This is the MODIS data with 499 scenes covering the whole Heihe River basin in 2008 and 2009. The acquisition time is from 2008-04-23 to 2008-09-30 (295 scenes), and from 2009-05-01 to 2009-10-01 (204 scenes). MODIS data products have 36 channels with resolutions of 250m, 500m and 1000m respectively. The data format is pds, unprocessed, and the MODIS processing software is filed together with the original data. MODIS remote sensing data of Heihe Integrated Remote Sensing Joint Test are provided by Gansu Meteorological Bureau.
Gansu Meteorological Bureau
The data set is from February 24, 2000 to December 31, 2004, with a resolution of 0.05 degrees, MODIS data, and the data format is .hdf. It can be opened with HDFView. The data quality is good. The missing dates are as follows: 2000 1 -54 132 219-230 303 2001 111 167-182 2002 079-086 099 105 2003 123 324 351-358 2004 219 349 The number after the year is the nth day of the year Pixel values are as follows: 0: Snow-free land 1-100: Percent snow in cell 111: Night 252: Antarctica 253: Data not mapped 254: Open water (ocean) 255: Fill An example of file naming is as follows: Example: "MOD10C1.A2003121.004.2003142152431.hdf" Where: MOD = MODIS / Terra 2003 = Year of data acquisition 121 = Julian date of data acquisition (day 121) 004 = Version of data type (Version 4) 2003 = Year of production (2003) 142 = Julian date of production (day 142) 152431 = Hour / minute / second of production in GMT (15:24:31) The corner coordinates are: Corner Coordinates: Upper Left (70.0000000, 54.0000000) Lower Left (70.0000000, 3.0000000) Upper Right (138.0000000, 54.0000000) Lower Right (138.0000000, 3.0000000) Among them, Upper Left is the upper left corner, Lower Left is the lower left corner, Upper Right is the upper right corner, and Lower Right is the lower right corner. The number of data rows and columns is 1360, 1020 Geographical latitude and longitude coordinates, the specific information is as follows: Coordinate System is: GEOGCS ["Unknown datum based upon the Clarke 1866 ellipsoid", DATUM ["Not specified (based on Clarke 1866 spheroid)", SPHEROID ["Clarke 1866", 6378206.4,294.9786982139006, AUTHORITY ["EPSG", "7008"]]], PRIMEM ["Greenwich", 0], UNIT ["degree", 0.0174532925199433]] Origin = (70.000000000000000, 54.000000000000000)
National Snow and Ice Data Center（NSIDC）
This dataset includes passive microwave remote sensing brightness temperatures data for longitude and latitude projections and 0.25 degree resolution from 2002 to 2008 in China. 1. Data processing process: NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. 2. Data format: Brightness temperature files: two-byte unsigned integers, little-endian byte order Time files: two-byte signed integers, little-endian byte order 3. Data naming: ID2rx-AMSRE-aayyyydddp.vnn.ccc (China-ID2r1-AMSRE-D.252002170A.v03.06V) ID2 Inverse Distance Squared r1 Resolution 1 swath input data AMSRE Identifies this an AMSR-E file D.25 Identifies this as a quarter degree file yyyy Four-digit year ddd Three-digit day of year p Pass direction (A = ascending, D = descending) vnn Gridded data version number (for example, v01, v02, v03) ccc AMSR-E channel indicator: numeric frequency (06, 10, 18, 23, 36, or 89) followed by polarization (H or V) 4. Cutting range: Corner Coordinates: Upper Left (60.0000000, 55.0000000) (60d 0'0.00 "E, 55d 0'0.00" N) Lower Left (60.0000000, 15.0000000) (60d 0'0.00 "E, 15d 0'0.00" N) Upper Right (140.0000000, 55.0000000) (140d 0'0.00 "E, 55d 0'0.00" N) Lower Right (140.0000000, 15.0000000) (140d 0'0.00 "E, 15d 0'0.00" N) Center (100.0000000, 35.0000000) (100d 0'0.00 "E, 35d 0'0.00" N) Origin = (60.000000000000000, 55.000000000000000) 5. Data projection: GEOGCS ["WGS 84", DATUM ["WGS_1984", SPHEROID ["WGS 84", 6378137,298.257223563, AUTHORITY ["EPSG", "7030"]], TOWGS84 [0,0,0,0,0,0,0], AUTHORITY ["EPSG", "6326"]], PRIMEM ["Greenwich", 0, AUTHORITY ["EPSG", "8901"]], UNIT ["degree", 0.0174532925199433, AUTHORITY ["EPSG", "9108"]], AUTHORITY ["EPSG", "4326"]]
Mary Jo Brodzik Matthew Savoie Richard Armstrong Ken Knowles
