Soil data are extremely important at both global and local scales, and in the absence of reliable soil data, land degradation assessments, environmental impact studies and sustainable land management interventions are severely hampered。By Soil information data in the urgent need of the World, especially under the background of the convention on climate change, international institute for applied systems analysis (IIASA) and the UN food and agriculture organization (FAO) and the Kyoto protocol on Soil carbon measurement and the United Nations food and agriculture organization (FAO)/international global agriculture ecological assessment (GAEZ v3.0) jointly established under the sponsorship of a new generation of World Soil Database (Harmonized World Soil Database version 1.2) (HWSD V1.2). The 2010 data set of soil texture on the qinghai-tibet plateau was culled from the world soil database.Data format :grid format, projected as WGS84.The main soil classification system used is fao-90.Unique verification identifier of core soil institution unit: Mu_global-hwsd database soil mapping unit identifier that connects GIS layers. MU_SOURCE1 and MU_SOURCE2- source database mapping unit identifiers； SEQ- soil unit sequence in the composition of soil mapping unit; Soil classification system USES fao-7 classification system or fao-90 classification system (SU_SYM74 resp.su_sym90) or fao-85 (SU_SYM85). The main fields of the soil property sheet include: ID(database ID) MU_GLOBAL(soil unit identifier) (global) SU_SYMBOL Soil mapping unit SU_SYM74(FAO74classify ); SU_SYM85(FAO85classify); SU_SYM90（FAO90The soil name in a soil classification system)； SU_CODE Soil mapping unit code SU_CODE74 Soil unit name SU_CODE85 Soil unit name SU_CODE90 Soil unit name DRAINAGE(19.5); REF_DEPTH(Soil reference depth); AWC_CLASS(19.5); AWC_CLASS(Soil available water content); PHASE1: Real (The soil phase); PHASE2: String (The soil phase); ROOTS: String (Depth classification of obstacles to the bottom of the soil)； SWR: String (Characteristics of soil moisture content)； ADD_PROP: Real (A specific soil type in a soil unit that is associated with agricultural use)； T_TEXTURE(Topsoil texture); T_GRAVEL: Real (Percentage of aggregate volume on top)；( unit：%vol.) T_SAND: Real (Top sand content)； ( unit：% wt.) T_SILT: Real (surface silt content);(unit: % wt.) T_CLAY: Real (clay content on top);(unit: % wt.) T_USDA_TEX: Real (top-level USDA soil texture classification);(unit: name) T_REF_BULK: Real (top soil bulk density);(unit: kg/dm3.) T_OC: Real (top organic carbon content);(unit: % weight) T_PH_H2O: Real (top ph) (unit: -log(H+)) T_CEC_CLAY: Real (the cationic exchange capacity of the clay layer at the top);(unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of topsoil) (unit: cmol/kg) T_BS: Real (top basic saturation);(unit: %) T_TEB: Real (top exchange base);(unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top-level sulfate content);(unit: % weight) T_ESP: Real (top layer exchangeable sodium salt);(unit: %) T_ECE: Real (top-level conductivity).(unit: dS/m) S_GRAVEL: Real (percentage of bottom gravel volume);(unit: % vol.) S_SAND: Real (content of underlying sand);(unit: % wt.) S_SILT: Real (substratum silt content);(unit: % wt.) S_CLAY: Real (clay content in the bottom layer);(unit: % wt.) S_USDA_TEX: Real (USDA underlying soil texture classification);(unit: name) S_REF_BULK: Real (bulk density of underlying soil);(unit: kg/dm3.) S_OC: Real (bottom organic carbon content);(unit: % weight) S_PH_H2O: Real (base ph) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying cohesive soil);(unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of underlying soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation);(unit: %) S_TEB: Real (underlying exchangeable base);(unit: cmol/kg) S_CACO3: Real (content of underlying carbonate or lime) (unit: % weight) S_CASO4: Real (substrate sulfate content);(unit: % weight) S_ESP: Real (underlying exchangeable sodium salt);(unit: %) S_ECE: Real (underlying conductivity).(unit: dS/m) This database is divided into two layers, in which the top layer (T) has a soil thickness of (0-30cm) and the bottom layer (S) has a soil thickness of (30-100cm).。 Refer to the instructions for other attribute values HWSD1.2_documentation.pdf，The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description andHWSD.mdb。
Food and Agriculture Organization of the United Nations（FAO）
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.
WANG Xufeng
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
China Centre for Resources Satellite Data and Application
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.
ZHANG Tongzuo
The method of aboveground biomass of grassland is zonal classification model. The data years were 2000, 2010 and 2015, and the fresh vegetation weight was based on the first ten days of August. Above-ground biomass is defined as the total amount of organic matter of vegetation living above the ground in a unit area. Unit: g/m². This data set is calculated from a statistical model based on the MODIS vegetation index by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. The spatial resolution is 250 m x 250 m. The data set is an important data source for vegetation monitoring in Three River Source National Park. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters
ZHU Weiwei
The data set contains agricultural economic data of all counties and regions in the Tibetan Plateau in 1980-2015, and covering the total number of households and total population in rural areas, agricultural population, rural labor force, cultivated land, paddy field area, the dry land area, power of agricultural machinery, agricultural vehicles, mechanical ploughing area, irrigation area, consumption of chemical fertilizers electricity use, gross output value of agriculture, forestry, animal husbandry and fishery, the output of cattle, pig, sheep, meat, poultry, and fish, the sown area of grain, the output of grain, cotton, oil and all kinds of crops, and characteristic agricultural products and livestock production and other relevant data.The data came from the statistical yearbook of the provinces included in the Tibetan Plateau.The data are of good quality and can be used to analyze the socio-economic and agricultural development of qinghai-tibet plateau.
