Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1099

  • Remote sensing data of NDVI changes in Central Asia (2010, 2015, 2020)

    Remote sensing data of NDVI changes in Central Asia (2010, 2015, 2020)

    This dataset includes the Normalized Difference Vegetation Index (NDVI) data of the five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) during 2010, 2015 and 2020. This data is derived from the image data of the Moderate Resolution Imaging Spectrometer (MODIS) used by the US Earth Observation System (EOS) project, product number MOD13A2.006. The product has a time resolution of 16 days and a spatial resolution of 1km. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16-day period. The criteria used are low clouds, low view angle, and the highest NDVI value.

    2021-02-02 265 23

  • Refined spatial distribution data set of population in hanbantota port area (HRSLv1.2)

    Refined spatial distribution data set of population in hanbantota port area (HRSLv1.2)

    The refined population spatial distribution data set of Hambantota port area is generated by reanalysis based on hrsl data of Sri Lanka. Hrsl data provides an estimate of the population distribution in 2015 at a resolution of 1 arcsec (about 30 meters). The latest census information and built-up area information based on satellite images are used in hrsl data. This data set is based on hrsl data. Firstly, the boundary of buildings is extracted from the 0.5m resolution remote sensing image by computer vision technology, and the building types (high-rise buildings, medium and low rise buildings, bungalows, etc.) are determined by combining with manual visual interpretation and field sampling. The population distribution area mask is constructed in the building area, and the 10 meter grid is used as the analysis unit to calculate the population distribution in the unit According to the proportion of different building types, the proportion of main land use types, building density, distance from road and other related indicators, the average density of building type consistent area is calculated from hrsl data, and the corresponding population density of each building is obtained by machine learning method. Then, the population data in the area is allocated to the corresponding unit by proportional allocation method, and the 10 meter resolution is obtained Population distribution products. The data is distributed in the form of GeoTIFF files. Population GeoTIFF represents population estimates (in person) and provides detailed estimates for population, infrastructure and Sustainability Studies in the humanitarian field.

    2021-01-29 238 1

  • Aboveground biomass data set of temperate grassland in northern China (1993-2019)

    Aboveground biomass data set of temperate grassland in northern China (1993-2019)

    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.

    2021-01-27 376 23

  • Data set of soil physical and chemical indexes of temperate grassland in Eurasia (1981-2019)

    Data set of soil physical and chemical indexes of temperate grassland in Eurasia (1981-2019)

    In the past 50 years, under the background of global climate change, with the increase of population and economic development, Eurasian grassland has been seriously degraded. One belt, one road surface, is a key indicator of grassland quality. Its spatial temporal pattern and distribution can directly reflect the degradation of grassland. Effective assessment of grassland quality is of great significance for the sustainable development of the countries along the border and the promotion of China's "one belt and one road" strategy. In previous studies, there is room for improvement in accuracy and accuracy of spatial and temporal distribution of soil properties. With the development of geographic information system, global positioning system, various sensors and soil mapping technology, digital soil mapping has gradually become an efficient method to express the spatial distribution of soil. Based on soil landscape science and spatial autocorrelation theory, this study combined multi-source sample data and environmental covariate data, and used machine learning model to predict the spatial distribution of surface soil attributes of warm grassland in Eurasia around 2000. In order to solve the problem of soil sample standardization, the equal area spline function was used to fit the soil properties of different profiles to the soil properties of 20 cm in the surface layer, and the soil particle distribution parameter model was used to transform the classification standards of different soil textures into the United States system. In order to solve the problem of insufficient number of soil samples, pseudo expert observation points were used to supplement soil organic matter and sand content samples in under sampling area; stepwise regression combined with support vector machine model was used, and effective soil bulk density simulation samples were screened by calculating threshold. According to the characteristics of complex terrain and climate conditions, combined with multi-source remote sensing data, ngboost model is applied to mine the relationship between soil attributes and environmental landscape factors (topography, climate, vegetation, soil type, etc.) and spatial location based on sample points, and to predict soil organic matter, sand content and bulk density in the study area from 1980 to 1999 and 2000 to 2019 respectively, and the uncertainty of corresponding indicators is given Spatial distribution of sex. The spatial distribution trend of the simulated soil attribute indexes is consistent with the actual situation. Before 2000, the soil organic matter content, bulk density and sand content were 0.64, 0.35 and 0.44 respectively, and the RMSE were 0.25, 0.07 and 13.94 respectively; after 2000, the RMSE were 0.79, 0.77 and 0.86 respectively, and the RMSE were 0.2, 0.13 and 6.61 respectively. The results show that this method can effectively retrieve the soil physical and chemical properties of temperate grassland in Eurasia, and provide a basis for the evaluation of grassland degradation and the construction of grassland quality evaluation system.

