Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1359

  • Landsat-based continuous monthly 30m FVC Dataset in Qilian mountain area in 2021 (V1.0)

    Landsat-based continuous monthly 30m FVC Dataset in Qilian mountain area in 2021 (V1.0)

    Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

    2022-06-21 782 51

  • Landsat-based continuous monthly 30m LAI Dataset in Qilian mountain area in 2021 (V1.0)

    Landsat-based continuous monthly 30m LAI Dataset in Qilian mountain area in 2021 (V1.0)

    Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

    2022-06-21 1123 30

  • The statistics of natural disasters in Qinghai (1950-2000)

    The statistics of natural disasters in Qinghai (1950-2000)

    This data set contains information on natural disasters in Qinghai over nearly 50 years, including the times, places and the consequences of natural disasters such as droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms, pest plagues, rats, and geological disasters. Qinghai Province is located in the northeastern part of the Tibetan Plateau and has a total area of 720,000 square kilometers. Numerous rivers, glaciers and lakes lie in the province. Because two mother rivers of the Chinese nation, the Yangtze River and the Yellow River, and the famous international river—the Lancang River—originated here, it is known as the "Chinese Water Tower"; there are 335,000 square meters of available grasslands in the province, and the natural pasture area ranks fourth in the country after those of Inner Mongolia, Tibet and Xinjiang. There are various types of grasslands, abundant grassland resources, and 113 families, 564 genera and 2100 species of vascular plants, which grow and develop under the unique climatic condition of the Tibetan Plateau and strongly represent the characteristics of the plateau ecological environment. As the main part of the Tibetan Plateau, Qinghai Province is one of the centers of the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the study of climate and ecological environment in the international field of sciences and technology. The terrain and land-forms in Qinghai are complex, with interlaced mountains, valleys and basins, widely distributed snow and glaciers, the Gobi and other deserts and grassland. Complex terrain conditions, high altitudes and harsh climatic conditions make Qinghai a province with frequent meteorological disasters. The main meteorological disasters include droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms. The data are extracted from the Qinghai Volume of Chinese Meteorological Disaster Dictionary, with manual entry, summarizing and proofreading.

    2022-06-21 8959 607

  • Computed tomography (CT) scan dataset of vegetation-soil-rock three-dimensional spatial structure of typical watersheds in Qilian Mountains (2021)

    Computed tomography (CT) scan dataset of vegetation-soil-rock three-dimensional spatial structure of typical watersheds in Qilian Mountains (2021)

    1) Data content: CT scan dataset of vegetation-soil-rock three-dimensional spatial structure of typical watersheds in Qilian Mountains, the data includes the volume density of moss layers at different depths, soil macroporosity and soil gravel volume density data in typical watersheds of Qilian Mountains; 2) Data Source and processing method: The moss layer and the undisturbed soil column with a depth of 30 cm under the moss cover were collected in a typical small watershed of the Qilian Mountains, and the moss layer and the undisturbed soil column were scanned with an industrial X-ray three-dimensional microscope; 3) Data quality description: The resolution of moss layer is 40 μm, and the resolution of undisturbed soil column is 68 μm; 4) Data application results and prospects: CT scan data set of vegetation-soil-rock three-dimensional spatial structure of typical small watersheds in Qilian Mountains is suitable for ecological restoration, water resources management and utilization in Qilian Mountains. It is of great significance and can provide basic data and theoretical support for elaborating the water conservation function and mechanism of the Qilian Mountains.

    2022-06-20 815 118

  • Ice core δ18O and accumulations dataset (1900-2011)

    Ice core δ18O and accumulations dataset (1900-2011)

    Among many indicators reflecting changes in climate and environment, the stable isotope index of ice core is an indispensable parameter in ice core record research, and it is one of the most reliable means and the most effective way to restore past climate change. Meanwhile, ice core accumulation is a direct record of precipitation on the glacier, and high-resolution ice core records ensure continuity of precipitation records. Therefore, ice core records provide an effective means of restoring changes in precipitation. Stable isotopes from ice cores drilled throughout the TP have been used to reconstruct climate histories extending back several thousands of years. This dataset provides data support for studying climate change on the Tibetan Plateau.

    2022-06-20 2454 306

  • Remote sensing inversion dataset of the spatial distribution of the Qilian Mountains "Mountains, Waters, Forests, Farmland, Lakes and Grassland"(1985-2020)

    Remote sensing inversion dataset of the spatial distribution of the Qilian Mountains "Mountains, Waters, Forests, Farmland, Lakes and Grassland"(1985-2020)

    The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.

    2022-06-19 1036 57

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2021)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2021)

    The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream of Heihe integrated observatory network from May 2 to December 26, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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.

    2022-06-16 1037 20

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2021)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2021)

    The dataset contains the phenological camera observation data of the Arou Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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.

    2022-06-16 1003 21

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Leaf area index of Daman Superstation, 2021)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Leaf area index of Daman Superstation, 2021)

    This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from July 22 to September 5 in 2021. The site (100.376° E, 38.853°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 are (100.374°E, 38.855°N), (100.371° E, 38.854°N), (100.369°E, 38.854°N). Four sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 5 days moving averaged method to obtain the processed LAI (level 2); 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.

    2022-06-16 1173 66

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2021)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2021)

    The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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.