LV Changhe
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 dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.
WANG Guangjun
This dataset is the spatial distribution map of the marshes in the source region 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 30 m, 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.
WANG Guangjun
The Tibetan Plateau in China covers six provinces including Tibet, Qinghai, Xinjiang, Yunnan, Gansu and Sichuan, including Tibet and Qinghai, as well as parts of Xinjiang, Yunnan, Gansu and Sichuan. The research on water and soil resources matching aims to reveal the equilibrium and abundance of water resources and land resources in a certain regional scale. The higher the level of consistency between regional water resources and the allocation of cultivated land resources, the higher the matching degree, and the superior the basic conditions of agricultural production. The general agricultural water resource measurement method based on the unit area of cultivated land is used to reflect the quantitative relationship between the water supply of agricultural production in the study area and the spatial suitability of cultivated land resources. The Excel file of the data set contains the generalized agricultural soil and water resource matching coefficient data of the Tibetan Plateau municipal administrative region in China from 2008 to 2015, the vector data is the boundary data of the Tibetan Plateau municipal administrative region in China in 2004, and the raster data pixel value is the generalized agricultural soil and water resource matching coefficient of the year in the region.
DONG Qianjin, DONG Lingxiao
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 ASTER Global Digital Elevation Model (ASTER GDEM) is a global digital elevation data product jointly released by the National Aeronautics and Space Administration of America (NASA) and the Ministry of Economy, Trade and Industry of Japan (METI). The DEM data were based on the observation results of NASA’s new generation of Earth observation satellite, TERRA, and generated from 1.3 million stereo image pairs collected by ASTER (Advanced Space borne Thermal Emission and Reflection Radio meter) sensors, covering more than 99% of the land surface of the Earth. These data were downloaded from the ASTER GDEM data distribution website. For the convenience of using the data, based on framing the ASTER GDEM data, we used Erdas software to splice and prepare the ASTER GDEM mosaic of the Tibetan Plateau. This data set contains three data files: ASTER_GDEM_TILES ASTERGDEM_MOSAIC_DEM ASTERGDEM_MOSAIC_NUM The ASTER GDEM data of the Tibetan Plateau have an accuracy of 30 meters, the raw data are in tif format, and the mosaic data are stored in the img format. The raw data of this data set were downloaded from the ASTERGDEM website and completely retained the original appearance of the data. ASTER GDEM was divided into several 1×1 degree data blocks during distribution. The distribution format was the zip compression format, and each compressed package included two files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif xx is the starting latitude, and yyy is the starting longitude. _dem.tif is the dem data file, and _num.tif is the data quality file. ASTER GDEM TILES: The original, unprocessed raw data are retained. ASTERGDEM_MOSAIC_DEM: Inlay the dem.tif data using Erdas software, and parameter settings use default values. ASRERGDEM_MOSAIC_NUM: Inlay the num.tif data using Erdas software, and parameter settings use default values. The original raw data are retained, and the accuracy is consistent with that of the ASTERGDEM data distribution website. The horizontal accuracy of the data is 30 meters, and the elevation accuracy is 20 meters. The mosaic data are made by Erdas, and the parameter settings use the default values.
METI, National Aeronautics and Space Administration
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The precipitation, its seasonal distribution and the change of precipitation forms have been one of the hot spots in the global climate change research. The data includes precipitation data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation of meteorological data of National Meteorological Science Information Center. The data can be used to analyze the temporal and spatial distribution of precipitation over the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the temporal and spatial variation of precipitation over the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
FANG Huajun
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
FANG Huajun
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
FANG Huajun, Ranga Myneni
The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.
FANG Huajun
The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. NPP estimation method based on algorithm of CASA model.
LIU Wenjun
The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. Based on pixel dichotomy model, the vegetation coverage model is developed by using multi-scale remote sensing images, combining with high precision remote sensing parameters such as vegetation community type and distribution characteristics, and the mixed pixel decomposition method is used to construct the vegetation coverage model. All data could be used only after the permission of the data distributor.
