The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient number). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. The dataset is a long-time series vegetation index dataset of Qinghai Lake Basin, which is mainly aimed at normalized difference vegetation index (NDVI). It includes spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI for 10 days, with a spatial resolution of 1km and a temporal resolution of 10 days.
Flemish Institute for Technological Research (VITO)
The monthly average vegetation index data of Heihe River Basin is based on MODIS 1 km and 250 m NDVI products. From 250 m products, the grid value of Heihe River Basin is proposed as precision control, and the 1 km product is modified by HASM method. The monthly average vegetation index of Heihe River Basin from 2001 to 2011 was obtained by fusing multi-source NDVI data using HASM method. Resolution: 1km * 1km The average precipitation data set of Heihe River Basin adopts the data information of 21 meteorological conventional observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River basin provided by Heihe planning data management center. The daily precipitation data of each station from 1961 to 2010 is calculated. If the coefficient of variation is greater than 100%, the daily precipitation distribution trend can be obtained by using the geographic weighted regression to calculate the relationship between the station and the geographical terrain factors; if the coefficient of variation is less than or equal to 100%, the relationship between the station precipitation value and the geographical terrain factors (longitude, latitude, elevation) is calculated by ordinary least square regression, and the daily precipitation score is obtained HASM (high accuracy surface modeling method) was used to fit and modify the residual error after removing the trend. Finally, the trend surface results and residual correction results are added to get the annual average precipitation distribution of Heihe River Basin from 1961 to 2010. Time resolution: annual average precipitation from 1961 to 2010. Spatial resolution: 500M.
YUE Tianxiang, ZHAO Na
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The National Center for Atmospheric Research
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
This dataset is based on the sixth edition of the MODIS normalized difference vegetation index product (2001-2014) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey USGS EROS. The NDVI has a time resolution of 16 days and a spatial resolution of 0.05 degree. First,the NDVI data products were re-sampled from the spatial resolution of 0.05 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
NASA EOSDIS LP DAAC, XU Xiyan
The ground sample data was collected by LAI-2000 canopy analyzer, and the collection area was located in Dayekou, Wuxing Village (2012) and other areas. The main measure of vegetation is corn. The LAI value of the corn was obtained using the LAI2000, and the observation was repeated twice in a pattern of “one up and four down”. The leaf area of each leaf of the corn plant was obtained using CD202, and a total of three corns were collected.
FAN Wenjie
The forest hydrology experimental area of Heihe River integrated remote sensing experiment includes the dense observation area of Dayekou basin and the dense observation area of Pailugou basin. Due to the concentrated distribution of the fixed sample plots in the drainage ditch basin, these sample plots lack of representativeness to the forest of the whole dayokou basin, so in June 2008, 43 temporary forest sample plots were set up in the whole dayokou basin. The data set is the ground observation data of the 43 temporary plots. In addition to the measurement and recording of stand status and site factors, Lai was also observed. The instruments used to measure each wood in the sample plot are mainly tape, DBH, flower pole, tree measuring instrument and compass. The DBH, tree height, height under branch, crown width in cross slope direction, crown width along slope direction and single tree growth were measured for each tree. WGS84 latitude and longitude coordinates of the center point of the sample plot were measured with different hand-held GPS, and the positioning error was about 5-30m. Other observation factors include: Forest Farm, slope direction, slope position, slope, soil thickness, canopy density, etc. The implementation time of these temporary sample plots is from 2 to 30 June 2008. The data set can provide ground data for the development of remote sensing inversion algorithm of forest structure parameters.
LING Feilong, HE Qisheng, ZHANG Xuelong, WANG Shunli, ZHAO Ming, LEI Jun, NIU Yun, LUO Longfa, CHEN Erxue
The data set mainly includes observation data of each tree in the super site, and the observation time is from June 2, 2008 to June 10, 2008. The super site is set around the Dayekou Guantan Forest Station. Since the size of the super site is 100m×100m, in order to facilitate the forest structure parameter survey, the super site is divided into 16 sub-sample sites, and tally forest measurement is performed in units of sub-samples. The tally forest measurement factors include: diameter, tree height, height under branch, crown width in transversal slope direction, crown width in up and down slope direction, and tindividual tree growth status. The measuring instruments are mainly: tape, diameter scale, laser altimeter, ultrasonic altimeter, range pole and compass. The data set also records the center point latitude and longitude coordinates of 16 sub-samples (measured by Z-MAX DGPS). The data set can be used for verification of remote sensing forest structure parameter extraction algorithm. The data set, together with other observation data of the super site, can be used for reconstruction of forest 3D scenes, establishment of active and passive remote sensing mechanism models, and simulation of remote sensing images,etc.
