As the roof of the world, the water tower of Asia and the third pole of the world, the Qinghai Tibet Plateau is an important ecological security barrier for China and even Asia. With the rapid development of social economy, human activities have increased significantly, and the impact on the ecological environment is growing. In this paper, eight factors including cultivated land, construction land, National Road, provincial road, railway, expressway, GDP and population density were selected as the threat factors, and the attributes of the threat factors were determined based on the expert scoring method to evaluate the habitat quality of the Qinghai Tibet Plateau, so as to obtain six data sets of the habitat quality of the agricultural and pastoral areas of the Qinghai Tibet Plateau in 1990, 1995, 2000, 2005, 2010 and 2015. The production of habitat quality data sets will help to explore the habitat quality of the Qinghai Tibet Plateau and provide effective support for the government to formulate sustainable development policies of the Qinghai Tibet Plateau.
LIU Shiliang, LIU Yixuan, SUN Yongxiu, LI Mingqi
This dataset includes boundary and topographic data of Southeastern Tibetan Plateau (SETP): 1. SETP_ Boundary: we centered on the traditional SETP region (i.e., the Parlung Tsangpo River basin or Bomi County) and used the surrounding river network (e.g., the Yarlung Zangbo-Brahmaputra River, Nujiang-Salween River, and their tributaries) to delineate the boundary of the SETP. This region covers the Eastern Nyainqentanglha Ranges, Eastern Himalayas, and Western Hengduan Mountains and hosts the largest maritime glacier concentration across China. 2. Topographic data: Based on NASADEM provided by NASA Earthdata, we mosaicked the DEM, slope, aspect, profile curvature (profc) and water Mask (SWB) of SETP. 3. Hillshade: We produced the hillshde with a altitude angle of 45° from the NASADEM of SETP.
ZHAO Fanyu, LONG Di, LI Xingdong, HUANG Qi, HAN Pengfei
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.
GAO Xin
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.
WANG Xiaoping
The data set is the distribution of the average roughness in Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. This is the MODIS-NDVI data set calculated by using the median particle diameter and the vegetation coverage. The space and time resolutions are 500 m and 16 days, respectively. The time is from 01, January, 2017 to 18, December, 2017. The data set uses the the Geodetic coordinate system. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
GAO Xin
This data set records the statistical data of the administrative divisions and the names of States, prefectures, cities, counties and districts in Qinghai Province from 1998 to 2000. The data are divided by industry, region, affiliation and registration type. The data were collected from the annual statistical inspection of Qinghai Province issued by Qinghai Provincial Bureau of statistics. The data set consists of four tables Administrative divisions and names of States, prefectures, cities, counties and districts.xlsx Administrative divisions and names of States, prefectures, cities, counties and districts, 1998.xls Administrative divisions and names of States, prefectures, cities, counties and districts, 1999.xls Administrative divisions and names of States, prefectures, cities, counties and districts, 2000.xls The data table structure is the same. For example, there are nine fields in the 1998 data table of administrative divisions and names of States, prefectures, cities and counties Field 1: Region Field 2: number of county administrative units Field 3: name of county administrative unit Field 4: sub district office Field 5: Town Field 6: Rural Township Government Field 7: Village Committee Field 8: Residents Committee Field 9: family Committee
Qinghai Provincial Bureau of Statistics
The natural resources dataset of the Qinghai-Tibetan Plateau covers 215 counties in this area. The observation intervals are 5 years from 2000-2015. The indicators are rainfall, temperature, humidity, population, and land area. The data sources are meteorological station data, regional statistical yearbook, etc., which are expressed by Excel. This data provides a reference for understanding the natural background conditions on the county scale in the Qinghai Tibet Plateau.
FENG Xiaoming
The natural resources dataset of the Qinghai-Tibetan Plateau covers 215 counties in this area. The observation intervals are 5 years from 2000-2015. The indicators are rainfall, temperature, humidity, population, and land area. The data sources are meteorological station data, regional statistical yearbook, etc., which are expressed by Excel. This data provides a reference for understanding the natural background conditions on the county scale in the Qinghai Tibet Plateau.
