This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 1 to September 20 in 2019. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 7 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.376°E, 38.853°N)、(100.377° E, 38.858°N)、(100.374°E, 38.855°N)、(100.374°E, 38.858°N)、(100.371°E, 38.854°N)、(100.369°E, 38.854°N)、(100.369°E, 38.854°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
This data is the plant diversity and distribution data of chnz020 grid on the Qinghai Tibet Plateau, including the Chinese name, Latin name, latitude and longitude, altitude, collection number, number of molecular materials, number of specimens, administrative division, small place, collector, collection time and creator of plants in this grid. The data is obtained from e scientific research website（ http://ekk.kib.ac.cn/web/index/#/ ）, and partially identificated. This data has covered the list and specific distribution information of 150 species belonging to 129 genera and 87 families in this flora. This data can be used not only to study the floristic properties of this region, but also to explore the horizontal and vertical gradient pattern of plants in this region.
In order to describe the distribution pattern of genetic diversity of main domesticated animals in the Qinghai Tibet Plateau and its surrounding areas, clarify their related genetic background, and establish the corresponding genetic resource bank. In 2021, the investigation and collection of genetic resources of domestic animals will be carried out in yinguoling Mongolian Autonomous Prefecture, Xinjiang. A total of 209 blood samples of 500 local domesticated animals such as sheep, pigeons, cattle, goats and chickens were collected. This data set contains basic sample information such as species, variety, detailed sampling place, sample type, collection time, collector and storage method, which are stored in Excel form. This data set also contains the appearance photos of sampled individuals, which are stored in JPG format.
This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
The seasonal synthetic satellite remote sensing image collection of the Qinghai-Tibet Plateau is obtained by de-clouding processing of the Sentinel-2 TOA time series product. It contains 4 bands (visible and near infrared bands), and has a spatial resolution of about 10 meters. Each year is divided into 4 quarters from January to March, April to June, July to September, and October to December, and the cloud mask of each image is obtained by synthesizing the visible band, cirrus band, aerosol band and NIR band information of Sentinel-2 data. Finally, the cloud-free satellite remote sensing images of the Tibetan Plateau region are obtained by synthesizes all the masked images in a quarter according to the median principle.
The atlas includes three thematic maps of the Distribution Map of Desert Ecosystem Types on the Tibetan Plateau, the Distribution Map of Suitable Areas for Agriculture and Animal Husbandry on the Tibetan Plateau, and the Desertification Development Trend Map of Desert Ecosystem on the Tibetan Plateau. The time of the maps spans from 2010 to 2020. The original climatic data come from the monthly TerraClimate dataset with a spatial resolution of 1/24° (about 4 km). The data were preprocessed to be those have a spatial resolution of 30-m. The well-known desertification assessment system and the desert ecosystem classification standards were integrated to formulate the classification rules of the desert ecosystem, which were calibrated and validated by the remote sensing data and field survey results. In addition, the algorithms such as machine learning, Random Forest (RF) and Support Vector Machine (SVM) were introduced to generate the Distribution Map of Desert Ecosystem Types on the Tibetan Plateau. The Distribution Map of Suitable Areas for Agriculture and Animal Husbandry on the Tibetan Plateau reflects the supply services of agricultural and animal husbandry products. The vegetation productivity of modern desert ecosystem on the Tibetan Plateau was estimated, which showed the spatial distribution of potential forage supply. The grazing red line is set based on the experience of USDA, including: 1) the potential annual mean vegetation biomass less than 225kg ha-1; 2) More than 1.6km away from water source; 3) Slope greater than 65%; 4) High intensity erosion area. Grazing activities will be strictly prohibited from the areas under the standard of the red line. The areas of main crops (highland barley, Lycium chinense and wheat) in and around the Tibetan Plateau over recent five years are excluded. Based on the maximum information entropy analysis of the climate and geological environment of the existing planting areas, the growth adaptability of the three crops in the desert ecological area of the Tibetan Plateau is assessed to develop new agricultural planting areas from the desert ecological area of the Tibetan Plateau. By the comparison between the modern desert ecosystem of the Tibetan Plateau and the historical desertification in the early 21st century, the Desertification Development Trend Map of Desert Ecosystem on the Tibetan Plateau diagnosed the evolution pattern of the desert ecosystem during the past 20 years, and simulated the generation and extinction probability of the desert ecosystem on the Tibetan Plateau under the assumption that the climate change trend will be stable in the next 50 years. The probability distribution will be an important tool for evaluating the suitability of desert ecosystem protection and development in the Tibetan Plateau in the next 50 years. This atlas has reference value for monitoring the desert ecosystem of the Tibetan Plateau and developing and utilizing the service function of the desert ecosystem of the Tibetan Plateau.
The dataset is the normalized difference moisture index (NDMI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDMI equation which use the difference ratio between the NIR band and SWIR2 band to quantitatively reflect the water content of vegetation canopy .And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDMI is highly correlated with canopy water content and can be used to estimate vegetation water content, and it is also used to analyze the change of land surface temperature because it is strongly correlated with land surface temperature.
The dataset is the Landsat enhanced vegetation index (EVI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the EVI equation which is added backgroud adjusted parameters C1 and C2, and atmospheric adjusted parameter L based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow. Compared with NDVI, EVI has stronger ability to resist atmospheric interference and noise,so it is more suitable for weather conditions with high aerosol content and lush vegetation areas.
The dataset is the soil adjusted vegetation index (SAVI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the SAVI equation which is added soil adjusted parameters S based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.SAVI is stable in the sparse vegetation area, but is not sensitive in the dense vegetation area .
The dataset is the normalized difference water index (NDWI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDWI equation which use the difference ratio between the green band and NIR band to enhance the water information, and then to weaken the information of vegetation, soil, buildings and other targets.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDWI is usually used to extract surface water information effectively, therefore it is widely used in water resoureces, hydrology, forestry and agriculture.