Original genome data sets of Halenia elliptica and Halenia grandiflora

The Qinghai-Tibet Plateau and its surrounding alpine areas have bred a high degree of plant diversity, and their composition sources are complex. The Qinghai-Tibet Plateau is not only the distribution center of modern alpine plants, but also inextricably linked with plants in other areas. The plants growing in this area have unique gene resources to adapt to the plateau environment. Due to the limit of technology, the mining and utilization of plant gene resources in this area is still in its infancy. Through comparative genomics research on two species of Gentianaceae, we can analyze the genomic effect of plant mating system evolution, discover the key genes related to selfing, and explore the maintenance mechanism of plant hybrid mating system. The content of this data collection is listed as follows: the original genome data of the Halenia elliptica and the Halenia grandiflora , including the third-generation pacbio sequencing data of Halenia elliptica and the Halenia grandiflora, and the second-generation Illumina sequencing data of the Halenia elliptica and the Halenia grandiflora.

0 2022-06-29

Types of surface vegetation within 500m of China Pakistan Economic Corridor and Tianshan Mountains (2020)

This data set collates and collects the surface vegetation type data with 500m spatial resolution in the Qinghai Tibet Plateau and its surrounding areas. The data source is from the official website of the United States Geological Survey (USGS)( https://lpdaac.usgs.gov/products/mod12q1v006/ )This data is the land use and cover product of MODIS level III data, with a spatial resolution of 500m. By using supervised classification of Terra and aqua reflectance data. By applying smoothing splines to the bottom bidirectional reflectance distribution function (BRDF) - adjusted reflectance (nbar) time series, the 6th mcd1201 product developed a new notch filling spectral time feature. In addition, the 6th edition also uses hidden Markov model (HMM) to reduce false changes in category labels. The data set contains 17 major land cover types, including 11 natural vegetation types, 3 land types for land development and mosaic and 3 non vegetation land type definition types according to the international geosphere biosphere program (IGBP). They are: 1-evergreen coniferous forest; 2-evergreen broad-leaved forest; 3-deciduous coniferous forest; 4-deciduous broad-leaved forest; 5-mixed forest; 6-dense shrub; 7- sparse shrub; 8- woody savanna; 9- savanna; 10- grassland; 11- permanent wetlands; 12- agricultural land; 13- cities and construction areas; 14- agricultural land / natural vegetation splicing; 15- snow and ice; 16- bare ground; 17- water.

0 2022-06-18

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.

0 2022-06-16

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.

0 2022-06-16

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.

0 2022-06-16

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.

0 2022-06-16

A dataset of net primary productivity of vegetation on the Qinghai-Tibet Plateau (2001-2020)

Vegetation primary productivity (Net Primary Production, NPP) dataset, source data from MODIS product (MOD17A3H), after data format conversion, projection, resampling and other preprocessing. The existing format is TIFF format, the projection is Krasovsky_1940_Albers projection, the unit is kg C/m2/year, and the spatial range is the entire Qinghai-Tibet Plateau. The spatial resolution of the data is 500 meters, the temporal resolution is every 5 years, and the time range is from 2001 to 2020. The NPP of the Qinghai-Tibet Plateau showed a trend of increasing gradually from northwest to southeast.

0 2022-06-08

Approximate vegetation restoration map of Qinghai Tibet Plateau

Mapping scope: the scope of Qinghai Tibet Plateau (2002 Edition) by Zhang Yili, etc. Data source: vegetation map of Qinghai Tibet Plateau in 1980s, climate, terrain, landform, soil data, etc. Mapping method: the restored vegetation map is a vegetation map that reflects the distribution of the original vegetation before it was damaged by human economic activities. Due to the lack of early vegetation distribution map of the Qinghai Tibet Plateau, based on the vegetation map of the Qinghai Tibet Plateau in the 1980s prepared by the project team, the approximate Restored Vegetation Map is prepared through the following methods. Based on the vegetation map of the Qinghai Tibet Plateau in the 1980s and the worldclim19 bioclimatic data in 1980, the relationship between bioclimatic data and natural vegetation is analyzed to determine the climate data change range corresponding to the distribution of various natural vegetation. For the artificial vegetation in the 1980's vegetation map, the earliest 1960 worldclim19 biological climate data are used to judge the corresponding natural vegetation according to the climate data of the artificial vegetation distribution area and the relationship between the vegetation distribution and climate, and replace the artificial vegetation in this area with natural vegetation. On this basis, further consider the zonal law of vegetation distribution and its relationship with terrain, landform and soil, analyze the previous judgment results according to the remaining natural vegetation around the artificial vegetation and the surrounding zonal vegetation, cross verify the accuracy of the artificial vegetation replacement results, and make appropriate corrections. The natural vegetation in the 1980's vegetation map, such as coniferous forest, broad-leaved forest, shrub, desert, grassland and meadow, remains unchanged. Based on the above analysis results, an approximate Restored Vegetation Map is obtained. The vegetation classification unit is the same as the vegetation map of Qinghai Tibet Plateau in 1980s. Based on the accuracy of the data used in the mapping, the maximum mapping scale of this drawing is 1:500000.

0 2022-06-04

Field investigation of elements (carbon, nitrogen, phosphorus, sulfur, potassium) of vegetation in the southeast edge of Qinghai Tibet Plateau and Hengduan Mountain Area

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 southeast edge of the Qinghai Tibet Plateau and Hengduan Mountain area, in order to provide data support for regional model simulation and ecological management.

0 2022-05-31

Field investigation of elements (carbon, nitrogen, phosphorus, sulfur, potassium) of vegetation in the Water tower area of Qinghai Tibet Plateau and Himalayan Mountains (2020s)

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.

0 2022-05-30