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).
Food and Agriculture Organization of the United Nations（FAO）
This data set is a digital elevation model of the Tibetan Plateau and can be used to assist in analysis and research of basic geographic information for the Tibetan Plateau. The raw data were the Shuttle Radar Topography Mission (SRTM) data, which were provided by Global Land Cover Network (GLCN), and the raw data were framing data , using the WGS84 coordinate system, including latitude and longitude, with a spatial resolution of 3″. After the mosaic processing, the Nodata (null data) generated in the mosaic process were interpolated and filled. After filling, the projection conversion process was performed to generate data as Albers equal area conical projection. After the conversion projection, the spatial resolution of the data was 90 m. Finally, the boundary of the Tibetan Plateau was used for cutting to obtain DEM data. This data table has two fields. Field 1: value Data type: long integer Interpretation: altitude elevation Unit: m Field 2: count Data type: long integer Interpretation: The number of map spots corresponding to the altitude elevation Data accuracy: spatial resolution: 90 m
Food and Agriculture Organization of the United Nations
This data set contains the digital slope aspect distribution and slope aspect degree data of the Tibetan Plateau, which can be used to assist in basic geographic information analysis and research work on the Tibetan Plateau region. The raw data were the Shuttle Radar Topography Mission (SRTM) data provided by Global Land Cover Network (GLCN) using the WGS84 coordinate system, and the raw data were framing data, including latitude and longitude data, with a spatial resolution of 3″. After the mosaic processing, the Nodata (null data) generated in the mosaic process were interpolated and filled, and after filling, a projection conversion process was performed to generate an equal-area conical projection of the data bit Albers, after conversion projection, the spatial resolution was 90 m. Finally, the boundary of the Tibetan Plateau was used for cutting to obtain DEM data. Use the spatial analysis module under ArcMap to calculate the slope aspect and generate the slope aspect map. Pixel data: value Data type: floating point Interpretation: slope degree Dimension: degree Data accuracy: spatial resolution 90 m
GLCN
This data set contains the digital slope distribution and slope degree data of the Tibetan Plateau, which can be used to assist in basic geographic information analysis and research work on the Tibetan Plateau region. The raw data were the Shuttle Radar Topography Mission (SRTM) data provided by Global Land Cover Network (GLCN) using the WGS84 coordinate system, and the raw data were framing data, including latitude and longitude data, with a spatial resolution of 3″. After the mosaic processing, the Nodata (null data) generated in the mosaic process were interpolated and filled, and after filling, a projection conversion process was performed to generate an equal-area conical projection of the data bit Albers, after conversion projection, the spatial resolution was 90 m. Finally, the boundary of the Tibetan Plateau was used for cutting to obtain DEM data. Use the spatial analysis module under ArcMap to calculate the slope aspect and generate the slope map. Field: value Data type: floating point Interpretation: slope degree Dimension: degree Data accuracy: spatial resolution 90 m
Food and Agriculture Organization of the United Nations
The DEMs of the typical glaciers on the Tibetan Plateau were provided by the bistatic InSAR method. The data were collected on November 21, 2013. It covered Puruogangri and west Qilian Mountains with a spatial resolution of 10 meters, and an elevation accuracy of 0.8 m which met the requirements of national 1:10 000 topographic mapping. Considering the characteristics of the bistatic InSAR in terms of imaging geometry and phase unwrapping, based on the TanDEM-X bistatic InSAR data, and adopting the improved SAR interference processing method, the surface DEMs of the two typical glaciers above were generated with high resolution and precision. The data set was in GeoTIFF format, and each typical glacial DEM was stored in a folder. For details of the data, please refer to the Surface DEMs for typical glaciers on the Tibetan Plateau - Data Description.
JIANG Liming
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.
