As a typical arid and semi-arid region, Central Asia is subject to varying degrees of hydrothermal constraints and environmental limitations for sustainable land and agricultural development. Analysis and prediction of land use potential is essential to guarantee regional food security and reduce the adverse effects of climate change. This dataset is oriented to the sustainable agricultural development of five Central Asian countries, and the potential evaluation of land use and agroecology from the perspective of land resource development and utilization potential is carried out with dry farming, irrigated agriculture, forestry, and grass-pastoralism as land use targets. The multi-objective land resource development and utilization evaluation factors include climate (heat and water resources), topography, irrigation and water extraction conditions, and soil conditions, which are greater than 10℃ cumulative temperature, average temperature in January, average temperature in July, precipitation, precipitation variation coefficient, elevation, slope, water extraction distance, groundwater level, soil organic matter, soil texture, soil acidity and alkalinity, among which the precipitation variation coefficient is based on The precipitation variation coefficient is based on precipitation conversion, and the slope information is extracted from the elevation data. Variable climate elements including future monthly-scale precipitation, mean temperature, maximum and minimum air temperature, and humidity are derived from bias-corrected and downscaled CMIP6's ACCESS-CM2, BCC-CSM2-MR, CanESM5, CAS-ESM2-0, CESM2-WACCM, EC-Earth3, GFDL-ESM4, KACE-1-0-G multi-model ensemble averaged data with experiments of r1i1p1f1. This data can provide a basis for future land resources development and utilization, agricultural development, etc. in the five Central Asian countries. The data can provide basic data support for the future development and utilization of land resources and agricultural development in five Central Asian countries.
YAO Linlin , ZHOU Hongfei
Based on ESA's CCI-LC Maps data, we mapped the agricultural landscape of Central Asia, including Kazakhstan, Turkmenistan, Tajikistan, Kyrgyzstan, and Uzbekistan, for sustainable agricultural development in the five Central Asian countries, and classified the existing agricultural land into six categories: rainfed cropland, rainfed cropland (herbaceous cover), rainfed cropland (forest cover), irrigated cropland, cropland (>50%)/natural vegetation (<50%), and cropland (<50%)/natural vegetation (>50%). 50%)/natural vegetation (<50%) and arable land (<50%)/natural vegetation (>50%). The data year is 2020 and the spatial resolution of the data is 300m × 300m, i.e., about 0.003° × 0.003°. The dataset can provide basic data support for future land resource development and utilization and agricultural development of the five Central Asian countries.
ZHANG Junjun , JIANG Xiaohui
Facing the sustainable agricultural development of the five Central Asian countries, with the goal of land resources, in order to explore the land resources evaluation in Central Asia under the climate change in the past 20 years and the land resources situation in Central Asia under the climate change in the next 30 years, we collected the land resources evaluation elements in Central Asia, including: soil elements (soil salinization degree, soil texture, soil organic matter content, soil pH value, soil total nitrogen), terrain elements (elevation, slope) Climatic elements (rainfall, temperature, solar radiation). Topographic elements and soil elements are based on 2020. Climate elements include 2000, 2010, 2020, and the average precipitation and temperature in 2030 and 2050 under the future SSP5-8.5 scenarios estimated by the ESM1 climate model in CMIP6, with a spatial resolution of 0.01 ° × 0.01°。 The data set can provide basic data support for the future development and utilization of land resources and agricultural development of the five Central Asian countries.
ZHANG Junjun , JIANG Xiaohui
The data set includes the implied water resources and land resource flows among 11 cities and counties in the Heihe River basin, including Ganzhou, Sunan, Minle, Linze, Gaotai, Shandan, Suzhou, Jinta, Jiayuguan and Ejina. Table 1 includes the transfer volume of virtual water resources and virtual land resources among multiple regions. Table 2 includes the virtual water resources export volume of each regional sub sector and the virtual water resources import volume of each regional sub sector. Table 3 includes the export volume of virtual land resources of each regional sub sector and the import volume of virtual land resources of each regional sub sector. Based on the input-output tables of 11 cities and counties in the Heihe River Basin, we investigate the consumption, loss and flow of water and land resources in each economic sector, construct a coupled water-land resource accounting statement, and calculate the virtual water resources and virtual land resources flow by sector in each region based on the input-output analysis method. The water consumption and land use data of each region and sector are obtained from official statistical yearbook data.
