This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
WANG Junbo
Vulnerability refers to a property of the system that is susceptible to changes in structure and function due to the system's sensitivity to internal and external disturbances and its lack of ability to respond, that is, the ability of the region to cope with disasters to reduce losses when heat waves occur. This dataset is based on the pan-third pole regional road network data, GDP data, medical facility spatial distribution data, vegetation coverage data, and water distribution data as basic data,and takes 2015 as the base year. The Euclidean Metric calculation method is adopted to determine the spatial distribution of road networks, water and medical facilities in the area. The distance from roads, water bodies, medical facilities, GDP, and vegetation coverage are used as evaluation indicators. The equal-weight overlapping addition is used to evaluate the vulnerability of heat waves at each node. In order to eliminate the impact of unit differences, the data of each index layer is normalized before the evaluation.Finally, the vulnerability level of each node is divided by the natural Jenks method.
GE Yong, YANG Fei, LIU Qingsheng
The data comes from the National Centers for environmental information (NCEI), which provides meteorological records of all stations in the world since they were built, including temperature, wind speed, dew point, precipitation and other information. There are four recorded stations near Dhaka city. The monitoring data of meteorological stations have the characteristics of high precision. Firstly, the monitoring data of stations in the world are downloaded from NCEI, and then four stations in Dhaka city are selected according to longitude and latitude. The data level records the daily meteorological station monitoring data from January 1, 2016 to December 31, 2019.
GE Yong, YANG Fei
The data source of this data set is the first, second and third bands of the atmospheric top layer reflectance data of Landsat-5 satellite. Landsat satellite is a sun synchronous satellite. The satellite moves from north to south. The earth rotates from west to East. The satellite circles the earth 14.5 times a day. Each circle moves 159km to the west of the equator. It covers every 16 days repeatedly. This data set mainly covers Dhaka City, Bangladesh. Based on the top layer reflectance data of Landsat-5 atmosphere in 2010, this data is downloaded from the geospatial data cloud platform, and uses ArcGIS to synthesize the data band. Finally, the 30 meter resolution multispectral remote sensing image data of Dhaka area 2010 in TIFF format is obtained.
GE Yong, YANG Fei
The basic data source of this dataset is from the website of the National Oceanic and Atmospheric Administration (NOAA). NOAA satellites are meteorological observation satellites. Provide meteorological environment information including temperature, precipitation, dew point, wind speed, etc. This dataset mainly covers key nodes in the pan-third pole Southeast Asia and Middle East regions. The main steps of data processing are as follows: First, according to the definition of high temperature heat waves in China's national standard "GB / T 29457-2012", based on basic meteorological data, determine the occurrence of high temperature heat waves, and then statistically obtain the frequency of high temperature heat waves. The time and occurrence intensity are collated to obtain the historical high temperature heat wave disaster event data set. This data set is helpful for clarifying the occurrence of extreme high temperature disasters in each study area, and provides reference materials and a strong basis for judging the intensity of high temperature heat waves in each area.
GE Yong, LIU Qingsheng
The basic data source of this dataset is from the website of the National Oceanic and Atmospheric Administration (NOAA). NOAA satellites are meteorological observation satellites. Provide meteorological environment information including temperature, precipitation, dew point, wind speed, etc. This dataset mainly covers key nodes in the Southeast Asia and Middle East regions. The main steps of data processing are as follows: firstly, the daily maximum temperature data is obtained by screening from a large number of basic meteorological data; the daily maximum temperature relative humidity relationship is integrated, and the daily relative humidity calculation is completed based on the dew point temperature data of the weather station. This data set provides basic information and a strong reference for evaluating the high temperature weather process in key node areas.
GE Yong, LIU Qingsheng
The data sources of this dataset are the first to seventh bands of the top-of-atmosphere (TOA) reflectance data of Landsat-5 and landsat-8 satellites. Landsat satellites are sun synchronous satellite with a repetition period of 16 days. Based on the data of Landsat-5 and landsat-8 TOA reflectance from 2000 to 2016, this dataset mainly covers the pan third polar key points region in Southeast Asia and the Middle East. It uses Google Earth engine cloud computing platform to clip the data of the study area, and finally gets the 30-meter resolution multi spectral remote sensing image data of the pan third polar region 2000-2016 in TIFF format.
