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
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
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
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
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
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
The data set is the basic data of the Qinghai Tibet Plateau in 2015. The original data comes from the National Basic Geographic Information Center, and the data of the Qinghai Tibet plateau region is formed by splicing and clipping the segmented data. The data content includes 1:1 million provincial administrative divisions, 1:1 million roads and 1:250000 water system. The data attributes of administrative divisions include name, code and Pinyin; Road data attributes include: GB, RN, name, rteg and type (basic geographic information classification code, road code, road name, road grade and road type); Water system data attributes include: GB, hydc, name, period (basic geographic information classification code, water system name code, name, season).
YANG Yaping
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
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 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
This data set includes the urban distribution, urban population and built-up areas of the Qinghai Tibet Plateau from 2000 to 2015. The urban distribution data is the county-level vector boundary in 2015, and the urban population and built-up area data years are 2000, 2005, 2010 and 2015. Among them, the data of urban distribution and built-up areas are from the research team of Kuang Wenhui, Professor of Institute of geography and resources, Chinese Academy of Sciences, and the data of urban population are from the census data of each year, the statistical yearbook of each province in the Qinghai Tibet Plateau, etc. The data quality is excellent, which can be used to analyze the population growth trend, urban expansion and the impact of human activities on the surrounding environment of cities and towns in the Qinghai Tibet Plateau.
KUANG Wenhui
Taking villages and towns as the basic division unit, the division map of agricultural development in the Tibetan Plateau comprehensively considers climate, topography, vegetation type and coverage, land use type and proportion, distribution of nature reserves, key points of ecological protection and direction of agricultural development, puts forward the zoning scheme of agricultural and animal husbandry regulation for ecological protection in Qinghai Tibet Plateau, and divides the Qinghai Tibet Plateau into 8 areas (3 areas are based on ecological protection) The protection areas are the key limited control areas of agriculture and animal husbandry, 5 moderate development areas of agriculture and 23 small areas, and the zoning is named by the way of protection + development direction of agriculture and animal husbandry. The purpose of the zoning map is to develop agriculture and animal husbandry moderately on the basis of effective ecological protection, which can provide reference information for the protection of ecological security barrier function and sustainable management.
LV Changhe, LIU Yaqun
Based on the vulnerability assessment framework of "exposure sensitivity adaptability", the vulnerability assessment index system of agricultural and pastoral areas in Qinghai Tibet Plateau was constructed. The index system data includes meteorological data, soil data, vegetation data, terrain data and socio-economic data, with a total of 12 data indicators, mainly from the national Qinghai Tibet Plateau scientific data center and the resource and environmental science data center of the Chinese Academy of Sciences. Based on the questionnaire survey of six experts in related fields, the weight of the indicators is determined by using the analytic hierarchy process (AHP). Finally, four 1km grid data are formed involving ecological exposure, sensitivity, adaptability and ecological vulnerability in the agricultural and pastoral areas of the Qinghai Tibet Plateau. The data can provide a reference for the identification of ecological vulnerable areas in the Qinghai Tibet Plateau.
ZHAN Jinyan, TENG Yanmin, LIU Shiliang
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
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 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
1. The data content is the monthly groundwater level data measured between the tail of chengdina River, Kuqa Weigan River and Kashgar river of Tarim River, which is required to be the water level data of 30 wells, but the number of wells in this data reaches 44; 2. The data is translated into CSV through hobo interpretation, and the single bit time-lapse value is found through MATLAB, and then extracted and calculated through Excel screening, that is, through the interpretation of original data, through the communication Out of date and daily data, calculated monthly data; 3. Data is measured data, 2 decimal places are reserved, unit is meter, data is accurate; 4. Data can be applied to scientific research and develop groundwater level data for local health.
CHEN Yaning, HAO Xingming
The Three-River-Source National Park with an area of 123,100 km2 and include three sub regions, they are source region of the Yangtze River in the national park, source region of Yellow River in the national park and source region of Lancang River in the national park. The national park is located between longitude 89°50'57" -- 99°14'57", latitude 32°22'36" -- 36°47'53". It accounts for 31.16% of the total area of Three-River-Source region. This data set is generated by digitizing the location map of Three-River-Source national park in the comprehensive planning of Three-River-Source national park. The data include the boundary for the national park. Data format is Shapefile. Arcmap is recommended to open the data.
WANG Xufeng
The Three-River-Source National Park with an area of 123,100 km2 and include three sub regions, they are source region of the Yangtze River in the national park, source region of Yellow River in the national park and source region of Lancang River in the national park. The national park is located between longitude 89°50'57" -- 99°14'57", latitude 32°22'36" -- 36°47'53". It accounts for 31.16% of the total area of Three-River-Source region. This data set is generated by digitizing the location map of Three-River-Source national park in the comprehensive planning of Three-River-Source national park. The data include the boundary for the national park. Data format is Shapefile. Arcmap is recommended to open the data.