CHEN Bin
Provide detailed spatial distribution of land cover types in China from 1990 to 2015, with spatial accuracy of 0.25 ° and geographic coordinate system of WGS84. Each grid shows the ratio of land use type to grid area (0-1). The data comes from the global land use spatial distribution map of the University of Maryland. The historical homogenized land use data of China is obtained by linear interpolation of the original data, extraction of Chinese regional mask and transformation of coordinate system, and saved in geotiff file format. The methods and standards of data over the years are consistent, the coverage is complete, and the collection and processing process is traceable and reliable. It has realized the homogenization of existing population data products, providing a basis for analyzing the laws of human elements, the interaction mechanism of human elements and natural elements.
WANG Can , WANG Jiachen
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2021. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2021. The overall accuracy of the product is better than 85%.
YANG Aixia, ZHONG Bo
The evaluation of the potential of cropland development under the influence of future climate change changes was carried out for the sustainable development of agriculture in five Central Asian countries, with cropland as the target. The evaluation factors of cropland development potential include: topographic factors (elevation, slope, slope direction, distance to water resources), soil factors (salinity, soil texture, soil organic matter content, soil pH), climate factors (rainfall, temperature, solar radiation), and economic factors (road density, population density). Using 2020 as the base year, the future potential for cropland development in Central Asia under the SSP5-8.5 scenario was estimated using the average precipitation and temperature from the ESM1 climate model in CMIP6, with other indicators held constant. The data provide evaluation results of the cropland development potential of the five Central Asian countries for the time periods 2020s, 2030s (2021-2040) and 2050s (2041-2060) with a spatial resolution of 0.01° × 0.01°. The dataset can provide basic data support for future land resource development and utilization and agricultural development in the five Central Asian countries.
JIANG Xiaohui, ZHANG Junjun
Facing the sustainable development of agriculture in the Central Asia, the risk assessment of land resources exploitation under the influence of future climate change and land use change is carried out with the goal of cultivated land. The evaluation indices of land resources exploitation risk for farmland include topographic factors (such as elevation and slope), land use type, soil texture, precipitation, GDP per capita, grain production per capita, growth rate of agricultural economy, urbanization rate, natural growth rate of population, soil organic matter content, etc. Taking 2015 as the baseline and keeping other indicators remain unchanged, we use multi-model ensemble mean precipitation of climate models in CMIP6 (BBC-CSM2-MR, CanESM5, IPSL-CM6A-LR, MIROC6 and MRI-ESM2-0) and the land cover data under different emission scenarios in the future to estimate the risk of land resources exploitation in Central Asia under different scenarios in the future (SSP1-2.6, SSP2-4.5 and SSP5-8.5). The datasets include land resources exploitation in 2030s (2021-2040) and 2050s (2041-2060) under three future scenarios, with a spatial resolution of 0.5°×0.5°. It is expected to provide basic information for future agricultural production and land resources exploitation in five countries in Central Asia.