HUANG Huabin
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope direction data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
Adopt aster with 30 meter resolution provided by Heihe project data management center GDEM data and 90 meter resolution SRTM data are two sets of grid data, as well as multi-source point data. These point data include radar point cloud elevation data in the middle and upper reaches; elevation data extracted from soil sample points and vegetation sample in the data management center of Heihe plan; elevation data extracted from climate and hydrological stations; and elevation sample data measured by the research group. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high-precision DEM data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final DEM surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
The landform near Qilian in the upper reaches of Heihe River includes the first level denudation surface (wide valley surface) and the Ninth level river terrace. The stage surface distribution data is mainly obtained through field investigation. GPS survey is carried out for the distribution range of all levels of geomorphic surface. The field data is analyzed in the room, and then combined with remote sensing image, topographic map, geological map and other data, the distribution map of all levels of geomorphic surface in the upper reaches of Heihe river is drawn. The age of the denudation surface is about 1.4ma, and the formation of Heihe terrace is later than this age, all of which are terraces since late Pleistocene.
HU Xiaofei, PAN Baotian
The section data of the upper reaches of Heihe River mainly show the structure and cross section distribution characteristics of the terrace of Heihe River. These data are mainly obtained through field investigation and measurement. The data include the forest farm section and raft section near Qilian County in the upper reaches of Heihe River, and the Heihekou section in Yingluoxia.
HU Xiaofei, PAN Baotian
DEM (digital elevation model) is the abbreviation of digital elevation model, which is an important original data for watershed terrain and feature recognition. The principle of DEM is to divide the watershed into M rows and N columns of quadrilateral (cell), calculate the average elevation of each quadrilateral, and then store the elevation in a two-dimensional matrix. Because DEM data can reflect the local terrain features of a certain resolution, a large amount of surface morphology information can be extracted by DEM, which includes the slope, slope direction and the relationship between cells of watershed grid unit [7]. At the same time, the surface water flow path, river network and watershed boundary can be determined by certain algorithm. Therefore, to extract basin features from DEM, a good basin structure model is the premise and key of the design algorithm.
XU Zongxue, HU Litang, XU Maosen
This data is based on the DEM data generated by 1:250,000 digital contour lines and elevation points in China released by national basic geographic information center, and the DEM data set of heihe river basin is generated by the nearest neighbor method resampling method of ARCGIS spatial analysis module with a spatial resolution of 30 SEC.
National Basic Geographic Information Center
Ⅰ. Overview This dataset is derived from the global 30m-resolution digital elevation product dataset, which is processed using the data of the first version (v1) of ASTER GDEM. Its spatial resolution is 30m. Due to the influence of clouds, lines, pits, bulges, dams or other anomalies generated by the boundary stacking, there are local anomalies in the first version of the original data of ASTER GDEM, so the digital elevation processed by ASTER GDEM v1 Data products have data anomalies in individual areas, and users need to pay attention to them during use. In addition, this data set can complement the SRTM global 90m resolution elevation dataset. Ⅱ. Data processing description ASTER GDEM is a fully automated method to process and generate ASTER archived data of 1.5 million scenes, including 1,264,118 ASTER DEM data based on independent scenes generated through stereo correlation. After de-cloud processing, residual outliers are removed, and the average value is taken as the final pixel value of ASTER GDEM object area. After correcting the remaining abnormal data, the global ASTER GDEM data was generated by 1°× 1° sharding. Ⅲ. Data content description The dataset covers the entire upper reaches of the Yellow River, and each data file name is generated based on the latitude and longitude of the lower left (southwest) Angle of the fractal geometry center. For example, the lower-left coordinate of the ASTGTM_N40E116 file is 40 degrees north latitude and 116 degrees east longitude. ASTGTM_N40E116_dem and ASTGTM_N40E116_num correspond to digital elevation model (DEM) and quality control (QA) data, respectively. Ⅳ. Data usage description ASTER GDEM data can be calculated and visualized. It has a broad application prospect in various fields, especially in mapping, surface deformation and military fields.Specifically, it mainly includes the following aspects: In scientific research, ASTER GDEM data plays an important role in geology, geophysics, seismic research, horizontal modeling, volcano monitoring and remote sensing image registration.The three-dimensional model of the ground is built by using high-precision digital terrain elevation data, which can be embedded and superimposed with the image of the ground to observe subtle changes of the earth surface. In civil and industrial applications, ASTER GDEM data can be used for civil engineering calculation, dam site selection, land use planning, etc. In communications, digital topographic data can help businesses build better broadcast towers and determine the best location of mobile phone booths.In terms of aviation safety, ASTER GDEM digital elevation data can be used to establish the enhanced aircraft landing alarm system, which greatly improves the aircraft landing safety coefficient. In the military, ASTER GDEM data is the basic information platform of C4ISR (army automatic command system), which is indispensable in the study of battlefield regional structure, combat direction, battlefield preset, combat deployment, troop concentration in projection, protection conditions, logistics support and other aspects.
