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.
KONG Weidong
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.
KONG Weidong
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
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.
ZHOU Huakun
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).
WU Guangjian
This data includes the image data of the second comprehensive field scientific investigation of the Qinghai Tibet Plateau. The image data includes the sample plot photos of the quadrats collected in the nature reserve during the scientific research, the images of forest ecosystem, grassland ecosystem and lake ecosystem in the nature reserve in Northwest Yunnan and Western Sichuan, the vegetation situation, wildlife habitat, and the data of animals, plants and fungi in the reserve. In addition, the image data also includes the sample collection process of the scientific research, the household survey of the scientific research team in the community survey and the image data of the interview with the local protection department. The data comes from UAV and camera shooting, which can provide evidence and reference for scientific research.
SU Xukun
1) Data content It includes the observation year, latitude and longitude, altitude, ecosystem type and soil layer (soc0-100 (kgcm-2); 0-100 represents soil layer), underground biomass content. 2) Data sources This part of the data is obtained from the literature, specific literature sources refer to the documentation. 3) Data quality description The data cover a wide range, including comprehensive indicators, showing the content of soil organic carbon under different soil layers, with high integrity and accuracy, which can meet the estimation of soil carbon storage of grassland in Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect of soil and realizing the sustainable development of ecosystem carbon in the future.
HU Zhongmin
1) Data content It includes the observation year, longitude and latitude, ecosystem type, annual rainfall, drought index, annual net primary productivity, aboveground biomass, underground biomass and other data. 2) Data sources One part is from literature (1980-1995), the other part is from field sampling (2005-2006). 3) Data quality description The data has a long observation year, a large time span, a wide coverage, and many indicators, which has high integrity and accuracy, and can meet the estimation of grassland carbon storage in the Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect and realizing the sustainable development of ecosystem carbon in the future.
HU Zhongmin
Carrying capacity refers to the carrying capacity of grassland calculated by actual carrying capacity and reasonable carrying capacity, that is, all overloading, balanced and non overloading. This data set includes two products: Grassland carrying capacity pressure index and grassland livestock balance index. Grassland carrying capacity pressure index = actual carrying capacity / reasonable carrying capacity, and grassland livestock balance index = (actual carrying capacity - reasonable carrying capacity) × 100% / reasonable carrying capacity, the actual carrying capacity data comes from the Qinghai Tibet Plateau actual carrying capacity data set (2000-2019), and the reasonable carrying capacity data comes from the Qinghai Tibet Plateau reasonable carrying capacity data set (2000-2019). This data set can analyze the temporal and spatial variation characteristics of livestock carrying status in the Qinghai Tibet Plateau, extract overgrazing areas, and evaluate the overload intensity of the Qinghai Tibet Plateau, which has important application value for ecological protection, monitoring and early warning of the Qinghai Tibet Plateau.
LIU Bintao
Actual carrying capacity refers to the actual number of livestock that can be raised on a certain area of grassland during a certain utilization period. The actual carrying capacity is obtained through statistical yearbooks from various provinces (regions) and cities (prefectures) on the Qinghai Tibet Plateau, as well as statistical data provided by animal husbandry management departments. There are multiple statistical calibers in the statistical data, such as inventory, output, output rate, and year-end livestock quantity. This dataset uniformly adopts the year-end livestock inventory as the calculation standard for actual carrying capacity based on the statistical data of each region. A multiple linear regression was conducted using the actual carrying capacity in the statistical yearbook with population density, NPP, and terrain undulation to establish a spatial model for actual carrying capacity. Grid data of actual carrying capacity (sheep units, MU/km2) were obtained, with a time series from 2000 to 2019 and a spatial resolution of 250 meters. Using statistical data from the core pastoral areas of the Qinghai Tibet Plateau, including Guoluo Prefecture, Yushu Prefecture, Changdu City, Nagqu City, Aba Prefecture, Ganzi Prefecture, and Gannan Prefecture, it was verified that the average absolute error of spatialization was 27.48 MU/km2, and the average relative error was 13.79%. This dataset can analyze the spatiotemporal variation characteristics of actual livestock carrying capacity on the Qinghai Tibet Plateau, evaluate the carrying characteristics of grasslands on the Qinghai Tibet Plateau, extract transitional grazing areas, and has important application value for ecological protection, monitoring, and early warning on the Qinghai Tibet Plateau.