LIU Wenjun
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
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
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
This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public 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. This data set is composed of 1:1 million water coefficient data in Sanjiangyuan area, including three layers: water system surface (HYDA), water system line (HYDL) and water system point (HYDP). The water system surface (HYDA) includes lakes, reservoirs and double-line rivers; the water system line (HYDL) includes single-line rivers, ditches, river structure lines; and the water system point (HYDP) includes springs and wells. HYDA Attribute Item Name and Definition: Attribute Item Description Fill in Example GB National Standard Classification Code 210101 HYDC Water System Name Code KJ2103 NAME Name Heihe WQL Water Quality PERIOD Seasonal Month 7-9 TYPE Type Pass HYDL property item name and definition: Attribute Item Description Fill in Example GB National Standard Classification Code 210101 HYDC Water System Name Code KJ2103 NAME Name Heihe PERIOD Seasonal Month 7-9 HYDP property item name and definition: Attribute Item Description Fill in Example GB National Standard Classification Code 210101 NAME TYPE Type ANGLE Angle 75 Water coefficient data GB code and its meaning: Attribute Item Code Description GB 210101 Surface rivers 210200 Seasonal River 210300 Dry River 230101 Lakes 230102 Ponds 230200 Seasonal Lake 230300 Dry Lake 240101 Build Reservoir 240102 Built-in Reservoir
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
This data is derived from the National Basic Geographic Information Resources Catalogue Service System, which provides 11 million national basic geographic databases free of charge 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. This data set is composed of 1:1 million administrative boundary layers (BOUA) and administrative boundary line layers (BOUL) in Sanjiangyuan area. Names and definitions of BOUA attribute items: Attribute Item Description Fill in Example PAC Administrative Division Code 513230 NAME Name Rangtang County Names and definitions of BOUL attribute items: Attribute Item Description Fill in Example GB National Standard Classification Code 630200 The meaning of BOUL attribute items: Attribute Item Code Description GB 630200 Provincial Boundary GB 640200 District, Municipal and State Administrative Region GB 650201 County administrative boundaries (determined)
National Catalogue Service for Geographic Information
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
National Catalogue Service for Geographic Information
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
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
National Catalogue Service for Geographic Information
This data was originated 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 AGNP data of 1:1 million residential place names 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 (AGNP): Attribute Item Description Fill in Example CLASS Geographical Name Classification Code AK NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township
National Catalogue Service for Geographic Information
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
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
On August 22, 2018, in the Lancang River Source Park, a camera was carried on DJI Elf 4 UAV to take aerial photographs of the sample area. A total of 20 routes (5 missing routes) were set up, flying at a height of 100 m, and the overlap degree of adjacent photos was not less than 70%. A total of 1160 aerial photographs were obtained and stored in two folders of "100 MEDIA" and "101 MEDIA".
WANG Xufeng, WEI Yanqiang
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
National Catalogue Service for Geographic Information
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 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.
National Catalogue Service for Geographic Information
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.
WANG Xufeng, WEI Yanqiang
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
National Catalogue Service for Geographic Information
On August 19, 2018, DJI UAV was used to aerial photograph the wetland sample in Qumalai County of the Yangtze River Source Park. 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, with a ground resolution of 2 cm, an area of 850 m x 1000 m and a resolution of 4.5 cm for DSM.
WANG Xufeng, WEI Yanqiang
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.
WEI Yanqiang
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, WEI Yanqiang, WANG Xufeng
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.
WANG Xufeng
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.
WEI Yanqiang, Establishing Developing and Applying of the Space-Air-Field Integrated Eco-Monitoring and Data Infrastructure of the Three-River-Source National Park
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.
WEI Yanqiang
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 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.
WANG Xufeng
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
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, LI Qi
This data set was derived from MODIS version 005 and the IMS data set. It is a daily cloudless snow area product processed by cloud removal. Value range: 0%-100%. 200: snow; 100: lake ice; 25: land; 37: sea. The spatial resolution is 0.005 degrees (approximately 500 m), and the temporal coverage is from July 5, 2002, to December 31, 2014.
HAO Xiaohua
The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00，t1960-90_2.e00，t1960-90_3.e00，t1960-90_4.e00，t1960-90_5.e00， t1960-90_6.e00，t1960-90_7.e00，t1960-90_8.e00，t1960-90_9.e00，t1960-90_10.e00， t1960-90_11.e00，t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00，t1991-20_2.e00，t1991-20_3.e00，t1991-20_4.e00，t1991-20_5.e00， t1991-20_6.e00，t1991-20_7.e00，t1991-20_8.e00，t1991-20_9.e00，t1991-20_10.e00， t1991-20_11.e00，t1991-20_12.e00， Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00，p1960-90_2.e00，p1960-90_3.e00，p1960-90_4.e00，p1960-90_5.e00， p1960-90_6.e00，p1960-90_7.e00，p1960-90_8.e00，p1960-90_9.e00，p1960-90_10.e00， p1960-90_11.e00，p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00，p1991-20_2.e00，p1991-20_3.e00，p1991-20_4.e00，p1991-20_5.e00， p1991-20_6.e00，p1991-20_7.e00，p1991-20_8.e00，p1991-20_9.e00，p1991-20_10.e00， p1991-20_11.e00，p1991-20_12.e00， The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.
ZHOU Caiping
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.
WANG Xufeng
The data set contains the rare animal survey data for the Sanjiangyuan area from 2016 to 2017, including the latitude and longitude of the survey site, the length of the sample line, animal discovery time, animal names, quantity, location of the occurrence, type of habitat, affiliated families, etc.
HU Linyong, ZHANG Tongzuo, ZHANG Tongzuo,
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