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
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