Coupling model of grass and livestock in Qilian Mountains and its optimal allocation results

In this data set, the effects of different proportions of oat grass and natural grass on digestion and metabolism of grazing Tibetan sheep in summer were studied with four proportions of oat grass and natural grass in Qilian alpine meadow. It includes dry matter (DM), organic matter (OM), crude protein (CP), crude fat (EE), neutral detergent fiber (NDF) and acid detergent fiber (ADF) intake and digestibility of grazing Tibetan sheep. Through the analysis of data, the natural forage in summer can meet the growth and metabolism of Tibetan sheep, and it is not suitable to feed oat grass.

0 2022-06-21

Field investigation of elements (carbon, nitrogen, phosphorus, sulfur, potassium) of vegetation in the Water tower area of Qinghai Tibet Plateau and Himalayan Mountains (2020s)

Carbon, nitrogen, phosphorus, sulfur and potassium are important basic life elements of ecosystem. It plays an important role in revealing the impact of its regional variation and spatial pattern on human activities and the sustainable development of ecosystem in the future. The Qinghai Tibet Plateau has unique alpine vegetation types and rich vertical zone landforms and surface cover types. The biogeographic pattern of surface elements (carbon, nitrogen, phosphorus, sulfur, potassium) is an important manifestation of the coupling of carbon, nitrogen and water cycle processes and related mechanisms of alpine ecosystems. This dataset focuses on the distribution pattern and spatial variation of surface materials (plant leaf branch stem root and litter) in the complex ecosystem of the Water tower area of Qinghai Tibet Plateau and Himalayan Mountains, in order to provide data support for regional model simulation and ecological management.

0 2022-05-30

Aboveground biomass data set of temperate grassland in northern China (1993-2019)

Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.

0 2022-04-18

Data set of grassland growth in Qilian Mountain Area

The data set is the measured data, which is obtained through the three-year field survey from 2019 to 2021. There are 59 sample points and 590 quadrats in total. It includes the grassland growth status of different grassland types in 14 typical counties in Qilian Mountain Area (Aksai, Dachaidan, Delingha, Dulan, Gangcha, Gaotai, Golmud, Huangcheng Town, Mangya City, Menyuan County, Qilian County, Shandan County, Sunan County and Wulan county). The indicators include species diversity, dominant species, edible forages, poisonous weeds Dry weight of edible forage and dry weight of poisonous weeds. This data set investigates edible forage and poisonous weeds separately, which can provide accurate data support for calculating effective livestock carrying capacity.

0 2022-04-02

Dataset of vegetation and soil plot survey in Jianghu Source region of Tibet  from 2019 to 2021

Using the quadrat survey method, natural grassland, fenced natural grassland and artificial grassland are arranged in the source area of rivers and lakes in Tibet to investigate grassland type, coverage, species composition, aboveground biomass, soil temperature, soil bulk density, soil water content, soil texture, soil pH, soil organic matter, soil total P and soil total K, The characteristics of vegetation community and soil quality under different grassland utilization modes were compared and analyzed to study the impact of grassland utilization on vegetation and soil environment. The data collection year is from August 2019 to August 2021, and the collection location is the source area of Jianghu and surrounding areas. The altitude of the sample point is the GPS recorded data, the vegetation type is the mapping of the sample point in the vegetation map of China, the soil temperature and humidity is the soil 4 parameter speedometer data, the soil bulk density is the measured data of the sample point, the number of herbaceous species, grassland coverage and aboveground biomass are the sample survey data, and the soil particle size, organic matter and nutrient content are the sample laboratory analysis data.