GE Yong, LING Feng, ZHANG Yihang
The data sources of this dataset are the first to seventh bands of the top-of-atmosphere (TOA) reflectance data of Landsat-5 and landsat-8 satellites. Landsat satellites are sun synchronous satellite with a repetition period of 16 days. Based on the data of Landsat-5 and landsat-8 TOA reflectance from 2000 to 2016, this dataset mainly covers the pan third polar key points region in Southeast Asia and the Middle East. It uses Google Earth engine cloud computing platform to clip the data of the study area, and finally gets the 30-meter resolution multi spectral remote sensing image data of the pan third polar region 2000-2016 in TIFF format.
GE Yong, LING Feng, ZHANG Yihang
The data sources of this dataset are the first to seventh bands of the top-of-atmosphere (TOA) reflectance data of Landsat-5 and landsat-8 satellites. Landsat satellites are sun synchronous satellite with a repetition period of 16 days. Based on the data of Landsat-5 and landsat-8 TOA reflectance from 2000 to 2016, this dataset mainly covers the pan third polar key points region in Southeast Asia and the Middle East. It uses Google Earth engine cloud computing platform to clip the data of the study area, and finally gets the 30-meter resolution multi spectral remote sensing image data of the pan third polar region 2000-2016 in TIFF format.
GE Yong, LING Feng, ZHANG Yihang
The data sources of this dataset are the first to seventh bands of the top-of-atmosphere (TOA) reflectance data of Landsat-5 and landsat-8 satellites. Landsat satellites are sun synchronous satellite with a repetition period of 16 days. Based on the data of Landsat-5 and landsat-8 TOA reflectance from 2000 to 2016, this dataset mainly covers the pan third polar key points region in Southeast Asia and the Middle East. It uses Google Earth engine cloud computing platform to clip the data of the study area, and finally gets the 30-meter resolution multi spectral remote sensing image data of the pan third polar region 2000-2016 in TIFF format.
GE Yong, LING Feng, ZHANG Yihang
The data include the carbonate content, carbon isotope and oxygen isotope analysis results of inorganic carbonates of 79 samples from 850 m natural section of the middle late Eocene in the salkuli basin. The carbon and oxygen isotopes of carbonate in the sediments record the hydrological and vegetation information in the geological history, which is one of the main indicators of paleoenvironmental tracer research. After grinding and sieving, the carbon and oxygen isotope analysis is completed by the sample processing unit (carbonate device) and MAT252 isotope mass spectrometry online automatic online system. The analytical accuracy of the sample is: carbon isotope is better than ± 0.06 ‰, and oxygen isotope is better than ± 0.08 ‰. Through the analysis of carbon and oxygen isotope data of solkuli section, the evolution history of arid environment since Eocene can be reconstructed, and the paleoclimate effect of the Tibetan Plateau uplift and global climate change can be discussed.
SUN Jimin
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CAO Guangzhen
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
This dataset contains monthly 0.05°×0.05° (1982, 1985, 1990, 1995, and 2000), 0.01°×0.01° (2005, 2010, 2015, 2017 and 2018), and daily 0.01°×0.01° (2018) LST products in Qilian Mountain Area. The dataset was produced based on SW algorithm by AVHRR BT from thermal infrared channels (CH4: 10.5µm to 11.3µm; CH5: 11.5µm to 12.5µm) at a resolution of 0.05°, MYD21A1 LST products at a resolution of 0.01° along with some auxiliary datasets. The auxiliary datasets include IGBP land cover type, AVHRR NDVI products, Modern Era Retrospective-Analysis for Research and Applications-2 (MERRA-2) reanalysis data, ASTER GED, Lat/Lon and the Julian Day information.
LI Hua
The data set contains agricultural economic data of all counties and regions in the Tibetan Plateau in 1980-2015, and covering the total number of households and total population in rural areas, agricultural population, rural labor force, cultivated land, paddy field area, the dry land area, power of agricultural machinery, agricultural vehicles, mechanical ploughing area, irrigation area, consumption of chemical fertilizers electricity use, gross output value of agriculture, forestry, animal husbandry and fishery, the output of cattle, pig, sheep, meat, poultry, and fish, the sown area of grain, the output of grain, cotton, oil and all kinds of crops, and characteristic agricultural products and livestock production and other relevant data.The data came from the statistical yearbook of the provinces included in the Tibetan Plateau.The data are of good quality and can be used to analyze the socio-economic and agricultural development of qinghai-tibet plateau.