LIU Bintao
Reasonable carrying capacity, also known as theoretical carrying capacity, refers to the maximum number of domestic animals that can be carried by a certain grassland area in a certain period of time under the premise of moderate grazing (or mowing) and maintaining sustainable production of grassland to meet the needs of normal growth, reproduction and production of livestock. Based on the MODIS inversion data of forage yield (fresh weight, kg / hm2), the reasonable carrying capacity of grassland (sheep unit, mu / km2) was evaluated according to the code for calculation of grassland carrying capacity and grass livestock balance (DB 51 / t1480-2012) and calculation of reasonable carrying capacity of natural grassland (NY / T 635-2015), The time series is 2000-2019, and the spatial resolution is 250m. This data set can analyze the temporal and spatial variation characteristics of the theoretical carrying capacity under the condition of rational utilization of grassland in the Qinghai Tibet Plateau, evaluate the carrying capacity characteristics of grassland in the Qinghai Tibet Plateau, and extract the overgrazing areas, which has important application value for ecological protection, monitoring and early warning of the Qinghai Tibet Plateau.
LIU Bintao
Grassland yield is an important ecological parameter of grassland, which is an important basis for monitoring grassland productivity, Estimating Grassland reasonable carrying capacity and evaluating grassland carrying status. Based on the grassland data collected in July and August, MODIS NDVI, precipitation and terrain parameters, multivariate statistical equations were established to invert the total grass yield (kg / hm2) and edible grass yield (kg / hm2). The time series is 2000-2019, and the spatial resolution is 250 meters. Based on the data of 50 quadrats distributed in Sichuan, Tibet, Qinghai, Gansu and other regions, the results show that the average absolute error of total grass yield is 734.75kg/hm2, and the average relative error is 24.85%. The average absolute error of edible grass yield is 715.81kg/hm2, and the average relative error is 30.52%. Due to the complexity of grassland types, high spatial heterogeneity and scale mismatch between the measured grassland quadrats and MODIS image pixels, this accuracy can meet the requirements of remote sensing monitoring of grassland in large areas. This data set can analyze the spatiotemporal variation characteristics of grassland productivity in the Qinghai Tibet Plateau, evaluate the carrying capacity characteristics of grassland in the Qinghai Tibet Plateau, and extract the overgrazing areas, which has important application value for ecological protection, monitoring and early warning of the Qinghai Tibet Plateau.
LIU Bintao
This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.
NIU Ben, ZHANG Xianzhou
This data is derived from the Supplementary Tables of the paper: Chen, F. H., Welker, F., Shen, C. C., Bailey, S. E., Bergmann, I., Davis, S., Xia, H., Wang, H., Fischer, R., Freidline, S. E., Yu, T. L., Skinner, M. M., Stelzer, S., Dong, G. R., Fu, Q. M., Dong, G. H., Wang, J., Zhang, D. J., & Hublin, J. J. (2019). A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau. Nature, 569, 409-412. This research is another breakthrough made by academician Fahu Chen and his team over the years research of human activities and environmental adaptation on the Tibetan Plateau. The research team analyzed the newly discovered hominid mandible fossils in Xiahe County, Gansu Province, China, and identified it belongs to Denisovan of the Tibetan Plateau, which suggested to call Xiahe Denisovan. The team conducted a multidisciplinary analysis of the fossil, including chronology, physique morphology, molecular archaeology, living environment and human adaptation. It is the first Denisovan fossil found outside the Denisova Cave in the Altai Mountains and the earliest evidence of human activity on the Tibetan Plateau (160 kyr BP). This study provides key evidence for further study of Denisovans' physical characteristics and distribution in East Asia, it also provides evidence of a deep evolutionary history of these archaic hominins within the challenging environment of the Tibetan Plateau. This data contains 6 tables, table name and contents are as follows: t1: Distances in mm between meshes generated from CT versus photoscans (PS). t2: Measurements of the Xiahe mandible after reconstruction. t3: Comparative Dental metrics. t4: Comparative crown morphology. t5: Uniprot accession numbers for protein sequences of extant primates used in the phylogenetic analyses. t6: Specimen names and numbers.
CHEN Fahu
The data set includes the spatial distribution of grass yield in the Qinghai-Tibetan Plateau in 1980, 1990, 2000, 2010, and 2017. The gross primary productivity (GPP) of grassland in the Qinghai-Tibetan Plateau was simulated based on the ecological hydrological dynamic model VIP (vegetation interface process) with independent intellectual property of Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. The net primary productivity (NPP) was estimated by empirical coefficient, and converted it into dry matter, and then the hay yield was estimated by root-shoot ratio. The spatial resolution is 1km. The data set will provide the basis for grassland resource management, development, utilization and the formulation of the strategy of "grass for livestock".
MO Xingguo
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