CHEN Erxue, BAI Lina, WANG Bengyu, TIAN Xin, LIU Qingwang, CAO Bin, Yang Yongtian, Zhihai Gao, Bingxiang Tan, GUO Zhifeng, WANG Xinyun, FU Anmin, ZHANG Zhiyu, NI Wenjian, WANG Qiang, BAO Yunfei, WANG Dianzhong, ZHANG Yang, ZHAO Liqiong, LIANG Dashuang, WANG Shunli, ZHAO Ming, LEI Jun, NIU Yun, LUO Longfa
The data set is the meteorological and observational data of hulugou shrub experimental area in the upper reaches of Heihe River, including meteorological data, albedo data and evapotranspiration data under shrubs. 1. Meteorological data: Qilian station longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3232.3m, scale meteorological data from January 1, 2012 to December 31, 2013. Observation items include: temperature, humidity, vapor pressure, net radiation, four component radiation, etc. The data are daily scale data, and the calculation period is 0:00-24:00 2. Albedo: daily surface albedo data from January 1, 2012 to July 3, 2014, including snow and non snow periods. The measuring instrument is the radiation instrument on the 10m gradient tower in hulugou watershed. Among them, the data from August 4 to October 2, 2012 was missing due to instrument circuit problems, and the rest data quality was good 3. Evapotranspiration: surface evapotranspiration data of Four Typical Shrub Communities in hulugou watershed. The observation period is from July 18 to August 5, 2014, which is the daily scale data. The data include precipitation data, evaporation and infiltration data observed by lysimeter. The data set can be used to analyze the evapotranspiration data of alpine shrubs and forests. The evapotranspiration of grassland under canopy was measured by a small lysimeter with a diameter of 25 cm and a depth of 30 cm. Two lysimeters were set up in each shrub plot, and one lysimeter was set for each shrub in transplanting experiment. The undisturbed undisturbed soil column with the same height as the barrel is placed in the inner bucket, and the outer bucket is buried in the soil. During the embedding, the outer bucket shall be 0.5-1.0 cm higher than the ground, and the outer edge of the inner barrel shall be designed with a rainproof board about 2.0 cm wide to prevent surface runoff from entering the lysimeter. Lysimeter was set up in the nearby meteorological stations to measure grassland evapotranspiration, and a small lysimeter with an inner diameter of 25 cm and a depth of 30 cm was also set up in the sample plot of Picea crassifolia forest to measure the evaporation under the forest. All lysimeters are weighed at 20:00 every day (the electronic balance has a sensing capacity of 1.0 g, which is equivalent to 0.013 mm evaporation). Wind proof treatment should be taken to ensure the accuracy of measurement. Data processing method: evapotranspiration is mainly calculated by mass conservation in lysimeter method. According to the design principle of lysimeter lysimeter, evapotranspiration is mainly determined by the quality difference in two consecutive days. Since it is weighed every day, it is calculated by water balance.
SONG Yaoxuan, LIU Zhangwen
The data set of atmospheric water vapor absorption and utilization of desert plants, all of which are original data, including the liquid flow and environmental data of wild desert plants (Sitan village and Ejina Banner, Jingtai County), such as Tamarix, Bawang, Baici, Hongsha, etc., including the data of meteorology, photosynthesis, fluorescence and leaf surface humidity, as well as the data of gene transcriptome and expression regulation.
XIAO Honglang
The dataset is Lai data of ground sample points in Heihe River Basin, collected by LAI-2000 canopy analyzer. The collection area is located in Zhangye rural demonstration base, Ejina Banner, Jiuquan Satellite Center (2011) and other areas. The main measured vegetation is corn. The Lai value of maize was obtained by using lai2000, and the observation was repeated twice in the mode of one up four down. Cd202 was used to obtain the leaf area of each leaf of maize plant, and three maize plants were collected.