FENG Xiaoming
Among the different regions in China, Tibet contains the largest number of natural ecosystem types. It is an ideal scientific research base and a natural laboratory for the geosciences, biology and other related disciplines. To better protect this precious natural heritage, to develop and utilize the natural resources rationally and to carry out scientific research, 13 national and autonomous regional nature reserves were established in the Tibetan Autonomous Region in 1984, covering an area of 326,000 square kilometres. These reserves account for 49.3% of the total area of nature reserves in China. By the end of 2012, Tibet had established 47 nature reserves of various types, including 9 national reserves, 14 provincial reserves, 3 municipal reserves, and 21 prefectural reserves, with a total area of 412,200 square kilometres. These reserves accounted for 34.35% of the land area of the Tibetan Autonomous Region and include 22 different types of ecological function reserves. The data were extracted from the Chinese Nature Reserve Specimen Information Sharing Infrastructure. Serial number: unified number of nature reserves Name of the nature reserves Administrative region: administrative region of the nature reserves Area (hectare) Primary protection objects Type: Type of nature reserves Class: Class of the nature reserves Established time: The date the nature reserves were established Responsible authority
Institute of forest ecological environment and protection
This data set includes the social, economic, resource and other relevant index data of Gansu, Qinghai, Sichuan, Tibet, Xinjiang and Yunnan in the Qinghai Tibet Plateau from 2000 to 2015. The data are derived from Gansu statistical yearbook, Qinghai statistical yearbook, Sichuan statistical yearbook, Xizang statistical yearbook, Xinjiang statistical yearbook, Yunnan statistical Yearbook China county (city) socio economic statistical yearbook And China economic network, guotai'an, etc. The statistical scale is county-level unit scale, including 26 county-level units such as Yumen City, Aksai Kazak Autonomous Region and Subei Mongolian Autonomous County in Gansu Province, 41 county-level units such as Delingha City, Ulan county and Tianjun County in Qinghai Province, 46 counties such as Shiqu County, Ruoergai County and ABA County in Sichuan Province, and 78 counties such as Ritu County, Gaize county and bango County in Tibet, 14 counties including Wuqia County, aktao county and Shache County in Xinjiang Province, and 9 counties including Deqin County, Zhongdian county and Fugong County in Yunnan Province; Variables include County GDP, added value of primary industry, added value of secondary industry, added value of tertiary industry, total industrial output value of Industrial Enterprises above Designated Size, total retail sales of social consumer goods, balance of residents' savings deposits, grain output, total sown area of crops, number of students in ordinary middle schools and land area. The data set can be used to evaluate the social, economic and resource status of the Qinghai Tibet Plateau.
CHEN Yizhong
Inland water system and river basin regional dataset are the key hydrological parameters in the study of global change. Waterr distribution is of great significance to the study of the characteristics, morphological characteristics, changes, time distribution of various types of water bodies at the nodes, and the law of regional differentiation. The basic data is downloaded from DIVA-GIS, and is subset and resampled by administrative boundary dataset of all 31 key nodes as the research areas. The data concludes the distribution of lakes and reservoirs (planar River system) and rivers (linear River basin) . Finally, the data of water system and river basin in 31 key node regions are stored and obtained. This data set serves as the research basis for all hydrological remote sensing data and provides hydrological base data for the project. This data set can be updated in real time according to the government information and the changing trend of water system where node is located.
SHANG Cheng
The spatial distribution data set of disaster prevention and mitigation facilities in hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the fusion of 1-2m remote sensing image data, combined with POI data, the distribution information of hospital, fire protection and refuge facilities were extracted respectively. On this basis, the relevant layers and poi layers of OSM were superimposed with the extracted results and images. Through visual inspection, errors were found and the extracted results were corrected. Finally, hambantuota was formed Vector layer data of disaster prevention and mitigation related facilities in the node and Colombo area.
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.
SHANG Cheng
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
1) In mountainous areas, due to the complex topographic and geological background conditions, landslides are very easy to occur triggered by external factors such as rainfall, snow melting, earthquake and human engineering activities, resulting in the loss of life and property and the destruction of the natural environment. In order to meet the safety of project site construction, the rationality of land use planning and the urgent needs of disaster mitigation, it is necessary to carry out regional landslide sensitivity evaluation. When many different evaluation results are obtained by using a variety of different methods, how to effectively combine these results to obtain the optimal prediction is a technical problem that is still not difficult to solve at present. It is still very lack in determining the optimal strategy and operation execution of the optimal method for landslide sensitivity evaluation in a certain area. 2) Using the traditional classical multivariate classification technology, through the evaluation of model results and error quantification, the optimal evaluation model is combined to quickly realize the high-quality evaluation of regional landslide sensitivity. The source code is written based on the R language software platform. The user needs to prepare a local folder separately to read and store the software operation results. The user needs to remember the folder storage path and make corresponding settings in the software source code. 3) The source code designs two different modes to display the operation results of the model. The analysis results are output in the standard format of text and graphic format and the geospatial mode that needs spatial data and is displayed in the standard geographic format. 4) it is suitable for all people interested in landslide risk assessment. The software can be used efficiently by experienced researchers in Colleges and universities, and can also be used by government personnel and public welfare organizations in the field of land and environmental planning and management to obtain landslide sensitivity classification results conveniently, quickly, correctly and reliably. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development
YANG Zhongkang
The urbanization rate data of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the urbanization rate statistical data at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between urbanization rate and covariables (e.g.,night lighting NPP-VIIRS). The spatial regression analysis method is used to model relationship between the urbanization rate data and covariables, and then the county-level urbanization rate data were downscaled and predicted. Based on statistical data and spatial analysis, it is finally integrated into urbanization rate data. The data can provide important basic data for the development of social and economic research on key area and regions along the Belt and Road.
