This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie JIANG Weiguo WU Jianjun ZHOU Hongmin
This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture （CIAT）with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.
The data was obtained from the 30-second global elevation dataset developed by the US Geological Survey (USGS) and completed in 1996. Downloaded the data from the NCAR and UCAR Joint Data Download Center (https://rda.ucar.edu/datasets/ds758.0/) and redistributed it through this data center. GTOPO30 divides the world into 33 blocks. The sampling interval is 30 arc seconds, which is 0.00833333333333333 degrees. The coordinate reference is WGS84. The DEM is the distance from the sea level in the vertical direction, ie the altitude, in m, the altitude range from -407 to 8752, the ocean depth information is not included here, the negative value is the altitude of the continental shelf; the ocean is marked as -9999, the elevation above the coastline is at least 1; the island less than 1 square kilometer is not considered. In order to facilitate the user's convenience, on the basis of the block data, splice 10 blocks in -10S-90N and 20W-180E without any resampling processing. This data file is DEM_ptpe_Gtopo30.nc
United States Geological Survey (USGS) UitedStateGeologicalSurvey UitedStateGeologicalSurvey HE Yongli
The sand drift potential data sets of Central Asia in 2017 is in tif format. It covers five countries in Central Asia, including Uzbekistan, Tajikistan, Kyrgyzstan, Kazakhstan and Turkmenistan. The sand drift potential is absolutely drift potential, that is, the sum of the flux in all directions, regardless of the direction of the potential. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The temporal resolution is month, the spatial resolution is 0.25°, and the time range is 2017. This data set can be used as an important reference data for sand storm disaster assessment.
The SRTM (Shuttle Radar Topography Mission) data were obtained from the Endeavour space shuttle jointly launched by NASA and NIMA in February 2000. The SRTM system on the Endeavour had been collecting data for 222 hours and 23 minutes. It covered more than 80% of the global land surface from 60° north latitude to 56° south Latitude, including the whole territory of China. The radar image data acquired by the program have been processed for more than two years to form a digital terrain elevation model. The raw data of this data set were downloaded from the SRTM data distribution website (http://srtm.csi.cgiar.org). For the convenience of using the data, based on the framing of STRM data, we use Erdas software to splice and prepare the STMR mosaic of the Tibetan Plateau. The accuracy is 30 meters, and the data are in geoTIFF format. The raw data of this data set was downloaded from the SRTM data distribution website (http://srtm.csi.cgiar.org). The SRTM data provides a file for each latitude and longitude square. There are two kinds of longitude files, which are 1 arc-second and 3 arc-second, denoted SRTM1 and SRTM3, or 30-m and 90-m data. This data set comprises SRTM3 data with a resolution of 90 m, and the version is SRTM V4.1 (GeoTIFF format).
Global Land Cover Network
The ASTER Global Digital Elevation Model (ASTER GDEM) is a global digital elevation data product jointly released by the National Aeronautics and Space Administration of America (NASA) and the Ministry of Economy, Trade and Industry of Japan (METI). The DEM data were based on the observation results of NASA’s new generation of Earth observation satellite, TERRA, and generated from 1.3 million stereo image pairs collected by ASTER (Advanced Space borne Thermal Emission and Reflection Radio meter) sensors, covering more than 99% of the land surface of the Earth. These data were downloaded from the ASTER GDEM data distribution website. For the convenience of using the data, based on framing the ASTER GDEM data, we used Erdas software to splice and prepare the ASTER GDEM mosaic of the Tibetan Plateau. This data set contains three data files: ASTER_GDEM_TILES ASTERGDEM_MOSAIC_DEM ASTERGDEM_MOSAIC_NUM The ASTER GDEM data of the Tibetan Plateau have an accuracy of 30 meters, the raw data are in tif format, and the mosaic data are stored in the img format. The raw data of this data set were downloaded from the ASTERGDEM website and completely retained the original appearance of the data. ASTER GDEM was divided into several 1×1 degree data blocks during distribution. The distribution format was the zip compression format, and each compressed package included two files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif xx is the starting latitude, and yyy is the starting longitude. _dem.tif is the dem data file, and _num.tif is the data quality file. ASTER GDEM TILES: The original, unprocessed raw data are retained. ASTERGDEM_MOSAIC_DEM: Inlay the dem.tif data using Erdas software, and parameter settings use default values. ASRERGDEM_MOSAIC_NUM: Inlay the num.tif data using Erdas software, and parameter settings use default values. The original raw data are retained, and the accuracy is consistent with that of the ASTERGDEM data distribution website. The horizontal accuracy of the data is 30 meters, and the elevation accuracy is 20 meters. The mosaic data are made by Erdas, and the parameter settings use the default values.