XUE Xian, DU Heqiang
Ⅰ. Overview The SRTM (Space Shuttle Radar Topographic Mapping Mission) was performed by NASA, the Geospatial Intelligence Agency, and German and Italian space agencies in February 2002. A total of 222 hours and 23 minutes of data collection was performed by the US space shuttle Endeavour onboard the SRTM system, and 9.8 trillion bytes of radar images were collected between 60 degrees in North America and 56 degrees in south latitude with an area of more than 119 million km2 Data, Fei changed more than 80% of the earth's surface, this data set covers the entire territory of China. It took two years to process, and finally obtained a global digital elevation model (DEM) with a plane longitude of ± 20m and an elevation longitude of ± 16m. Ⅱ. Data processing description The processing of SRTM data is done by the Ground Data Processing System (GDPS). The GDPS consists of three parts: (1) an interferometric processor, which uses the interferometric processor to convert the data into elevation maps and radar image bands; (2) a mosaic processor, which is used to compile collected global airborne data Draw a mosaic map of continental elevation data and images; (3) Verification system is responsible for checking the quality of the mosaic map and providing accuracy maps. These processors are currently installed on JPL workstations, and the next step is to install them on a set of supercomputers for the systematic processing of real SRTM data. As this work progresses, JPL will release auxiliary data to the work. Ⅲ. Data content description SRTM data provides a file for each latitude and longitude grid. There are two types of longitude: 1 arc-second and 3 arc-second. Called SRTM1 and SRTM3, or 30m and 90m data. This dataset uses SRTM3 data with 90m resolution. Each file contains elevation data of 1201 × 1201 sampling points. The data format is DEM format. The spatial position of each picture frame is shown in the attached picture (1-25 thousand pictures in the country). Ⅳ. Data usage description SRTM data has computable and visual functions, and has broad application prospects in various fields, especially in the fields of surveying and mapping, surface deformation, and military. Specifically, it mainly includes the following aspects: In scientific research, SRTM data plays a very important role in geology, geophysics, seismic research, level modeling, volcano monitoring, and registration of remote sensing images. Using high-precision digital terrain elevation data to build a three-dimensional three-dimensional model of the ground, which is superimposed on the ground image, can observe slight changes in the earth's surface. In civil and industrial applications, SRTM data can be used for civil engineering calculations, reservoir dam site selection, land use planning, etc. In terms of communications, digital terrain data can help businesses build better broadcast towers and determine the best In terms of aviation safety, the use of SRTM digital elevation data can establish an enhanced aircraft landing alarm system, which greatly improves the aircraft landing safety factor. In the military, SRTM data is the basic information platform of C4ISR (Army Automatic Command System). It is necessary to study the structure of the battlefield, the direction of the battlefield, the presetting of the battlefield, the deployment of operations, the concentration of forces in the delivery, the protection conditions, and logistics support Essential.
XUE Xian, DU Heqiang
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. Heihe River Basin water system map is one of the hydrological and water resources part of the atlas, with a scale of 1:2500000, positive axis isometric conic projection and standard latitude of 25 47 n. Data sources: river data of Heihe River Basin, reservoir distribution data of Heihe River Basin, residential area data of Heihe River Basin in 2009, administrative boundary data of one million Heihe River Basin in 2008, Lake data of Heihe River Basin and other basic geographic data. The upper reaches of Heihe River Basin are located in Qilian County, Haibei Tibetan Autonomous Prefecture, Qinghai Province, and the northern foot of Qilian Mountain in Zhangye, Jiuquan City, Sunan and Subei counties of Gansu Province. The middle reaches are located in Shandan, Minle, Ganzhou, Linze, Gaotai, Sunan, Suzhou, Jiayuguan and Yumen counties of Gansu Province. The lower reaches are located in Jinta, Gansu Province, Ejina Banner and Alxa Right Banner of Inner Mongolia, involving three provinces (autonomous regions), 16 cities and counties (District, banner), 56 towns, 45 townships and 4 Sumu. Table 1 shows the information about the administrative divisions of Heihe River Basin.
