The Second Tibetan Plateau Scientific Expedition (STEP) program

Brief Introduction: Second Tibetan Plateau Scientific Expedition Program

Number of Datasets: 111

  • 1km grid data set of ecological vulnerability in agricultural and pastoral areas of Qinghai Tibet Plateau

    1km grid data set of ecological vulnerability in agricultural and pastoral areas of Qinghai Tibet Plateau

    Based on the vulnerability assessment framework of "exposure sensitivity adaptability", the vulnerability assessment index system of agricultural and pastoral areas in Qinghai Tibet Plateau was constructed. The index system data includes meteorological data, soil data, vegetation data, terrain data and socio-economic data, with a total of 12 data indicators, mainly from the national Qinghai Tibet Plateau scientific data center and the resource and environmental science data center of the Chinese Academy of Sciences. Based on the questionnaire survey of six experts in related fields, the weight of the indicators is determined by using the analytic hierarchy process (AHP). Finally, four 1km grid data are formed involving ecological exposure, sensitivity, adaptability and ecological vulnerability in the agricultural and pastoral areas of the Qinghai Tibet Plateau. The data can provide a reference for the identification of ecological vulnerable areas in the Qinghai Tibet Plateau.

    2021-04-09 903 40

  • Glacier coverage data on the Tibetan Plateau in 2013 (TPG2013, Version1.0)

    Glacier coverage data on the Tibetan Plateau in 2013 (TPG2013, Version1.0)

    The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.

    2021-04-09 3075 91

  • Glacier coverage data  on the Tibetan Plateau  in 1970s (TPG1976, Version 1.0)

    Glacier coverage data on the Tibetan Plateau in 1970s (TPG1976, Version 1.0)

    The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes(92% coverage on TP)in 1972-79,including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s(1981,1986-89,which covers 6.5% of TP) and 2 scenes in 1994(by 1.5% coverage on TP).Among all satellite data,77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.

    2021-04-09 3529 74

  • Glacier coverage data on the Tibetan Plateau in 2017 (TPG2017, Version1.0)

    Glacier coverage data on the Tibetan Plateau in 2017 (TPG2017, Version1.0)

    The Tibetan Plateau Glacier Data –TPG2017 is a glacial coverage data on the Tibetan Plateau from selected 210 scenes of Landsat 8 Operational Land Imager (OLI) images with 30-m spatial resolution from 2013 to 2018, among of which 90% was in 2017 and 85% in winter. Therefore, 2017 was defined as the reference year for the mosaic image. Glacier outlines were digitized on-screen manually from the 2017 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2017. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2017 if they were identifiable on images in all other three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.

    2021-04-09 3596 216

  • Contiguous solar induced chlorophyll fluorescence (CSIF) dataset of Tibetan Plateau (2000-2018)

    Contiguous solar induced chlorophyll fluorescence (CSIF) dataset of Tibetan Plateau (2000-2018)

    The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.

    2021-04-09 316 7

  • Oxygen content in the atmosphere of the Tibetan Plateau (1980-2019)

    Oxygen content in the atmosphere of the Tibetan Plateau (1980-2019)

    Based on the meteorological data of 105 meteorological stations in and around the Qinghai Tibet Plateau from 1980 to 2019 (data from China Meteorological Administration and National Meteorological Science Data Center), the oxygen content was calculated. It was found that there was a significant linear correlation between oxygen content and altitude, y = -0.0263x + 283.8, R2 = 0.9819. Therefore, the oxygen content distribution map can be calculated based on DEM data grid. Due to the limitation of the natural environment in the Qinghai Tibet Plateau, there are few related fixed-point observation institutions. This data can reflect the distribution of oxygen content in the Qinghai Tibet Plateau to a certain extent, and has certain reference significance for the research of human living environment in the Qinghai Tibet Plateau.

