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
This dataset subsumes sustainable livestock carrying capacity in 2000, 2010, and 2018 and overgrazing rate in 1980, 1990, 2000, 2010, and 2017 at county level over Qinghai Tibet Plateau. Based on the NPP data simulated by VIP (vehicle interface process), an eco hydrological model with independent intellectual property of the institute of geographic sciences and nature resources research(IGSNRR), Chinese academy of Sciences(CAS), the grass yield data (1km resolution) is obtained. Grass yield is then calculated at county level, and corresponding sustainable livestock carring capacity is calculated according to the sustainable livestock capacity calculation standard of China(NY / T 635-2015). Overgrazing rate is calculated based on actual livestock carring capacity at county level.The dataset will provide reference for grassland restoration, management and utilization strategies.
MO Xingguo
The data set is obtained by UAV aerial photography during five field visits to the Qinghai Tibet Plateau in 2018-2019. The data size is 77.6 GB, including more than 11600 aerial photos. The aerial film was shot in five times, from July 19, 2018 to July 26, 2018, September 9, 2018 to September 16, 2018, April 24, 2019 to May 10, 2019, July 6, 2019 to July 20, 2019, September 1, 2019 to September 7, 2019. The shooting location mainly includes the roads and surrounding areas between major cities in Lhasa, shigaze, Naqu, Shannan, Linzhi, Changdu, Diqing, Ganzi, ABA, Gannan and Golog. The aerial photos clearly reflect the local land use / cover type, vegetation distribution, grassland degradation, vegetation coverage, river and lake distribution and other information. The aerial photos have longitude and latitude and altitude information, which can provide better verification information for the remote sensing interpretation of land use / cover, and also can be used for the estimation of vegetation coverage, and for the study of land use in the study area Good reference information is provided.
LV Changhe, LIU Yaqun
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
In this study, the cultivated land, forest land and grassland of the Qinghai Tibet Plateau in 2015 were taken as the evaluation objects to analyze the terrain, climate, soil and vegetation factors (terrain: altitude, slope; climate: sunshine hours, ≥ 0 ℃ accumulated temperature, annual average precipitation; soil: soil texture, soil erosion intensity, soil layer thickness; vegetation: vegetation type, NDVI) that have significant impact on land sensitivity and establish agriculture Land sensitivity evaluation index system. Using AHP method to determine the weight of evaluation factors, according to the ArcGIS Jerks classification method to get the sensitivity level of cultivated land, forest land and grassland, output 250m resolution of the Qinghai Tibet Plateau agricultural land sensitivity map, and verify the results.
YAO Minglei
According to the characteristics of the Qinghai Tibet Plateau and the principles of scientificity, systematization, integrity, operability, measurability, conciseness and independence, the human activity intensity evaluation index system suitable for the Qinghai Tibet Plateau has been constructed, which mainly includes the main human activities such as agricultural and animal husbandry activities, industrial and mining development, urbanization development, tourism activities, major ecological engineering construction, pollutant discharge, etc, On the basis of remote sensing data, ground observation data, meteorological data and social statistical yearbook data, the positive and negative effects of human activities are quantitatively evaluated by AHP, and the intensity and change characteristics of human activities are comprehensively evaluated. The data can not only help to enhance the understanding of the role of human activities in the vegetation change in the sensitive areas of global change, but also provide theoretical basis for the sustainable development of social economy in the Qinghai Tibet Plateau, and provide scientific basis for protecting the ecological environment of the plateau and building a national ecological security barrier.
ZHANG Haiyan, XIN Liangjie, FAN Jiangwen, YUAN Xiu
This data set is the data set of land resource elements in the Qinghai Tibet Plateau from 1990 to 2015. It records the change of land use proportion of 15 built-up areas of prefecture level units in Qinghai and Tibet every five years. The data is excel file, and the spatial resolution is the scale of prefecture level administrative unit. This data is based on the land use type data of the Qinghai Tibet Plateau, and is obtained by calculating the proportion of the built-up area in the area of each grade unit to the area of the grade unit. The data set can be used to study the spatial pattern, development process and evolution mechanism of the urbanization of the Qinghai Tibet Plateau, and provide data support for the study of the impact of the urbanization of the Qinghai Tibet Plateau on the ecological environment.
DU Yunyan, YI Jiawei
Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).
XU Erqi
The dataset is the land cover of Qing-Tibet Plateau in 2015. 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 dataset is the land cover of Qing-Tibet Plateau in 2011. 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 dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of the Yangtze River (in the south 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
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