The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
The remote sensing monitoring database of land use status in China is a multi-temporal land use status database covering the land area of China, which has been established after many years of accumulation under the support of the National Science and Technology Support Plan and the Key Direction Project of the Knowledge Innovation Project of the Chinese Academy of Sciences. It is the most accurate remote sensing monitoring data product of land use in China at present, which has played an important role in the national land resources survey, hydrology and ecological research. This data set covers the six western provinces in China: Xinjiang, Tibet, Qinghai, Yunnan, Sichuan and Gansu. Based on Landsat TM/ETM remote sensing images in the late 1970s、1980s、1995、2000、2005、2010、2015， 1KM raster data are generated by using the professional software and manual visual interpretation on the basis of vector data. The land use types include six primary land types which are cultivated land, forest land, grassland, water area, residential land and unused land, and 25 secondary types.
LIU Jiyuan
This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.
RAN Youhua, LI Xin
This dataset contains cultivated land and impermeable surface products in Qilian Mountain key Area from 1990 to 2015 every 5 years. The dataset came from land cover products in Qilian Mountain key Area.
YANG Aixia
The dataset is the land cover of Qing-Tibet Plateau in 2012. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
The data are construction land index of countries along the "the Belt and Road" in 2010 and 2015, also known as the construction land rate. It refers to the proportion of land used for construction in the total land area, including land for urban and rural housing and public facilities, land for industrial and mining purposes, land for energy, transportation, water conservancy, communications and other infrastructure, land for tourism and land for military purposes. The data come from the international statistics website. The area of construction land and relevant land that it had provided, divide the result of total land area of the country to get. It reflects the degree of development of a country's land area and the intensity of infrastructure development. At the same time, its value is also closely related to the national and regional economic development level, population density, urban residential density, traffic network density and so on. In the coordinated development of "the Belt and Road", they can provide important reference for the planning and implementation of national policies and programs, so as to accelerate the economic development of all countries.
CHEN Shaohui, LIU Zhenwei
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
Based on high-resolution satellite images of Google earth of theTibetan plateau, facility agricultural land in the whole region in 2018 was obtained through visual interpretation.The video shooting time was concentrated from November, 2017 to November, 2008.Among them, the area of facility agriculture based on image extraction in 2018 accounts for about 70.47% of the total area.Based on the image taken since November 2017, the proportion of the agricultural area of facilities for extraction is as high as 86.87%.In some areas, the time of image shooting is relatively early, but most of them are sparsely populated with little or no distribution of facility agriculture, which has little impact on the research results.This data is conducive to fully understanding the spatial distribution of facility agriculture in qinghai-tibet plateau region and to the adjustment of local facility agriculture spatial planning.
LV Changhe, WEI Hui
The data of Land Resources Productivity for “B&R” includes: 1. Areas of cultivated land resources in regions and countries along the “B&R”; 2. Data on grain planting area and total grain output in regions and countries along the “B&R”; 3. Major crops (rice, wheat, corn) in regions and countries along the route Planting area and production data; 4. Areas of grassland resources in the region and along the country; 5. Number of livestock (bovine, sheep) in the region and along the country. Source: Cultivated land and population data from the World Bank database; food, crop, grassland, and livestock data are from FAO. Data application: According to the data provided, the basic characteristics analysis of land resources and the analysis of land resource output can be carried out in the Belt and Road region and the countries along the route, so that the land resource productivity evaluation analysis can be carried out.
YANG Yanzhao
Land use and land cover map of Amu river Basin includes four periods: 1990, 2000, 2010 and 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The land use map of Amu river basin is based on Landsat TM and ETM image data in 1990, 2000, 2010 and 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually. Finally, the data validation methods are field validation and high-precision image validation.