The dataset of CMA operational meteorological stations observations in the Heihe river basin were provided by Gansu Meteorological Administration and Qinghai Meteorological Administration. It included: (1) Diurnal precipitation, sunshine, evaporation, the wind speed, the air temperature and air humidity (2, 8, 14 and 20 o'clock) in Mazongshan, Yumen touwnship, Dingxin, Jinta, Jiuquan, Gaotai, Linze, Sunan, Zhangye, Mingle, Shandan and Yongchang in Gansu province (2) the wind direction and speed, the temperature and the dew-point spread (8 and 20 o'clock; 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 and 50hpa) in Jiuquan, Zhangye and Mingqin in Gansu province and Golmud, Doulan and Xining in Qinghai province (3) the surface temperature, the dew point, the air pressure, the voltage transformation (3 hours and 24 hours), the weather phenomena (the present and the past), variable temperatures, visibility, cloudage, the wind direction and speed, precipitation within six hours and unusual weather in Jiuquan, Sunan, Jinta, Dingxin, Mingle, Zhangye, Gaotai, Shandan, Linze, Yongchang and Mingqin in Gansu province and Tuole, Yeniugao, Qilian, Menyuan, Xining, Gangcha and Huangyuan in Qinhai province.
Gansu meteorological bureau Qinghai Meteorological Bureau
I. Overview The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. Ⅱ. Data processing description The VEGETATION sensor was launched by SPOT-4 in March 1998, and has received SP0T VGT data for global vegetation coverage observation since April 1998. It has a very complete and efficient image ground processing mechanism system. The VEGETATION data is mainly received by the Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides related parameters (such as calibration coefficients). Finally, the image processing and archiving center of VITO Institute in Belgium Global VEGETATION data archiving and user orders. Among them, VGT-P (prototype) data products mainly provide scientific researchers with high-quality physical quantity prototype data in order to facilitate their research and development of algorithms and application models. The data undergoes strict systematic error correction and resampling into a longitude and latitude network projection, the pixel resolution is lkm, and the pixel brightness value is the reflectivity of the ground features on the top layer of the atmosphere. In addition to providing four bands of raw data, relevant auxiliary parameters such as atmospheric conditions, system information (solar zenith angle, azimuth, field of view, and reception time) and terrain data are also provided according to user needs. VGT-S (synthesis) products provide atmospheric-corrected surface reflectance data, and use multi-band synthesis techniques to obtain a normalized vegetation index (w) data set with lkm resolution. VGI-S products include the spectral reflectance and NDVI data set (s1) of four bands synthesized daily, the spectral reflectance of four bands synthesized every 10 days, and the maximum NDVI data set (S10) every 10 days to reduce cloud and The impact of BRDF, while S10 was also resampled into 4km resolution (S10.4) and 8km resolution (S10.8) datasets. VGT-S products are widely used for their high time resolution. This data set contains the spectral reflectance of four bands synthesized every 10 days and the 10-day maximized NDVI data set (S10). The pre-processing of SPOT source data includes atmospheric correction, radiation correction, and geometric correction. NDVI data with a maximum of 10 days of synthesis is generated, and the values of -1 to -0.1 are set to -0.1, and then formula YDN = (JNDVI +0.1) /0.004 Convert to a YDN value from 0 to 250. Ⅲ. Data content description The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. The SPOT-VEGETATION-NDVI data set contains .zip compressed files with time resolution from April 1, 1998 to December 31, 2011. After decompression, it is an ESRI-GRID file with a scene every 10 days. The SPO-VEGETATION-NDVI data set naming rules are: v-yymmdd, where v is the abbreviation of vegetation, yymmdd represents the date of the file, and is the main identifier that distinguishes other files. Ⅳ. Data usage description An important feature of the Vegetation Index product is that it can be converted into leaf crown biophysical parameters. Vegetation index (VI) also plays an "intermediate variable" in the acquisition of vegetation biophysical parameters (such as foliar index LAI, green shade, fAPAR, etc.). The relationship between vegetation indices and vegetation biophysical parameters is currently being studied using globally representative ground, aircraft and satellite observation datasets. These data can be used to evaluate the performance of the VI algorithm before satellite launch, and also provide the conversion coefficient between the vegetation index product and the biophysical characteristics of the leaf crown. The use of biophysical data is part of the Vegetation Index Verification Program. Vegetation index products will play a major role in several Earth Observation System (EOS) studies and are also part of global and regional biosphere model products in recent years.
