This phenological data is based on the MOD13A2 data of the Qinghai Tibet Plateau from 2000 to 2015 (with a temporal resolution of 16 days and a spatial resolution of 1km). The NDVI curve is fitted using the segmented Gaussian function in the TIMESAT software. The spring phenology, autumn phenology and the length of the growth season are extracted using the dynamic threshold method. The thresholds of spring phenology and autumn phenology are set to 0.2 and 0.7 respectively. The phenological data were masked. Among them, the mask rules are: 1) The maximum value of NDVI must be met between June and September; 2) The average value of NDVI from June to September shall not be less than 0.2; 3) The average NDVI in winter shall not exceed 0.3.
ZU Jiaxing , ZHANG Yangjian
This dataset is the growing season NDVI and vegetation phenology dataset of the Tibetan Plateau during during the past 20 years (2001-2020). The data source is MODIS (MOD13A2, Collection 6) products, and the spatial resolution is 1km. The dataset includes: the average NDVI during the growing season (May-September), the start date of the growing season (SOS), the end date of the growing season (EOS) and the duration of the growing season (DOS) for each year from 2001 to 2020. Two methods were used to extract vegetation phenology: dynamic threshold approach and double logistic function method. The data format is TIFF and the projection is Sphere_ ARC_ INFO_ Lambert_ Azimuthal_ Equal_ Area.
WANG Taihua, YANG Dawen
This data includes the distribution data of soil bacteria in Namco region of the Qinghai Tibet Plateau, which can be used to explore the seasonal impact of fencing and grazing on soil microorganisms in Namco region. The sample was collected from May to September 2015, and the soil samples were stored in ice bags and transported back to the Ecological Laboratory of Beijing Institute of Qinghai Tibet Plateau Research; This data is the result of amplification sequencing, using MoBio Powersoil ™ Soil DNA was extracted with DNA isolation kit, and the primers were 515F (5 '- GTGCCAAGCGCCGGTAA-3') and 806R (5'GGACTACNVGGGTWTCTAAT-3 '). The amplified fragments were sequenced by Illumina Miseq PE250. The original data is analyzed by Qiime software, and then the similarity between sequences is calculated, and the sequences with a similarity of more than 97% are clustered into an OTU. The Greengenes reference library is used for sequence alignment to remove the sequence that only appears once in the database. The soil moisture content and soil temperature were measured by a soil hygrometer, and the soil pH was measured by a pH meter (Sartorius PB-10, Germany). The soil nitrate nitrogen (NO3 −) and ammonium nitrogen (NH4+) concentrations were extracted with 2 M KCl (soil/solution, 1:5), and analyzed with a Smartchem200 discrete automatic analyzer. This data set is of great significance to the study of soil microbial diversity in arid and semi-arid grasslands.
Data on soil bacterial diversity of grassland in Qinghai Tibet Plateau. The samples were collected from July to August 2017, including 120 samples of alpine meadow, typical grassland and desert grassland. The soil surface samples were collected and stored in ice bags, and then transported back to the ecological laboratory of the Beijing Qinghai Tibet Plateau Research Institute. The soil DNA was extracted by MO BIO PowerSoil DNA kit. The 16S rRNA gene fragment amplification primers were 515F (5 '- GTGCCAAGCCGGTAA-3') and 806R (5 ´ GGACTACNVGGGTWTCTAAT-3 ´). The amplified fragments were sequenced by Illumina Miseq PE250. The original data is analyzed by Qiime software, and the sequence classification is based on the Silva128 database. Sequences with a similarity of more than 97% are clustered into an operation classification unit (OTU). This data systematically compares the bacterial diversity of soil microorganisms in the Qinghai Tibet Plateau transect, which is of great significance to the study of the distribution of microorganisms in the Qinghai Tibet Plateau.
This data set is the conventional meteorological observation data of Maqu grassland observation site in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity（specific humidity in 2020）, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021). This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30. The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.
CAO Ruyin , XU Zichao , CHEN Yang , SHEN Miaogen , CHEN Jin
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.
