Land use and land cover map of Amu river Basin includes four periods: 1990, 2000, 2010 and 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The land use map of Amu river basin is based on Landsat TM and ETM image data in 1990, 2000, 2010 and 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually. Finally, the data validation methods are field validation and high-precision image validation.
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.
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
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. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters
The Randolph Glacier Inventory (RGI) is a complete inventory of global glacier outlines published by GLIMS (Global Land Ice Measurements from Space). It is currently available in six versions: Version 1.0 was published in February 2012, version 2.0 was published in June 2012, version 3.0 was published in April 2013, version 4.0 was published in December 2014, version 5.0 was published in July 2015, and version 6.0 was published in July 2017. The data sets include four versions, which are 6.0, 5.0, 4.0 and 3.2 (revision, August 2013). The data are organized according to different regions. In each region, each glacier record includes a shape file (.shp file and its corresponding .dbf, .prj, and .shx files) and a .csv file of height measurement data. The data are from GLIMS: Global Land Ice Measurements from Space (http://www.glims.org/RGI/) Data quality checks include geometry, topology, and certain attributes, and the following checks were performed: 1) All polygons were checked by the ArcGIS Repair Geometry tool. 2) Glaciers with areas less than 0.01 square kilometres were removed. 3) The topology was checked with the Does Not Overlap rule. 4) The attribute sheet was checked by Fortran subroutines and Python scripts for data quality.
The two regions of North Pole are defined by the Arctic Monitoring and Assessment Programme (AMAP) working group and Arctic Human Development Report (AHDR). The AMAP Arctic’s geographical coverage extends from the High Arctic to the sub Arctic areas of Canada, the Kingdom of Denmark (Greenland and the Faroe Islands), Finland, Iceland, Norway, the Russian Federation, Sweden and the United States, including associated marine areas. The AHDR Arctic encompasses all of Alaska, Canada North of 60°N together with northern Quebec and Labrador, all of Greenland, the Faroe Islands, and Iceland, and the northernmost counties of Norway, Sweden and Finland. The situation in Russia is harder to describe in simple terms. The area included, as demarcated by demographers, encompasses the Murmansk Oblast, the Nenets, YamaloNenets, Taimyr, and Chukotka autonomus okrugs, Vorkuta City in the Komi Republic, Norilsk and Igsrka in Krasnoyarsky Kray, and those parts of the Sakha Republic whose boundaries lie closest to the Arctic Circle.