXT Detailed format descriptions for each data file are given in the SURF_CLI_CHN_MUL_DAY_FORMAT.doc file. The meteorological site information contained in this data set is as follows: Site_id lat lon ELV name_En 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Uran 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Chabcha 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wu Daoliang 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 Toto River 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 Maddo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98 98.10 9200.00 Shiqu 56 043 34.47 100.25 3719.00 Golo 56 046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56 067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Marqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suoxian 56116 31.42 95.60 3873.10 Ding Qing 56125 32.20 96.48 3643.70 Xiangqian 56128. 31.22. 96.60. 3810.00 Leiwuqi 56 137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Saida

    2020-06-24 0 View Details

  • The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. 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 with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.

    2020-06-16 0 View Details

  • 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.

    2020-06-03 0 View Details

  • 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.

    2020-06-03 0 View Details

  • Vegetation quadrat survey dataset in the Three-River Headwater Region (2017)

    The data set contains vegetation quadrat survey data for Qumalai, Mado and Hoh Xil from August 3, 2017, to August 9, 2017. The main survey contents are coverage, altitude and above-ground biomass. It covers three vegetation types: alpine grassland, alpine wetland and alpine meadow. The latitude, longitude, altitude, total coverage, species name and quantity of the quadrat were recorded, and three samples of each species were selected to measure the altitude, the total above-ground biomass, and the above-ground biomass of each category.

    2020-06-03 0 View Details

  • 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.

    2020-06-03 0 View Details

  • Drone photoes of Qumalai wetland plot (2018)

    On August 19, 2018, the wetland sample in Qumali County, located in the source area of the Yangtze River, was aerially photographed by DJI Elf 4 UAV. A total of 31 routes were set up, flying at a height of 100 m, and the overlap of adjacent photographs was not less than 70%. A total of 1551 aerial photographs were obtained and stored in two folders named "Drone Photoes Part1" and "Drone Photoes Part2".

    2020-06-03 0 View Details

  • 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.

    2020-06-03 0 View Details

  • Spot vegetation NDVI dataset for Sanjiangyuan (1998-2013)

    The data set is extracted from the NDVI data of long time series acquired by VEGETATION sensor on SPOT satellite. The time range of the data set is from May 1998 to 2013. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 10 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 1 km, temporal resolution is 10 days, time range: May 1998 to December 2013.

    2020-06-03 0 View Details