HUANG Farong, LI Lanhai
Facing the sustainable development of agriculture in the Central Asia, the risk assessment of the land resources exploitation is carried out with the aim of cultivated land. The evaluation indices of land resources exploitation risk for farmland include topographic factors (such as elevation and slope), precipitation, land use type, soil texture, 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 above indices are normalized dimensionless, and the weight of each index to the risk of land resources exploitation is determined based on the multiple linear regression model between grain production and each index. The datasets include land resources exploitation risk in 1995, 2000, 2005, 2010 and 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
This data set is a 30m land cover classification product in the Qilian Mountains in 2021. This product is based on the land cover classification product in 2021, based on the Landsat series data and strong geodetic data processing capability of Google Earth engine platform, and is produced by using the ideas and methods of change detection. The overall accuracy is better than 85%. This product is the continuation of land cover classification products from 1985 to 2020. Land cover classification products from 1985 to 2020 can also be downloaded from this website. Among them, the land use products from 1985 to 2015 are five years and one period, and the land use products from 2015 to 2021 are one year and one period.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
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 is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
The Huanghuang Valley was one of the most important agricultural development areas on the Qinghai-Tibet Plateau, especially by the Qing Dynasty, the land cover of the area underwent significant changes. By collating and correcting the 1726 cropland data recorded in the historical documents of the area, with a view to revealing the basic conditions of arable land changes and human activities in the typical river valley agricultural area of the Qinghai-Tibet Plateau, we provide a theoretical basis. This data contains raster data on the spatial distribution pattern of arable land in the Huanghuang Valley in 1726 with a spatial resolution of 1km*1km. The area of cropland is mainly obtained from the New Records of Xining Prefecture，Records of Xuanhua Hall，New Records of Gansu， which were recorded during the Qianlong period of 20 years. The determination of county administrative boundaries refers to Atlas of Chinese History edited by Tan Qixiang and Comprehensive Table of Administrative Region Evolution in Qing Dynasty edited by Niu Hanping. The original data on cropland collected from the historical literature was corrected and then the quantitative data was assigned to space using a grid drawing method.
LIU Fenggui, LUO Jing
Tibetan Plateau with high altitude,cold climate,poor natural conditions and fragile ecological environment become the sensitive and promoter region of global climate change.Studying for Land reclamation of historical period in Qinghai-Tibet Plateau is not only the specific way to participate in the global environmental change, but also can provide the comprehensive research of land use change with abundant regional information,there is important significance for studying history in our country even the whole world of land use/cover change research.The region of Brahmaputra River and its two tributaries in Tibetan Plateau pastoral transitional zone is one of the important typical agricultural area, and is the area with the most intense land reclamation activities and the fastest population growing.Proceeding deep historical data mining in the study area to reconstruct the cropland spatial patterns over the past 300 years has important significance to study the human land use activities under the background of global climate change. This data contains raster data on the spatial distribution pattern of arable land in Brahmaputra River and Its Two Tributaries in 1730 with a spatial resolution of 500m*500m.The data of cultivated land in 1730 comes from tiehu Inventory，the missing data of two counties were interpolated.The land area recorded in the data is converted into modern mu units, and the missing counties are calculated using the area's per capita cultivated land and population.
LIU Fenggui, GU Xijing
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin
The supply capacity of land resources is an important index to determine the carrying capacity of land resources. The data set includes: (1) the supply capacity of cultivated land resources in the Qinghai Tibet Plateau; (2) Data on grassland resource supply capacity of Qinghai Tibet Plateau. The supply capacity of cultivated land resources is based on the output of main agricultural products of Tibet Bureau of statistics, and summarizes the output of grain, meat, eggs and dairy livestock products at key nodes; The grassland resource supply capacity is based on the grassland area and livestock quantity data of Tibet Bureau of statistics, combined with field sampling data and climate data, and based on the aboveground biomass model to calculate the average biomass and total biomass of grassland in typical counties at key nodes. The data can be used to analyze the spatial difference of land supply capacity of the Qinghai Tibet Plateau, which is of great significance to the study of land carrying capacity of the Qinghai Tibet Plateau.
