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.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
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.
XU Zhenzhu, YANG Yuanhe, ZHANG Feng
Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.
NIU Ben, ZHANG Xianzhou
This dataset is derived from the paper: Tang, H. et al. (2020). Early Oligocene vegetation and climate of southwestern China inferred from palynology. Palaeogeography, Palaeoclimatology, Palaeoecology, 560, 109988. doi:10.1016/j.palaeo.2020.109988 This data is part of Supplementary data of the paper, maily contains: Supplementary table 1) Pollen percentages, which were calculated using the collected pollen samples. Supplementary table 2) Plant functional types (PFTs) for the reconstructed paleovegetation of three sites : Wenshan (Early Oligocene), Jianchuan (Early Oligocene) and Lühe (Late Eocene). Recently, in the town of Lühe, central Yunnan, SW China, a new fossil-bearing section was found and dated as early Oligocene (~33–32 Ma) according to U-Pb isotope of volcanic tuff. The fossil-bearing section totals about 18 m in thickness. Fifty-five pollen samples were collected vertically throughout this Lühe town section. For each sample, 2–2.5 g of sediment were treated with KOH (10%,) HCl (10%) and HF (39%), then sample residues were sieved through a 5 μm nylon mesh in an ultrasonic tank. Spore and pollen grains were identified using both a light microscope (LM, Leica DM1000 microscope) and a scanning electronic microscope (SEM). Single grains were picked up by a capillary tube and then transferred to a copper stub, coated with gold and observed with a Zeiss EVO LS10 SEM. At least 300 pollen grains were counted for each sample under the LM at ×400 magnification. Then the pollen percentages were calculated using the sum of total terrestrial pollen. The paleovegetation was reconstructed following the method described by Prentice et al., 1996, Prentice and Jolly, 2000 and Ni et al. (2010). The paleobiomes were reconstructed by comparing the similarity of the palaeoflora with modern plant functional types (PFTs), according to the data published by Ni et al. (2010). The similarity between the palaeoflora and modern PFTs data was explored using Euclidean distances (Prentice et al., 1996) and the Jaccard Index Coefficient (Pound and Salzmann, 2017). The Jaccard Index Coefficient in the R package “clusteval” was used here to calculate the similarity. The palaeoflora was assigned to the biome with the highest similarity scores, taking into account dominant or key taxa.
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
The data include the Cenozoic plant fossils collected from Gansu, Qinghai and Yunnan by the Department of paleontology, School of Geological Sciences and mineral resources, Lanzhou University from 2019 to 2020. All the fossils were collected by the team members in the field and processed in the laboratory by conventional fossil restoration methods and cuticle experiment methods. The fossils are basically well preserved, some of which are horned The study of these plant fossils is helpful to understand the Cenozoic paleoenvironment, paleoclimate, paleogeographic changes and vegetation features of the eastern Qinghai Tibet Plateau.
Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.
This dataset is derived from the paper: Deng, W. et al. (2020). Sharp changes in plant diversity and plant-herbivore interactions during the Eocene–Oligocene transition on the southeastern Qinghai-Tibetan Plateau. Global and Planetary Change, 194, 103293. doi:10.1016/j.gloplacha.2020.103293 This data contains herbivore damage patterns on fossil leaves of plant assemblages from the latest Eocene layer and the earliest Oligocene layer in Kajun Village, Markam County, southeastern margin of the Qinghai-Tibetan Plateau. Herbivore damage patterns on fossil leaves are essential to explore the evolution of plant-herbivore interactions under paleoenvironmental changes and to better understand the evolutionary history of terrestrial ecosystems. The Eocene–Oligocene transition (EOT) is a period of dramatic paleoclimate changes that significantly impacted global ecosystems, Researchers identified taxonomic composition of the flora, and investigated well-preserved herbivore damage on fossil leaves from two layers(the latest Eocene layer (MK-3, ~34.6 Ma) and the earliest Oligocene layer (MK-1, ~33.4 Ma)) of the Lawula Formation in Markam County, southeastern Qinghai-Tibetan Plateau (QTP), China. The data contains tables of the records of the leaves fossil, the fileds of the tables are as following: Basic Code; Database RFID; Family code; Genera code; Species code; Marks; Plant-herbivore; Leaves for damage; FFGs & DTs; Code marks; Hole feeding; Margin feeding; Skeletonization; Surface feeding; Piercing & Sucking; Oviposition; Mining; Galling; Fungal; Incertae Sedis; Boring; Undefined This dataset also contains some figures in the article.
DENG Weiyudong, SU Tao
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
This dataset records The experiment of soil water content in the lower reaches of the Tarim River (Karl) was carried out by the members of the Xinjiang salt water Regiment (Karl) from September to September, 2020 In order to study the phenotypic characteristics of different plants under high salinity saline water irrigation, and to explore the feasibility of high salinity saline water for vegetation construction.
LI Xinrong, HE Mingzhu, ZHAO Zhenyong
The data set includes the start time (year, month), location (longitude and latitude), duration (month), drought intensity and vulnerability data of vegetation response to drought in Central Asia from 1982 to 2015, with a spatial resolution of 1 / 12 °. The drought events were identified by the standardized precipitation evapotranspiration index at the time scale of 12 months (spei12) < - 1.0. The specific algorithm of drought characteristics and vegetation vulnerability is detailed in the citation. The dataset has been applied in the study of vegetation vulnerability to drought in Central Asia, and has application prospects in the research fields of spatial-temporal characteristics of drought events, drought-vegetation interaction mechanism, drought risk assessment and so on.
