Precipitation data on Tibetan Plateau (2000-2015)

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.

0 2020-08-25

Prefecture-level adminstrative units boundary of Qinghai-Tibet Plateau (2015)

This dataset is the boundary vector data of the prefecture-level administrative units in the Qinghai-Tibet Plateau in 2015. The data is in the Shapefile format and includes provincial-level administrative units such as the Tibet Autonomous Region, Qinghai Province, Gansu Province, Yunnan Province, and Xinjiang Uygur Autonomous Region in the Qinghai-Tibet Plateau. The 38 prefecture-level administrative units can be used for the geographical background research of the urbanization and ecological environment interaction stress of the Qinghai-Tibet Plateau. It is the basic geographic data for the statistics of urbanization indicators such as social, economic and population levels of the Qinghai-Tibet Plateau. The data is obtained by means of data capture and collected through the administrative interface data acquisition API interface provided by the high-tech map. The data set uses the geographic coordinate system of WGS84.

0 2020-08-15

1:100000 vegetation map of Heihe River Basin(2015) (version 3.0)

1:100000 vegetation map of Heihe River Basin, the regional scope is subject to the Heihe river boundary of Huangwei Committee, the area is about 14.29 × 104km2, the data format is GIS vector format, this version is version 3.0. The data is mainly based on ground observation data, integrated with all kinds of remote sensing data, 1:1 million vegetation map, climate, terrain, landform, soil data mapping, and compiled by cross validation. The classification standard, legend unit and system of vegetation map of the people's Republic of China (1:1000000), 2007 are adopted, including vegetation type group, vegetation type, formation and sub formation. The new version mainly unifies the codes of the new formation (74 codes in total, distinguishing the formation and the sub formation). 9 vegetation type groups, 22 vegetation types and 74 formations (sub formations) in version 2.0 were changed into 9 vegetation type groups, 22 vegetation types and 67 formations (7 sub formations). The data includes versions 2.0 and 3.0

0 2020-07-30

Monthly temperature grid data set of Qinghai Tibet Plateau (2000-2015)

The Qinghai Tibet Plateau belongs to the plateau mountain climate. The temperature and its seasonal variation have been one of the hot spots in the global climate change research. The data includes the temperature 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 on the data of national weather station in Qinghai Tibet Plateau. The data can be used to analyze the temporal and spatial distribution of air temperature in the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the law of temperature change with time in the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.

0 2020-07-23

Photosynthetically active radiation absorption coefficient dataset in Qinghai Tibet Plateau (2000-2015)

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.

0 2020-07-23

Pan third pole 31 key nodes regional administrative boundary dataset (2015)

The Administrative boundary dataset is the base in the global change research, and it is important for the whole project.At present, DIVA-GIS is the basic source of administrative boundary. Whole national administrative boundary shapefiles were downloaded from DIVA-GIS. Based on the official administrative units (municipalities) as the basic units, the administrative units at the higher level (provincial level) where the municipalities are located are stored and reserved as the research objects.If the provincial unit area of the node has exceeded 10,000 square kilometers, the provincial unit will be retained as the research area. At the same time, if the provincial unit area of the node is small, then considering the political and economic impact of the provincial level and its surrounding areas, neighboring provincial units are also combined by merging and retaining to at least 10,000 square kilometers as the research object. Finally, the administrative region data of all 31 key node regions (Abbas, Alexander, Ankara, Astana, Bangkok, Chittagong, Colombo, Dhaka, Djibouti, Ekaterinburg, Gwadar, Hambantota, Karachi, Kolkata, Kuantan, Kyaukpyu, Maldives, Mandalay, Melaka, Minsk, Mumbai, Novosibirsk, Piraeus, Rayong, Sihanouk, Tashkent, Teheran, Valencia, Vientiane, Warsaw, Yangon) are produced. This data set serves as the research basis for all remote sensing data and provides baseline data for the project. This dataset can be updated in real time according to the official or government information of the node.

0 2020-07-08

Land cover data of the Belt and Road's region (Version 1.0) (2015)

Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 300 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), we build the LUCC classification system in the Belt and Road's region and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the integration and modification of land cover products and finally completed the land use data in the Belt and Road's region V1.0 (64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).

0 2020-06-11

1-km gridded datasets for gross domestic product of five key nodes along One Belt One Road (2015)

Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. We have collected the published GDP data, then obtained the 1-km gridded datasets for GDP of 2015 in five key nodes over Bengal and Myanmar, including Dacca, Chittagong, Kyaukpyu, Rangoon and Mandalay. To solve the problem of missing data existing in the current datasets, we will apply kriging and function interpolation methods to fill gaps. We will also develop the multi-source data fusion method based on geostatistics to achieve the GDP predictions of time continuously and high spatial resolution.

0 2020-06-11

Basic dataset of Great Lakes in Central Asia - Ecology (2015)

Net Primary Productivity (NPP) reflects the efficiency of plant fixation and conversion of light energy as a compound. It refers to the amount of organic matter accumulated per unit time and unit area of green plants. It is the organic matter produced by plant photosynthesis. The remainder of the Gross Primary Productivity (GPP) minus Autotrophic Respiration (RA), also known as net primary productivity. As an important part of the surface carbon cycle, NPP not only directly reflects the production capacity of vegetation communities under natural environmental conditions, but also is an important component to measure regional land use/cover change. The net primary productivity data product uses the light energy utilization (GLOPEM) model algorithm to invert multiple scale raster data products obtained from various satellite remote sensing data (Landsat, MODIS, etc.), which is also the main factor for determining and regulating ecological processes.

0 2020-05-29

Basic dataset of great lakes in Central Asia-social economy (2015)

The data summarizes the agricultural and socio-economic status of the five Central Asian countries ( Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan ) in 2015.The data comes from the statistical yearbooks of the five Central Asian countries ( Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan ), including the Total Population, Cultivated Land Area, Grain production area, GDP, The proportion of agricultural GDP to total GDP and The proportion of industrial GDP to total GDP and Forest Area. Detailed statistics of the six socio-economic factors of the five Central Asian countries are given. Statistics show that each of the six elements of the five Central Asian countries has its own focus. The data provides basic data for the project, facilitates analysis of the ecological and social situation in Central Asia, and provides data support for the future data analysis of the project.

0 2020-05-29