This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
The seasonal synthetic satellite remote sensing image collection of the Qinghai-Tibet Plateau is obtained by de-clouding processing of the Sentinel-2 TOA time series product. It contains 4 bands (visible and near infrared bands), and has a spatial resolution of about 10 meters. Each year is divided into 4 quarters from January to March, April to June, July to September, and October to December, and the cloud mask of each image is obtained by synthesizing the visible band, cirrus band, aerosol band and NIR band information of Sentinel-2 data. Finally, the cloud-free satellite remote sensing images of the Tibetan Plateau region are obtained by synthesizes all the masked images in a quarter according to the median principle.
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.
The Northwest Institute of Ec-Environment and Resources of the Chinese Academy of Sciences organized a team of 9 and 5 people to carry out the research on "key technologies and demonstration for vegetation restoration and reconstruction in desertification land " from the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea from April 3, 2019 to April 30, 2019 and from September 16 to 28, 2019, respectively, and investigated the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea The site includes Tashkent, Samarkand, Navoi, Bukhara, Nukus, muinak, etc., with a total length of more than 4000 kilometers. It mainly conducts UAV low altitude remote sensing, plant community investigation, soil type, climate and soil moisture status comprehensive investigation in different degree of degradation desertification areas, and samples of plant, soil are taken. A total of 30 sample plots were investigated, and data sets of desertification degree and distribution characteristics, vegetation type and distribution, soil type and physical and chemical properties were obtained.
This dataset is the FPAR observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 19 July, 2012 (UTC+8). Measurement instruments: AccuPAR (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: For corn, to measure the incoming PAR on the canopy, transmission PAR under the canopy, reflected PAR on the canopy, reflected PAR under the canopy. For orchard and white poplar forest, to measure the incoming PAR outside of the canopy, transmission PAR under the canopy. Corresponding data: Land cover, plant height, crop rows identification
A total of 52 sample sites were selected in the desert belts of Qinghai and Tibet for field sampling of aboveground biomass of vegetation during the vegetation growing season in 2019 and 2020. At the same time, the longitude, latitude and altitude of the experimental site were recorded using handheld GPS devices. The field setting method of the quadrate is as follows: select a section with uniform vegetation. When the vegetation is relatively abundant, the quadrate is set as a 10 m x10 m square plot, and when the vegetation is relatively sparse, the quadrate is set as a 30 m x30 m square plot or a 30 m x90 m rectangular plot. 3-5 small sample boxes (1m x 1m) were randomly thrown into the set sample plot to determine the specific location of the sample. Collect plant samples by sample harvesting method: register plant species, number of plants of each species and other information in sample area of 1 square meter. All kinds of plants in the quadrate were planted and mowed on the ground, and the collected herbaceous plant samples were placed in archives and marked with species, sample site name and number, collection time and other information. They were brought back to the laboratory and dried to a constant weight in a constant temperature drying oven at 65 ℃. The dry weight of the plant samples was measured. Finally, the aboveground biomass of the vegetation was calculated. In addition, two kinds of remote sensing net primary productivity (NPP) data of the 52 sample points were extracted by the longitude and latitude of the sampling points. (1) Enhanced Vegetation Index (EVI) from 2000 to 2018, and calculated the annual Integrated Enhanced Vegetation Index (IEVI). IEVI was highly correlated with net primary productivity (NPP). Can be used as a proxy indicator of net primary productivity (He et al. 2021, Science of The Total Environment). (2) Percentage of remote sensing net primary productivity (NPP) and its quality control (QC) in 2001-2020, NPP remote sensing data from MOD17A3HGF Version 6 product (https://lpdaac.usgs.gov/products/mod17a3hgfv006/), the net photosynthetic value (the total primary productivity - keep breathing) is calculated. In the sample sites with low vegetation coverage, there may be null value (NA) of remote sensing net primary productivity.
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage (June to October) in 2020 (The data of Huazhaizi station in August is not available because of technical problem). The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from phenology camera observation data of the Alpine meadow and grassland ecosystem Superstation from January 1 in 2020 to December 31 in 2020. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The phenology camera adopts a vertical downward method to collect data, with the resolution of 2592*1944. Phenology photos in this data set were taken at 12:10 a day, which has a time error of ±10 min. The image is named as BSDCJZ BEIJING_IR_Year_Month_Day_Time.
The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream reaches of Heihe integrated observatory network before May 31 and after September 2, 2020. Due to the power failure of phenological camera, the time is lost from May 31 to September 2, 2020. In addition, after the camera was moved and reinstalled, the object in the field of view before May 31 and after September 2 would change, which may cause the inconsistency of the data before and after. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. 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 phenology, firstly, The phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. 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. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from July 25 to October 20 in 2020. 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.