0 2022-02-10

Analysis data of plant carbon and nitrogen cycle (2019-2020)

The data were collected from the sample plot of Haibei Alpine Meadow Ecosystem Research Station (101°19′E,37°36′N,3250m above sea level), which is located in the east section of Lenglongling, the North Branch of Qilian Mountain in the northeast corner of Qinghai Tibet Plateau. Alpine meadow is the main vegetation type in this area. The data recorded the light, air temperature and humidity, wind temperature and wind speed above the alpine plant canopy. The radiation intensity above the alpine plant canopy was recorded by LI-190R photosynthetic effective radiation sensor (LI-COR, Lincoln NE, USA) and LR8515 data collector (Hioki E. E. Co., Nagano, Japan), and the recording interval was once per second. S580-EX temperature and humidity recorder (Shenzhen Huatu) and universal anemometer are used (Beijing Tianjianhuayi) record the daily dynamics of air temperature and humidity, wind temperature and wind speed every three seconds. The recording time is from 10:00 on July 13 to 21:00 on August 17, Beijing time. Due to the need to use USB storage time and replace the battery every day, 3-5min of data is missing every day, and the missing time period is not fixed. At present, the data has not been published. Through research on the data The data can further explore the microenvironment of alpine plant leaves and its possible impact on leaf physiological response.

0 2022-01-18

Comparison of soil physical and chemical properties data set inside and outside grassland fence project (2006-2011)

1) Data content: data set of soil physical and chemical properties compared inside and outside the grassland fence project, including quadrat number, grassland type, survey County, survey location, project type, sampling time, project start time, duration, "longitude (° E)", "latitude (° n)", "altitude (m)", "pH (0-15cm)", "pH (15-30cm)", "SOM (0-15cm (‰))," SOM 15-30cm (‰)) "TN(0-15(‰))"、"TN(15-30(‰))"、"TP(0-15(‰))"、"TP(15-30(‰))" 2) Data source: field sampling data 3) Data quality: high quality 4) Data application prospect: the grassland fence project on the Qinghai Tibet Plateau will achieve remarkable results in protecting grassland and restoring regional vegetation productivity. The implementation of the project provides a broader space for the development of regional animal husbandry and ensures the stable growth of local farmers' and herdsmen's income and regional economy. In addition, the implementation of the project ensures and supports the normal production and life of herdsmen in Tibet, and realizes the grassland protection in the pastoral area and the stable development of herdsmen's animal husbandry production, which is of great significance to maintaining the overall stability of Tibetan society and promoting the sound and rapid development of Tibet

0 2021-12-14

The desertification risk map of the Arabian Peninsula in 2020

The gridded desertification risk data of The Arabian Peninsula in 2021 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 500m 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 the Arabian Peninsula in 2021.

0 2021-12-12

PM2.5 concentration data set of aerosol particles at different stations on the Qinghai Tibet Plateau (2020)

This data set includes the PM2.5 mass concentration of atmospheric aerosol particles at Southeast Tibet station, Ali station, mostag station, Everest station and Namuco station (unit: mm) μ g/m3)。 Aerosol PM2.5 fine particles refer to particles with aerodynamic equivalent diameter less than or equal to 2.5 microns in the ambient air. It can be suspended in the air for a long time, which has an important impact on air quality and visibility. The higher its concentration in the air, the more serious the air pollution. The concentration characteristic data of PM2.5 is output at the frequency of obtaining a set of data every 5 minutes, which can realize the analysis of aerosol mass concentration at different time scales such as hour, day and night, season and interannual, which provides the analysis of changes and influencing factors of aerosol mass concentration at different locations in the Qinghai Tibet plateau at different time scales, as well as the evaluation of local air quality, It provides important data support. This data is an update of the published data set of PM2.5 concentration of aerosol particles at different stations on the Qinghai Tibet Plateau (2018 and 2019).

0 2021-11-28

Demonstration data set of automatic plant phenology observer at Heihe Daman station (2019-2021)

The demonstration data set of automatic plant phenology observer at Heihe Daman station is the corn phenology observation data set collected by the plant phenology observer at Heihe Daman station. The plant phenology observer can collect phenology images through the phenology observation hardware system based on multispectral imager and wireless transmission module, and through online calculation and visual image management Phenological information processing and system control software can realize the automatic identification of key phenological periods at individual and community scales. Through the data collected by the automatic plant phenology observer, the indexes such as vegetation greenness index and NDVI index can be calculated, the change process of key plant phenology can be monitored, and the change law of vegetation phenology can be reflected.

0 2021-11-16