LV Changhe
This study takes the land resources in the Qinghai-Tibet Plateau as the evaluation object, and clarifies the current situation in the region suitable for agriculture, forestry, animal husbandry production and the quantity, quality and distribution of the reserve land resources. Through field investigations, collect relevant data from the study area, and combine relevant literature and expert experience to determine the evaluation factors (altitude, slope, annual precipitation, accumulated temperature, sunshine hours, soil effective depth, texture, erosion, vegetation type, NDVI). The grading and standardization are carried out, and the weights of each evaluation factor are determined by principal component analysis. The weighted index and model are used to determine the total score of the evaluation unit. Finally, the ArcGis natural discontinuity classification method is used to obtain the Qingshang Plateau. And the grades of farmland, forestry and grassland suitability drawings of the Qinghai-Tibet Plateau with a resolution of 90m were given. Finally, the results are verified and analyzed.
YAO Minglei
The remote sensing image interpretation mark is also called the interpretation factor, which can directly reflect the image features of the ground object information. The interpreter uses these marks to identify the nature, type or condition of the feature or phenomenon on the image, so it is for the remote sensing image data. Human-computer interactive interpretation is of great significance. The image used in the data to establish the interpretation mark avoids the summer with high vegetation coverage, and avoids the data with more snow cover, cloud cover or smog influence.According to the basic geographic information data extraction requirements, the combination of the remote sensing image band combination order and the full color band are selected.Avoid data loss when enhancing data. The requirement for selecting a typical marker-building area on an image is that the range is moderate to reflect the typical features of the type of landform, including as many basic geographic information elements as possible in the type of landform and the image quality is good. After the selection of the marking area is completed, look for all the basic geographic information element categories contained in the marking area, and then select various typical maps as the collection marks, then go to the field for field verification,including 3429 sampling reference points and 1,870 photos, and the translation of the library was established, and the unreasonable parts were modified until they were consistent with the field. At the same time, the ground photo of the map is taken to make the image and the actual ground elements relate to each other, expressing the authenticity and intuitiveness of the remote sensing image interpretation mark, and to deepen the user's understanding of the interpretation mark.
LIU Tie
The data set was acquired by uav aerial photography during the field investigation on the Tibetan Plateau in 2018. The data size was 5.72 GB, including more than 800 photos.The photo was taken from July 19, 2008 to July 26, 2008. The shooting locations mainly include yambajing, keshi village, apaixin village, zhongguo village, mirin village, ri village, chongkang village, kesong village, semi village, yamzhuo yoncho and the surrounding areas.Aerial photos more clearly reflect the local land cover, land use type distribution density, rivers and lakes, vegetation, etc.), work for land use remote sensing provides better validation information, can also be used for the estimation of vegetation coverage, for the study of land use in the study area provided a good reference information.
LV Changhe, LIU Yaqun
(1) Content: This data contains two tables: SyrDarya_201705_part.xlsx and SyrDarya_201709_part.xlsx attribute field: "Point", "Longitude", "Latitude", and "SAL" represent "Point Id", "Longitude", "Latitude", and "Total salt (‰)". (a) SyrDarya_201705_part.xlsx is sample data of the Syr Dayra River Basin in May 2017, which lack of the "PH" field. (b) SyrDarya_201709_part.xlsx is sample data of the Syr Dayra River Basin in September 2017, it contains the "PH" field. (2) According to the standard process of data processing(①Soil Testing Part 16: Method for determination of total water-soluble salt，②Soil Testing Part 2:Method for determination of soil PH), the soil sampling data in the Syr Dayra River Basin are processed, analyzed and sorted out, so as to select the soil sample collection and detection data set in the Syr Dayra River Basin. (3) This data is soil sampling data, which can be used to monitor soil salinization and other studies.
LUO Geping
Land use data of almaty, with a resolution of 30 meters, was in the form of TIF and the time was 1990.05.03 and 2018.04.14, respectively. Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
HUANG Jinchuan, MA Haitao
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