This dataset is derived from the paper: Su, T. et al. (2019). No high tibetan plateau until the Neogene. Science Advances, 5(3), eaav2189. doi:10.1126/sciadv.aav2189 This data contains supplementary material of this article. Researchers discovered well-preserved palm fossil leaves from the Lunpola Basin (32.033°N, 89.767°E), central Tibetan Plateau at a present elevation of 4655 m in 2016. Researchers compared the newly discovered fossil with those present fossil that are most similar, find that there is no similar leaves among present fossil, therefore, researchers proposed the new species <em>S. tibetensis</em> T. Su et Z.K. Zhou sp. nov. Using the climate model, combined with the research of the fossil, researchers rebuilt the paleoelevation of the central Tibetan Plateau, it shows that a high plateau cannot have existed in the core of Tibet in the Paleogene. The data contains the following tables: 1) Table S1. Fossil records of palms around the world. 2) Table S2. Morphological comparisons between fossils from Lunpola Basin and modern palm genera. 3) Table S3. Climate ranges of 12 living genera that show the closest morphological similarity to <em>S. tibetensis</em> T. Su et Z.K. Zhou sp. nov. This dataset also contains the figures in the supplementary material in the article.
This dataset is collected from the paper: Chen, J.*#, Huang, Y.*#, Brachi, B.*#, Yun, Q.*#, Zhang, W., Lu, W., Li, H., Li, W., Sun, X., Wang, G., He, J., Zhou, Z., Chen, K., Ji, Y., Shi, M., Sun, W., Yang, Y.*, Zhang, R.#, Abbott, R. J.*, & Sun, H.* (2019). Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot. Nature Communications, 10(1), 5230. doi:10.1038/s41467-019-13128-y. This data contains the genome assembly of alpine species Salix brachista on the Tibetan Plateau, it contains DNA, RNA, Protein files in Fasta format and the annotation file in gff format. Assembly Level: Draft genome in chromosome level Genome Representation: Full Genome Reference Genome: yes Assembly method: SMARTdenovo 1.0; CANU 1.3 Sequencing & coverage: PacBio 125.0; Illumina Hiseq X Ten 43.0; Oxford Nanopore Technologies 74.0 Statistics of Genome Assembly: Genome size (bp): 339,587,529 GC content: 34.15% Chromosomes sequence No.: 19 Organellas sequence No.: 2 Genome sequence No.: 30 Maximum genome sequence length (bp): 39,688,537 Minimum genome sequence length (bp): 57,080 Average genome sequence length (bp): 11,319,584 Genome sequence N50 (bp): 17,922,059 Genome sequence N90 (bp): 13,388,179 Annotation of Whole Genome Assembly: Protein：30,209 tRNA：784 rRNA：118 ncRNA：671 Please see attachments for more details of annotation. The tables in the Supplementary Information of this article can also be found in this dataset. The table list is represented in attachments. The accession no. of genome assembly is GWHAAZH00000000 (https://bigd.big.ac.cn/gwh/Assembly/663/show).
CHEN Jiahui, YANG Yongping, Richard John Abbott, SUN Hang
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 May 1 in 2019 to December 31 in 2019. 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.
This data set includes the normalized vegetation index, vegetation coverage, vegetation net primary productivity, grassland biomass, forest stock vegetation parameter remote sensing products in the key area of Qilian mountain from May 2019 to October 2019, and the spatial resolution is 10m. In this data set, remote sensing data sources such as GF-1, GF-6, Sentinel-2, and ZY-3, combined with basic meteorological and ground monitoring data, are used to retrieve vegetation parameters such as band ratio method, mixed pixel decomposition model and CASA model to generate monthly vegetation index remote sensing products of Qilian Mountain in the growing season. This data set provides data support for the diagnosis of regional eco-environmental problems and the dynamic assessment of eco-environment by constructing a high spatial-temporal resolution eco-environmental monitoring data set based on high-resolution satellites.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
Land surface temperature is a critical parameter in land surface energy balance. This dataset provides the monthly land surface temperature of UAV remote sensing for typical ground stations in the middle reaches of Heihe River basin from July to September in 2019. The land surface temperature retrieval algorithm is an improved single-channel algorithm, which was applied to the land surface brightness temperature data obtained by the UAV thermal infrared remote sensing sensor, and finally the land surface temperature data with a spatial resolution of 0.4m was obtained.
ZHOU Ji, LIU Shaomin, WANG Ziwei
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 in 2019. 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.
ZHOU Ji, LIU Shaomin, DONG Weishen
This data set includes the normalized vegetation index, vegetation coverage, vegetation net primary productivity, grassland biomass, forest stock vegetation parameters of the Heihe River Basin from May 2019 to October 2019, and the spatial resolution is 10m. In this dataset, remote sensing data sources such as GF-1, GF-6, Sentinel-2, and ZY-3, combined with basic meteorological and ground monitoring data, are used to retrieve vegetation parameters such as band ratio method, mixed pixel decomposition model and CASA model to generate monthly vegetation index remote sensing products of Qilian Mountain in the growing season. This data set provides data support for the diagnosis of regional eco-environmental problems and the dynamic assessment of eco-environment by constructing a high spatial-temporal resolution eco-environmental monitoring data set based on high-resolution satellites.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides a monthly land surface albedo of UAV remote sensing with a spatial resolution of 0.2 m. It measured in the midstream of Heihe River Basin during the vegetation growth season over typical stations in 2019. The pix4D mapper software was used for image mosaic and NDVI calculation.
ZHOU Ji, LIU Shaomin, JIN Zichun