YANG Yanzhao
Quantitative evaluation and comprehensive measurement of resource and environment carrying capacity is the key technical link of resource and environment carrying capacity research from classification to synthesis. Based on the evaluation of the suitability of human settlements, the limitation of resource carrying capacity and socio-economic adaptability, and according to the research idea and technical route of "suitability zoning restrictive classification adaptability classification warning classification", a three-dimensional tetrahedral model for the comprehensive evaluation of resource and environmental carrying capacity with balanced significance is constructed. Based on the 10km grid, a comprehensive study on the resource and environment carrying capacity was carried out, and the resource and environment carrying capacity index of the areas along the silk road was quantitatively simulated. Taking 1 as the equilibrium significance, it provided support for the comprehensive evaluation of the resource and environment carrying capacity of the areas along the silk road.
YOU Zhen
The data sources of this dataset mainly include domestic satellite images such as HJ-1A/B, GF-1/2, ZY-3, and Landsat TM/ETM+/OLI series satellite image data. Using the domestic satellite images supplemented by Google Earth images to generate the component training sample and validation sample data of different geographical divisions. Using Google Earth Engine (GEE) to test and correct the model algorithm parameters. The normalized settlement density index (NSDI) is obtained based on random forest algorithm, Landsat TM/ETM+/OLI series satellite images and auxiliary data. The vector boundary of urban built-up area is obtained by density segmentation method after manual interactive interpretation and correction. The NSDI, vegetation coverage index and vector boundary of the Tibetan Plateau are used to produce the original data of urban impervious surface and urban green space fractions in the Tibetan Plateau. After correction and accuracy evaluation, the datasets of urban impervious surface area and green space fractions in the Tibetan Plateau from 2000 to 2020 are generated. The resolution of the data product is 30 m, and the coordinate system and storage format of the data files are unified. The geographic coordinate system is WGS84, the projected coordinate system is Albers, and the data storage format is GeoTIFF, the data unit is percentage (the value range is 0~10000), and the scale factor is 0.01. In order to quantify the change of urban land cover more accurately, samples from several typical cities are selected to verify the dataset. The specific verification methods and accuracy are shown in the published results. The data can be used to analyze and reveal the impact of land cover change and future scenario simulation on the Tibetan Plateau, to provide a scientific basis for building environmentally livable cities and improving the quality of human settlements on the Tibetan Plateau.
KUANG Wenhui, GUO Changqing, DOU Yinyin
The data set records the statistical data of grassland construction in Qinghai Province in the main years, covering the period from 2011 to 2017. The data are divided by fenced grassland area, new enclosure area in the current year, reserved area of artificial grass planting, area of new species in the current year, rodent damage area in the year, rodent damage control area in the year, etc. The data set contains seven data tables, which are: Grassland Construction in major years (2011), grassland construction in major years (2012), grassland construction in major years (2013), grassland construction in major years (2014), grassland construction in major years (2015), grassland construction in major years (2016) and grassland construction in major years (2017). The data table structure is similar. For example, the data sheet of grassland construction in major years (2011) has 10 fields: Field 1: indicator Field 2: 1995 Field 3: year 2000 Field 4: year 2005 Field 5: year 2006 Field 6: year 2007 Field 7: year 2008 Field 8: year 2009 Field 9: year 2010 Field 10: year 2011
AGRICULTURAL AND RURAL Department of Qinghai Province
Sediment ancient DNA is biological ancient DNA scattered in Paleoenvironmental samples, which is different from ancient DNA directly extracted from ancient animal bones and plant remains. Paleoenvironmental DNA is mainly mixed with multi species ancient DNA extracted from environmental samples such as glaciers, frozen soil, lake sediments, peat sediments, site cultural layer, dental calculus and fecal fossils. These DNA enter the environment with biological residues (including remains, hair, feces and urine), degrade rapidly and denature slowly in the environment, and finally adsorb on minerals and other particles or integrate into their own genome by microorganisms for long-term preservation, thus forming paleoenvironmental DNA. Sediment DNA is a new ancient DNA analysis technology. The sediments of archaeological sites can track the DNA preservation status of relevant sites and possible humans, make up for the shortcomings that human fossils are generally available but not available, greatly expand the research object and open a new window to study the population evolution of Paleolithic archaeological sites. The ancient DNA of stratum sediments from baishiya karst cave site where Xiahe human mandible was found was systematically sampled and analyzed.