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
Field description: Num_code (Frozen soil attribute code) Combo (Permafrost properties) extent (Extent of frozen ground) content (Ice content) Attributes comparison are as follows: (1) Comparison table of frozen soil properties: 0 (No information) 1 - chf (Continuous permafrost extent with high ground ice content and thick overburden) 2 - dhf (Discontinuous permafrost extent with high ground ice content and thick overburden) 3 - shf (Sporadic permafrost extent with high ground ice content and thick overburden) 4 - ihf (Isolated patches of permafrost extent with high ground ice content and thick overburden) 5 - cmf (Continuous permafrost extent with medium ground ice content and thick overburden) 6 - dmf (Discontinuous permafrost extent with medium ground ice content and thick overburden) 7 - smf (Sporadic permafrost extent with medium ground ice content and thick overburden) 8 - imf (Isolated patches of permafrost extent with medium ground ice content and thick overburden) 9 - clf (Continuous permafrost extent with low ground ice content and thick overburden) 10 - dlf (Discontinuous permafrost extent with low ground ice content and thick overburden) 11 - slf (Sporadic permafrost extent with low ground ice content and thick overburden) 12 - ilf (Isolated patches of permafrost extent with low ground ice content and thick overburden) 13 - chr (Continuous permafrost extent with high ground ice content and thin overburden and exposed bedrock) 14 - dhr (Discontinuous permafrost extent with high ground ice content and thin overburden and exposed bedrock) 15 - shr (Sporadic permafrost extent with high ground ice content and thin overburden and exposed bedrock) 16 - ihr (Isolated patches of permafrost extent with high ground ice content and thin overburden and exposed bedrock) 17 - clr (Continuous permafrost extent with low ground ice content and thin overburden and exposed bedrock) 18 - dlr (Discontinuous permafrost extent with low ground ice content and thin overburden and exposed bedrock) 19 - slr (Sporadic permafrost extent with low ground ice content and thin overburden and exposed bedrock) 20 - ilr (Isolated patches of permafrost extent with low ground ice content and thin overburden and exposed bedrock) 21 - g (Glaciers) 22 - r (Relict permafrost) 23 - l (Inland lakes) 24 - o (Ocean/inland seas) 25 - ld (Land) (2) Comparison table of frozen soil scope c = continuous (90-100%) d = discontinuous (50- 90%) s = sporadic (10- 50%) i = isolated patches (0 - 10%) (3) Ice content comparison table h = high (>20% for "f" landform codes) (>10% for "r" landform codes) m = medium (10-20%) l = low (0-10%)
National Snow and Ice Data Center（NSIDC） WU Lizong
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan ZHUANG Dafang WANG Jianhua ZHOU Wancun WU Shixin
Reservoir refers to the artificial water area formed in valley, river or low-lying area by dam, dike, sluice, weir and other projects. It is the main measure used for runoff regulation to change the distribution process of natural water resources and plays an important role in social and economic development. Many reservoirs have been built in Heihe River Basin, which has an important impact on the utilization of water resources in this area. In order to facilitate the mapping needs of users, we use topographic map and remote sensing image to prepare the reservoir distribution map of the Heihe River Basin. The location and shape of the reservoir are mainly obtained by manual interpretation based on Google map image, which basically shows the current situation of the reservoir distribution in the Heihe River Basin around 2010.