The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei HU Xiaonong
DEM is the English abbreviation of Digital Elevation Model, which is an important source of data for river basin terrain and feature recognition. The principle of DEM is to divide the watershed into m rows and n columns of quadrilaterals (CELLs), calculate the average elevation of each quadrilateral, and then store the elevations in a two-dimensional matrix. Because DEM data can reflect local terrain features with a certain resolution, a large amount of surface morphological information can be extracted through DEM. These information include the slope, aspect, and relationship between cells in a watershed grid cell. At the same time, a certain algorithm can be used to determine the surface water flow path, the river network and the boundary of the watershed. Therefore, to extract watershed characteristics from DEM, a good watershed structure model is the premise and key of designing algorithms. The data includes: 1. 1: 1KM basic DEM Data based on China's 1: 250,000 contours and elevation points, including DEM, mountain shadows, slopes, and aspect maps 2. SRTM 1km DEM Cut from SRTM data of 1KM worldwide, including DEM, mountain shadow, slope, aspect map 3. ASTER GDEM According to the 30-meter ASTER GDEM, stitching, cutting, and resampling into 1KM The file formats are: geotiff Data set projection: Projection = Albers Conical Equal Area ", GEOGCS ["Krasovsky", DATUM ["Krasovsky", SPHEROID ["Krasovsky", 6378245,298.3000003760163]], PRIMEM ["Greenwich", 0], UNIT ["degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["standard_parallel_1", 25], PARAMETER ["standard_parallel_2", 47], PARAMETER ["latitude_of_center", 0], PARAMETER ["longitude_of_center", 105], PARAMETER ["false_easting", 0], PARAMETER ["false_northing", 0], UNIT ["metre", 1,] Data range: Corner Coordinates: Upper Left (-3656885.097, 6579746.944) (51d 4'21.50 "E, 51d19'19.71" N) Lower Left (-3656885.097, 1560746.944) (73d20'22.18 "E, 9d42'56.35" N) Upper Right (3405114.903, 6579746.944) (155d50'50.17 "E, 52d29'29.44" N) Lower Right (3405114.903, 1560746.944) (134d36'43.08 "E, 10d27'15.15" N) Center (-125885.097, 4070246.944) (103d32'28.11 "E, 37d57'32.64" N)
National Basic Geographic Information Center
The SRTM sensor has two bands, namely C-band and X-band. The SRTM we are using now comes from the C-band. The publicly released SRTM digital elevation products include DEM data at three different resolutions: * SRTM1 covers only the continental United States, with a spatial resolution of 1s; * SRTM3 data covers the world with a spatial resolution of 3s. This is the most widely used dataset. The elevation reference of SRTM3 is the geoid of EGM96 and the horizontal reference is WGS84. The nominal absolute elevation accuracy is ± 16m, and the absolute plane accuracy is ± 20m. * SRTM30 data also covers the world, with a resolution of 30s. There are multiple versions of SRTM data. The early SRTM data was completed by NASA's "JPL" (Jet Propulsion Laboratory) ground data processing system (GDPS). The data is called SRTM3- 1. The National Geospatial Intelligence Agency has further processed the data, and the lack of data has been significantly improved. The data is called SRTM3-2. This dataset is mainly the fourth version of SRTM terrain data obtained by CIAT (International Center for Tropical Agriculture) using a new interpolation algorithm. This method better fills the SRTM 90 data hole. The interpolation algorithm comes from Reuter et al. (2007). The data of SRTM is organized as follows: every 5 latitude and longitude grids is divided into a file, which are divided into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees). The file naming rule is srtm_XX_YY.zip, where XX indicates the number of columns (01-72), and YY indicates the number of rows (01-24). The resolution of the data is 90 m. Data use: SRTM data uses a 16-bit value to represent the elevation value (-/ + / 32767 meters), the maximum positive elevation is 9000 meters, and the negative elevation (12,000 meters below sea level). -32767 standard for empty data.