WANG Xufeng
Taking the villages or towns as the basic division unit, taking into account the forest topography (elevation, slope), vegetation type and coverage, land use status and agricultural utilization type, distribution of natural reserves, key points of ecological protection and agricultural development direction, the preliminary scheme of the agricultural and animal husbandry regulation and Control Division of the ecological protection on the Qinghai Tibet Plateau is proposed, which divides the Qinghai Tibet Plateau into 8 regions (3 regions With ecological protection as the key agricultural and animal husbandry limited control area, 5 agricultural moderate development areas) and 23 residential areas, the way of protection + agricultural and animal husbandry development direction is adopted in the naming of zones. Based on the effective protection of ecology and the moderate development of agriculture and animal husbandry in the Qinghai Tibet Plateau, the map can provide reference information for the protection of ecological security barrier function and sustainable management.
LV Changhe, LIU Yaqun
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
This data is originated from the 1:100,000 national basic geographic database, which was open freely for public by the National Basic Geographic Information Center in November 2017. The boundary of the Qinghai-Tibet Plateau was spliced and clipped as a whole, so as to facilitate the study on the Qinghai-Tibet plateau. This data set is the 1:100,000 administrative boundaries of the qinghai-tibet plateau, including National_Tibet_line、 Province_Tibet、City_Tibet、County_Tibet_poly and County_Tibet_line. Administrative boundary layer (County_Tibet_poly) property name and definition: Item Properties Describe Example PAC Administrative division code 513230 NAME The name of the County line name Administrative boundary layer (BOUL) attribute name and definition: Item Properties Describe Example GB classification code 630200 Administrative boundary layer (County_Tibet_line) attribute item meaning: Item Properties Describe Example GB 630200 Provincial boundary GB 640200 Prefectural, municipal and state administrative boundaries GB 650201 county administrative boundaries (determined)
National Basic Geographic Information Center
This dataset contains monthly and daily 0.01°×0.01° (2018) LST products in Qilian Mountain Area. The dataset was produced based on MYD21A1 LST products at a resolution of 0.01° along with some auxiliary datasets. The auxiliary datasets include Lat/Lon and the Julian Day information. MYD21A1 is the official LST product of MODIS, and the data is divided into day and night, using TES algorithm. Download URL: https://urs.earthdata.nasa.gov.
LI Hua
This dataset contains five types of boundaries. 1. TPBoundary_ 2500m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 2500m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 2. TPBoundary_ 3000m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 3000m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 3. TPBoundary_ HF (high_frequency): This boundary is defined according to 2 previous studies. Bingyuan Li (1987) had a systematic discussion on the principles for determining the extent of the Tibetan Plateau and the specific boundaries. From the perspective of the formation and basic characteristics of the Tibetan Plateau, he proposed the basic principles for determining the extent of the Tibetan Plateau based on the geomorphological features, the plateau surface and its altitude, while considering the integrity of the mountain. Yili Zhang (2002) determined the extent and boundaries of the Tibetan Plateau based on the new results of research in related fields and years of field practice. He combined information technology methods to precisely locate and quantitatively analyze the extent and boundary location of the Tibetan Plateau, and concluded that the Tibetan Plateau in China extends from the Pamir Plateau in the west to the Hengduan Mountains in the east, from the southern edge of the Himalayas in the south to the northern side of the Kunlun-Qilian Mountains in the north. On April 14, 2017, the Ministry of Civil Affairs of the People's Republic of China issued the Announcement on Adding Geographical Names for Public Use in the Southern Tibetan Region (First Batch), adding six geographical names in the southern Tibetan region, including Wo’gyainling, Mila Ri, Qoidêngarbo Ri, Mainquka, Bümo La, and Namkapub Ri. ４. TPBoundary_ New (2021): Along with the in-depth research on the Tibetan Plateau, the improvement of multidisciplinary research and understanding inside and outside the plateau, and the progress of geographic big data and Earth observation science and technology, the development of the 2021 version of the Tibetan Plateau boundary data by Yili Zhang and et al. was completed based on the comprehensive analysis of ASTER GDEM and Google Earth remote sensing images. The range boundary starts from the northern foot of the West Kunlun Mountain-Qilian Mountain Range in the north and reaches the southern foot of the Himalayas and other mountain ranges in the south, with a maximum width of 1,560 km from north to south; from the western edge of the Hindu Kush Mountains and the Pamir Plateau in the west to the eastern edge of the Hengduan Mountains and other mountain ranges in the east, with a maximum length of about 3,360 km from east to west; the latitude and longitude range is 25°59′30″N~40°1′0″N, 67°40′37″E~104°40′57″E, with a total area of 3,083,400km2 and an average altitude of about 4,320m. Administratively, the Tibetan Plateau is distributed in nine countries, including China, India, Pakistan, Tajikistan, Afghanistan, Nepal, Bhutan, Myanmar, and Kyrgyzstan. ５. TPBoundary_ Rectangle: The rectangle was drawn according to the range of Lon (63~105E) and Lat (20~45N). The data are in latitude and longitude projection WGS84. As the basic data, the boundary of the Tibetan Plateau can be used as a reference basis for various geological data and scientific research on the Tibetan Plateau.