    2021-01-26 1043 27

  • Data on soil temperature, humidity and carbon Flux obtained from a station in southeast Tibet (2007-2019)

    Data on soil temperature, humidity and carbon Flux obtained from a station in southeast Tibet (2007-2019)

    This data set includes daily average data on soil temperature, humidity and carbon flux obtained from a station in Southeast Tibet from 2007 to December 2019. The data collection site is the atmospheric environment observation site of the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet, which is run by the Chinese Academy of Sciences. The site is located at longitude 94°44'18", latitude 29°45'56" and is at an elevation of 3326 m. The observation instrument models are as follows: Soil temperature: Campbell Co 107; Soil humidity: Campbell Co CS616; Carbon flux: Collector model: C3000, Measurement interval: 10 seconds; The observations and data collection were performed in strict accordance with the instrument operating specifications, and the data have been published in relevant academic journals. Data with obvious errors were removed, and missing data were replaced with null values. Observation of the soil thermal flux was stopped in 2013. In 2015, due to damage to the station probe, soil temperature and humidity data were recorded only for the first two months, the probe was repaired in April 2016.

    2021-01-26 1805 79

  • Maximum leaf area index data set of northern Eurasia (1981-2017)

    Maximum leaf area index data set of northern Eurasia (1981-2017)

    The global land surface characteristic parameter (LAI) product was used with a spatial resolution of 5 km. The product uses generalized regression neural network method to retrieve Lai from AVHRR surface reflectance data. In this study, 12 issues of Lai data products from June to August of each year in five Central Asian countries, Mongolia and Northern China from 1981 to 2017 were downloaded from the national science and technology infrastructure platform National Earth System Science Data Center. These images are cropped by ArcGIS software, and the maximum value is calculated to obtain the spatiotemporal data set of the largest Lai. Among them, five Central Asian countries include Turkmenistan, Kyrgyzstan, Kazakhstan, Tajikistan and Uzbekistan; northern China refers to the area north of the Yangtze River in China.

    2021-01-26 387 6

  • Maximum leaf area index data set of northern Eurasia (1981-2017)

    Maximum leaf area index data set of northern Eurasia (1981-2017)

    The global land surface characteristic parameter (LAI) product was used with a spatial resolution of 5 km. The product uses generalized regression neural network method to retrieve Lai from AVHRR surface reflectance data. In this study, 12 issues of Lai data products from June to August of each year in five Central Asian countries, Mongolia and Northern China from 1981 to 2017 were downloaded from the national science and technology infrastructure platform National Earth System Science Data Center. These images are cropped by ArcGIS software, and the maximum value is calculated to obtain the spatiotemporal data set of the largest Lai. Among them, five Central Asian countries include Turkmenistan, Kyrgyzstan, Kazakhstan, Tajikistan and Uzbekistan; northern China refers to the area north of the Yangtze River in China.

    2021-01-26 387 6

  • The global land surface characteristic parameter (LAI) product was used with a spatial resolution of 5 km. The product uses generalized regression neural network method to retrieve Lai from AVHRR surface reflectance data. In this study, 12 issues of Lai data products from June to August of each year in five Central Asian countries, Mongolia and Northern China from 1981 to 2017 were downloaded from the national science and technology infrastructure platform National Earth System Science Data Center. These images are cropped by ArcGIS software, and the maximum value is calculated to obtain the spatiotemporal data set of the largest Lai. Among them, five Central Asian countries include Turkmenistan, Kyrgyzstan, Kazakhstan, Tajikistan and Uzbekistan; northern China refers to the area north of the Yangtze River in China.

    2021-01-25 387 6

  • Aboveground biomass data set of temperate grassland in northern China (1993-2019)

    Aboveground biomass data set of temperate grassland in northern China (1993-2019)

    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. This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.

    2021-01-25 376 23

  • Railway data of the key areas along One Belt One Road (2015)

    Railway data of the key areas along One Belt One Road (2015)

    The railway data of 34 key areas along the Belt and Road is collected from the Internet and reprocessed. First, we download the linear railway data from the country where the key node areas along the One Belt One Road are located from the OpenStreetMap, and cut and extracted them by region. Meanwhile, it is compared and analyzed with the railway extraction result based on high resolution remote sensing images, and then updated with data from regional statistical bureaus. It is finally integrated into a railway infrastructure element data product. The format of data is linear shapefile data. The spatial coordinate system of the railway data is WGS84, and it contains attribute fields such as name, class and so on. This data can be used to calculate the length of railways and analyze the distribution of railways in cities. The railway data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.

    2021-01-24 243 22

  • Lithofacies analysis data set of Tulsipur and Butwal sections in the Late Cretaceous, Nepal

    Lithofacies analysis data set of Tulsipur and Butwal sections in the Late Cretaceous, Nepal

    Lithofacies analysis is an important research method to explore the source region, background, and nature of sedimentary basins. Through the systematic investigation of several late Cretaceous strata in Nepal, situated on the south flank of the Himalayas, the Tulsipur and Butwal sections conducted detailed lithology and sedimentary facies analysis. Continuous strata include the Taltang Fm. , Amile Fm. , Bhainskati Fm. and Dumri Fm. from bottom to top. The lithology contains terrigenous clastic rocks such as conglomerate, sandstone, siltstone and mudstone, chemical rocks such as limestone and siliceous rock, as well as special lithology such as coal seam, carbonaceous layer and oxidation crust. Both sections have various colors and sedimentary structures, which are good materials for the analysis of lithofacies evolution. According to the characteristics of lithofacies and sedimentary assemblage revealed that the Nepal sedimentary environment evolution during the late Cretaceous, which experienced the marine, fluvial, lacustrine, and delta evolution process.