National Basic Geographic Information Center
On July 23, 1972, the United States launched the world's first resource satellite "Landsat 1" , and Landsat 2 and Landsat 3 were launched in the following 10 years. These three satellites were the first generation of resource satellites. They were equipped withreturn-beam vidicon cameras and multi-spectral scanners (MSS) with 3 and 4 spectral segments respectively, a resolution of 79m and a width of 185Km. There are 28 scenes of MSS data in Heihe River Basin currently which were obtained on the following dates: 1972-10-14, 1972-10-30, 1973-01-10, 1973-01-31, 1973-02-16, 1973-06-04, 1973. -10-07, 1973-10-28 (2 scenes), 1973-12-22, 1974-01-05, 1975-10-07, 1975-10-09, 1976-07-04, 1976-10-18 , 1976-11-07, 1976-11-27, 1976-12-30, 1977-01-19, 1977-02-07, 1977-04-20, 1977-05-06 (2 scenes), 1977-05 -08, 1977-06-10, 1977-06-29, 1977-07-18, 1978-10-09. Ortho rectification was performed on the images.
LP DAAC User Services
Data overview: This set of data mainly includes six prefecture level cities and 16 counties (Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugu Autonomous County, Jinta County, Subei Mongolian Autonomous County, Suzhou District, Yumen City, Jiayuguan City, Yongchang County, Qilian County, Alxa Left Banner, Ejina Banner, Alxa Right Banner) in Heihe River Basin ）The 12 social and economic data are: GDP, output value of primary industry, output value of secondary industry, output value of tertiary industry, per capita GDP, per capita disposable income of urban residents, per capita net income of rural residents, fixed asset investment, total retail sales of social consumer goods, fiscal revenue, fiscal expenditure, and total grain output (including all kinds of work) Output of the product). It is divided into county level and township level. The data period is 2000-2009.
ZHAO Jun
Railway distribution map is the basic data in the mapping process. In order to facilitate the use of users, we compiled the railway data set of Heihe River basin according to the railway data set distributed by the National Basic Geographic Information Center, the atlas of Gansu Province compiled by the Gansu Provincial map Geographic Information Center, the sky map and Guge map published by the China Surveying and Mapping Bureau. This data basically reflects the distribution of Railways around the Heihe River basin around 2010. The national standard of data classification and coding of national basic geographic information system - Classification and code of basic land information data (GB / T 13923-92) is adopted for railway coding, and the code is five digit code (National Basic Geographic Information Center 2010).