ZHANG Yili
The data set was produced based on the SRTM DEM data collected by Space Shuttle Radar terrain mission in 2016, the reference data such as river, lake and other water system auxiliary data , using the arcgis hydrological model to analyze and extract the river network. There are 12 sub-basins over the Tibet Plateau, including AmuDayra、Brahmaputra、Ganges、Hexi、Indus、Inner、Mekong、Qaidam、Salween、Tarim、Yangtze、Yellow. The outer boundary is based on the 2500-metre contour line and national boundaries.
ZHANG Guoqing
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.
DU Yunyan
Antarctic administrative boundary datasets consist of the properties of the state boundaries of the Antarctic states (properties properties), and the corresponding names and types of those properties :(CITY_POP), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (YEAR). The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
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.
DU Yunyan
Arctic administrative boundary data sets include Arctic_National, Arctic_Provincial, and Arctic_Prefecture vector spatial data sets of arcti-bound countries and Its corresponding name, TYPE related attribute data :(LOCAL_NAME), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (CONTINENT) The data comes from the 1:1,000,000 ADC_WorldMap global data set, which is a comprehensive, up-to-date and seamless geographic digital data. The world map coordinate system is latitude and longitude, WGS84 datum surface, and the arctic data set is the special projection parameter for the arctic (North_Pole_Stereographic).
ADC WorldMap
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.
DU Yunyan
The two regions of North Pole are defined by the Arctic Monitoring and Assessment Programme (AMAP) working group and Arctic Human Development Report (AHDR). The AMAP Arctic’s geographical coverage extends from the High Arctic to the sub Arctic areas of Canada, the Kingdom of Denmark (Greenland and the Faroe Islands), Finland, Iceland, Norway, the Russian Federation, Sweden and the United States, including associated marine areas. The AHDR Arctic encompasses all of Alaska, Canada North of 60°N together with northern Quebec and Labrador, all of Greenland, the Faroe Islands, and Iceland, and the northernmost counties of Norway, Sweden and Finland. The situation in Russia is harder to describe in simple terms. The area included, as demarcated by demographers, encompasses the Murmansk Oblast, the Nenets, YamaloNenets, Taimyr, and Chukotka autonomus okrugs, Vorkuta City in the Komi Republic, Norilsk and Igsrka in Krasnoyarsky Kray, and those parts of the Sakha Republic whose boundaries lie closest to the Arctic Circle.
Arctic Monitoring And Assessment Programme
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
Geomatics Center of Qinghai Province
Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
This data set is the concentrations of atmospheric and water POPs in Nam Co, including time series of gas phase OCP, PCBs, PAHs and particulate PAHs in the atmosphere; dissolve and particulate POPs in water. An active air sampler (AAS) was deployed on the roof of Nam Co Monitoring and Research Station for Multisphere Interactions (NCMORS) and the air monitoring was conducted for two consecutive years from September 2012 to September 2014. The flow rate of AAS was 60 L min-1 and the air samples were collected every 2 weeks with a volume of approximately 600 m3 for each sample. The air stream passes first through glass fiber filters (GFFs 0.7 μm, Whatman) to collect the total suspended particulates (TSP) and then through polyurethane foam (PUF, 7.5×6 cm diameter) to retain the POPs in gas phase. Fifteen sites around the Nam Co Lake (surface lake water, 0–1 m depth) were selected to obtain the spatial distribution of POPs in lake water. The water samples (200 L) were filtered with GFFs to obtain the total suspended particulate matter (SPM), and then pumped through an XAD-2 resin column to collect the dissolved phase compounds. 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 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% forPAHs. The reported concentrations were not corrected for recoveries.
WANG Xiaoping
Land use data of Dushanbe, with a resolution of 30 meters, was in the form of TIF and the time was 1990.03.03 and 2018.03.16, 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).
ESA Glob Cover, MA Haitao, HUANG Jinchuan
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
Land use data of Bishkek, with a resolution of 30 meters, was in the form of TIF and the time was 1990.03.30 and 2018.04.12, 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).
