This data set includes two infrared cameras and environmental parameter data sets of three terrestrial vertebrates deployed in Qilian Mountain reserve. The equipment is deployed near Sidalong in Qilian Mountain reserve, with a time span of (2020.8-2021.10). Due to equipment maintenance and insufficient illumination, some data are discontinuous, but the data of the two equipment can complement each other and reconstruct all the information of observation points in Qilian Mountain reserve from August 2020 to October 2021. One of the two devices is equipped with an infrared camera, which collects 4994 photos, which can be matched with the above sensor photos, or the ecological factor information before and after taking photos. 1. Wild animals and temperature, humidity, light, pressure and network signal strength information in Qilian Mountain reserve. The acquisition interval is once every half an hour 2. Data source: "development of terrestrial vertebrate monitoring equipment", 2016yfc0500104, completed by: Institute of zoology, Chinese Academy of Sciences, raw data, unprocessed 3. The sensor data acquisition interval is every half an hour. The temperature accuracy is plus or minus 0.1 degrees and the humidity accuracy is plus or minus 0.5%. The photo data is divided into trigger and timing. The trigger data is generally triggered by wild animals in the field of vision of the infrared camera; the timing photo data is dynamically adjusted according to the battery power, and the acquisition interval is between 1-12 hours. 4. This data can be used to record the ambient temperature in the reserve. Combined with the infrared camera data, it can be used to analyze the activity rhythm of wild animals, coexistence analysis and distribution limiting factors.
QIAO Huijie
The data set is based on the field observation and survey along the roads in Sichuan, Qinghai and Tibet. 100 * 100m sample plots are selected along the roads, and 1m * 1m or 2m * 2m sample plots are selected according to the vegetation distribution. The survey content involves the weather, geographical location, geomorphic characteristics, slope direction, slope position, soil type, vegetation type, plant community name, surface characteristics, human activity mode and vegetation status in the sample plot. For the investigation of basic information and vegetation status of the sample plot, the methods of artificial observation and tool measurement are adopted. In the vegetation status, the vegetation name refers to "herb species in Qinghai Province", mainly investigating its height, coverage, life form and other information. The summary of the survey results of the data set can be used as a reference to supplement the herb diversity of the Qinghai Tibet Plateau. The data set is the vegetation survey content of the actual sample plot, one file per day, and the file naming method is: year + day. For example, 20200712 represents the questionnaire content on July 12, 2020, and 202007023 represents the questionnaire content on July 23, 2020.
LI Jingji
The data set is the physiological and ecological parameters of the dominant species of each ecosystem in Heihe River Basin. According to the requirements of tesim model, the data set divides Heihe River basin into seven ecosystems: deciduous broad-leaved forest ecosystem (BRD), evergreen coniferous forest ecosystem (CNF), agricultural field ecosystem (CRP), desert ecosystem (DST), meadow grassland ecosystem (MDS) Shrubbery ecosystem (SHB) and grassland ecosystem (STP). Some of the data in this data set are based on the measured data, some are obtained by reference documents, but after verification, they are applied to the Heihe River Basin. For the data in this data, each parameter of each ecosystem has three values, which are the value in the model, the minimum value and the maximum value of this parameter. The data can provide input parameters for the ecological process model, and the data set is still in further optimization.
PENG Hongchun
The dataset is the 30m resolution burned area product from 1980s to 2019 over the Tibetan Plateau. The dataset is produced using Landsat time series land surface reflectance and machine learning algorithm, and the overall accuracy is over 90%. It can provide data product support for the research and applications in fire monitoring, carbon emission studies, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
Data content: the data set product contains the 30-meter resolution product of suspended solids concentration in the water body of the Qinghai-Tibet Plateau, which can be used as the key parameters for ecosystem-related research in Qinghai-Tibet Plateau. Data sources and processing methods: Product inversion is mainly based on the Landsat series data, by extracting the effective aquatic reflectance, to obtain the water composition information. This product is the preliminary result of extracting the concentration information of suspended solids in water using the empirical / semi-empirical method. Data quality: the overall accuracy is high, and the product will be further optimized in combination with the measured data of scientific research. Results and prospects of data application: the data set will be continuously updated and can be used for the study and analysis of ecosystem change in the Qinghai-Tibet Plateau.