Data files are in 7Z compressed package format, which can be decompressed and opened by 7-zip software. There are three files in total, namely file 1, text version of grassland Degradation classification on The Qinghai-Tibet Plateau, file type is Word, and file 2, named As Map, with seven maps in total. The type of the image is PNG, and the name of the image is the trend rate of average NDVI change in the growing season of grass, grassland, meadow, grassland, alpine vegetation, desert and swamp on the Tibetan Plateau from 2010 to 2019. File 3. The folder named as data is filled with pictures. There are 7 kinds of pictures with the same names as above.
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.
The dataset includes climate suitability zonation, climate and soil suitability zonation and climate and soil terrain suitability zonation dataset of 8 forages. The dataset can provide important data support for artificial grassland construction. Based on the climate index model and maximum entropy model, the climate suitability index of each pasture was constructed by using the temperature, precipitation data of the last 40 years and elevation data, and considering the soil type, soil organic matter content and topographic factors, the planting zonation of eight pasture species was established in Tibet. Eight forages are important forage resources in alpine areas. The accuracy of climate suitability index was ensured by field investigation, and the practicability of dataset of pasture planting zonation was ensured by comprehensive consideration of climate factors and soil topographic factors. Artificial grassland planting is not only the main means of ecological restoration of degraded grassland, but also an important part of grassland production structure adjustment. Reasonable and scientific grass planting is the foundation. The dataset of forage planting zonation has an important application prospect in the implementation of major ecological projects and the scientific management of grassland.
ZHOU Huakun , SHI Mingming , ZHOU Bingrong , SU Wenjiang , SUN Weijie
The dataset includes climate suitability zonation, climate and soil suitability zonation and climate and soil terrain suitability zonation dataset of 8 forages. The dataset can provide important data support for artificial grassland construction. Based on the climate index model and maximum entropy model, the climate suitability index of each pasture was constructed by using the temperature, precipitation data of the last 40 years and elevation data, and considering the soil type, soil organic matter content and topographic factors, the planting zonation of eight pasture species was established in Qinghai. Eight forages are important forage resources in alpine areas. The accuracy of climate suitability index was ensured by field investigation, and the practicability of dataset of pasture planting zonation was ensured by comprehensive consideration of climate factors and soil topographic factors. Artificial grassland planting is not only the main means of ecological restoration of degraded grassland, but also an important part of grassland production structure adjustment. Reasonable and scientific grass planting is the foundation. The dataset of forage planting zonation has an important application prospect in the implementation of major ecological projects and the scientific management of grassland.
ZHOU Huakun , SU Wenjiang , ZHOU Bingrong , SHI Mingming , ZHAO Huifang
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.
The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
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.
XU Zengrang, JIN Mingming , QIAO Tian
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.
TANG Yanhong, ZHENG Tianyu
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.
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
HONG Jiangtao, WANG Xiaodan
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).
This data set includes two infrared cameras and environmental parameter data sets of three terrestrial vertebrates deployed in Qilian Mountain reserve. The equipment is deployed near Sidalong in Qilian Mountain reserve, with a time span of (2020.8-2021.10). Due to equipment maintenance and insufficient illumination, some data are discontinuous, but the data of the two equipment can complement each other and reconstruct all the information of observation points in Qilian Mountain reserve from August 2020 to October 2021. One of the two devices is equipped with an infrared camera, which collects 4994 photos, which can be matched with the above sensor photos, or the ecological factor information before and after taking photos. 1. Wild animals and temperature, humidity, light, pressure and network signal strength information in Qilian Mountain reserve. The acquisition interval is once every half an hour 2. Data source: "development of terrestrial vertebrate monitoring equipment", 2016yfc0500104, completed by: Institute of zoology, Chinese Academy of Sciences, raw data, unprocessed 3. The sensor data acquisition interval is every half an hour. The temperature accuracy is plus or minus 0.1 degrees and the humidity accuracy is plus or minus 0.5%. The photo data is divided into trigger and timing. The trigger data is generally triggered by wild animals in the field of vision of the infrared camera; the timing photo data is dynamically adjusted according to the battery power, and the acquisition interval is between 1-12 hours. 4. This data can be used to record the ambient temperature in the reserve. Combined with the infrared camera data, it can be used to analyze the activity rhythm of wild animals, coexistence analysis and distribution limiting factors.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center