This dataset is the spatial distribution map of the marshes in the source region of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
WANG Guangjun
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%.
YE Qinghua
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.
ZHANG Yao
Based on 2015 ESA global land cover data (ESA GlobCover), combined with the Tsinghua university global land cover data (FROM GLC)、NASA MODIS global land cover data (MCD12Q1)、University of Maryland global land data (UMD)、USGS global land data (IGBP DISCover),we build the LUC classification system in the Tibet Plateau and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Tibet Plateau V1.0.
XU Erqi
The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.
National Meteorological Information Center, Tibet Meteorological Bureau, China
Based on the Tibetan Plateau wetland pattern in the 1970s interpreted using the Mire Map of China compiled by the scientific expeditions and the Tibetan Plateau wetland pattern in the 2000s interpreted using Landsat TM (resolution: 30 m) satellite image data, The Mire Map of China in the 1970s was interpreted. Visual interpretation of Landsat TM images from 2006 to 2009: a) Based on the natural zoning of the whole district, the interpretation keys of different wetland types were established with reference to the data obtained by different physical geography units and actual surveys. b) Based on the established interpretation keys, wetlands with an area greater than 10 square kilometers were primarily extracted by artificial visual interpretation method (excluding permanent, seasonal rivers and riverbeds). c) According to the interpretation results in combination with the topographic map (resolution: 90 m) of the study area and the actual situation of the wetland plaque investigation within the study area, the plaque modification and supplementation were artificially carried out. The data of the 1970s were obtained by interpretation of the Mire Map of China compiled by the Tibetan Plateau scientific expeditions of the Changchun Institute of Geography. The wetland data of the 2000s was derived from Landsat TM (resolution: 30 m) satellite image data. The data are of good quality.
ZHOU Caiping
This data set is the water resources data of the Qinghai Tibet Plateau from 1990 to 2010, which is the sum of renewable surface and groundwater resources. The data is in vector format and the spatial resolution is in the scale of prefecture level administrative units. The data is obtained by checking the results of VIC (variable injection capacity) hydrological model. The simulated water resources are the sum of the surface runoff and underground runoff in the output results of hydrological simulation. The simulation results are verified by comparing with the runoff data of the measured stations. According to the statistics of water resources at the provincial level in China water resources bulletin, a correction coefficient α is introduced at the provincial level, so that the product of water resources and α in the hydrological model simulation province is equal to the statistics of water resources. Then the amount of water resources in the administrative unit is the product of the total amount of water resources and α.
DU Yunyan, YI Jiawei
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, Wu Tonghua
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
QIU Yubao
The data set is remote sensing image of Resource 3 No. 02 (ZY3-02). ZY3-02 was successfully launched from Taiyuan Satellite Launch Center at 11:17 on May 30, 2016 by Long March 4 B carrier rocket. China-made satellite imagery will be further strengthened in the areas of land surveying and mapping, resource survey and monitoring, disaster prevention and mitigation, agriculture, forestry and water conservancy, ecological environment, urban planning and construction, transportation and other fields. List of files: ZY302_PMS_E98.8_N37.4_201707_L1A0000156704 ZY302_PMS_E100.4_N37.0_20171127_L1A0000217243 ZY302_TMS_E99.5_N37.0_20170717_L1A0000160059 ZY302_TMS_E100.3_N36.6_20171127_L1A0000217279 ZY302_TMS_E100.4_N37.0_20170529_L1A0000139947 Folder Naming Rules: Satellite Name Sensor Name Central Longitude Central Latitude Acquisition Time L1****
China Centre for Resources Satellite Data and Application
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
China Centre for Resources Satellite Data and Application
Gf-2 satellite is the first civil optical remote sensing satellite independently developed by China with a spatial resolution better than 1 meter. It is equipped with two high-resolution 1-meter panchromatic and 4-meter multi-spectral cameras, and the spatial resolution of the sub-satellite can reach 0.8 meters. This data set is the remote sensing image data of 6 jing gaofen-2 satellite in 2017.The folder list is: GF2_PMS1_E100.5_N37.2_20171013_L1A0002678101 GF2_PMS1_E100.5_N37.4_20171013_L1A0002678097 GF2_PMS1_E100.6_N37.6_20171013_L1A0002678096 GF2_PMS2_E100.3_N37.4_20170810_L1A0002534662 File naming rules: satellite name _ sensor name _ center longitude _ center latitude _ imaging time _L****
China Centre for Resources Satellite Data and Application
This data set is the remote sensing data of gaofan-1 satellite, including the data of two scenes of PMS1 camera on 2017-8-13 and 2017-10-5, one scene of PMS2 camera on 2017-5-27, and one scene of WFV2 and WFV3 camera on September 23, 2018.File list: GF1_PMS1_E99.1_N37.2_20170813_L1A0002539236 GF1_PMS1_E101.2_N36.4_20171005_L1A0002653985 GF1_PMS2_E100.3_N37.7_20170527_L1A0002384098 GF1_WFV2_E98.4_N37.6_20180927_L1A0003481737 GF1_WFV3_E100.4_N37.3_20180927_L1A0003481706
ZHOU Shengming
The data set is extracted from the NDVI data of long time series acquired by VEGETATION sensor on SPOT satellite. The time range of the data set is from May 1998 to 2013. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 10 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 1 km, temporal resolution is 10 days, time range: May 1998 to December 2013.
Image Processing Centre for SPOT-VGT
The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.
National Oceanic and Atmospheric Administration
The data set is MODIS vegetation index data (MOD13Q1). The source areas of the three rivers are extracted to carry out the research and analysis of the source areas of the three rivers separately. MOD13Q1 is a 16-day composite vegetation index, including normalized vegetation index (NDVI) and enhanced vegetation index (EVI). The spatial scope of Sanjiang Source covers two MODIS files (h25v05 and h26v05). Data storage format is hdf. Each file contains 12 bands: Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Data Quality (VI Quality), Red Reflectance, Near Infrared Reflectance (NIR Reflectance), Blue Reflectance, Mid Infrared Reflectance, Observation. Viewzenith angle, sun zenith angle, relative azimuth angle, composite day of the year and pixel reliability. The data format of this data set is hdf, spatial resolution is 250m, temporal resolution is 16 days, time range: February 2000 to October 2018.
Kamel Didan*, Armando Barreto Munoz, Ramon Solano, Alfredo Huete
This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
LI Fei, Fei Li, Zhijun Zhang
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 Tibetan 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.
ZHANG Yao
The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.
WANG Xufeng
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