ESA Glob Cover, HUANG Jinchuan, MA Haitao
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 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
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
The data set contains the boundaries of the three source regions of the Yellow River, the Yangtze River and the Lancang River, the boundary of the whole Sanjiangyuan region and the boundaries of the counties within the basin. The observation projects include the boundaries of the three source regions of the Yellow River, the Yangtze River and the Lancang River, the boundary of the whole Sanjiangyuan region and the boundaries of the counties within the basin.
WEI Yanqiang, Establishing Developing and Applying of the Space-Air-Field Integrated Eco-Monitoring and Data Infrastructure of the Three-River-Source National Park
The data set contains the Chinese name, English name and the affiliation between the districts and counties in each administrative division of Qinghai. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. Table 1: The table of administrative divisions in Qinghai has 5 fields. Field 1: Regions Interpretation: Chinese names of the regions Field 2: English names of the regions Interpretation: English names of the regions Field 3: Districts and counties Interpretation: Chinese names of the districts and counties Field 4: English names of the districts and counties Interpretation: English names of the districts and counties Field 5: Land area Unit: square kilometers Table 2: The table of division changes of each county has 5 fields. Field 1: Districts and counties Field 2: Year Field 3: Area Unit: square kilometers Field 4: Number of townships Field 5: Number of Village Committees
Qinghai Provincial Bureau of Statistics
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
Based on the Global 1,000,000 Basic Geographic Data (2010) of the Resource and Environment Science Data Center of the Chinese Academy of Sciences, the administrative divisions of Arctic countries (USA, Canada, Russia, Norway (including Greenland and the Faroe Islands), Denmark, Sweden, Finland, and Iceland) at the national and provincial levels are extracted via ArcGIS. The data are stored separately by nation. The data format is the .shp format of ArcGIS, and the projection mode is GCS_WGS_1984. The national data are from http://www.resdc.cn/data.aspx?DATAID=205. The provincial data are from http://www.resdc.cn/data.aspx?DATAID=206.
YANG Linsheng, WANG Li
"Hydrologic - ecological - economic process coupling and evolution of heihe Basin governance under the framework of water rights" (91125018) project data exchange 4-basin-plan-mdb 1. Data overview: a watershed plan revision for the Murray darling river in Australia, adopted in 2012, for catchment comparisons 2. Data content: the public plan
WANG Zhongjing
I. Overview The Yellow River is the second longest river in our country. The problem of the Yellow River's sediment has attracted the attention of people all over the world. The watershed is an important natural unit. Using the SRTM-DEM and ASTER-GEDEM data sets as the data source, under the ArcGIS software platform, the method of combining river burning method and river scalar method is used to extract the upper reaches of the Yellow River basin. The boundary of the basin from the source area of the Yellow River to the upper reaches of the Yellow River in Hekou Town. Ⅱ. Data processing description Using SRTM-DEM and ASTER-GDEM issued by the United States as data sources, under the ArcGIS software platform, the method of combining river burning method and river scalar method was used to extract the upper reaches of the Yellow River basin. Because the ratio of the rivers from the Three Lakes Estuary to Hekou Town is extremely small, there is a certain error in the boundary of the basin. Ⅲ. Data content description The map is stored in ArcGIS and .shp files. The river basin boundary spans five provinces (autonomous regions) of Qinghai, Sichuan, Gansu, Ningxia, and Inner Mongolia, with a total area of 55.06 × 104 km2. Ⅳ. Data usage description Watershed boundary is an important natural unit for hydrology, soil erosion, and non-point source pollution research. By extracting watershed boundaries, the migration range of soil erosion and non-point source pollution can be delineated.
XUE Xian, DU Heqiang
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. Heihe River Basin water system map is one of the hydrological and water resources part of the atlas, with a scale of 1:2500000, positive axis isometric conic projection and standard latitude of 25 47 n. Data sources: river data of Heihe River Basin, reservoir distribution data of Heihe River Basin, residential area data of Heihe River Basin in 2009, administrative boundary data of one million Heihe River Basin in 2008, Lake data of Heihe River Basin and other basic geographic data. The upper reaches of Heihe River Basin are located in Qilian County, Haibei Tibetan Autonomous Prefecture, Qinghai Province, and the northern foot of Qilian Mountain in Zhangye, Jiuquan City, Sunan and Subei counties of Gansu Province. The middle reaches are located in Shandan, Minle, Ganzhou, Linze, Gaotai, Sunan, Suzhou, Jiayuguan and Yumen counties of Gansu Province. The lower reaches are located in Jinta, Gansu Province, Ejina Banner and Alxa Right Banner of Inner Mongolia, involving three provinces (autonomous regions), 16 cities and counties (District, banner), 56 towns, 45 townships and 4 Sumu. Table 1 shows the information about the administrative divisions of Heihe River Basin.
WANG Jianhua, ZHAO Jun, WANG Xiaomin, FENG Bin
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