Dataset of key elements of desertification in Central and Western Asia (Amu River Basin) (1990、2000、2010)

Data set of key elements of desertification in Central and Western Asia (Amu river Basin), includes three parts: lakes distribution data, vegetation coverage data and NPP data. The spatial resolution of lakes distribution data and vegetation coverage data is 30 m, and includes three periods: 1990, 2000 and 2010. It is based on the interpretation and calculation of TM/ETM data; The spatial resolution of NPP data is 500 m, and includes two periods: 2000 and 2010 (Since no MODIS data in 1990, there is no 1990 data). It is based on the calculation of MODIS data. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.

0 2020-05-29

Dataset of relief degree of land surface in the Green Silk Road

The Relief Degree of Land Surface is a comprehensive representation of regional altitude and surface cutting degree. Using the resampled Digital Elevation Model data and the Feng(2007) Model, the Relief Degree of Land Surface dataset of the Green Silk Road in 1km resolution was developed. The dataset includes the spatial distribution data of the Relief Degree of Land Surface, elevation, and flat area at 1km covering the Green Silk Road, as well as each country of the Green Silk Road.

0 2020-05-29

Dataset of key elements of desertification in typical watershed of Central and Western Asia (Amu River Basin)

Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.

0 2020-05-29

Dataset of the multi-year average of relative humidity for the Green Silk Road (Version 1.0)

Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. The relative humidity isone of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of relative humidity is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.

0 2020-05-29

Remote sensing monitoring dataset of land use status in six provinces in western China for many years (1970s, 1980s, 1995, 2000, 2005, 2010, 2015)

The remote sensing monitoring database of land use status in China is a multi-temporal land use status database covering the land area of China, which has been established after many years of accumulation under the support of the National Science and Technology Support Plan and the Key Direction Project of the Knowledge Innovation Project of the Chinese Academy of Sciences. It is the most accurate remote sensing monitoring data product of land use in China at present, which has played an important role in the national land resources survey, hydrology and ecological research.   This data set covers the six western provinces in China: Xinjiang, Tibet, Qinghai, Yunnan, Sichuan and Gansu. Based on Landsat TM/ETM remote sensing images in the late 1970s、1980s、1995、2000、2005、2010、2015, 1KM raster data are generated by using the professional software and manual visual interpretation on the basis of vector data.   The land use types include six primary land types which are cultivated land, forest land, grassland, water area, residential land and unused land, and 25 secondary types.

0 2020-05-28

Land cover of Qinghai-Tibet Plateau (2010)

The dataset is the land cover of Qing-Tibet Plateau in 2010. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.

0 2019-09-15

Cattle stock at the end of the year (2000-2010)

The data include the data of cattle stock at the end of the year of Tibetan Plateau . The spatial area is divided by counties on the Tibetan Plateau. The time resolution is 5 years, and the time coverage is 2000, 2005, 2010.This data is obtained through statistics and collection of relevant literature, historical database records and other materials, and other data are acquired through purchase.The data can be used to analyze the agricultural production and meat price changes of each county on the Tibetan Plateau. In addition, the development differences of each county on the Tibetan Plateau can also be analyzed by comparing the data of the counties.

0 2019-09-13

Landsat-based continuous monthly 30m×30m land surface NDVI dataset in Qilian Mountain area (1986-2017)

This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2017. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 5, Landsat 8 and sentinel 2 channels from Red and NIR channels. The data are synthesized monthly through Google Earth Engine cloud platform, and the missing pixels are interpolated by calculating the index of the model. The quality of the data is good, and it can be used in environmental change monitoring and other fields.

0 2019-09-12