    2022-06-16 1134 20

  • Long-term series of daily snow depth dataset in China (1979-2021)

    Long-term series of daily snow depth dataset in China (1979-2021)

    This data set is an upgraded version of "China snow depth long time series data set (1978-2012)". The long time series data set of snow depth in China (1979-2021) adopts longitude and latitude projection, and the data is floating-point. Data sets are stored by year. Each year is a compressed package, and each compressed package contains daily snow depth files. The daily snow depth is stored in a TXT file named "yyyyddd.txt", where yyyy stands for year, DDD stands for Julian date, and the unit of snow depth is cm. For example, 2005001 Txt represents this ASCII file to describe the snow cover in China on the first day of 2005. The ASCII code file of the data set is composed of a header file and the main content. The header file consists of 6 lines of description information, such as the number of rows, the number of columns, the coordinates of the x-axis center point, the y-axis center point, the grid size, and the label value of the no data area. The main content is a two-dimensional group composed of the number of rows and columns. The unit of snow depth is cm. Because the space described by all ASCII files in the data set is nationwide in China, the header files of these files are unchanged. Now the header files are excerpted as follows (where xllcenter, yllcenter and cellsize are in degrees): Ncols 321 Nrows 161 Xllcenter 60 Yllcenter 15 Cellsize 0.25 NoData_ Value -1

    2022-06-15 46360 2269

  • Reconstructed streamflow data of the Gunt River River in the upper reaches of the Amu River (1495-2018)

    Reconstructed streamflow data of the Gunt River River in the upper reaches of the Amu River (1495-2018)

    This data is the annual average runoff data from 1495 to 2018 of Khorog Hydrometric Station of gunte River, a tributary of Amu Darya River, reconstructed based on tree ring data. The data obtained from the tree ring hydrology research carried out by the Urumqi desert Meteorology Institute of the China Meteorological Administration and the Institute of water issues, hydropower and ecology of the National Academy of Sciences of Tajikistan can be used for scientific research such as water resources assessment and water conservancy projects in mountainous areas of Central Asia.

    2022-06-14 2097 145

  • The glacier inventory of Qilian Mountain Area (v3.0, 2019)

    The glacier inventory of Qilian Mountain Area (v3.0, 2019)

    This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

    2022-06-13 1102 0

  • Remote sensing products of vegetation parameters in Heihe River Basin (2021)

    Remote sensing products of vegetation parameters in Heihe River Basin (2021)

    This dataset includes the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), vegetation net primary productivity (NPP), grassland biomass, forest stock volume remote sensing products of vegetation parameters in the Heihe River Basin from May 2021 to October 2021, with a spatial resolution of 8m. This dataset uses remote sensing data sources such as Gaofen-1, Gaofen-6, Sentinel, and Resource-3, combined with basic data such as meteorology and ground monitoring, and uses the band ratio method, mixed pixel decomposition model, CASA model and other vegetation parameters to reflect Algorithms and models are used to generate remote sensing products of monthly vegetation indices in key areas of Qilian Mountains during the growing season. This dataset provides data support for the diagnosis of regional ecological and environmental problems and dynamic assessment of the ecological environment by constructing a high-resolution satellite-based ecological environment monitoring data set.

    2022-06-07 496 23

  • Remote sensing products of vegetation parameters in key areas of Qilian Mountains (2021)

    Remote sensing products of vegetation parameters in key areas of Qilian Mountains (2021)

    This dataset includes the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), vegetation net primary productivity (NPP), grassland biomass, forest stock volume and vegetation parameter remote sensing products of key areas in the Qilian Mountains from May 2021 to October 2021, with a spatial resolution of 8m . This dataset uses remote sensing data sources such as Gaofen-1, Gaofen-6, Sentinel, and Resource-3, combined with basic data such as meteorology and ground monitoring, and uses the band ratio method, mixed pixel decomposition model, CASA model and other vegetation parameters to reflect Algorithms and models are used to generate remote sensing products of monthly vegetation indices in key areas of Qilian Mountains during the growing season. This dataset provides data support for the diagnosis of regional ecological environment problems and dynamic assessment of the ecological environment by constructing a high-resolution satellite-based ecological environment monitoring dataset with high spatial and temporal resolution.

    2022-06-07 499 35

  • Reconstruction data of narun river runoff in the upper reaches of SYR River (1753-2017)

    Reconstruction data of narun river runoff in the upper reaches of SYR River (1753-2017)

    This data is the runoff data of nalun hydrological station in the upper reaches of the SYR River from 1753 to May to August 2017 reconstructed based on tree ring data. It is obtained from the tree ring hydrological research jointly carried out by Urumqi desert Meteorological Institute of China Meteorological Administration and the Institute of water and Hydropower of the Kyrgyz National Academy of Sciences. The data can be used for scientific research such as water resources assessment and water conservancy projects in mountainous areas of Central Asia, and the observation time is the calibration period, The linear transformation equation of runoff and tree ring data is established to reconstruct the path quantity. Data period: 1753 to 2017. Data element: average runoff from May to August (m3 / s) Station location: 41.43 ° ″ n, 76.02 ° ″ e, 2039m

    2022-06-07 711 9

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Cosmic-ray observation system of soil moisture of Daman Superstation, 2021)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Cosmic-ray observation system of soil moisture of Daman Superstation, 2021)

    This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Daman Superstation from January 1 to December 31, 2021. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, ℃), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.

    2022-06-05 1074 16

  • Water index in the Qilian Mountain Area (2021)

    Water index in the Qilian Mountain Area (2021)

    This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2021. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2021 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2021, and can be used for quantitative estimation of water resource.

    2022-06-05 831 0

  • The glacier inventory of Qilian Mountain Area(2021)

    The glacier inventory of Qilian Mountain Area(2021)

    This dataset contains the glacier outlines in Qilian Mountain Area in 2021. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2021 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2021, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

    2022-06-05 941 0

  • The glacier inventory of Qilian Mountain Area (v2.0, 2020)

    The glacier inventory of Qilian Mountain Area (v2.0, 2020)

    This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

    2022-06-05 1073 0