GE Yong, LING Feng
The urbanization rate data of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the urbanization rate statistical data at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between urbanization rate and covariables (e.g.,night lighting NPP-VIIRS, road network density). The spatial regression analysis method is used to model relationship between the urbanization rate data and covariables, and then the county-level urbanization rate data were downscaled and predicted. Based on statistical data and spatial analysis, it is finally integrated into urbanization rate data. The data can provide important basic data for the development of social and economic research on key area and regions along the Belt and Road.
GE Yong, LING Feng
The data set records the administrative divisions of Qinghai Province, and the data are divided according to the administrative divisions of Qinghai Province. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Administrative divisions of the whole province (end of 2001). Xls Administrative divisions of the whole province (end of 2002). Xls Administrative divisions of the whole province (end of 2003). Xls Provincial administrative divisions (end of 2006). Xls Provincial administrative divisions (end of 2007). Xls Provincial administrative divisions (end of 2008). Xls Provincial administrative divisions (end of 2009). Xls Provincial administrative divisions (end of 2013). Xls Administrative divisions of the whole province (end of 2004). Xls The data table structure is the same. For example, there are five fields in the data table of the provincial administrative divisions (at the end of 2001) Field 1: Region Field 2: land area (km2) Field 3: number of administrative units at county level Field 4: name of county administrative unit (region) Field 5: sub district office
Qinghai Provincial Bureau of Statistics
1) The work of automatically dividing a wide and complex geospatial area or even a complete watershed into repeatable and geomorphically consistent topographic units is still in the stage of theoretical concept, and there are great challenges in practical operation. Terrain unit is a further subdivision of topography and geomorphology, which can ensure the maximum uniformity of geomorphic features in slope unit and the maximum heterogeneity between different units. It is suitable for geomorphic or hydrological modeling, landslide detection in remote sensing images, landslide sensitivity analysis and geological disaster risk assessment. 2) Slope unit is an important type of topographic unit. Slope unit is defined as the area surrounded by watershed and catchment line. In fact, the area surrounded by watershed and catchment line is often multiple slopes or even a small watershed. Theoretically, each slope unit needs to ensure the maximum internal homogeneity and the maximum heterogeneity between different units. The slope unit is an area with obviously different topographic characteristics from the adjacent area. These topographic characteristics can be based on the characteristics of catchment or drainage boundary, slope and slope direction, such as ridge line, valley line, platform boundary, valley bottom boundary and other geomorphic boundaries. According to the high-precision digital elevation model, the slope unit with appropriate scale and quality can be drawn manually, but the manual drawing method is time-consuming and error prone. The quality of the divided slope unit depends on the subjective experience of experts, which is suitable for small-scale areas and has no wide and universal application value. Aiming at the gap in practical operation in this field, we propose an innovative modeling software system to realize the optimal division of slope units. Automatic division system of slope unit based on confluence analysis and slope direction division v1 0, written in Python programming language, runs and calculates as the grass GIS interpolation module, and realizes the automatic division of slope units in a given digital elevation data and a set of predefined parameters. 4) Based on python programming language, the code is flexible and changeable, which is suitable for scientific personnel with different professional knowledge to make a wide range of customization and personalized customization. In addition, the software can provide high-quality slope unit division results, reflect the main geomorphic characteristics of the region, and provide a based evaluation unit for fine landslide disaster evaluation and prediction. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development.
YANG Zhongkang
The data set records the basic situation of counties (cities) in Qinghai Province from 2013 to 2014. The data is divided by year, and the statistical area covers 46 counties and cities, including Xining, Haidong, Hainan, Huangnan, Yushu, Guoluo, Haixi, Haibei, etc. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains two data tables, namely: basic situation of county (city) (2013). XLS, basic situation of county (city) (2014). XLS. The data table structure is the same. For example, the data table in 2013 has five fields: Field 1: Region Field 2: administrative area Field 3: number of villages Field 4: number of towns Field 5: number of sub district offices
Qinghai Provincial Bureau of Statistics
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