Current Browsing: Vegetation


Potassium transporter in ammopiptanthus mongolicus (2015-2016)

A typical Shaker type potassium ion absorption channel gene AmKAT1 was cloned from the leaves of Ammopiptanthus mongolicus. Electrophysiological studies of AmKAT1 show that AmKAT1 is a K+ absorption channel regulated by potassium ion concentration. the system can only input K+ into guard cells when the extracellular potassium ion concentration is high (above 10 mmol/L). This distinctive feature has important physiological significance for xerophytes such as Ammopiptanthus mongolicus: under the condition of low concentration of extracellular potassium ions (no matter how high the concentration of sodium ions), AmKAT1 is difficult to open, potassium ions cannot enter guard cells, the guard cells will not absorb water and expand, and stomata will be difficult to open, thus reducing the transpiration and loss of water in Ammopiptanthus mongolicus and enhancing the viability of Ammopiptanthus mongolicus in arid environment. We have further studied the mechanism of extracellular potassium ion regulating the activity of AmKAT1 and found that at least two sites in AmKAT1 are involved in potassium ion induction, and now one site has been determined to be located in the channel pore region. In addition, we cloned a guard cell export-oriented K+ channel AmGORK and a slow anion channel AmSLAC1. Fluorescence quantitative PCR results showed that AmGORK was mainly expressed in the upper part of the ground, and its transcription level was affected by PEG simulated water stress, ABA, NaCl and osmotic stress treatments to varying degrees. Electrophysiological studies in xenogeneic system of Xenopus laevis oocytes show that AmGORK channel of Mongolian Ammopiptanthus mongolicus guard cells can mediate efficient efflux of K+ when membrane potential is depolarized. The activation of this channel has typical voltage dependence and potassium ion concentration dependence, and is inhibited by potassium ion channel inhibitors TEA and Ba2+; In addition, the activity of AmGORK is regulated by extracellular pH, but not by extracellular calcium concentration. These results show that although Ammopiptanthus mongolicus is an ancient drought-resistant leguminous shrub originated millions of years ago, it is highly similar to the existing common model plant Arabidopsis thaliana in the stomatal closure mechanism dominated by K+. These results provide evidence to preliminarily reveal the functional conservatism of GORK-like stomatal regulatory channels in different species and long-term evolution.

2020-07-28

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Leaf area index of Daman Superstation, 2018)

This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 11 to September 18 in 2018. The site (100.372° E, 38.856°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude ranges are (100.373297°E~100.374205°E, 38.857871°N~38.858390°N), (100.373918°E~100.373897°E, 38.854025°). N~38.854941°N), (100.368007°E~100.369044°E, 38.850678°N~38.851580°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The LAI data is obtained from LAINet measurements following four steps: (1) the raw data is light quantum (level 0); (2) the daily LAI can be obtained using the software LAInet (level 1); (3) the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); (4) for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

2020-07-25

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao, 2018)

This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 16 to October 18 in 2018. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 2 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N) and Mixed forest station (101.1335E, 41.9903N), each of which is about 30m×30m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The LAI data is obtained from LAINet measurements following four steps: (1) the raw data is light quantum (level 0); (2) the daily LAI can be obtained using the software LAInet (level 1); (3) the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); (4) for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

2020-07-25

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation dataset of Sidaoqiao superstation, 2018)

The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

2020-07-25

Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (phenology camera observation dataset of Daman superstation, 2018)

The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (GCC), phenological phase and fractional cover (FC). Please refer to Liu et al. (2018) for sites information in the Citation section.

2020-07-25

Qilian mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation dataset of Arou superstation, 2018)

The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

2020-07-25

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of mixed forest superstation, 2018)

The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

2020-07-25

The vegetation map at the 1:4,000,000 of China (1979)

This dataset: Editor-in-Chief: Hou Xueyu Drawing: Hou Xueyu, Sun Shizhou, Zhang Jingwei, He Miaoguang. Wang Yifeng, Kong Dezhen, Wang Shaoqing Publishing: Map Press Issue: Xinhua Bookstore Year: 1979 Scale: 1: 4,000,000 It took five years to complete from May 1972 to July 1976. In the process of drawing legends and mapping, referring to the vast majority of vegetation survey data (including maps and texts) after 1949 in China, we held more than a dozen mapping seminars involving researchers from inside and outside the institute. During the layout after the mapping work was completed, many new survey data were added, especially vegetation data in western Tibet. The nature of this map basically belongs to the current vegetation map, including two parts of natural vegetation and agricultural vegetation. The legend of natural vegetation is arranged according to the seven vegetation groups. They are mainly divided according to the appearance of plant communities and certain ecological characteristics. The concept of agricultural vegetation community, like the natural vegetation community, also has a certain life form (appearance, structure, layer), species composition and a certain ecological location. In 1990, the State Key Laboratory of Resources and Environmental Information Systems of the Institute of Geographical Sciences and Resources, Chinese Academy of Sciences completed the digitization of this map, and wrote relevant data description documents. The digitized data also adopt equal product cone projection and can be converted into other projections by GIS software. This data includes a vector file in e00 format, a Chinese vegetation coding design description, a dataset description, a vegetation data layer attribute data table, and a scanned "People's Republic of China Vegetation Map-Brief Description" and other files. Data projection: Projection: Albers false_easting: 0.000000 false_northing: 0.000000 central_meridian: 110.000000 standard_parallel_1: 25.000000 standard_parallel_2: 47.000000 latitude_of_origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: Unknown Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Unknown Spheroid: Clarke_1866 Semimajor Axis: 6378206.400000000400000000 Semiminor Axis: 6356583.799999999800000000 Inverse Flattening: 294.978698213901000000

2020-06-09

Vegetation map (1:1,000,000) in the Heihe River basin (2001)

The data is the digitization of the Heihe River basin part of the 1:1 million Vegetation Atlas of China, 1:1000, 000 Vegetation Atlas of China is edited by academician Hou Xueyu, a famous vegetation ecologist (Hou Xueyu, 2001). It is jointly compiled by more than 250 experts from 53 units such as research institutes of Chinese Academy of Sciences, relevant ministries and commissions, relevant departments of various provinces and regions, colleges and universities. It is another summative achievement of vegetation ecologists in China over 40 years after the publication of monographs such as vegetation of China Basic map of natural resources and natural conditions of the family. It is based on the rich first-hand information accumulated by vegetation surveys carried out throughout the country over the past half century, and the materials obtained by modern technologies such as aerial remote sensing and satellite images, as well as the latest research achievements in geology, soil science and climatology. It reflects in detail the distribution of vegetation units of 11 vegetation type groups, 796 formations and sub formations of 54 vegetation types, horizontal and vertical zonal distribution laws, and also reflects the actual distribution of more than 2000 dominant species of plants, major crops and cash crops in China, as well as the close relationship between dominant species and soil and ground geology. The atlas is a kind of realistic vegetation map, reflecting the recent quality of vegetation in China.

2020-06-05

The investigation data on the ground and underground biomass and distribution characteristics of the desert plant communities (2014)

In the previous project, three different types of desert investigation and observation sites in the lower reaches of Heihe River were set up. Different kinds of desert plants with the same average growth and size as the observation site were selected for the above ground biomass and underground biomass total root survey. The dry weight was the dry weight at 80 ℃, and the root shoot ratio was the dry weight ratio of the underground biomass to the aboveground biomass. Species: Elaeagnus angustifolia, red sand, black fruit wolfberry, bubble thorn, bitter beans, Peganum, Tamarix and so on.

2020-06-01