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.
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
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.
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.
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.
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.
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.
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.
WEI Yanqiang, 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.
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
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
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.
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 land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to email@example.com.
This dataset provides the estimated results of land cover change (IGBP classification) in 2040, 2070 and 2100 of Heihe River under the latest cmip5 based greenhouse gas emission scenario RCPs (representative concentration pathways). Spatial resolution: 1km. Time period: RCP (2.6, 4.5, 8.5) three scenarios, each scenario corresponding to three time periods: t1:2040, t2:2070, t3:2100. File naming rules: take "HLCs rcp26_" as an example to explain: in the naming, "HLCs" refers to the land cover scenario of Heihe River Basin, rcp26 refers to the rcp2.6 scenario of cmip5, "_40" refers to the future scenario period of 2040, the complete file name means the land cover prediction data of Heihe River Basin in 2040 under the rcp26 scenario, and so on.
FAN Zemeng, YUE Tianxiang
Irrigation area data of Zhangye City from 1999 to 2011, including total irrigation area (effective irrigation area, forest irrigation area, orchard irrigation area, forage irrigation area and other irrigation areas), water-saving irrigation area (sprinkler irrigation area, micro irrigation area, low-pressure pipe irrigation area, canal seepage prevention area and other water-saving irrigation areas), effective irrigation area data, and Ganzhou District, Shandan District Corresponding data of county, Gaotai County, Sunan County, Linze County and Minle County
"Coupling and Evolution of Hydrological-Ecological-Economic Processes in Heihe River Basin Governance under the Framework of Water Rights" (91125018) Project Data Convergence-MODIS Products-Land Use Data in Northwest China (2000-2010) 1. Data summary: Land Use Data in Northwest China (2000-2010) 2. Data content: Land use data of Shiyanghe River Basin, Heihe River Basin and Shulehe River Basin in Northwest China from 2000 to 2010 obtained by MODIS
1. Overview of data Based on the Google earth image data in 2012, the land use types of wetland parks were vectorized by visual interpretation method, which provided the data basis for wetland ecosystem service assessment. 2. Data content Land use types include wetland, farmland (corn, vegetables, wheat), water area, forest land, construction land, bare land, etc. Scale: 1: 50,000; Coordinate system: WGS84; Data type: vector polygon; Storage format: Dbf/Shp/Jpeg 3. Space-time range Coverage: Zhangye National Wetland Park; Total area: 46.02 square kilometers.
According to the statistical yearbook, different types of land use change areas in the middle reaches of China since liberation were collected and sorted out.
The Landuse/Landcover data of the Heihe River Basin in 2000 ( newly compiled in 2012), was finished by the Remote Sensing Laboratory of Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, using satellite remote sensing, based on the LandsaTM and ETM remote sensing data around 2000, combing field investigation and verification, thus leading to the establishment of the Heihe River Basin 1:10. 10,000 land use/land cover imagery and vector database. The main contents are: 1:100,000 land use graphic data and attribute data in the Heihe River Basin. The Heihe River Basin 1:100,000 (2011) land cover data and the previous land cover data use the same layered land cover classification system, the whole basin is divided into six first-class categories (cultivated land, woodland, grassland, waters, urban and rural residents, industrial and mining land and unused land), 25 secondary classes; data types are vector polygons, stored as Shape format. Land cover classification attributes: Primary type, secondary type, attribute coding, spatial distribution position Cultivated land: Plain dry land, 123, is mainly distributed in basin, Piedmont zone, river alluvial, diluvial plain or lacustrine plain (lack of water, irrigation condition is poor). Hilly dry land, 122, is mainly distributed in Hilly areas. Generally speaking, land blocks distribute on gentle slopes, ridges and mats of hills. Mountainous dry land, 121, is mainly distributed in mountainous areas, with the elevation below 4000 meters (gentle slope, mountainside, steep slope platform, etc.) and the Piedmont zones. Woodland: There is woodland (arbor), 21, is mainly distributed in the mountains (below 4000 meters ) or on the slopes of the mountains, valleys, hills, plains and so on. Shrub land, 22, is mainly distributed in higher mountain areas (below 4500 meters), most of which distribute in hillsides, valleys and sandy land. Sparse forest land, 23, is mainly distributed in the mountains, hills, plains and sandy land, and on the edge of the Gobi (loam, gravel). Other woodlands, 24, are mainly distributed in the oasis field, around rivers, roadsides and rural settlements. Grassland: Highly covered grassland, 31, is mainly distributed in mountainous areas (slow slopes), hills (steep slopes) and inter-river beaches, Gobi, sand dunes, etc. Mid-covered grassland, 32, is mainly distributed in relatively dry areas (Gobi, low-lying land and sandy land,sand dunes, etc.). The low-cover grassland, 33, grows mainly in drier areas (on the loess hills and on the edge of the sand). Waters: Channel, 41 is mainly distributed in plains, inter-river cultivated land and inter-mountain valleys. Lake, 42, is mainly distributed in low-lying areas. Reservoir pit, 43, is mainly distributed in plains and valleys between rivers, surrounded by residential areas and cultivated land. Glacier and permanent snow cover, 44, mainly distribute at the top of (over 4000) alpine regions. Flood land, 46, is mainly distributed in the high and low hillside gullies, the piedmont, the plain lowlands, and the edge of the river and lake basins. Residents land: Urban land, 51, is mainly distributed in plains, mountain basins, slopes and valleys. Rural residential land, 52, are mainly distributed in oases, cultivated land and roadsides, on the tablelands and the slopes. Industrial land and traffic land, 53, are generally distributed in the periphery of towns, areas with fairly developed transportation and industrial mining areas. Unutilized land: Sandy land, 61, is mostly distributed in the basin, on both sides of the river, in the river bay and on the periphery of the Piedmont and Gobi. Gobi, 62, is mainly distributed in the Piedmont belt with strong wind erosion and sediment transport. Saline and alkaline land, 63, is mainly distributed in dry lakes, lakeside and areas relatively low with easy water accumulation. Swamp, 64, is mainly distributed in relatively low areas with easy water accumulation. Bare soil, 65, is mainly distributed in arid areas (steep hillsides, hills and gobi), with vegetation coverage less than 5%. Bare rock, 66, is mainly distributed in extremely arid rocky mountainous areas (windy and rainless). The other, 67 mainly distributes in bare rocks formed by freezing and thawing above 4000 meters, also known as alpine tundra.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center