FAN Wenjie
This data includes three parts of data, namely shrub water holding experiment, shrub interception experiment and shrub transpiration experiment data. Shrub water holding experiment: select the two shrub types of Caragana jubata and Potentilla fruticosa, respectively pick the branches and leaves of the two vegetation types, weigh their fresh weight, carry out water holding experiment, measure the saturated weight of branches and leaves, dry weight of branches and leaves, dry weight of branches and leaves after completion, and finally obtain the data of branches, leaves and total water holding capacity. Shrub interception experiment: two shrubs, Caragana jubata and Potentilla fruticosa, were also selected and investigated. 30 rain-bearing cups were respectively arranged under the two shrubs. after each rainfall, penetration rainfall was measured and observed from June 1, 2012 to September 10, 2012. Shrub Transpiration Experiment: Potentilla fruticosa on July 14, Caragana jubata on August 5, Salix gilashanica on August 15, 2012. The measurement is made every hour according to the daily weather conditions.
ZHAO Chuanyan, MA Wenying
The leaf cross-sectional structure of constructive species in arid area of the middle and lower reaches of Heihe River Basin. The material number is consistent with the sampling table. Refer to the sampling table number to determine the material and its distribution position. A semi thin section of 65 dominant plants. The mesophyll structure of C3 and C4 plants, the characteristics of palisade tissue and sponge tissue, as well as the special structure including crystalloid cells can be reflected.
LIU Yubing
1:100000 vegetation map of Heihe River Basin, the regional scope is subject to the Heihe river boundary of Huangwei Committee, the area is about 14.29 × 104km2, the data format is GIS vector format, this version is version 3.0. The data is mainly based on ground observation data, integrated with all kinds of remote sensing data, 1:1 million vegetation map, climate, terrain, landform, soil data mapping, and compiled by cross validation. The classification standard, legend unit and system of vegetation map of the people's Republic of China (1:1000000), 2007 are adopted, including vegetation type group, vegetation type, formation and sub formation. The new version mainly unifies the codes of the new formation (74 codes in total, distinguishing the formation and the sub formation). 9 vegetation type groups, 22 vegetation types and 74 formations (sub formations) in version 2.0 were changed into 9 vegetation type groups, 22 vegetation types and 67 formations (7 sub formations). The data includes versions 2.0 and 3.0
ZHENG Yuanrun, ZHOU Jihua
In the ecosystem, soil and vegetation are two interdependent factors. Plants affect soil and soil restricts vegetation. On the one hand, there are a lot of nutrients such as carbon, nitrogen and phosphorus in the soil. On the other hand, the availability of soil nutrients plays a key role in the growth and development of plants, directly affecting the composition and physiological activity of plant communities, and determining the structure, function and productivity level of ecosystems. Soil moisture content (or soil moisture content): In the 9 sections from Daxihaizi to taitema lake in the lower reaches of Tarim River, plant sample plots are set in the direction perpendicular to the river channel according to the arrangement of groundwater level monitoring wells. Dig one soil profile in each sample plot, collect one soil sample from 0-5 cm, 5-15 cm, 15-30 cm, 30-50 cm, 50-80 cm, 80-120 cm and 120-170cm soil layers from bottom to top in each profile layer, each soil sample is formed by multi-point sampling and mixing of corresponding soil layers, each soil layer uses aluminum boxes to collect soil samples, weighs wet weight on site, and measures soil moisture content (or soil moisture content) by drying method. Soil nutrient: the mixed soil sample is used for determining soil nutrient after removing plant root system, gravel and other impurities, air-drying indoors and sieving. Organic matter is heated by potassium dichromate, total nitrogen is treated by semi-micro-Kjeldahl method, total phosphorus is treated by sulfuric acid-perchloric acid-molybdenum antimony anti-colorimetric method, total potassium is treated by hydrofluoric acid-perchloric acid-flame photometer method, effective nitrogen is treated by alkaline hydrolysis diffusion method, effective phosphorus is treated by sodium bicarbonate leaching-molybdenum antimony anti-colorimetric method, effective potassium is treated by ammonium acetate leaching-flame photometer method, PH and conductivity are measured by acidimeter and conductivity meter respectively (water to soil ratio is 5: 1). Soil water-soluble total salt was determined by in-situ salinity meter. Drought stress is the most common form of plant adversity and is also the main factor affecting plant growth and development. Plant organs will undergo membrane lipid peroxidation under adverse circumstances, thus accumulating malondialdehyde (MDA), the final decomposition product of membrane lipid peroxide. MDA content is an important indicator reflecting the strength of membrane lipid peroxidation and the damage degree of plasma membrane, and is also an important parameter reflecting the damage of water