River networks dataset at 1:250 000 in Three Rivers Source Region (2015)

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

0 2020-03-12

Primary road network dataset at 1:250,000 of the Three Rivers Source Region (2015)

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

0 2020-03-12

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

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

0 2019-09-15

Vegetation quadrat survey dataset in Maduo County (2016)

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

0 2019-09-15

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

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

0 2019-09-15

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

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

0 2019-09-15

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.

0 2019-09-15

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

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

0 2019-09-15

MODIS NDVI based phpenology for Sanjiangyuan (2001-2014)

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

0 2019-09-14

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.

0 2019-09-14