Brief Introduction: Pan-third-polar environmental change and green silk road construction
Number of Datasets: 661
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.
2020-08-15 2329 98 View Details
This data contains part of the economic indicators of Qinghai province and Tibet Autonomous Region. The data statistics based on provinces can be used to construct the evaluation index system for the coupling coordination relationship between urbanization and eco-environment on the Tibetan Plateau. The data of the Tibet Autonomous Region contains seven indicators, including the gross domestic product (GDP), the primary, secondary and tertiary industries, industry, construction industry, and the per capita GDP, the time span is 1951-2016. The time span of the data set of Qinghai province is from 1952 to 2015, besides the above seven indicators, there is one more indicator of Qinghai province called agriculture forwdtry animal husbandry and fishery. All data are derived from the statistical yearbook, which is calculated at current prices. The gross domestic product (GDP) for 2005-2008 has been revised based on data from the second economic census.
2020-08-15 1562 40 View Details
The dataset is the land cover of Qing-Tibet Plateau in 2013. 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.
2020-08-15 887 34 View Details
The dataset is the land cover of Qing-Tibet Plateau in 2012. 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.
2020-08-15 1776 32 View Details
The dataset is the land cover of Qing-Tibet Plateau in 2011. 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.
2020-08-15 2100 31 View Details
Black carbon(BC) is a carbonaceous aerosol that mainly emitted from the incomplete combustion of fossil fuels or biomass. As fine particles in the atmosphere with light-absorbing characteristic, BC can significantly reduce the surface albedo when deposits on snow and ice and accelerate the melting of glaciers and snow cover. New Aethalometer model AE-33 acquires the real-time BC concentration according to the light absorption and attenuation characteristics from the different wavelengths. In addition, AE-33 uses dual-spot measurements, which can compensate for the “spot loading effect” and obtain high-quality BC concentrations. By using the real-time observation data measured by AE-33 at Mt. Everest Station, we analyzed the seasonal and diurnal variations of BC and its sources and transport processes, and we also investigated the transport mechanisms of serious polluted episodes. That can provide basis for future works on assessment of climate effects caused by BC in this region.
2020-08-15 1005 12 View Details
This study takes the land resources in the Qinghai-Tibet Plateau as the evaluation object, and clarifies the current situation in the region suitable for agriculture, forestry, animal husbandry production and the quantity, quality and distribution of the reserve land resources. Through field investigations, collect relevant data from the study area, and combine relevant literature and expert experience to determine the evaluation factors (altitude, slope, annual precipitation, accumulated temperature, sunshine hours, soil effective depth, texture, erosion, vegetation type, NDVI). The grading and standardization are carried out, and the weights of each evaluation factor are determined by principal component analysis. The weighted index and model are used to determine the total score of the evaluation unit. Finally, the ArcGis natural discontinuity classification method is used to obtain the Qingshang Plateau. And the grades of farmland, forestry and grassland suitability drawings of the Qinghai-Tibet Plateau with a resolution of 90m were given. Finally, the results are verified and analyzed.
2020-08-15 1665 46 View Details
The Tibetan Plateau in China covers six provinces including Tibet, Qinghai, Xinjiang, Yunnan, Gansu and Sichuan, including Tibet and Qinghai, as well as parts of Xinjiang, Yunnan, Gansu and Sichuan. The research on water and soil resources matching aims to reveal the equilibrium and abundance of water resources and land resources in a certain regional scale. The higher the level of consistency between regional water resources and the allocation of cultivated land resources, the higher the matching degree, and the superior the basic conditions of agricultural production. The general agricultural water resource measurement method based on the unit area of cultivated land is used to reflect the quantitative relationship between the water supply of agricultural production in the study area and the spatial suitability of cultivated land resources. The Excel file of the data set contains the generalized agricultural soil and water resource matching coefficient data of the Tibetan Plateau municipal administrative region in China from 2008 to 2015, the vector data is the boundary data of the Tibetan Plateau municipal administrative region in China in 2004, and the raster data pixel value is the generalized agricultural soil and water resource matching coefficient of the year in the region.
2020-08-15 1544 34 View Details
This data set is the long-term concentrations of atmospheric POPs in southeast Tibet, including OCP, PCBs and PAHs. The sampling period in this study was from August 2008 to July 2014. The data was gained from the continuous air monitoring program in STORS. In this program, a low-volume air sampler (~100 L/min) was set at STORS to trap particle- and gas-phase chemicals by a glass fiber filter (GFF, diameter of 9 cm) and polyurethane foam plugs (PUFs, 7.5 cm × 6 cm diameter), respectively. Typically, ~700 m3 of air was collected over a two-week period. Total (gas + particle) phase concentrations are reported. POPs were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The air samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently. The samples were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs and PCBs and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Field blanks and procedural blanks were prepared. The recoveries ranged from 64% to 112% for OCPs, and 65% to 92% for PAHs. The reported concentrations were not corrected for recoveries.
2020-08-15 861 3 View Details
This data set is the spatial distribution of soil POPs in the Tibetan Plateau, including OCPs, PCBs, PBDEs and PAHs. Fourty soil samples were taken from remote sites (i.e., away from towns, roads, or other human activity) in 8 soil zones of the Tibetan Plateau in 2007. The samples were collected using a stainless steel hand-held corer.Five cores (0-5 cm), taken over an area of ~100 m2, were bulked together to form one sample. The samples were wrapped in aluminum foil twice and sealed in two plastic bags to minimize the possibility for contamination. All the samples were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently, and were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs, PCBs and PBDEs, and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Procedural blanks were prepared. The recoveries ranged from 53% to 130% for OCPs, and 58% to 92% for PAHs. The reported concentrations were not corrected for recoveries.