stress to plants. At the same time, under adverse conditions, the increased metabolism of reactive oxygen species in plants will lead to the accumulation of reactive oxygen species or other peroxide radicals, thus damaging cell membranes. Superoxide dismutase (SOD) and peroxidase (POD) in plants can remove excess active oxygen in plants under drought and other adversities, maintain the metabolic balance of active oxygen, protect the structure of the membrane, and finally enhance the resistance of plants to adversities. The analysis samples take Populus euphratica, Tamarix chinensis and Phragmites communis as research objects. According to the location of groundwater monitoring wells, six sample plots are set up starting from the riverside, with an interval of 50 m between each sample plot, which are sample plots 1, 2, 3, 4, 5 and 6 in turn. Fresh leaves of plants are collected, stored at low temperature, and pretreated (dried or frozen) on the same day. PROline (Pro), cell membrane system protective enzymes superoxide dismutase (SOD) and peroxidase (POD) were tested indoors. Preparation of enzyme solution: weigh 0.5g of fresh material and add 4.5mL pH7.8 with ph 7.8. The materials were homogenized in a pre-frozen mortar, which was placed in an ice bath. Centrifuge at 10000 r/min for 15 min. The supernatant was used for determination of superoxide dismutase, peroxidase and malondialdehyde (MDA). PRO determination: put 0.03 g of material into a 20 mL large test tube, add 10mL ammonia-free distilled water, seal it, put it in a boiling water bath for 30min, cool it, filter, filtrate 5 mL+ ninhydrin 5 mL, develop color in boiling water for 60min, and extract with toluene. The extract was colorized with Shimadzu UV-265 UV spectrophotometer at 515 nm. SOD activity was measured by NBT photoreduction. The order of sample addition for enzyme reaction system is: pH 7.8 PBS 2.4mL+ riboflavin 0.2 mL+ methionine 0.2 mL+EDTA0.1 mL+ enzyme solution 0.1 mL+NBT0.2 mL. Then the test tube was reacted under 40001ux light for 20 min, and photochemical reduction was carried out. SOD activity was measured at 650 nm wavelength by UV-265 ultraviolet spectrophotometer. POD activity determination: the reaction mixture was 50 ml PBS with pH 6.0+28 μ L guaiacol+19 UL30% H2O2. 2 mL of reaction mixture +1 mL of enzyme solution, immediately start timing, reading every 1 min, reading at 470 nm. Determination of chlorophyll: ethanol acetone mixed solution method. After cutting the leaves, the mixed solution of 0.2 g and acetone: absolute ethanol = 1: 1 was weighed as the extraction solution. After extracting in the dark for 24 h, the leaves turned white and chlorophyll was dissolved in the extraction solution. The OD value of chlorophyll was measured by spectrophotometer at 652nm. Determination method of soluble sugar: phenol sulfate method is adopted. (1) The standard curve is made by taking 11 20 ml graduated test tubes, numbering them from 0 to 10 points, and adding solution and water according to Table 1 respectively. Then add 1 ml of 9% phenol solution to the test tube in sequence, shake it evenly, then add 5 ml of concentrated sulfuric acid from the front of the tube for 5 ~ 20 s, the total volume of the colorimetric solution is 8 ml, and leave it at constant temperature for 30 minutes for color development. Then, with blank as control, colorimetric determination was carried out at 485 nm wavelength. With sugar as abscissa and optical density as ordinate, a standard curve was drawn and the equation of the standard curve was obtained. (2) Extraction of soluble sugar: fresh plant leaves are taken, surface dirt is wiped clean, cut and mixed evenly, 0.1-0.3 g are weighed, 3 portions are respectively put into 3 calibration test tubes, 5-10 ml distilled water is added, plastic film is sealed, extraction is carried out in boiling water for 3O minutes, the extraction solution is filtered into a 25 ml volumetric flask, repeated flushing is carried out, and the volume is fixed to the calibration. (3) Absorb 0.5 g of sample solution into the test tube, add 1.5 ml of distilled water, and work out the content of soluble sugar in the same way as the standard curve. The amount of solution and water in each test tube Pipe number 0 1-2 3-4 5-6 7-8 9-10 1.100μg/L sugar solution 0.20 0.40 0.60 1.0 2. water/ml 2.0 1.8 1.6 1.4 1.2 1.0 3. Soluble sugar content/μ g 0 20 40 60 80 100 Determination of malondialdehyde: thiobarbituric acid method. Fresh leaves were cut to pieces, 0．5 g was weighed, 5% TCA5 ml was added, and the homogenate obtained after grinding was centrifuged at 3 000 r／rain for 10 rain. Take 2 ml supernatant, add 0.67% TBA 2 ml, mix, boil in 100 water bath for 30 rain, cool and centrifuge again. Using 0.67% TBA solution as blank, the OD values at 450, 532 and 600 nm were determined. Methods for analysis and testing of plant hormones (GA3, ABA, CK, IAA): 0.1 0.005 g plant samples were taken and ground in liquid nitrogen. 500μl methanol was extracted overnight at 4℃. Centrifuge the sample and freeze-dry the supernatant. 30μl10％% CH3CN dissolved the sample. 10μl of sample solution was analyzed by HPLC. The external standard method was used to quantify plant hormones. Standard plant hormones were purchased from sigma Company. See (Ruan Xiao, Wang Qiang, et al., 2000, Journal of Plant Physiology.26 (5), 402-406) for analysis methods.