WANG Jianhua, ZHAO Jun, WANG Xiaomin, FENG Bin
Based on the field survey results of this project, the previous hydrogeological survey results and the prediction and judgment of desert depressions, we obtained more than 600 known water level points in badain jaran desert and its surrounding areas, and drew a first-order approximate contour map of the groundwater level in badan jaran desert by using the measured or predicted groundwater level data.This isometric chart fills a gap in the study of groundwater in badain jaran desert. The so-called first-order approximation is the distribution of the macroscopic groundwater level, which reaches a resolution of 1 km on the spatial scale, and it is assumed that the groundwater level in the shallow and deep layers is the same, and the groundwater in the quaternary and bedrock distribution areas remains continuous.The error level of the first-order approximate contour is ± 10 m, which mainly comes from the uncertainty of ground elevation data. This data set contains a vector diagram of the groundwater level contour line and a raster data file.
WANG Xusheng, HU Xiaonong
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
From 2012 to 2013, the geomorphic surface near the Zhengyi gorge in the middle reaches of the Heihe River was investigated, mainly including the 4-level river terrace. The data are mainly obtained through field investigation, and analyzed and mapped indoors to obtain the distribution map of geomorphic surface at all levels near the middle reaches of Zhengyi gorge.
HU Xiaofei, PAN Baotian
The dataset contains all individual glacial storage (unit: km3) over the Qinghai-Tibetan Plateau in 1970s and 2000s. It is sourced from the resultant data of the paper entitled "Consolidating the Randolph Glacier Inventory and the Glacier Inventory of China over the Qinghai-Tibetan Plateau and Investigating Glacier Changes Since the mid-20th Century". The first draft of this paper has been completed and is planned to be submitted to Earth System Science Data journal. The baseline glacier inventories in 1970s and 2000s are the Randolph Glacier Inventory 4.0 dataset, and the Glacier Inventory of China, respectively. Based on the individual glacial boundaries extracted from the above-mentioned two datasets, the grid-based bedrock elevation dataset (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m), and the glacier surface elevation obtained by a slope-dependent method, the individual glacier volumes in 1970s and 2000s are then calculated. In addition, the calculated results of individual glacier volumes in this study have been compared and verified with the existent results of several glacier volumes, relevant remote sensing datasets, and the global glacier thickness dataset based on the average of multiple glacier model outputs (https://www.research-collection.ethz.ch/handle/20.500.11850/315707, doi:10.3929/ethz-b-000315707), and the errors in the calculations have also been quantified. The established dataset in this study is expected to provide the data basis for the future regional water resources estimation and glacier ablation-involved researches. Moreover, the acquisition of the data also provides a new idea for the future glacier storage estimation.
HU Xiaofei, PAN Baotian
ASTER Global Digital Elevation Model (ASTER GDEM) is a global digital elevation data product jointly released by National Aeronautics and Space Administration (NASA) and Japan's Ministry of Economy, Trade and Industry (METI) .The DEM data is based on the observation results of the new generation of Earth observation satellite TERRA Completed, it is produced by 1.3 million stereo pair data collected by ASTER (Advanced Space borne Thermal Emission and Reflection Radio meter) sensors, and its coverage area exceeds 99% of the earth's land surface. The data has a horizontal accuracy of 30 meters (95% confidence) and an elevation accuracy of 20 meters (95% confidence). This data is the third global elevation data, which is a significant improvement over the previous SRTM3 DEM and GTOPO30 data. ASTER GDEM released two versions. The first version was released in June 2009 and the second version was released in October 2011. Compared with the first version, the second version has make further progress in water coverage and deviation removal. The quality of the data has been greatly improved. This dataset is the second version of the ASTER GDEM dataset in the Shule River Basin, including DEM, mountain shadow, slope, and aspect data. Spatial resolution: 1 radian second (about 30 meters), accuracy: vertical accuracy of 20 meters, horizontal accuracy of 30 meters.
National Aeronautics and Space Administration
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.
