Vegetation environmental research data set in key areas of Asian water tower area of Qinghai Tibet Plateau (2019-2020)

This data set includes six data files, which are: (1) soil temperature and moisture data of alpine meadow elevation gradient_ Dangxiong, Tibet (2019-2020). This data is the hourly observation data of temperature and water content at different soil depths (5cm and 20cm) of the alpine meadow at 4400m, 4500m, 4650m, 4800m, 4950m and 5100m above sea level in Dangxiong, Tibet during 2019-2020. (2) Meteorological environment data of Sejila Mountain Forest line_ Linzhi, Tibet (2019), the data is the hourly meteorological environment (including wind speed, air temperature 1 m away from the surface, relative humidity 1 m away from the surface, air temperature 3 m away from the surface, relative humidity 3 m away from the surface, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, 660 nm) of the forest line of Sejila Mountain in Linzhi, Tibet in 2019 Hourly observation data of red light radiation, 730nm infrared radiation, surface temperature, atmospheric long wave radiation, surface long wave radiation, underground 5cm-20cm-60cm heat flux, underground 5cm-20cm-60cm soil temperature and humidity, rainfall and snow thickness, among which some observation data are missing due to equipment power failure in plateau area, which has been explained in the data. (3) NDVI of vegetation at major meteorological stations_ In the Qinghai Tibet Plateau (2020), NDVI survey data and average values of vegetation near 25 meteorological stations are included. (4) Land use survey data set_ Along the Sichuan Tibet Railway (2019), including 35 survey points along the Sichuan Tibet railway land use survey data, including survey time, location, latitude and longitude, altitude, slope aspect, main vegetation types and dominant species. (5) Leaf area index survey data_ The leaf area index (LAI) of main vegetation types along Sichuan Tibet Railway (2019) was measured by SunScan canopy analyzer and lai-2200. (6) Survey data of soil temperature and humidity_ Along the Sichuan Tibet Railway (2019), including 34 survey points along the Sichuan Tibet Railway: location, longitude and latitude, altitude, soil surface temperature, soil moisture at 30cm, the data were recorded as 3 repeated measurements at each survey point. The data set can be used to analyze and study the change law of vegetation environment in Qinghai Tibet Plateau.

0 2021-04-20

Dataset of above ground biomass in Sanjiangyuan region (2000, 2010, 2015 )

The method of aboveground biomass of grassland is zonal classification model. The data years were 2000, 2010 and 2015, and the fresh vegetation weight was based on the first ten days of August. Above-ground biomass is defined as the total amount of organic matter of vegetation living above the ground in a unit area. Unit: g/m². This data set is calculated from a statistical model based on the MODIS vegetation index by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. The spatial resolution is 250 m x 250 m. The data set is an important data source for vegetation monitoring in Three River Source National Park. Projection information: Albers isoconic projection Central meridian: 105 degrees First secant: 25 degrees First secant: 47 degrees West deviation of coordinates: 4000000 meters

0 2021-04-20

Dataset of growing season average NDVI changing trends in Three River Source National Park (2000-2018)

Based on the average NDVI (spatial resolution 250m) of MODIS during the growing season from 2000 to 2018, the trend of NDVI was calculated by using Mann-Kendall trend detection method. Three parks of Three River Source National Park are calculated (CJYQ: Yangtze River Park; HHYYQ: Yellow River Park; LCJYQ: Lancang River Park). CJYQ_NDVI_trend_2000_2018_ok.tif: Changjiang Source Park NDVI trend. CJYQ_NDVI_trend_2000_2018_ok_significant.tif: Changjiang Source Park NDVI change trend, excluding the area that is not significant (p > 0.05). CJYYQ_gs_avg_NDVI_2000.tif: The average NDVI of the Yangtze River Source Park in 2000 growing season. Unit NDVI changes every year.

0 2021-04-20

SeaWiFS NDVI dataset for Sanjiangyuan (1997-2007)

The data set is NDVI data of long time series acquired by SeaWiFS. The time range of the data set is from September 1997 to 2007. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 15 days. 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, spatial resolution is 4 km, temporal resolution is 15 days, time range: 256 days in 1997 to 365 days in 2007.

0 2021-04-20

Survey data set of plant and soil carbon and nitrogen cycle in representative sites (2019-2020)

