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.
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.
Airborne pollen is mainly produced and disseminated during the process of plant flowering, controlled by plant phenology and climatic conditions. As an important bioindicator of plant behavior, airborne pollen can supply information about reproductive phenology, climate and atmospheric circulations. From 2011 to 2013, airborne pollen samples were collected using a volumetric Burkard pollen trap at the Qomolangma Station for Atmospheric and Environmental Observation and Research, Chinese Academy of Sciences (QOMS, 28.21°N, 86.56°E; 4276 m a.s.l.), on the northern slope of the Himalayas. The sampler is a volumetric air-suction device capable of continuously gathering pollen and spore particles. Air is drawn in at a speed of 10 l/min, and airborne particles are deposited on a sticky tape mounted on a drum that makes one complete rotation per week. The tape is changed weekly after a complete rotation. Then, the tape is removed and cut into seven pieces, with each piece representing one day of sampling. The pieces are mounted on slides using glycerin and safranin. Identification and counting of pollen grains were performed under an Olympus BX41 microscope at 400× magniﬁcation; all pollen grains on each slide were counted . Pollen concentration was expressed as the daily pollen grains per cubic meter of air using a constant air intake speed of 10 l/min. The pollen concentration and percentage of each pollen taxon in each year were calculated. The pollen sampling and lab process were followed the standard methods to ensure the authenticity and reliability of the data. The pollen data can provides insights into vegetation response to climate change and has significance for interpreting fossil pollen records.
The data set includes meteorological data from the Ngari Desert Observation and Research Station from 2009 to 2017. It includes the following basic meteorological parameters: temperature (1.5 m from the ground, once every half hour, unit: Celsius), relative humidity (1.5 m from the ground, once every half hour, unit: %), wind speed (1.5 m from the ground, once every half hour, unit: m/s), wind direction (1.5 m from the ground, once every half hour, unit: degrees), atmospheric pressure (1.5 m from the ground, once every half hour, unit: hPa), precipitation (once every 24 hours, unit: mm), water vapour pressure (unit: kPa), evaporation (unit: mm), downward shortwave radiation (unit: W/m2), upward shortwave radiation (unit: W/m2), downward longwave radiation (unit: W/m2), upward longwave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). The temporal resolution of the data is one day. The data were directly downloaded from the Ngari automatic weather station. The precipitation data represent daily precipitation measured by the automatic rain and snow gauge and corrected based on manual observations. The other observation data are the daily mean value of the measurements taken every half hour. Instrument models of different observations: temperature and humidity: HMP45C air temperature and humidity probe; precipitation: T200-B rain and snow gauge sensor; wind speed and direction: Vaisala 05013 wind speed and direction sensor; net radiation: Kipp Zonen NR01 net radiation sensor; atmospheric pressure: Vaisala PTB210 atmospheric pressure sensor; collector model: CR 1000; acquisition interval: 30 minutes. The data table is processed and quality controlled by a particular person based on observation records. Observations and data acquisition are carried out in strict accordance with the instrument operating specifications, and some data with obvious errors are removed when processing the data table.
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
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.
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In ﬁeld measurements, a long stick with the camera mounted on one end is beneﬁcial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller ﬁeld of view. The stick can be used to change the camera height; a ﬁxed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small ﬁeld of view (<30 ), rows of more than two cycles should be included in the ﬁeld of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the ﬁeld of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classiﬁcation of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identiﬁcation are important factors that affect the efﬁciency of ﬁeld measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
The MODIS Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 m reflectance bands is a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.Based on MCD12Q1 data from 2001 to 2016, MatLab was used to tailor the masks of 18 key nodes in Southeast Asia and middle East. Finally. This dataset is based on the data of MOD09A1 V6 synthesized in 8 days from 2001 to 2016 downloaded by the National Aeronautics and Space Administration (NASA). The spatial resolution is 500 meters, and MatLab is used to mask cut the data in the research area, and Finally, the land cover data of 18 key nodes from 2001 to 2016 were obtained.. The 18 key regions covered by the data mainly include: Bangkok, Port of Myanmar, Chittagong, Colombo, Dhaka, Gwadar, Hambantot, Huangjing and Malacca, Kwantan, Maldives, Mandalay, Sihanouk, Vientiane, Yangon, etc.).
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize monthly NDVI products and calculate FVC by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.