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.
ZHANG Chi Geping Luo
The data content mainly includes the main and micro data of the whole rock of some magmatic rocks in the Hoh Xil Lhasa plate of the Qinghai Tibet Plateau. The samples were mainly distributed in Hoh Xil lake, South Qiangtang guoganjianian, Dugur, and Gangdise Nasongduo and Saga counties. There are more than 300 major and trace elements in the samples, including olivine leucite, quartz monzonite, diorite and granite, which are of great significance to the study of the lithospheric evolution of the Qinghai Tibet Plateau. Data mainly come from published articles or being accepted. XRF spectroscopy was used to determine the major elements and ICP-MS was used to determine the trace elements. The data quality is highly reliable, and the testing units include the State Key Laboratory of Guangzhou Institute of geochemistry, Chinese Academy of Sciences, etc. The data are published in high-level journals, including geology, BSA bulletin and Journal of petroleum.
TANG Gongjian WANG Jun QI Yue ZHOU Jinsheng DAN Wei
This data set mainly includes the non-traditional B-Mo isotopic data of Himalayan Leucogranites, which is mainly used to study the mechanism of B-Mo isotopic fractionation during the melting process, and is of great significance to the genetic study of Himalayan Leucogranites. The rocks are mainly from the granite in the Cuonadong area. Among them, there are 34 Mo samples and 48 B samples, including repeated samples. MC-ICP-MS was used for B-Mo isotopic analysis. ICP-AES and MC-ICP-MS were used for B and Mo contents in solution. The testing unit is Guangzhou Institute of geochemistry, Chinese Academy of Sciences. The data are from accepted articles published in the Journal of geochimica et cosmochimica Acta, and the data are true and reliable. It can be applied to the study of unconventional isotope fractionation and the genesis of magmatic rocks.
FAN Jingjing
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.
XIE Yaowen
The single mineral dating data set of magmatic rocks in the Qinghai Tibet Plateau is mainly zircon dating in some areas of the Hoh Xil Lhasa plate, with 34 zircon dating samples. The samples are mainly from baohu, guoganjianianshan and Dugur areas of South Qiangtang, Saga county and narusongduo areas of Lhasa plate. The rocks are mainly quartz monzonite, granite and diorite. The zircon dating methods include Sims and LA-ICPMS. The data comes from published articles or in the acceptance stage. The data were published in SCI or Ni journals, including geology, BSA bulletin and Journal of petroleum, and the data results were true and reliable. The main testing unit is Guangzhou Institute of geochemistry, Chinese Academy of Sciences. The data set can be used to study the age of magmatic rocks in different areas of the Qinghai Tibet Plateau.
TANG Gongjian DAN Wei ZHOU Jinsheng QI Yue WANG Jun
Zircon HF-O data sets of magmatic rocks in the Qinghai Tibet Plateau are mainly based on zircon HF-O isotopic data of local areas from the South Qiangtang to Lhasa plate. Zircon HF-O test points are mainly concentrated in guoganjianian mountain, baohu, Duguer of South Qiangtang and saga County of Lhasa plate. The rocks are mainly mafic dyke swarms, gneissic granite and diorite. Zircon HF-O was measured by MC-ICP-MS and Sims, respectively. The data comes from published articles or in the acceptance stage. The data were published in SCI or Ni journals, including geology, BSA bulletin and Journal of petroleum, and the data results were true and reliable. The main testing unit is Guangzhou Institute of geochemistry, Chinese Academy of Sciences. The data set can be used to study the petrogenesis and lithospheric evolution of magmatic rocks in different areas of the Qinghai Tibet Plateau.
TANG Gongjian DAN Wei WANG Jun QI Yue
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
WANG Zhaofeng GONG Dianqing
The data are collected from the automatic weather station (AWS, Campbell company) in the moraine area of garongla glacier by the comprehensive observation and research station of alpine environment in Southeast Tibet, Chinese Academy of Sciences. The geographic coordinates are 29.765 ° n, 95.712 ° E and 3950 m above sea level. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), net radiation (w / m2) and air pressure (kPa). In the original data, an average value is recorded every 30 minutes before October 2018, and then an average value is recorded every 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The net radiation probe is nr01, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Due to the thick snow cover and low temperature on the ice surface in winter and spring, some parameter data periods are missing, which can be used by scientific researchers studying climate, glacier and hydrology.
LUO Lun
According to the distribution of cultivated land in 18 districts and counties in the "One River and Two Tributaries" region of Tibet Autonomous Region, a 5km × 5km grid was adopted, covering all cultivated land and greenhouse land. A total of 1092 5km × 5km grids were set up, and each grid contains a number. Data processing method: the fishnet tool in ArcGIS 10.3 is used to generate the grid covering the administrative boundaries of 18 districts and counties in the "one river, two rivers" region of Tibet Autonomous Region, and then the intersect tool is used to generate the grid covering cultivated land. The data can be used to collect soil samples of cultivated land in "One River and Two Tributaries" area of Tibet Autonomous Region.
WANG Zhaofeng GONG Dianqing
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.
FENG Zhiming
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2018. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
DAI Liyun
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.
XU Wenqiang
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
LI Guoshuai LI Xin
The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2 [U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the effective sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, i.e., the minimum wind velocity at the standard height to cause sand particle rolling; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in the effective sand-moving wind (sand-moving wind >6 m s-1 at the height of 10 m) directions, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, to sum all the above results to obtain the final DP in the wind direction. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 2000 to 2008 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.25°×0.25° generated with the fifth generation of ECMWF atmospheric ReAnalysis of the global climate (ERA5).
LI Guoshuai
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.
LI Yalin
1) Data content: Paleomagnetic data, magnetic index data, major element percentage data and chemical weathering index can establish the paleomagnetic age framework of the Dahonggou section and restore the precipitation change and chemical weathering history in geological history. 2) Data sources and processing methods The data source is experimental data. Paleomagnetic data: a cylindrical sample of 2x2x2cm was drilled with a small gasoline drill and measured with a low-temperature superconducting magnetometer in a magnetic shielding room. Magnetic data: the samples collected in the field were ground into fine particles by mortar and put into 2x2x2 non-magnetic plastic box, and tested by kappa bridge susceptibility meter, pulse magnetometer and rotating magnetometer. Mass percentage content and chemical weathering index data of major elements in the whole sample and particle size fraction: firstly, the whole sample and particle size fraction sample were pretreated with acetic acid and hydrogen peroxide to remove carbonate and organic matter, and then pressed into a round cake with a diameter of about 4cm and a thickness of about 8mm by a pressure apparatus, and finally XRF fluorescence analysis was carried out. 3) Data quality The sample collection and experimental processing are carried out according to strict standards, and the data quality is reliable. 4) Data application achievements and Prospects Three SCI papers were published using this set of data, one of which is Ni.
NIE Junsheng
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.
LV Changhe LIU Yaqun
The data set is the land cover data set of 2010 and 2020. The spatial range is Dhaka City, Bangladesh. The spatial resolution is 30m and the temporal resolution is year. The data comes from globeland30 (Global geographic information public goods, http://www.globallandcover.com/ ）, acquired after mosaic and reorganization. The data accuracy evaluation of the source data is led by Tongji University and Institute of aerospace information innovation, Chinese Academy of Sciences. The overall accuracy of the data is more than 83.50%. The data set can provide high-precision basic geographic information for related research, and has important applications in resource and environment bearing state identification, natural disaster risk assessment, disaster prevention and mitigation, etc.
YANG Fei YIN Cong
The data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