CGIAR-CSI
A total of 137 soil samples of different vegetation types, different altitudes and different terrains were collected from June 2012 to August 2012. The soil layer of each sample point was divided into three layers of 0-10cm, 10-20cm and 20-30cm, with an altitude of 2700-3500m m. The vegetation types were divided into five types: Picea crassifolia forest, Sabina przewalskii, subalpine scrub meadow, grassland and dry grassland. At the same time of sampling, hand-held GPS is used to record the location information and environmental information of each sampling point, including longitude, latitude, altitude, slope, aspect, terrain curvature, vegetation type, soil thickness, maximum root depth, etc. Soil bulk density: The measurement method of soil bulk density is to put the sample into an envelope and dry it in an oven at 105℃ for 24 hours, then take it out and place it for 30 minutes to weigh. The ratio of the weighing result to the volume of the ring cutter is the soil bulk density, and the unit is g/cm3. Soil mechanical composition: hydrometer method is used to measure the soil mechanical composition, which includes the content of soil sand, silt and clay.
ZHAO Chuanyan, MA Wenying
SRTM (Shuttle Radar Topography Mission) is by NASA and the national geospatial intelligence agency (NGA) cooperation to build the global 3 d graphics data project.In February 2000, the SRTM system mounted on the U.S. space shuttle endeavour collected radar image data between latitude 60 ° north and latitude 57 ° south, and acquired radar image data covering more than 80% of the world's land surface.After more than two years of processing, the digital terrain elevation model was made. This data set including the heihe river basin SRTM points picture and Mosaic two kinds of data, and the points of the graph is SRTM version 4 data by the CGIAR - CSI (international centre for tropical agriculture, http://srtm.csi.cgiar.org/) treatment, compared with the previous version has greatly improved, including: 1) use a lot of interpolation algorithm, 2) use more auxiliary DEM data to fill the blank spots and blank area, 3) compared with the third version of the data and migration half a yuan.The Mosaic map is obtained by splicing on the basis of sub-map. The sub-charts include srtm_56_04,srtm_56_05,srtm_57_04 and srtm_57_054. The data are 16 bit values representing the elevation value (-/+/32767 m). The maximum positive elevation is 9000 m and the maximum negative elevation is 12,000 m below sea level.Null data is identified by -32767.Divide the file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) per 5 latitude and longitude squares.
TYLER B. STEVENS
The data set includes ASTER GDEM data and its Mosaic. ASTER Global DEM (ASTER GDEM) is a Global digital elevation data product jointly released by NASA and Japan's ministry of economy, trade and industry (METI) on June 29, 2009. The DEM data is based on the observation results of NASA's new earth observation satellite TERRA.It is produced by the ASTER(Advanced Space borne Thermal Emission and Reflection Radio meter) sensor, which collects 1.3 million stereo image data, covering more than 99% of the earth's land surface.The data has a horizontal accuracy of 30 m (95% confidence) and an elevation accuracy of 7-14 m (95% confidence).This data is the third global elevation data, which is significantly higher than previous SRTM3 DEM and GTOPO30 data. We from NASA's web site (http://wist.echo.nasa.gov/api) to download the data of heihe river basin, and through the data center to distribute.The data distributed by the center completely retains the original appearance of the data without any modification to the data.If users need details about ASTER GDEM preparation process, please refer to the data documents of metadata connections, or visit http://www.ersdac.or.jp/GDEM/E/3.html or directly from https://lpdaac.usgs.gov/ reading and ASTER Global DEM related documents. ASTER GDEM is divided into several data blocks of 1×1 degree in distribution, and the distribution format is zip compression format. Each compressed file includes three files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif reademe.pdf Where xx is the starting latitude and yyy is the starting longitude._dem. Tif is the dem data file, _num. Tif is the data quality file, and reademe is the data description file. In order to facilitate users to use the data, on the basis of the fractional ASTER GDEM data, we splice fractional SRTM data to prepare the ASTER GDEM Mosaic map of the black river basin, which retains all the original features of ASTER GDEM without any resamulation. This data includes two files: heihe_aster_gdem_mosaic_dem.img Heihe_Aster_GDEM_Mosaic_num. Img The data is stored in the format of Erdas image, where the file _dem.img is the dem data file and the file _num. Img is the data quality file.