    2021-03-22 196 13

  • Tibetan Plateau Sheep lambing record data set (2019-2020)

    Tibetan Plateau Sheep lambing record data set (2019-2020)

    In order to collect the characteristic Domestic Animal Germplasm Resources in Qinghai Tibet Plateau and explore the molecular markers that affect the quality of the germplasm resources, the scientific research team of task five sub project 2019qzk05010704 extensively collected samples of Qinghai Tibetan sheep and Qinghai fine wool sheep in Haibei and Haixi of Qinghai Province from 2019 to 2020, and established the first and second core groups in Ledu agricultural experimental station and Sanjiaocheng sheep breeding farm of Qinghai Province. This data set contains a lambing information table, which records the lambing records of 2074 sheep. The information table includes basic sample information such as gender, lambing time, birth weight, etc., which is saved in Excel form. Individual photos were saved in JPG format and submitted to the "photo video of the second Qinghai Tibet scientific expedition (2019qzk0501) (2020)" dataset. This data set can be used in combination with physical samples to screen individual sheep with superior heterotopic sites for marker assisted selection, propagation and generation selection, and to cultivate families with special germplasm resources.

    2021-03-15 100 0

  • Survey data set of alien fish invasion in the lower reaches of Yarlung Zangbo River (2019-2020)

    Survey data set of alien fish invasion in the lower reaches of Yarlung Zangbo River (2019-2020)

    In order to investigate the species, dispersal location and ecological impact of alien fish on the local indigenous fish in the Qinghai Tibet Plateau, the scientific research team of task 5 sub project 2019qzk05010304 investigated the lower reaches of Lhasa River and Yarlung Zangbo River from 2019 to 2020. This dataset contains a sample information table, which includes fish species, sample point information, sampling time, collector and other basic sample information, and is saved in the form of Excel. A metadata description document, saved in the form of Excel table. 160 photos were saved in JPG format and submitted to the "photo video of the second Qinghai Tibet scientific expedition (2019qzk0501) (2020)" dataset.

    2021-03-12 74 0

  • The daily albedo product coupling topographic effects and combining multi-sensory data over the Tibet Plateau (2016)

    The daily albedo product coupling topographic effects and combining multi-sensory data over the Tibet Plateau (2016)

    This daily land surface albedo proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the Multisensor Combined BRDF Inversion Model developed from a kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF / albedo of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himiwarri-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days, and then the daily resolution albedo is estimated. The validation results show that it meets the accuracy requirements of albedo application. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity. It can effectively support the study of radiation energy balance and environmental change in the Tibet Plateau.

    2021-03-08 442 4

  • The Daily  kernel-driven BRDF model  coefficients retrieved from  5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

    The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

    This daily land surface kernel-driven BRDF model's coeciffients proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the the kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himawari-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity with the shortest composition period. It can effectively support angular effects correction and the BRDF-releated parameters' retrieval.

    2021-03-04 491 6

  • The daily albedo product coupling topographic effects and combining multi-sensory data over the Tibet Plateau

    The daily albedo product coupling topographic effects and combining multi-sensory data over the Tibet Plateau

    This daily land surface albedo proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the Multisensor Combined BRDF Inversion Model developed from a kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF / albedo of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himiwarri-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days, and then the daily resolution albedo is estimated. The validation results show that it meets the accuracy requirements of albedo application. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity. It can effectively support the study of radiation energy balance and environmental change in the Tibet Plateau.

    2021-03-02 442 4

  • Glacier temperature dataset of Xiaodong Kemadi (2012-2015)

    Glacier temperature dataset of Xiaodong Kemadi (2012-2015)

    Xiaodongkemadi glacier, located in Tanggula Mountain, is a continental glacier. The glacier is a compound valley glacier formed by the confluence of a southward main glacier (also known as dadongkemadi glacier) and a Southwest Branch glacier (also known as xiaodongkemadi glacier). The daily temperature and humidity observation data of 6 points in xiaodongkemadi, 4 points in Yangbajing and 4 points in hariqin from 2012 to 2015.

    2021-02-25 2116 32

  • Urban landuse pattern simulation of Xining City (2050)

    Urban landuse pattern simulation of Xining City (2050)

    Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).

    2021-02-03 296 1

  • Urban landuse pattern simulation of Xining City (2050)

    Urban landuse pattern simulation of Xining City (2050)

    Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill)_ There are four.

    2021-02-03 296 1

  • Urban expansion simulation of Xining City (2050)

    Urban expansion simulation of Xining City (2050)

    Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).