LIU Huichan
On the basis of physiological and biochemical analysis of photosynthetic organs (leaves or assimilating branches) of typical desert plants in heihe river basin collected in mid-july 2011, some photosynthetic organs of desert plants were collected in mid-july 2012 and put into a liquid nitrogen tank and brought back to the laboratory for determination. Physiological analysis indexes mainly include: soluble protein unit: mg/g;Free amino acid unit: g/g;Chlorophyll content unit: mg/g;Superoxide dismutase (SOD) unit: U/g FW;Catalase (CAT) unit: U/(g•min);POD unit: U/(g•min);Proline (Pro) unit: g/g; Soluble sugar unit: g/g;Malondialdehyde (MDA) is given in moles per liter.
SU Peixi
As determined in mid-august 2013, planting species: bubbly spines (different habitats are mid-range intermountain lowland and gobi), red sand (different habitats are mid-range gobi and downstream gobi). Using the brother company of LI - 6400 Portable Photosynthesis System (Portable Photosynthesis System, LI - COR, USA) and LI - 3100 leaf area meter, etc., to the desert plant photosynthetic physiological characteristics were observed. The symbolic meaning of the observed data is as follows: Obs，observation frequency ； Photo ，net photosynthetic rate，μmol CO2•m–2•s–1； Cond stomatal conductance，mol H2O•m–2•s–1 ; Ci, Intercellular CO2 concentration, μmol CO2•mol-1； Trmmol，transpiration rate，mmol H2O•m–2•s–1； Vpdl，Vapor pressure deficit，kPa； Area，leaf area，cm2； Tair，free air temperature ，℃； Tleaf，Leaf temperature，℃； CO2R，Reference chamber CO2 concentration，μmol CO2•mol-1； CO2S，Sample chamber CO2 concentration，μmol CO2•mol-1； H2OR，Reference chamber moisture，mmol H2O•mol-1； H2OS，Sample chamber moisture，mmol H2O•mol-1； PARo，photon flux density，μmol•m–2•s–1； RH-R，Reference room air relative humidity，%； RH-S，Relative humidity of air in sample room，%； PARi，Photosynthetic effective radiation，μmol•m–2•s–1； Press，barometric pressure，kPa； Others are the state parameters of the instrument at the time of measurement.
SU Peixi
Net Primary Productivity (NPP) reflects the efficiency of plant fixation and conversion of light energy as a compound. It refers to the amount of organic matter accumulated per unit time and unit area of green plants. It is the organic matter produced by plant photosynthesis. The remainder of the Gross Primary Productivity (GPP) minus Autotrophic Respiration (RA), also known as net primary productivity. As an important part of the surface carbon cycle, NPP not only directly reflects the production capacity of vegetation communities under natural environmental conditions, but also is an important component to measure regional land use/cover change. The net primary productivity data product uses the light energy utilization (GLOPEM) model algorithm to invert multiple scale raster data products obtained from various satellite remote sensing data (Landsat, MODIS, etc.), which is also the main factor for determining and regulating ecological processes.
LIU Tie
At the end of September and the beginning of October, 2013, desert plants in typical areas of heihe basin stopped their growth period to conduct year-end ecological survey. There are altogether 8 survey and observation fields, which are: piedmont desert, piedmont gobi, middle reaches desert, middle reaches gobi, middle reaches desert, lower reaches desert, lower reaches gobi and lower reaches desert, with a size of 40m×40m. Three 20m×20m large quadrats were fixed in each observation field, named S1, S2 and S3, and regular shrub surveys were conducted.Each large quadrat was fixed with 4 5m x 5m small quadrats, named A, B, C, D, for the herbal survey.