National Basic Geographic Information Center
Eo-1 (Earth Observing Mission) is a new Earth Observing satellite developed by NASA to replace Landsat7 in the 21st century. It was launched on November 21, 2000.The orbit of eo-1 satellite is basically the same as that of Landsat7, which is a solar synchronous orbit with an orbital altitude of 705km and an inclination Angle of 98.7°, which is 1min less than that of Landsat7 and crosses the equator.On board of EO 1 3 kinds of sensors, namely, the Advanced Land Imager (ALI (the Advanced Land Imager), atmospheric correction instrument AC (Atmosp heric Corrector) and compose a specular as spectrometer (Hyperion), Hyperion sensor is first spaceborne hyperspectral mapping measurement instrument, the hyperspectral data a total of 242 bands, spectral range is 400 ~ 2500 nm, spectral resolution up to 10 nm, ground resolution of 30 m. Currently, there are 6 scenes of eo-1 Hyperion data in heihe river basin.The coverage and acquisition time were: 4 scenes in the encrypted observation area of zhangye urban area + yingke oasis encrypted observation area (2007-09-10, 2008-05-12, 2008-05-20, 2008-07-15).Two scenes of the iceditch watershed observation area were encrypted, the time was 2008-03-17, 2008-03-22, respectively. Product grade is L1 without geometric correction. The eo-1 Hyperion remote sensing data set of heihe integrated remote sensing joint experiment was acquired by researcher wang jian and Beijing normal university through purchase. (note: "+" represents simultaneous coverage)
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The spot satellite series in France consists of five stars, of which spot 5 is the best. It was launched in May 2002, with a height of 830km, an orbit inclination of 98.7 degrees, and a sun synchronous quasi regression orbit, with a regression period of 26 days. Linear array sensor (CCD) and push scan scanning technology were used for imaging. SPOT5 satellite carries two high-resolution geometric imagers (HRG), one high-resolution Stereo Imager (HRS) and one wide field vegetation detector (VGT). It has five working bands, multi spectral band spatial resolution is 10m (short wave infrared spatial resolution is 20m), panchromatic band spatial resolution is 2.5m. At present, there are three spots of SPOT5 data in Heihe River Basin. The coverage and acquisition time are respectively: 1 scene in Linze area, including multispectral image with resolution of 10m and panchromatic image with resolution of 2.5m, with time of 2008-07-04; 1 scene in Zhangye City, with resolution of 2.5m, with time of 2008-03-29; 1 scene of multispectral data with resolution of 10m, with time of 2008-08-10. The product level is L1, and the product has undergone rough geometric correction. SPOT5 image is mainly used as the base map of geometric precision correction in Heihe experiment. The spot 5 remote sensing data set of Heihe comprehensive remote sensing joint experiment was purchased by Beijing Normal University.
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The dataset is the HWSD Soil texture data set of the qaidam basin. The data is from the Harmonized World Soil Database (HWSD) constructed by the United Nations food and agriculture organization (FAO) and Vienna institute for international applied systems (IIASA), which was released in version 1.1 on March 26, 2009.The data resolution is 1km.The main soil classification system adopted is fao-90.The main fields in the soil property list include SU_SYM90 (soil name in the FAO90 soil classification system) SU_SYM85(FAO85 classification) T_TEXTURE(top layer soil texture) (19.5);ROOTS: String(deep classification of obstacles to the bottom of the soil);SWR: String (soil moisture content characteristics);ADD_PROP: Real (specific type of soil in a soil unit related to an agricultural use);T_GRAVEL: Real (percent by volume);T_SAND: Real;T_SILT: Real (silt content);T_CLAY: Real;T_USDA_TEX: Real (USDA soil texture classification);T_REF_BULK: Real (soil bulk density);T_OC: Real (organic carbon content);T_PH_H2O: Real T_CEC_CLAY: Real;T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation);T_TEB: Real (commutative base);T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content);T_ESP: Real (exchangeable sodium);T_ECE: Real.The attribute field beginning with T_ represents the upper soil attribute (0-30cm), and the attribute field beginning with S_ represents the lower soil attribute (30-100cm) (FAO 2009).This data can provide model input parameters for earth system modelers, and agricultural perspectives can be used to study eco-agricultural zoning, food security and climate change.
Food and Agriculture Organization of the United Nations（FAO）
The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient number). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. The dataset is a long-time series vegetation index dataset of Qinghai Lake Basin, which is mainly aimed at normalized difference vegetation index (NDVI). It includes spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI for 10 days, with a spatial resolution of 1km and a temporal resolution of 10 days.
Flemish Institute for Technological Research (VITO)
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
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