2020-08-15 802 9 View Details
In order to explore how and when turnip was successfully domesticated the Qinghai-Tibet Plateau and what is the relationship between turnip domestication and early human settlement on the Qinghai-Tibetan Plateau and human migration along the ancient Silk Road, the whole genome De Novo sequencing of a self-bred F1 variety on Qinghai-Xizang Plateau was conducted, with the assembled genome size of 409.69 Mb,Contig N50 was 1.21 Mb in June 2018 using Pacbio sequencing. Those data will provide a genetic basis for elucidating the relationship between plant disperse and human activities. As we know, traditional turnip landrace is influenced by human domestication and nature selection. Hopefully, the study will help to understand the impacts of human selection on turnip genetic differentiation, and the adaptation mechanism of turnip in the Qinghai-Tibetan Plateau.
2020-08-15 1701 1 View Details
The sustainable development of husbandry industry depends on the conservation of local species, in which the protection of genetic resource is the core. The unique natural environment and long-term artificial selection shape the exclusive characteristics in endemic husbandry animals that well adapt to the local environments in Qinghai and Gansu Provinces. Currently, the introduction of commercial breeds leads to the loss of species diversity of local breeds and challenges the protection of genetic resources. In the present study, extensive field investigations are conducted to assess production performances and species resources, aiming to identify native breeds facing degradations. The achievements of the current study propose the conservative strategies for local domestic animals, which lay the foundation for purification and rejuvenation of endemic species/strains and promote the progression of husbandry industry in Qinghai-Tibetan Plateau and surrounding areas.
2020-08-15 1209 4 View Details
To analyzing the demographic history and the genetic mechanism underlying local adaptation of the domestic Equus animals in Qinghai-Tibet Plateau and surrounding regions and building a genetic resources bank of Equus in Pan-Third Pole, we resequenced 236 domestic Equus animal samples collected until 2018, including Tibet horse, Tibet ass, domestic horses and donkeys in the plains. By applying mitochondrial DNA sequencing and D-loop sequencing on 75 samples, including 73 ass and two horses, , a batch of genetic and genome data were generated. It provides basic genetic data to analysis on domestication, immigration and expansion of domestic animals in Qinghai-Tibet Plateau. Meanwhile it helps better understand the adaption of domestic Equus animal to Qinghai-Tibet Plateau environment.
2020-08-15 1627 4 View Details
To analyzing the distribution pattern and genetic background of domain domestic animals in Qinghai-Tibet Plateau and surrounding regions and building a genetic resources bank of animals and plants in Pan-Third Pole, we collected 343 domain domestic animal samples in 2018, including Tibet pigs, Tibet dogs, Tibet sheep and Tibet chickens in Yunnan, Sichuan and Tibet Province. By applying mitochondrial DNA sequencing on 159 chickens from northwest Yunnan and southeast Tibet, genome resequencing on 11 wild and domestic pigs and GBS sequencing on 193 domestic cattle, a batch of genetic and genome data were generated. It provides basic genetic data to analysis on domestication, immigration and expansion of domestic animals in Qinghai-Tibet Plateau. Meanwhile it helps better understand the adaption of domestic animals to Qinghai-Tibet Plateau environment.
2020-08-15 1648 14 View Details
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
2020-08-08 743 2 View Details
Land use data of Astana, with a resolution of 30 meters, was in the form of TIF and the time was 1989.08.06 and 2017.07.26, respectively.Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
2020-08-07 885 0 View Details
Land use/cover maps are one of the most basic and fundamental datasets used in environmental issues and disaster risk prevention and control research. Based on the analysis of existing land use products, Sentinel datasets with a 10m resolution were used to classify in key node areas by object-oriented classification method based on eCognition platform software. At present, the Land use datasets (10-meter resolution ) of 8 key nodes include Kyaukpyu port, yangon,Djibouti, mandalay, hambantota port, Colombo port, rayong industrial zone, and Bangkok,were obtained. The accuracy of the data is analyzed based on a limited sample, the classification overall accuracy is about 90%. The classification system is: 10. Forest; 20. Cultivated Land; 21. Paddy filed; 22. Dry farmland; 30. Water; 31. River; 32. Lake (reservoir, pond); 33 .wetland marsh wetland; 40. Artificial surface; 43. Mining area; 50. Bareland bare soil, bare rock, desert, etc.; 60. Grassland; 70. Shrubland.
2020-08-06 512 7 View Details
Evapotranspiration (ET) is the process which changes from liquid or solid to vapor returning to the atmosphere in hydrological cycles since precipitation arrives at the ground. It is usually the sum of evaporation of surface soil moisture and transpiration (T) in plants. It is the key parameter in the study of global change. At present, THE EVAPotranspiration data product of MODIS satellite is an important data source for monitoring the temporal and spatial changes of the surface, and surface evapotranspiration is an important part of water balance in the earth-gas interaction. Book which has high space-time resolution MODIS16 products as the foundation, global land evaporation in area along the whole area separated from 31 key nodes and Laos, Cambodia's railway, China and myanmar oil and gas pipeline and elegant high iron three key verification area ET cutting, estimation, get the key node area of 8 to 16 days ET products, time range is 2000-2016. Is mainly used in the areas related to all the way the surface of water and energy balance in the process of simulation and dynamic monitoring and management of regional water resources rationally, especially to the scientific allocation of water resources and realize the efficient utilization of water resources has important practical significance, to be able to have a purpose of the related research of area along the area to provide data support and reference.
2020-08-06 2145 0 View Details
China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.
2020-07-29 61661 802 View Details
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from August 31 to December 24, 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
2020-07-25 4500 84 View Details