SU Peixi
This data is digitized from the "Naiman Banner Desertification Types and Land Consolidation Zoning Map" of the drawing. The specific information of this map is as follows: * Editors: Zhu Zhenda and Qiu Xingmin * Editor: Feng Yushun * Re-photography and Mapping: Feng Yushun, Liu Yangxuan, Wen Zi Xiang, Yang Taiyun, Zhao Aifen, Wang Yimou, Li Weimin, Zhao Yanhua, Wang Jianhua * Field trips: Qiu Xingmin and Zhang Jixian * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Shanghai China Printing House * Scale: 1: 150000 * Published: May 1984 * Legend: Severe Desertification Land, Intensely Developed Desertification Land, Developing Desertification Land, Potential Desertification Land, Non-desertification Land, Fluctuating Sandy Loess Plain, Forest and Shrub, Saline-alkali Land, Mountain Land, Cultivated Land and Midian Land 2. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Naiman banner desertification type map, rivers, roads, reservoirs, railways, zoning 3. Data Attributes Desertification Class Vegetation Background Class Desertified land and cultivated sand dunes under development. Midland in Saline-alkali Land Severely desertified land Reservoir Trees and shrubbery Mountain Strongly developing desertified land Potential desertified land Lakes Non-desertification land Undulating sand-loess plain 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
ZHU Zhenda, QIU Xingmin, FENG Yusun, ZHAO Yanhua, WANG Jianhua, ZHAO Aifen, WANG Yimou, LI Weimin, ZHANG Jixian, LIU Yangxuan, WEN Zixiang
Carbon, nitrogen, phosphorus, sulfur and potassium are important basic life elements of ecosystem. It plays an important role in revealing the impact of its regional variation and spatial pattern on human activities and the sustainable development of ecosystem in the future. The Qinghai Tibet Plateau has unique alpine vegetation types and rich vertical zone landforms and surface cover types. The biogeographic pattern of surface elements (carbon, nitrogen, phosphorus, sulfur, potassium) is an important manifestation of the coupling of carbon, nitrogen and water cycle processes and related mechanisms of alpine ecosystems. This dataset focuses on the distribution pattern and spatial variation of surface materials (plant leaf branch stem root and litter) in the complex ecosystem of the Water tower area of Qinghai Tibet Plateau and Himalayan Mountains, in order to provide data support for regional model simulation and ecological management.
LI Mingxu
In mid-july 2011, photosynthetic organs (leaves or assimilating branches) of typical desert plants were collected and brought back to the laboratory in a liquid nitrogen tank for determination. The analysis indexes mainly include soluble protein unit: mg/g;Free amino acid unit: g/g;Chlorophyll content unit: mg/g;Superoxide dismutase (SOD) unit: U/g FW;Catalase (CAT) unit: U/(g•min);POD unit: U/(g•min);Proline (Pro) unit: g/g; Soluble sugar unit: g/g;Malondialdehyde (MDA) is given in moles per liter.
SU Peixi
1) Data content: species list and distribution data of Phrynocephalus and Eremais in Tarim Basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tarim Basin from 2008 to 2020, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
GUO Xianguang
The gridded desertification risk data of The Arabian Peninsula in 2021 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in the Arabian Peninsula in 2021.
XU Wenqiang
The dataset investigated the growth status of plants and leaf morphological indexes of single and conjoined red sand and pearl in the middle and lower reaches of heihe river basin in 2013. The growth indexes were crown width, plant height, and biomass of fine roots and thick roots.Leaf shape indicators are: length, width, thickness, and leaf area, volume, etc.The experimental observation indexes are: leaf nitrogen content, water potential, gas exchange data, chlorophyll fluorescence data. Data include: field observation data and explanatory documents.