    2021-02-02 296 1

  • Urban expansion simulation of Xining City (2050)

    Urban expansion simulation of Xining City (2050)

    Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development, and protecting cultivated land (infill).

    2021-02-01 296 1

  • Urban expansion simulation of Xining City (2050)

    Urban expansion simulation of Xining City (2050)

    Based on the future population forecast data, urban expansion driving factor data (road network density, residential area, night light, GDP) and so on, the future urban expansion model is used to simulate and predict the urban expansion pattern and land use distribution of Xining City in 2050. The data set contains four data results corresponding to the urban pattern of Xining in 2050 under different scenarios. They are maintaining the status quo (BAU), urban compact development (infill), continuing the existing pattern and protecting cultivated land (protect), compact development and protecting cultivated land (infill).

    2021-02-01 296 1

  • Mitochondrial genome sequencing data of Tibetan population in Lhasa

    Mitochondrial genome sequencing data of Tibetan population in Lhasa

    The whole mitochondrial genomes of 68 Tibetan samples were sequenced by high-throughput second-generation sequencing. The average depth of sequencing was 1000 ×, ensuring that the mitochondrial genome of each sample was completely covered (100%). Based on the phylogenetic analysis, we control the quality of these data to ensure that there is no sample pollution and other quality problems. According to the phylogenetic tree, each individual was allocated into haplogroups. The results showed that in Lhasa Tibetan population, M9a1c1b1a was the highest (19.12%), followed by G2 (13.23%), M13a (11.76%), C4a (7.35%), D4 (7.35%), A11a1a (5.88%), M9a1b (5.88%), and F1c, F1g, B4, F1d, M62b, F1a, F1b, G1, M11, M8a, U7a, Z3a. These haplogroups have different originations, including Paleolithic components (M13a, M62b, M9a1b, etc.), northern China millet farmers’ components (M9a1c1b1a and A11a1a), components distributed mainly in southern East Asia (F1a, etc.), northern East Asian haplogroups (C4a, D4, etc.). It is worth noting that the maternal component of Lhasa Tibetans is mainly composed of millet agricultural population in northern China, indicating the important impact of genetic input of millet agricultural population in northern China on the genetic structure of the population in this area. Taken together, the maternal genetic structure of Lhasa Tibetan population exhibits time stratification, which may represent the genetic imprint of different population entering the region in different periods.

    2021-02-01 200 1

  • Flood distribution of historical streams and rivers in Qinghai Tibet scientific research area

    Flood distribution of historical streams and rivers in Qinghai Tibet scientific research area

    The flood distribution data of historical streams and rivers in Qinghai Tibet scientific research area include longitude and latitude, location of occurrence, basic triggering type, date, damage and other attribute information. Data source: survey statistics of disaster investigation department. On the basis of the original data, the necessary data quality control. According to the type description of the original data, the main triggering factors, the location of the occurrence, combined with the 30 meter foundation terrain, the flood type is analyzed and divided. The data can be used as a reference for the analysis of historical flood disasters. The data format is point vector SHP format, which can be directly opened with ArcGIS. The data can be used for flood risk analysis in the corresponding area of the Qinghai Tibet Plateau.

    2021-01-29 791 7

  • Dataset of urban distribution, urban population and built-up area in Tibetan Plateau (2000-2015)

    Dataset of urban distribution, urban population and built-up area in Tibetan Plateau (2000-2015)

    This data set includes the urban distribution, urban population and built-up areas of the Qinghai Tibet Plateau from 2000 to 2015. The urban distribution data is the county-level vector boundary in 2015, and the urban population and built-up area data years are 2000, 2005, 2010 and 2015. Among them, the data of urban distribution and built-up areas are from the research team of Kuang Wenhui, Professor of Institute of geography and resources, Chinese Academy of Sciences, and the data of urban population are from the census data of each year, the statistical yearbook of each province in the Qinghai Tibet Plateau, etc. The data quality is excellent, which can be used to analyze the population growth trend, urban expansion and the impact of human activities on the surrounding environment of cities and towns in the Qinghai Tibet Plateau.

    2021-01-28 1202 66