This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from July 22 to September 5 in 2021. 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 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.374°E, 38.855°N), (100.371° E, 38.854°N), (100.369°E, 38.854°N). Four 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 5 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.
LIU Shaomin, CHE Tao, Qu Yonghua, XU Ziwei, TAN Junlei
The multi-scale dataset of environment and element-at-risk for the Qinghai-Tibet Plateau includes geomorphic data, normalized vegetation index data, annual temperature and rainfall data, and disaster bearing value grade data, covering an area of 6.56 million square kilometers. The data set is mainly prepared for disaster and risk assessment. Due to the huge coverage, the geomorphic data adopts 150m spatial resolution and other data adopts 1000m spatial resolution. Geomorphology, vegetation index, temperature and rainfall data are mainly produced by processing open source data, and disaster bearing value grade data are produced by superposition calculation, comprehensively considering population data, night light index, buildings and surface cover types.
TANG Chenxiao
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
1) Data content (including elements and significance): the data includes the daily values of air temperature (℃), precipitation (mm), relative humidity (%), wind speed (M / s) and radiation (w / m2) 2) Data source and processing method; Air temperature, relative humidity, radiation and wind speed are daily mean values, and precipitation is daily cumulative value; Data collection location: 29 ° 39 ′ 25.2 ″ n near the forest line on the east slope of Sejila Mountain; 94°42′25.62″E; 4390m； The underlying surface is natural grassland; Collector model Campbell Co CR1000, acquisition time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; The wind speed sensor is 05103; The precipitation is te525mm; The radiation is li200x; 3) Data quality description; The original data of air temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; The daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of the sensor, the precipitation in winter may have a certain error. 4) Data application achievements and prospects: this data is the update of the existing data "Sejila Mountain meteorological data (2007-2017)" and "basic meteorological data of Sejila east slope forest line of South Tibet station of Chinese Academy of Sciences (2018)". The data time scale span is large, which is convenient for scientists or graduate students in Atmospheric Physics, ecology and atmospheric environment. This data will be updated from time to time every year.
Luo Lun
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
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
The data set includes the start time (year, month), location (longitude and latitude), duration (month), drought intensity and vulnerability data of vegetation response to drought in Central Asia from 1982 to 2015, with a spatial resolution of 1 / 12 °. The drought events were identified by the standardized precipitation evapotranspiration index at the time scale of 12 months (spei12) < - 1.0. The specific algorithm of drought characteristics and vegetation vulnerability is detailed in the citation. The dataset has been applied in the study of vegetation vulnerability to drought in Central Asia, and has application prospects in the research fields of spatial-temporal characteristics of drought events, drought-vegetation interaction mechanism, drought risk assessment and so on.
DENG Haoyu
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Da Wei
Land surface temperature is a critical parameter in land surface energy balance. This dataset provides the monthly land surface temperature of UAV remote sensing for typical ground stations in the middle reaches of Heihe River basin from July to September in 2019. The land surface temperature retrieval algorithm is an improved single-channel algorithm, which was applied to the land surface brightness temperature data obtained by the UAV thermal infrared remote sensing sensor, and finally the land surface temperature data with a spatial resolution of 0.4m was obtained.
ZHOU Ji, LIU Shaomin, WANG Ziwei
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 1 to September 20 in 2019. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Mixed forest station (101.1335E, 41.9903N), which is about 30 m×30 m in size. 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.
LIU Shaomin, Qu Yonghua, XU Ziwei
The dataset contains the phenological camera observation data of the Liancheng station in the midstream of Datonghe integrated observatory network from April 26 to October 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 2592×1944. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc), phenological period and coverage (Fc).
ZHAO Changming, ZHANG Renyi
The dataset contains the phenological camera observation data of the Guazhou station in the midstream of Shulehe integrated observatory network from March 26 to October 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 2592×1944. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc), phenological period and coverage (Fc).
ZHAO Changming, ZHANG Renyi
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 1 to September 20 in 2019. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N), which is about 30m×30m in size. 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.
LIU Shaomin, Qu Yonghua, XU Ziwei
The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from August 28,2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc), phenological period and coverage (Fc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, XU Ziwei
The dataset contains the phenological camera observation data of the Mixed forest station in the downstream of Heihe integrated observatory network from August 28, 2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, XU Ziwei
The dataset contains the phenological camera observation data of the Arou Superstation in the midstream of Heihe integrated observatory network from August 28, 2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, XU Ziwei
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.
LIU Shaomin, Qu Yonghua, XU Ziwei
The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream of Heihe integrated observatory network from August 28,2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, XU Ziwei
This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2020. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.
WANG Xufeng
This dataset is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The dataset contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. The data contains fileds as follows: year Tmin.recon (℃) See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
HUANG Ru, ZHU Haifeng, LIANG Eryuan
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