SU Peixi
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
1. The data is digitized in the map of the development degree of desertification in daqintara (1974) from the drawing. The specific information of the map is as follows: * chief editor: zhu zhenda, qiu xingmin * editor: wang yimou * drawing: feng yu-sun, yao fa-fen, wu wei, wang jianhua, wang zhou-long * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house, unified isbn: 12461.26 二. The data is stored in ESRI Shapefile format, including the following layers: 1, * desertification development degree map (1974) : desertification1974.shp 2, * double river: river_double-shp 3, * single river: river_single-shp 4, Road: SHP 5, Lake: lake.shp 6, street: Stree. SHP 7, Railway: Railway. SHP 8, forest belt: Tree_networks 9. Residential land: residential. SHP 10. Map: map_margin.shp 三, desertification development degree figure property fields and encoding attribute: (1) desertification degree (Type) : a flow of sand (Semi - shifting Sandy Land), sand form class (Shapes), grass (Grassland), forest Land, Woodland and forest density (W_density), the cultivated Land (Farmland) (2) sand Shapes: Barchan Dunes, Flat Sandy Land, undulated Sandy Land, Vegetated Dunes (3) the grass (Grassland) (4) Woodland: Woodland. (5) woodland density (W_density): Sparse Woodlot (6) Farmland: Dryfarming and Abandoned Farmland, Irrigated Fields
WANG Jianhua, ZHU Zhenda, QIU Xingmin, FENG Yusun, YAO Fafen
1) data content: distribution map of Amphipoda in the Tibetan Plateau; 2) data source and processing method: based on the list of Amphipoda in Tibetan and its basic database of distribution, including longitude and latitude, altitude, and the ArcView software has been used to make the distribution map of Amphipoda in the Tibetan Plateau; 3) data quality description: sample collection, longitude and latitude, altitude information are checked to ensure the quality of distribution data, all analysts have received strict training in the laboratory; 4) data application achievements and prospects: comprehensively analyze the distribution data, species diversity and genetic diversity of Amphipoda in Tibetan Plateau, discuss the impact of climate change on Amphipoda diversity and the response of Amphipoda to environmental change from the perspective of evolution and genetics, and provide scientific basis for biodiversity assessment and ecological protection in the Tibetan Plateau; 5) legend: brown circles for samples from Tian Shan, pink circles for samples at north side of the Yarlung Zangbo River with diversification age of 2-4 Ma, greeen triangles for samples at south side of the Yarlung Zangbo River with diversification age of 4-6 Ma, yellow circles for samples from Himalayas with diversification age around 3 Ma, orange square for samples from Hengduan Mt. with diversificaiton age of 5-7 Ma, blue circles for samples from east of the Tibetan Plateau.
HOU Zhonge
The data of farmland distribution on the Qinghai-Tibet Plateau were extracted on the basis of the land use dataset in China (2015). The dataset is mainly based on landsat 8 remote sensing images, which are generated by manual visual interpretation. The land use types mainly include the cultivated land, which is divided into two categories, including paddy land (1) and dry land (2). The spatial resolution of the data is 30m, and the time is 2015. The projection coordinate system is D_Krasovsky_1940_Albers. And the central meridian was 105°E and the two standard latitudes of the projection system were 25°N and 47°N, respectively. The data are stored in TIFF format, named “farmland distribution”, and the data volume is 4.39GB. The data were saved in compressed file format, named “30 m grid data of farmland distribution in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for farmland ecosystem management on the QTP.
LIU Shiliang, SUN Yongxiu, LI Mingqi
The data includes the basic survey data of the sampling points, the community species coverage, height and density of the sample square, as well as the aboveground biomass of the species, the temperature, moisture, pH, available nitrogen, available phosphorus, total carbon of the 0-10cm surface soil , Total nitrogen content (the basic information of the sample site includes the collection site, date, and soil condition of the collection site. The CK in the process is sampled without species (5), and D is the sample sample for degraded grassland (5). A), litter, dead, sand-covered...information of saline-alkali spots are respectively 0 for no, 1 for less, 2 for more, bare land area as a percentage; species height, coverage, density, and above-ground biomass collection The survey sample area is 50cm*50cm, each site has 10 samples, the coverage is expressed as a percentage, the height is cmcm, the density is expressed by the number of species, 0-10cm surface soil information for each The site has 3 repetitions. The degree of degradation is divided into high degradation (HG), moderate degradation (MG), and light degradation (LG). The utilization rate is heavy and light. Units are marked in the title). The data are all collected and measured on the spot. The total carbon is the elemental analysis method, the total nitrogen is the Kjeldahl method, the effective nitrogen is the alkaline solution diffusion method, the effective phosphorus is the extraction-molybdenum antimony colorimetric method, and the PH is the electric potential method. , Temperature and moisture are measured by soil thermometer and soil moisture meter. The data is of good quality and can be used to calculate biodiversity and analysis of driving factors for species existence.
TIAN Dashuan
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