    2021-01-21 947 1

  • Glacier coverage data on the Tibetan Plateau in 2001 (TPG2001, Version1.0)

    Glacier coverage data on the Tibetan Plateau in 2001 (TPG2001, Version1.0)

    The Tibetan Plateau Glacial Data –TPG2001 is a glacial coverage data on the Tibetan Plateau in around 2000 from 150 scenes of Landsat7 TM/ETM+ images by 30 m spatial resolution. The selected Landsat7 TM/ETM+ images were within the period between 1999 and 2002, including 61 scenes (41%) in 2001 and 47 scenes (31%) in 2000. Among all the images, 71% was taken in winter. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2001. Glacier outlines were digitized on-screen manually from the 2001 image mosaic, relying on false-colour image composites (RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2001. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2001 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.8%.

    2021-01-20 2949 81

  • Dataset of sustainable livelihood-Public infrastructure

    Dataset of sustainable livelihood-Public infrastructure

    This data includes the accessibility of 15 kinds of public facilities and services, such as roads and schools, in the communities of 1280 households at domestic and abroad, as well as the farmers' satisfaction with these public facilities and public services by comparing that with 3 years ago and current status with neighboring village. This data is used to support the analysis of the material capital part of sustainable livelihood. The data was collected by the research group through field survey in 2019. Before collecting the data, the research group and invited experts conducted a pretest and improved the survey questionnaire; Before the formal investigation, the members participating in the data collection were strictly trained; In the formal survey, each questionnaire is checked three times before it is filed. This data is of great value for understanding the physical capital accessibility and satisfaction of rural households in environment-economic fragile areas, and is an important supplement to national and macro data.

    2021-01-20 169 1

  • Dataset of sustainable livelihood-Public infrastructure

    Dataset of sustainable livelihood-Public infrastructure

    This data includes the accessibility of 15 kinds of public facilities and services, such as roads and schools, in the communities of 1280 households at domestic and abroad, as well as the farmers' satisfaction with these public facilities and public services by comparing that with 3 years ago and current status with neighboring village. This data is used to support the analysis of the material capital part of sustainable livelihood. The data was collected by the research group through field survey in 2019. Before collecting the data, the research group and invited experts conducted a pretest and improved the survey questionnaire; Before the formal investigation, the members participating in the data collection were strictly trained; In the formal survey, each questionnaire is checked three times before it is filed. This data is of great value for understanding the physical capital accessibility and satisfaction of rural households in environment-economic fragile areas, and is an important supplement to national and macro data.

    2021-01-20 169 1

  • Land use data set in Central Asia l(1970, 2005, 2015)

    Land use data set in Central Asia l(1970, 2005, 2015)

    In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.

    2021-01-19 922 20

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

    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.

    2021-01-15 1173 35

  • The desertification risk map of Iranian plateau in 2019

    The desertification risk map of Iranian plateau in 2019

    The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.

    2021-01-14 908 1

  • Paleogeographic map of paleoclimate, lithofacies and Cretaceous of Pan tertiary (130mA, 90mA)
  • Paleogeographic map of paleoclimate, lithofacies and Cretaceous of Pan tertiary (130mA, 90mA)

    Paleogeographic map of paleoclimate, lithofacies and Cretaceous of Pan tertiary (130mA, 90mA)

    Guided by the theory of plate tectonics, paleogeography, petroliferous basin analysis and sedimentary basin dynamics, we have collected a large number of data and achievements of geological research and petroleum geology in recent years, including strata, sedimentation, paleontology, paleogeography, paleoenvironment, paleoclimate, structure, oil and gas (potash) geology and other basic materials, especially paleomagnetism, Paleogene Based on the data of detrital zircon and geochemistry, combined with the results of typical measured stratigraphic sections, the lithofacies and climate paleogeographic pattern of Cretaceous were restored and reconstructed, and two lithofacies paleogeographic maps of early and late Cretaceous of Pan tertiary and two climate paleogeographic maps of early and late Cretaceous of Pan tertiary were obtained, aiming at discussing the influence of paleogeography, paleostructure and paleoclimate In order to reveal the geological conditions and resource distribution of oil and gas formation, and provide scientific basis and technical support for China's overseas and domestic oil and gas exploration deployment.

    2021-01-13 828 12

  • UAV-derived raster data of the Tibetan Plateau in 2020

    UAV-derived raster data of the Tibetan Plateau in 2020

    The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.

    2021-01-13 1250 22