XU Wenqiang
This data is the land use data covering the six economic corridors, which can reflect the spatial distribution of different land use types in the six economic corridors, mainly including 12 land types (irrigated farmland, dry cultivated land, cultivated land / forest grassland, woodland, shrub, grassland / lichen / moss, sparse vegetation, grassland, artificial surface and related areas, bare land) Land, water, permanent snow and glacier, lack of data (fire). The data space resolution one belt, one road, is about 0.0027 degrees, about 300m, longitude range 12.09 degree E-180 degrees, latitude 10.99 degree S-90 degrees N, data from Global Relief Model constructed by National Oceanic and Atmospheric Administration, and based on the "one belt and one road" national boundary cutting. This data is one of the basic data necessary to assess the land planning and natural disaster risk (including debris flow, landslide, mountain torrents and other disasters) in the six economic corridors, with high application frequency and wide prospects.
ZOU Qiang, The National Oceanic and Atmospheric Administration of the United States (NOAA)
The gridded desertification risk data in Central-Western Asia was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 1km and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Central-Western Asia.
XU Wenqiang
Land use data of Ashkabad, with a resolution of 30 meters, was in the form of TIF and the time was 1990.05.03 and 2018.04.14, respectively.Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
HUANG Jinchuan, MA Haitao
Land use data of Astana, with a resolution of 30 meters, was in the form of TIF and the time was 1989.08.06 and 2017.07.26, respectively.Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
HUANG Jinchuan, MA Haitao
The data were passed through the data center of institute of Tibetan Plateau research, Chinese Academy of Sciences（ http://www.data.tpdc.ac.cn/ ）China's land use status remote sensing monitoring database products are obtained. The Irtysh River and Tarim River Basin are all seven periods of data in 1980, 1990, 1995, 2000, 2005, 2010 and 2015. The data production is based on the Landsat of each period TM / ETM Remote sensing image is the main data source, which is generated by manual visual interpretation. The spatial resolution is 1km, and the projection parameter is Albers_ Conic_ Equal_ Area central meridian 105, standard weft 1:25, standard weft 2:47. The land use types include six first-class types of cultivated land, woodland, grassland, water area, residential land and unused land, and 25 second-class types.
Chinese Academy of Sciences Resource and Environmental Science Data Center(http://www.resdc.cn/) 111
1) Data content: Vector data of urban built-up areas in 65 countries of the pan-third pole region from 1992 to 2015. 2) Data source and processing method: Based on the global land cover data of the 300-meter resolution of the ESA JCR from 1992 to 2015, we integrated the global urban land use data of Gong Peng, Liu Xiaoping and Chen Jun to obtained a correction data set. 3) Data quality description: The accuracy of data in 65 countries is about 75%, and there may be differences in data accuracy in different regions. 4) Data application results and prospects: It can be used for urbanization related research in 65 countries in the Pan-Third region, such as urban land expansion analysis and future scenario simulation.
LI Guangdong
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 comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
The data set includes data of land and natural resources in Qinghai from 1984 to 2012. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. P.S: The land use data have not been updated in the yearbook since 2008. The 2008 data have been cited; therefore. The accuracy of the data is consistent with that of the statistical yearbook. There are two tables, one for natural resources data of every year, and the other is for land use data in different regions. “The land and natural resources in Qinghai” table contains the following information: Year, land, total land area, mountain, basin, river valley, Gobi desert, hilly land; cultivated land area, irrigated land; total grassland area, usable grassland, winter and spring grassland, summer and autumn grassland; forest area, forest coverage ratio, sparse forestland, shrub land, wood stocks; annual total surface runoff, Yellow River Basin, Yangtze River Basin, hydraulic theoretical reserves, installed capacity, annual power generation; coal reserves, iron ore reserves, asbestos reserves, pool salt, magnesium salt, potassium salt, boron, gold ore, lead ore, zinc ore, antimony ore, and limestone for cement. The “Land use in different regions” table includes the following information for each prefecture from 2003 to 2012: Year, region name, total area, cultivated land, garden land, forestland, grassplot, residential land use and industrial and mining land use, land for transportation, land for water conservancy facilities, and unused land.
Qinghai Provincial Bureau of Statistics
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