The data set includes three types of data, which are: (1) the data of soil physical and chemical indexes, carbon and nitrogen, plant carbon and nitrogen, and microbial carbon and nitrogen in the collapse area of the Qinghai Tibet Plateau in 2020. These data provide an important reference for the assessment of the carbon and nitrogen cycle in the Tibetan Plateau. This data is mainly obtained through field observation during the investigation in Gangcha, Qinghai Province in 2020. The obtained plant and soil samples were taken back to the laboratory for preliminary classification and impurity removal, and then dried to constant weight in an oven at 65 ° C. Carbon and nitrogen components in soil and plants were measured. A total of 40 quadrats of 4 typical plots were obtained. The data can be used to reveal the spatial variation of soil and plant carbon and nitrogen components, and understand the distribution of carbon and nitrogen components in soil plant microbial system. (2) Data of soil nutrient composition of grassland horizontal transect in Qinghai Tibet Plateau in 2019. This data is mainly obtained from the field drilling during the sample belt investigation in 2019. The soil samples were taken back to the laboratory for preliminary classification, root removal and stone screening. The soil samples were dried naturally, then mixed evenly and divided into two parts (about 100g each). One part was sieved with 2mm soil sieve to obtain sieved samples, and the other part was ground with ball mill to obtain ground samples. The content elements included: the contents of total C, N, P, K, Fe, Mn, Cu, Zn, CA, Na and total Mg; the contents of available P, K, Fe, Mn, Cu, Zn, CA, Na and Mg. Determination of soil total C and N: the grinding samples were packed, and then the contents of total C and N were determined by chnos elemental analyzer (vario El III, GmbH, Hanau, Germany). Determination of total elements in soil: the grinding samples were pressed by a tablet press, and then the contents of total P, K, Fe, Mn, Cu, Zn, CA, Na and total Mg in the samples were determined by X-ray fluorescence spectrometry (XRF, panalytical Axios max, Almelo, the Netherlands). Determination of soil available elements: the sieved samples were extracted, and the contents of available P, K, Fe, Mn, Cu, Zn, CA, Na and Mg were determined by inductively coupled plasma atomic emission spectrometry (ICAP 6300, Thermo Electron Corporation, Waltham, Ma, USA). A total of 13 transects were obtained. Each plot obtained three soil layers (0-10 cm, 10-20 cm, 20-30 cm). Therefore, there are 117 data of each soil nutrient element (C, N, P, Mn, Zn, etc.) in each quadrat. The data are directly obtained from the field soil samples obtained by this scientific research. After air drying, screening and grinding, the data are determined by the relevant analyzer (above) according to the corresponding test specifications, and the quality is reliable, which can be used to analyze the distribution law of soil carbon and nitrogen content or density in different regions and to evaluate soil nutrient In particular, it can be used for the research and modeling of carbon and nitrogen cycle driven by precipitation change, which has a wide range of application value and application prospects. (3) Vegetation productivity data of grassland horizontal transect in Qinghai Tibet Plateau in 2019. This data is mainly obtained from the field observation during the transect survey in 2019. After obtaining the plant samples, they were taken back to the laboratory for preliminary classification, gravel and other impurities were removed, and then dried in the oven at 65 ° C to constant weight. According to the biomass of the sample, it was converted into the key element of ecosystem carbon cycle vegetation productivity (NPP). A total of 13 transect points and 39 quadrats were obtained. The content elements of the data include aboveground biomass, aboveground biomass and NPP. The unit is gram per square meter; this data is the field observation data obtained from this scientific research, with reliable quality, which can be used to analyze the distribution law of vegetation productivity, vegetation cover, carbon storage assessment of ecosystem in different regions, especially for the study of carbon cycle driven by precipitation change and its modeling, and has a wide application value and application prospect.

0 2021-04-19

Dataset of plant distribution investigation in Three-River-Source National Park (2008-2017)

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.

0 2021-04-19

Vegetation quadrat survey dataset in Maduo County (2016)

The data set includes the sample survey data of alpine grassland and alpine meadow in Maduo County in September 2016. The sample size is 50cm × 50cm. The investigation contents include coverage, species name, vegetation height, biomass (dry weight and fresh weight), longitude and latitude coordinates, slope, aspect, slope position, soil type, vegetation type, surface characteristics (litter, gravel, wind erosion, water erosion, saline alkali spot, etc.), utilization mode, utilization intensity, etc.

0 2021-04-18

Monthly net primary productivity (NPP) dataset of the Qinghai Tibet Plateau (2012-2015)

The data set contains the monthly net primary productivity data of 2012-2015. The data is based on the temperature, precipitation, solar radiation and other climatic elements of the daily value data set of China's surface climate data, as well as the data of evapotranspiration et, potential PET, photosynthetic effective absorption ratio FPAR, NDVI and maximum light utilization rate, which are calculated by CASA model. The calculation results are verified by the data of Sanjiangyuan sampling point, The correlation coefficient is 0.718. The data set can be directly used for the analysis of grassland vegetation change in the Qinghai Tibet Plateau, providing the basis for dynamic monitoring of grassland change, and for the management of Grassland Change in the Qinghai Tibet Plateau.

0 2021-04-09

Hyperspectral remote sensing data of typical vegetation along Sichuan Tibet Railway (2019)

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.

0 2021-04-07

Intensity data of human activities on the plateau in 2012-2017

According to the characteristics of the Qinghai Tibet Plateau and the principles of scientificity, systematization, integrity, operability, measurability, conciseness and independence, the human activity intensity evaluation index system suitable for the Qinghai Tibet Plateau has been constructed, which mainly includes the main human activities such as agricultural and animal husbandry activities, industrial and mining development, urbanization development, tourism activities, major ecological engineering construction, pollutant discharge, etc, On the basis of remote sensing data, ground observation data, meteorological data and social statistical yearbook data, the positive and negative effects of human activities are quantitatively evaluated by AHP, and the intensity and change characteristics of human activities are comprehensively evaluated. The data can not only help to enhance the understanding of the role of human activities in the vegetation change in the sensitive areas of global change, but also provide theoretical basis for the sustainable development of social economy in the Qinghai Tibet Plateau, and provide scientific basis for protecting the ecological environment of the plateau and building a national ecological security barrier.

0 2021-03-29