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).
LIU Shiyin GUO Wanqin XU Junli
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
WANG Xufeng National Meteorological Information Center
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
WANG Xufeng National Meteorological Information Center
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
Sher Muhammad Sher Muhammad
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.
National Oceanic and Atmospheric Administration
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.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
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.
HU Linyong XU Shixiao
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.
HU Linyong LI Qi HU Linyong XU Shixiao LI Qi
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.
WANG Xufeng WANG Xufeng WANG Xufeng WEI Yanqiang
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".
WANG Xufeng WEI Yanqiang WANG Xufeng WANG Xufeng
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.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
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.
Image Processing Centre for SPOT-VGT
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.
Charles R. Mcclain
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
Geomatics Center of Qinghai Province
The data set is remote sensing image of Resource 3 No. 02 (ZY3-02). ZY3-02 was successfully launched from Taiyuan Satellite Launch Center at 11:17 on May 30, 2016 by Long March 4 B carrier rocket. China-made satellite imagery will be further strengthened in the areas of land surveying and mapping, resource survey and monitoring, disaster prevention and mitigation, agriculture, forestry and water conservancy, ecological environment, urban planning and construction, transportation and other fields. List of files: ZY302_PMS_E98.8_N37.4_201707_L1A0000156704 ZY302_PMS_E100.4_N37.0_20171127_L1A0000217243 ZY302_TMS_E99.5_N37.0_20170717_L1A0000160059 ZY302_TMS_E100.3_N36.6_20171127_L1A0000217279 ZY302_TMS_E100.4_N37.0_20170529_L1A0000139947 Folder Naming Rules: Satellite Name Sensor Name Central Longitude Central Latitude Acquisition Time L1****
China Centre for Resources Satellite Data and Application
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.
This data set contains statistical tables on the community situation of each county in Three-River-Source National Park. The specific contents include: Table 1 includes: number of administrative villages, number of natural villages, number of households, population, number of rural labor force, total value of primary and secondary industries, net income per capita, and number of livestock. Table 2 includes: the ethnic composition of the population (population of each ethnic group), education-related statistics (number of primary and secondary schools and number of students), health-related statistics (number of hospitals, health rooms and medical personnel), and statistics on the education level of the population (number of people with different education levels); Table 3 includes: the grassland (total grassland area, usable grassland area, moderately degraded area and grassland vegetation coverage), woodland (total area, arbor forest area, shrub forest area and sparse forest area), water area (total area, river area, lake area, glacier area, snowy mountain area and wetland area). A total of four counties were designed: Maduo, Qumalai, Zaduo and Zhiduo. This data comes from statistics of government departments.
National Bureau of Statistics
This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
This data comes from the National Catalogue Service for Geographic Information, which was provided to the public free of charge by the National Basic Geographic Information Center in November 2017. We spliced and trimmed Three Rivers Source Region as a whole to facilitate its use in the study of Three Rivers Source Region. The current status of the data is 2015. This dataset is the Three Rivers Source Region 1: 250,000 water system data, including three layers of water system surface (HYDA), water system line (HYDL) and water system point (HYDP). The water system surface (HYDA) includes lakes, reservoirs, double-line rivers, and ditches; the water system line (HYDL) includes single-line rivers, ditches, and river structure lines; and the water system points (HYDP) include springs and wells. HYDA attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 HYDC Water system name code KJ2103 NAME Name Heihe WQL Water quality Fresh PERIOD Seasonal months 7-9 TYPE Type Pass HYDL attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 HYDC Water system name code KJ2103 NAME Name Heihe PERIOD Seasonal months 7-9 HYDP attribute item name and definition: Attribute item Description Sample GB National standard classification code 210101 NAME Name Unfreezing spring TYPE Type Fresh ANGLE Angle 75 Water system GB code and its meaning: Attribute item Code Description GB 210101 Ground river 210200 Seasonal river 210300 Dry up river 230101 Lake 230102 Pond 230200 Seasonal lake 230300 Dry lake 240101 Built reservoir 240102 Reservoir in building
National Catalogue Service for Geographic Information
This data comes from the National Catalogue Service for Geographic Information, which was provided to the public free of charge by the National Basic Geographic Information Center in November 2017. We spliced and trimmed Three Rivers Source Region as a whole to facilitate its use in the study of Three Rivers Source Region. The current status of the data is 2015. This dataset is 1:25 million traffic data in the Three Rivers Source Region area, including two layers of highway (LRDL) and railway (LRRL). Highways (LRDL) include national, provincial, county, rural, and other highways; railways (LRRL) include standard-gauge, narrow-gauge, subway, and light rail. Highway (LRDL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 420301 RN Road number X828 NAME Road name Zhuoxiao fork-Baola Peak fork RTEG Road Level 4 TYPE Road type elevated Meaning of highway attribute items: Attribute item Code Description GB 420101 National road 420102 National road in building 420201 Provincial road 420102 Provincial highway in building 420301 County road 420302 County road in building 420400 Country road 420800 Machine tillage 440100 Simple road 440200 Village road 440300 Trail Railway (LRRL) attribute item name and definition: Attribute item Description Sample GB National standard classification code 410101 RN Railway number 0907 NAME Railway name Qinghai-Tibet Railway TYPE Rail type
National Catalogue Service for Geographic Information