The data set records the land and natural resources of Qinghai Province, and the data is divided by land and natural resources. 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 five data tables Land and natural resources 1998.xls Land and natural resources 1999.xls Land and natural resources 2000.xls Natural resources 2001.xls Natural resources 2002. XLS, data table structure is the same. For example, the 1998 data table of land and natural resources has three fields: Field 1: Indicators Field 2: Unit Field 3: 1998
Qinghai Provincial Bureau of Statistics
The data set records the per capita income and expenditure of households in Qinghai Province from 2007 to 2013. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains six data tables with the same structure. For example, there are six fields in the data table from 1978 to 2004 Field 1: Project Field 2: 2007 Field 3: 2008 Field 4: 2009 Field 5: 2010 Field 6: 2011
Qinghai Provincial Bureau of Statistics
The data set is mainly included the population, arable land and animal husbandry data of Qinghai Province and Tibet Autonomous Region in the past 100 years. The data mainly comes from historical documents and modern statistics. The data quality is more reliable. It mainly provides arguments for the majority of researchers in the development of agriculture and animal husbandry on the Qinghai-Tibet Plateau.
LIU Fenggui
This dataset includes year-on-year data on urban construction land changes in five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) from 1985 to 2018. The data has a spatial resolution of 30m and a temporal resolution of one year. It is derived from the Global Artificial Impervious Area (GAIA) change data extracted from Landsat images from 1985 to 2018 (Gong Peng et al.). The researchers evaluated 7 sets of data every 5 years from 1985 to 2015. The average overall accuracy is over 90%, and it is the only urban construction land dataset spanning 30 years.
XU Xiaofan, TAN Minghong
Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).
SHEN Shi
Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill)_ There are four.
SHEN Shi
Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).
SHEN Shi
Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).
SHEN Shi
Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development, and protecting cultivated land (infill).
SHEN Shi
The data includes the area and attributes of different types of land, such as cultivated land, grassland and woodland, of 1280 households at domestic and abroad, which is used to support the analysis of the natural capital part of sustainable livelihoods. The field survey data is collected by the research group. Before collecting the data, the research group and the invited experts conducted a pretest to improve the questionnaire; before the formal survey, the members participating in the data collection were strictly trained; during the formal survey, each questionnaire could be filed after three times of inspection. The data is of great value to understand the natural capital and land endowment of farmers in the vulnerable areas of environment and economy, and is an important supplement to the national and macro data in this area.
ZHANG Linxiu, BAI Yunli
The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.
LV Changhe, LIU Yaqun
This data set records the land strategy of Qinghai Province from 2019 to 2020. The data set contains four PDF files, which are collected from the Department of natural resources of Qinghai Province. They are the first phase of Qinghai land economic strategy in 2019, the second phase of Qinghai land economic strategy in 2019, the third phase of Qinghai land economic strategy in 2019, the fourth phase of Qinghai land economic strategy in 2019, the fifth phase of Qinghai land economic strategy in 2019, the sixth phase of Qinghai land economic strategy in 2019, the first phase of Qinghai land economic strategy in 2020, and Qinghai land economic strategy in 2020 No. 2, 2005. Qinghai land economics is a bimonthly magazine founded in 2002, The organizer is Qinghai provincial land and resources science and Technology Information Center, which publicizes national policies and laws, carries out academic and theoretical research, exchanges grass-roots practical experience, displays the land features of Qinghai, and guides the development of land and resources. It is received by the staff and scientific workers of the national land and resources system, large and medium-sized mining enterprises, scientific research institutes and people from all walks of life who are concerned about land and resources I'm a gentleman.
Department of Natural Resources of Qinghai Province
Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.
WU Hua
Aiming at sustainable agriculture and food production in Central Asia, the vulnerability of land resources is investigated from the view of exploitation risk of land resources. The evaluation indices of land resources for farmland include topographic factors (such as elevation and slope), land use type, soil texture, etc. The evaluation indices of sustainable agriculture include GDP per capita, grain production per capita, growth rate of agricultural economy, urbanization rate, natural growth rate of population, soil organic matter content, etc. The evaluation indices above which can indicate the properties of land resources directly are used as the evaluation indices of land resources vulnerability. Further, the weighted average of these indices is taken as the land resources vulnerability. The land resources vulnerability is one element of land resources exploitation risk, and the weights of land resources vulnerability evaluation indices are determined with multiple linear regression when the land resources exploitation risk is evaluated. The datasets include land resources vulnerabilities in 1995s (1992-1996), 2000s (1997-2001), 2005s (2002-2006), 2010s (2007-2011), 2015s (2012-2017) and 1995-2015 with a spatial resolution of 0.5°×0.5°. It is expected to provide basic information for agricultural production and land resources exploitation in five countries in Central Asia.