ZHANG Dongju , FU Qiaomei
The basic principle of ancient recipe analysis based on carbon and nitrogen stable isotope analysis method is you are what you eat, that is, the chemical composition of animal tissues and organs is closely related to their diet. Through the detection of isotope ratio of relevant elements, the food structure of ancient people and animals can be directly revealed Then it discusses the research means of people's livelihood and livestock domestication. The collagen of human and animal bones from shilinggang site in Nujiang, Yunnan Province in the southwest of Qinghai Tibet Plateau was analyzed by carbon and nitrogen stable isotopes.
DONG Guanghui , REN Lele
The alpine and anoxic environment of the Qinghai Tibet Plateau is a major challenge for human survival and life. When human beings boarded the Qinghai Tibet Plateau and adapted to the extreme environment of the plateau has always been a hot issue in the academic circles. At present, in the study of prehistoric culture of the Qinghai Tibet Plateau, except the northeast, most areas of the Qinghai Tibet Plateau have not established archaeological cultural sequences. Yajiang river basin is one of the areas with dense distribution of human activity relics, but there are few archaeological excavations and studies, and the activity history of the ancients in this area is not clear. Based on the systematic dating of cultural archaeological sites in Linzhi Area, Southeast Tibet, 33 carbon fourteenth age data were obtained.
YANG Xiaoyan, WANG Yanren
The data set of land desertification distribution in Sanjiangyuan area is derived from the desertification pattern and change data of Qinghai Tibet Plateau. This data is obtained based on the integration of remote sensing images, auxiliary data and other multi-source data. The main data used and referred to include: 1) remote sensing image data: Landsat was selected to extract the images from June to September as the main data source for land desertification monitoring on the Qinghai Tibet Plateau, and five images were selected to monitor land desertification in 1980, 1990, 2000, 2010 and 2015. 2) auxiliary data: terrain data, soil type data, vegetation type data Land use data, Google Earth image and other auxiliary data are important data in the interpretation of desertification land; 3) The indicators of desertification are wind erosion rate, percentage of quicksand area and vegetation coverage; 4) The area of the source area of the three rivers is 382312 km2. The data set is cut out from the land desertification distribution data of the Qinghai Tibet Plateau, so as to carry out the research and analysis of the source area of the three rivers separately; 5) This data format is ShapeFile format. It is recommended to use ArcMap to open data.
NAN Weige
Land cover data of typical mineral development project areas include land cover data set of Gannan Tibetan Autonomous Prefecture (2000), land cover data set of Gannan Tibetan Autonomous Prefecture (2010) and land cover data set of Gannan Tibetan Autonomous Prefecture (2020). The data format is shape file with a spatial resolution of 30m, including ten categories: cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier and permanent snow, and the time resolution is years. The data comes from globeland30 (global geographic information public product), http://www.globallandcover.com/ ）, obtained by mosaic and reorganization. The data accuracy evaluation of source data is led by Tongji University and Aerospace Information Innovation Research Institute of Chinese Academy of Sciences, and the overall accuracy of data exceeds 83.50%. The data set can provide high-precision basic geographic information for relevant research, and can be applied to the comprehensive effect assessment of land cover in typical mineral development areas of super large gold belt in Qilian Mountain metallogenic belt in the northeast of Qinghai Tibet Plateau. It has important applications in the environmental effect assessment of mineral development, natural disaster risk assessment and disaster prevention and reduction.
CHENG Hao
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
To collect, integrate and integrate data and information on human activities, geographical conditions, environmental quality, natural disasters, medical and health care, and natural resources in the Qinghai Tibet Plateau（ Meteorological data (air temperature, air pressure, wind speed, precipitation, evaporation, sunshine hours, air humidity) from 1980 to 2019, air oxygen content, solar radiation, 4 million digital landform data set, soil erosion, concentration data set of soil persistent organic pollutants in Qinghai Tibet Plateau, natural disasters, medical resources, economic data in Tibet and Qinghai Water resources data of Qinghai Tibet Plateau (1990, 1995, 2000, 2005, 2010, etc.)