National Aeronautics and Space Administration
I. Overview This data set contains the terrain data, soil data, meteorological data, land use data, NDVI data, etc. required for the operation of the IWEMS model. All maps and relevant point coordinates (weather stations) use the isometric projection UTM / WGS94 coordinate system. Ⅱ. Data processing description All maps and related point coordinates (weather stations) use the isometric projection UTM / WGS84 coordinate system. Ⅲ. Data content description The data content mainly includes: The basic terrain data includes the Cuneiform Desert (DEM) and the river network. The river network is used as the boundary for wind and sand transmission. The size of the DEM grid is 250 * 250 m. The river network was extracted using the ASTER-GDEM terrain data with the river burning method. Soil data, including soil physics, chemistry, and spatial distribution of soil types. It is derived from 1: 1 million soil database of China and converted to ESRI-grid format with a grid size of 250 * 250 m. Meteorological data, including daily data from Baotou, Dongsheng and Linhe meteorological stations around the Kubuqi Desert, from 2002 to 2010. Includes precipitation, wind speed and wind direction data. Land use data, 2000 land use data, scale is 1: 100,000. Convert it to ESRI-grid format with a grid size of 250 * 250 m. Ⅳ. Data usage description Evaluate wind and sand hazards along the Yellow River, estimate the amount of wind and sand entering the upper reaches of the Yellow River, and provide data support for establishing an early warning system for wind and sand hazards in the region.
XUE Xian, DU Heqiang
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The Shiyang River Basin Information System thematic data set is one of the results of the technical assistance project “Optimization of Desertification Control in Gansu Province” assisted by the Asian Development Bank, including 5 folders including document, investigation_point, maps, photo, and spatial. Each file The folder contains several files. The document folder includes the target design, data processing, thematic summary report, and projection information.The gpspoint folder includes files recorded in shapefile point format sampled by gps according to different purposes.The maps folder contains Chinese, english, and fonts files. Folder, the first two folders represent 14 Chinese and English maps stored in A4 format and pdf format, and fonts contain some special fonts: the photo folder contains field survey digital photos stored in bmp format: spatial The folder contains the dem folder of the digital elevation model, the gansu folder of the outline map of Gansu Province and the Hexi Corridor, the generate folder of the site data file shapefile, the grid folder of the raster data of various geographic features, and the remote sensing image. image folder, meteoHydro folder for original site text data, and vector folder for vector data for various geographic features. The data includes: 1. DEM folder: 100m dem, hillshade, divided into GRID and geotif formats 2. Gansu folder: Gansu border, Hexi border 3. Grid folder: NDVI (vegetation index), lndchange (land transfer matrix), landscape86 (land landscape map in 86 years), landscape2k (land landscape map in 2000), Desertiftype (desert type landscape map), Desersevrt (desert type map ), Annprecip 4. Meteohydro folder: Minqin, Wuwei, Yongchang meteorological data (1) daily daily observation items: Airpress (humidity), Precipitation (radiation), Sunlight (sunlight), Temperature (temperature) ), Wind (wind speed) (2) Months (monthly): Airpress (air pressure), Humidity (humidity), Rain (precipitation), Sunlight (sunlight), Temperature (temperature), Wind (wind speed) (3) tendays: Airpress, Humidity, Rain, Sunlight, Temperature, Wind (4) years (year by year): Precipitation, Temperature 5. Vectro folder: (1) Admwhole (county boundary map), (2) Lake (lake), (3) Hydrasta (hydrological site), (4) Basin (watershed boundary), (5) Landscape2000 (land use 200 (Year), (6) landscape86 (land use 1986), (7) Meteosta (meteorological station), (8) Lakep (reservoir point), (9) Place (residential point), (10) Rainfallcontour (railway), ( 11) Rainfallcontour (rainfall contour map), (12) Road (highway), (13) Stream (water system map), (14) Town (county name), (15) Township (county township boundary), (16) Vegetation (vegetation map) Data projection information: PROJCS ["Albers", GEOGCS ["GCS_Krasovsky_1940", DATUM ["Not_specified_based_on_Krassowsky_1940_ellipsoid", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]] For detailed data description, please refer to the data file
LI Xin
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