LI Lanhai, HUANG Farong
The data set includes the road condition, water system condition and land use situation of Yangon deep water port central city. The road dataset includes both roads and railways, while the water system dataset includes rivers and lakes. The road data set and water system data set are vector data, and the land use data set is grid data with 10m resolution. The classification system of land use is: 10. Forest forest; 20. Cultivated land; 21. Paddy filed paddy field; 22. Dry farmland; 30. Water body; 31. River river river; 32. Lake Lake (including reservoirs and ponds); 33. Wetland; 40. Artificial surface; 43. Mining area; 50. Bareland Bare soil, bare rock, desert and so on, based on the limited sample accuracy analysis of the data, the classification accuracy is about 90%.
GE Yong, LI Qiangzi, LI Yi
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
The dataset of restrictive classification/zoning of land resource carrying capacity of countries along the “Belt and Road” includes: 1. Restrictive classification/zoning data of land resource carrying capacity based on human-food balance; 2. Restrictive classification/zoning data of land resource carrying capacity based on equivalent balance, divided into two categories based on heat supply and demand balance and protein supply and demand. Source：Obtained using FAO food production/consumption data and land resource carrying capacity model. Data application：Based on this data, the human-land relationship of the countries along the route can be judged from cultivated land resources to land resources, providing scientific guidance and decision-making basis for optimizing the allocation of regional functions and improving the spatial layout of construction.
YANG Yanzhao
The data defines LC classes using a set of classifiers. The system was designed as a hierarchical classification, which allows adjusting the thematic detail of the legend to the amount of information available to describe each LC class, whilst following a standardized classification approach. As the CCI-LC maps are designed to be globally consistent, their legend is determined by the level of information that is available and that makes sense at the scale of the entire world. The “level 1” legend – also called “global” legend – presented in Table 3-1 meets this requirement. This legend counts 22 classes and each class is associated with a ten values code (i.e. class codes of 10, 20, 30, etc.). The CCI-LC maps are also described by a more detailed legend, called “level 2” or “regional”. This level 2 legend makes use of more accurate and regional information – where available – to define more LCCS classifiers and so to reach a higher level of detail in the legend. This regional legend has therefore more classes which are listed in Appendix 1. The regional classes are associated with nonten values (i.e. class codes such as 11, 12, etc.). They are not present all over the world since they were not properly discriminated at the global scale.
YANG Yu
It is summarized that the agricultural and socio-economic status of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan) in 2016. This data comes from the statistical yearbook of five Central Asian countries, including six elements: total population, cultivated land area, grain production area, GDP, proportion of agricultural GDP to total GDP, proportion of industrial GDP to total GDP, and forest area. Detailed statistics of the six socio-economic elements of the five Central Asian countries. It can be seen from the statistics that there are different emphases among the six elements of the five Central Asian countries. This data provides basic data for the project, facilitates the subsequent analysis of the ecological and social situation in Central Asia, and provides data support for the project data analysis.
LIU Tie
This dataset subsumes sustainable livestock carrying capacity in 2000, 2010, and 2018 and overgrazing rate in 1980, 1990, 2000, 2010, and 2017 at county level over Qinghai Tibet Plateau. Based on the NPP data simulated by VIP (vehicle interface process), an eco hydrological model with independent intellectual property of the institute of geographic sciences and nature resources research(IGSNRR), Chinese academy of Sciences(CAS), the grass yield data (1km resolution) is obtained. Grass yield is then calculated at county level, and corresponding sustainable livestock carring capacity is calculated according to the sustainable livestock capacity calculation standard of China(NY / T 635-2015). Overgrazing rate is calculated based on actual livestock carring capacity at county level.The dataset will provide reference for grassland restoration, management and utilization strategies.
MO Xingguo