Topographic relief is a comprehensive representation of regional altitude and surface cutting degree. Based on the definition and calculation formula of topographic relief under the background of China's human settlements assessment, the digital elevation model (Aster GDEM 30 m) data is resampled into 1 km, The data set includes: (1) kilometer grid spatial data of Tibetan Plateau topographic relief（ 2) Terrain suitability evaluation data of Qinghai Tibet Plateau. The data can be used to analyze the spatial difference of topographic relief of the Qinghai Tibet Plateau, which is of great significance to the study of human settlements and Natural Suitability of the Qinghai Tibet Plateau.
XIAO Chiwei, LI Peng,
The data set records the basic information of cultivated land in the Tibet Autonomous Region and contains two data tables. Among them, the data table 1 has 7 fields, and the data table has 5 fields, respectively recording the cultivated land area, dry land area, paddy field area, effective irrigation area, and national infrastructure area of Tibet Autonomous Region and each district and county from 1959 to 2016. , The units are all hectares. The data comes from: "Tibet Statistical Yearbook" and "Tibetan Social and Economic Statistical Yearbook", with the same accuracy as the statistical yearbook extracted from the data. This data set has important value for understanding the situation of cultivated land in the Tibet Autonomous Region, evaluating the level of cultivated land utilization, and researching agricultural production and food security.
SU Zhengan
Nanfeng region is a vast area with a sparse population, diverse land types and abundant natural resources. It is an important part of the natural forest region in Southwest China, and also one of the grain bases and emerging industrial bases in Tibet Autonomous Region. As it is located in the southwest border of the motherland, the mountains in the territory are high and the valleys are deep, the transportation is extremely inconvenient, the large area of natural forests have not been fully utilized, and the degree of land use is very low. In recent years, although the national economic construction and industrial and agricultural production in Nanfeng region have increased significantly, the economic foundation is still quite weak, the production technology and management level are backward, the developed and utilized land has not fully brought into play, and the per mu yield of grain crops is far lower than the national average. Moreover, soil erosion and debris flow activities have been strengthened and expanded, land resources have been damaged, biological production has been reduced, and pasture has been degraded, resulting in the deterioration of human ecological environment, which has affected social and economic development to a certain extent. Therefore, we should deeply investigate and study the land resources and the natural attributes of various types of land in the Nanfeng area, fully consider the socio-economic and technical conditions and management levels, follow the objective laws of natural environment development, propose measures and ways to rationally use and protect land resources according to local conditions, give full play to the potential of land production, In order to seek the best economic, ecological and social benefits, it is of practical significance to improve the economic outlook of the Nanfeng region and promote the development of the national economy of the Tibet Autonomous Region. According to the analysis of investigation data, the macro structure of land in Nanfeng area is obviously restricted by geomorphic factors, and geomorphic conditions control the redistribution of heat and water, resulting in regional differentiation of plant community appearance and soil physical and chemical properties, forming various land types with different production potentials. In addition, the Nanfeng area is vast and sparsely populated, and the degree of land development is extremely low. The natural attributes of most of the land have not been significantly changed by human activities. Therefore, the classification of land types in Nanfeng area should be based on landform as the dominant factor, with reference to climate characteristics and natural vegetation. According to this principle, the Nanfeng area can be divided into two land types, humid mountain type and semi humid mountain type, and 24 land types. The data includes the area, distribution range, main characteristics and main utilization direction of each land type. The original data of this data set is digitized from the book "natural geography and natural resources of the namgyabawa peak area".
PENG Buzhuo, YANG Yichou
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.
Linxiu ZHANG, 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
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
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
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