CHEN Yaning, HAO Xingming
The survey data of vegetation quadrat in the middle reaches of Heihe River consists of the field survey data in 2013 and 2014, including the vegetation and soil data of the survey quadrat. The data of each survey sample includes the following information: sample longitude and latitude, sample size, elevation, sample overview, plant name, plant height, crown width, coverage, total coverage, number of trees, plant spacing, row spacing, large row spacing, DBH. The soil is divided into 6 layers according to 0-100cm below the ground, which are 0-10cm, 10-20cm, 20-40cm, 40-60cm, 60-80cm and 80-100cm respectively.
WANG Zifeng, XU Zongxue, ZHANG Shurong
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects. Resolution 1m
YUE Tianxiang, ZHAO Na
一. Data overview This data interchange is the second data interchange of "genomics research on drought tolerance mechanism of typical desert plants in heihe basin", a key project of the major research program of "integrated research on eco-hydrological processes in heihe basin".The main research goal of this project is a typical desert sand Holly plants as materials, using the current international advanced a new generation of gene sequencing technology to the whole genome sequence and gene transcription of Holly group sequence decoding, so as to explore related to drought resistance gene and gene groups, and transgenic technology in model plants such as arabidopsis and rice) verify its drought resistance. 二, data content 1.Sequencing of the genome and transcriptome of lycophylla SPP. The genome size of Mongolian Holly was about 926 Mb, GC content 36.88%, repeat sequence proportion 66%, genome heterozygosity rate 0.56%, which indicated that the genome has many repeat sequences, high heterozygosity and belongs to a complex genome.Based on the predicted sequence results, we subsequently carried out in-depth sequencing of the genome of lysiopsis SPP. The obtained data were assembled to obtain a 937 Mb genome sequence (table 1), which was basically the same as the predicted genome size.Through to the sand Holly transcriptome sequencing and sequence assembly (table 2), received more than 77000 genes coding sequence (Unigene), these sequences are comments found that most of the gene sequence and legumes and soybean, garbanzo beans and bean has a higher similarity (figure 1), consistent with the fact of sand ilex leguminous plants. 一), and the sand Holly is a leguminous plants consistent with the fact. 2.Discovery of simple repeat sequence (SSR) molecular markers of sand Holly: There is a transcriptome data set of sand Holly in the network public database, and the sample collection site is zhongwei city, ningxia.But this is the location of the project team samples in minqin county, gansu province, in order to study whether this sand in different areas of the Holly sequence has sequence polymorphism, we first identify the minqin county plant samples in the genomes of simple sequence repeat (SSR) markers (table 3), and then, compares the transcriptome sequences of plant sample, found in part of SSR molecular marker polymorphism (table 4), these molecular markers could be used for the species of plant genetic map construction, QTL mapping and genetic diversity analysis in the study. 三, data processing instructions Sample collection place: minqin county, gansu province, latitude and longitude: N38 ° 34 '25.93 "E103 ° 08' 36.77".Genome sequencing: a total of 8 genomic DNA libraries of different sizes were constructed and determined by Illumina HiSeq 2500 instrument.Transcriptome sequencing: a library of 24 transcriptome mrnas was constructed and determined by Illumina HiSeq 4000. 四, the use of data and meaning We selected a typical desert plant as the research object, from the Angle of genomics, parse the desert plant genome and transcriptome sequences, excavated its precious drought-resistant gene resources, and to study their drought resistance mechanism of favorable sand Holly this ancient and important to the utilization of plant resources, as well as the heihe river basin of drought-resistant plant genetic breeding, ecological restoration and sustainable development.
HE Junxian, FENG Lei
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects.
YUE Tianxiang, ZHAO Na
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