Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
0 2021-01-27
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
0 2022-04-18
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
0 2022-04-18
The atmospheric forcing dataset for along the Belt and Road from 2000 to 2015 comes from CRUNCEP. CRUNCEP is an atmospheric forcing dataset used forcing the land surface models. Specifically, this long time series data set (including temperature, precipitation, temperature, etc.) is used to drive the Community Land Model (CLM) Land Model in the long term. The CRUNCEP is a combination of two existing datasets; the CRU TS3.2 0.5 X 0.5 monthly data covering the period 1901 to 2002 and the NCEP reanalysis 2.5 X 2.5 degree 6-hourly data covering the period 1948 to 2016. The CRUNCEP dataset has been used to force CLM for studies of vegetation growth, evapotranspiration, and gross primary production and for the TRENDY (trends in net land-atmosphere carbon exchange over the period 1980-2010) project, among many other use cases. The CRUNCEP data archived in this dataset is Version 7.
0 2022-04-18
This data set includes grid emission inventories of sulfur dioxide, nitrogen oxides and PM2.5 in 2019 in China's third polar region (Tibet, Xinjiang, Yunnan and Qinghai). The emission inventory comes from the emission inventory database of the research group of Professor Wang Shuxiao of Tsinghua University. The emission inventory is processed into a 1km * 1km grid dataset by using ArcGIS software technology. The basic data of emission calculation is calculated by the emission factor method based on public data collection, satellite observation data and literature collection. The data are from the data of the National Bureau of statistics and the statistical yearbook of other industries, and its quality can be guaranteed. The data can be used for further study of climate and air quality in the third polar region.
0 2022-04-18
In order to investigate the variation characteristics of agricultural water resources vulnerability in Central Asia, an index system was established with 18 indicators from three components, namely exposure, sensitivity and adaptation, according to the scheme of vulnerability assessment. Based on the socio-economic, topography, land cover and soil data, agricultural water resources vulnerability were calculated using the Equal-Weights and Principal Component Analysis (PCA) method. Each original raster data is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes 1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2017and 1992-2017with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for agricultural water supply and demand, development and utilization analysis in five central Asian countries.
0 2022-04-18
1) Data content (including elements and meanings): Gridded multiyear-average monthly air temperature lapse rate data over the Tibetan Plateau at three kinds of resolutions (i.e. 0.25°, 0.75° and 2°) 2) Data source and processing method: Locally reliable temperature lapse rates are created from filtered MODIS LST-elevation samples by using the thresholds of standard error of elevation and correlation coefficient 3) Data quality description: For ERA-Interim, the validation accuracy (based on 1980-2014 daily mean aire temperature records from 113 stations across the Tibetan Plateau) decreases from ~4℃ to ~2℃ after using the 0.75° temperaturel lapse rate. 4) Data application results and prospects: This dataset can be used for downscaling air temperature from multiple reanalysis datasets.
0 2022-04-18
A long-term (1980-2017) land evaporation (E) product with a spatial resolution of 0.25 degree. This is a merged product from three model-based E products using the Reliability Ensemble Averaging (REA) method which minimizes errors. These include the fifth-generation ECMWF Re-Analysis (ERA5), the second Modern-Era Retrospective analysis for Research and Applications (MERRA2), and the Global Land Data Assimilation System (GLDAS). To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain daily and monthly mean data (e.g., REA_1980_day.nc and REA_1980_mon.nc). The dataset is stored in NetCDF format, containing the variable E, representing land evaporation, produced in millimeters (mm) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.875E to 179.875E, the latitude from -59.875N to 89.875N. Complete time coverage is from January 1, 1980, to December 31, 2017.
0 2022-04-18
Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.
0 2022-04-18
The data set records the statistical table of groundwater level dynamic changes in various monitoring areas of Qinghai Province from 2015 to 2018. The data are recorded from the Department of natural resources of Qinghai Province, and the data set contains four data tables, which are: the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2015, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2016, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2017, and the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2018 The data table has the same structure and contains 7 fields Field 1: "geographic location" Field 2: "basic balance area (km2)" Field 3: "percentage of monitoring area (%)" Field 4: "weak descent area (km2)" Field 5: "percentage (%) of monitored area" Field 6: "strong uplift area (km2)" Field 7: "percentage (%) of monitored area"
0 2022-04-18
The data consists of three fields: longitude, latitude and lake depth. Using sonar equipment to measure the depth of water on the lake, GPS synchronous measurement of longitude and latitude. The salinity and temperature data of lake water are used to correct the depth data measured by sonar, and the outliers are eliminated. The underwater topographic map of lake can be formed by interpolation of water depth data. Using the underwater topographic map, the water storage of lakes can be calculated and the total water quantity of lakes in the Qinghai Tibet Plateau can be evaluated. The underwater topographic map combined with remote sensing data can also be used to study the characteristics and influencing factors of lake water quantity variation in the Qinghai Tibet Plateau, which is an important part of the study of water quantity variation in the Asian water tower.
0 2022-04-18
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
