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

Basic dataset of great lakes in Central Asia –mark dataset of remote sensing interpretation (2015)

The remote sensing image interpretation mark is also called the interpretation factor, which can directly reflect the image features of the ground object information. The interpreter uses these marks to identify the nature, type or condition of the feature or phenomenon on the image, so it is for the remote sensing image data. Human-computer interactive interpretation is of great significance. The image used in the data to establish the interpretation mark avoids the summer with high vegetation coverage, and avoids the data with more snow cover, cloud cover or smog influence.According to the basic geographic information data extraction requirements, the combination of the remote sensing image band combination order and the full color band are selected.Avoid data loss when enhancing data. The requirement for selecting a typical marker-building area on an image is that the range is moderate to reflect the typical features of the type of landform, including as many basic geographic information elements as possible in the type of landform and the image quality is good. After the selection of the marking area is completed, look for all the basic geographic information element categories contained in the marking area, and then select various typical maps as the collection marks, then go to the field for field verification,including 3429 sampling reference points and 1,870 photos, and the translation of the library was established, and the unreasonable parts were modified until they were consistent with the field. At the same time, the ground photo of the map is taken to make the image and the actual ground elements relate to each other, expressing the authenticity and intuitiveness of the remote sensing image interpretation mark, and to deepen the user's understanding of the interpretation mark.

0 2020-05-29

Basic dataset of great lakes in Central Asia – meteorology (2015)

Meteorological data are a set of weather data, which can be divided into climatological data and weather data. This data set mainly includes rainfall data and temperature data in meteorological data (In the data set, ‘pre’ represents rainfall and ‘T2’ represents temperature).The data set is from CRU(Climate Research Unit)global grid data provided by the university of east Anglia in the UK(http://www.cgiar-csi.org/). The CRU data set is interpolated from observations at 365 sites across central Asia, Many researchers have found that the data is relatively accurate in central Asia. This data set uses CRU to obtain rainfall and temperature data of five central Asian countries through Arcgis batch cutting.Meteorological data is widely used and can be integrated with resources in different fields. It plays an important role in the development and construction of transportation, new energy, agriculture, mobile Internet software development and service, public management and smart city, smart transportation, smart food and other fields based on big data technology.

0 2020-05-28

The map of fractional vegetation cover in the Yellow River source region of Tibet Plateau (2015)

This dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.

0 2020-05-28

Dataset for country level water resources in 2015 in Belt and Road Region (2015)

The main idea of water resources estimation is to establish a machine learning model using runoff coefficient and runoff impact factors (climate, topography, land use, soil), and then convert the estimated runoff coefficient to runoff depth, and then converted to water resources volumn. Based on global public open accessed data, establish the runoff coefficient topography, climate, soil, and land use, and the machine learning model for. Long-term annual runoff coefficient in the Belt and Road region was estimated and country level water resources was derived from precipitation of 2015 , The area of the country is estimated by the amount of water resources in the countries along the Belt and Road. A high-resolution runoff coefficient distribution map of the Belt and Road region was generated, which provided basic data support for water resources assessment and cross-border water distribution in the Belt and Road region.

0 2020-05-28

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

This dataset is the boundary vector data of the provincial-level administrative units in the Qinghai-Tibet Plateau in 2015. The data is in the Shapefile format and includes provincial administrative units such as Tibet Autonomous Region, Qinghai Province, Gansu Province, Yunnan Province, Xinjiang Uygur Autonomous Region, and Sichuan Province. The administrative boundary within the plateau 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 the urbanization indicators of the provincial, forest, and population sectors 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 2020-05-28

County-level adminstrative units boundary of Tibetan Plateau(2015)

This dataset is the boundary vector data of county-level administrative units in Tibetan Plateau in 2015. The data is in Shapefile format and includes provincial administrative units such as Tibet Autonomous Region, Qinghai Province, Gansu Province, Yunnan Province, Xinjiang Uygur Autonomous Region, and Sichuan Province. The county-level administrative unit boundary within the plateau 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 the urbanization indicators of the county-level units 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 2020-05-28