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
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
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
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
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
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
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 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
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
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
The Tibetan Plateau in China covers six provinces including Tibet, Qinghai, Xinjiang, Yunnan, Gansu and Sichuan, including Tibet and Qinghai, as well as parts of Xinjiang, Yunnan, Gansu and Sichuan. The research on water and soil resources matching aims to reveal the equilibrium and abundance of water resources and land resources in a certain regional scale. The higher the level of consistency between regional water resources and the allocation of cultivated land resources, the higher the matching degree, and the superior the basic conditions of agricultural production. The general agricultural water resource measurement method based on the unit area of cultivated land is used to reflect the quantitative relationship between the water supply of agricultural production in the study area and the spatial suitability of cultivated land resources. The Excel file of the data set contains the generalized agricultural soil and water resource matching coefficient data of the Tibetan Plateau municipal administrative region in China from 2008 to 2015, the vector data is the boundary data of the Tibetan Plateau municipal administrative region in China in 2004, and the raster data pixel value is the generalized agricultural soil and water resource matching coefficient of the year in the region.
DONG Qianjin, DONG Lingxiao
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
Current Situation Data of Agricultural Water and Soil Resources in the Five Central Asia Countries from 2000 to 2015 are derived from the Food and Agriculture Organization of the United Nations (FAO) food statistics database. The main elements include: water resources, temperature, soil, fertilization management, biomass, rice cultivation and land use information such as farmland, woodland and grassland. It can be used to support the analysis of the supply and demand situation of agricultural water resources in Central Asia, the study of land resource types and spatial distribution patterns, the study on the characteristics of agricultural land pattern changes, the evaluation of land resources exploitation and utilization degree and the evaluation of land resources quality, etc. It is helpful to understand the potential of agricultural land resources development in Central Asia and ensure the safety of agricultural production in Central Asia.
LI Fadong
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
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
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 matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
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
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