The research project on land surface data assimilation system in western China belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation. the person in charge is Li Xin, researcher of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. One of the data collected in this project is the reanalysis data of surface climate factors in western China in 2002. This data set is generated based on the daily 1 × 1 provided by the National Environmental Prediction Center (NCEP). However, the re-analysis of the data has the following problems: (1) the temporal and spatial resolution is not high enough (the horizontal resolution is 1 degree and the time is 6 hours); (2) The low-level errors in plateau areas are large; (3) The data are standard isosurface data and need interpolation. The 2002 reanalysis data set of surface climate elements in western China was generated by combining NCEP reanalysis data and MM5 model by Dr. Longxiao and Professor Qiu Chongjian of Lanzhou University using Newton relaxation data assimilation method (Nudging), including 10m horizontal and vertical wind speed (m/s), 2m air temperature (k), 2m mixing ratio, surface pressure (Pa), upstream and downstream short wave and long wave radiation (w/m2), convective precipitation and large scale precipitation (mm/s) at 0.25 degree per hour throughout 2002. I. preparation background The quality of the driving data seriously affects the ability of the land surface model to simulate the land surface state, so a very important component of the land surface modeling research is the driving data used to drive the land surface model. No matter how realistic these models are in describing the surface process, no matter how accurate the boundary and initial conditions they input, if the driving data are not accurate, they cannot get the results close to reality. Land surface models are so dependent on the quality of externally provided data that any error in these externally provided data will seriously affect the ability of land surface models to simulate soil moisture, runoff, snow cover and latent heat flux. These externally provided data include: precipitation, radiation, temperature, wind field, humidity and pressure. The 2002 reanalysis data set of surface climate elements in western China uses Newton relaxation data assimilation method (Nudging) to combine NCEP reanalysis data and MM5 model to generate driving data with higher spatial and temporal resolution suitable for complex terrain in western China. Second, the basic parameters of the operation mode 1. Using the US PSU/NCAR mesoscale model MM5 as a simulation model; The selection of simulation grid domain: center (32°N, 90°E), grid distance of 36km, number of horizontal grid points of 131*151, vertical resolution of 25 layers, and mode top of 100hPa；; 2. The data used for initialization are 1 * 1 GRIB grid data of NCEP in the United States. 3. The time step is 120s. Third, the physical process 1. physical process treatment of cloud and precipitation: Grell cumulus cloud parameterization scheme is adopted for sub-grid scale precipitation, and Reisner mixed phase microphysical explicit scheme is adopted for distinguishable scale precipitation; 2. MRF parameterization scheme is adopted for planetary boundary layer process. 3. the radiation process adopts CCM2 radiation scheme. IV. File Format and Naming It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: 2002***&.forc, where * * * is Julian day and 2002***& is time (in hours), where. forc is the file extension. V. data format Stored in binary floating point type, each data takes up 4 bytes.
LONG Xiao, QIU Chongjian
The research project on land surface data assimilation system in western China belongs to the major research plan of "environmental and ecological science in western China" of the national natural science foundation. the person in charge is researcher Li Xin of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. The output data set of the Land Surface Assimilation System in Western China is one of the data achievements of the project. It is a Chinese Land Surface Data Assimilation System constructed by Dr. Huang Chun Lin and researcher Li Xin of the Institute of Cold and Arid Region Environment and Engineering, Chinese Academy of Sciences. CoLM model is used as a model operator to couple microwave radiation transmission models for different surface states such as soil (including melting and freezing), snow cover, etc. and to assimilate passive microwave observations (SSM/I and AMSR-E), so that the system can finally output assimilation data of soil moisture, soil temperature, snow cover, frozen soil, sensible heat, latent heat, evaporation, etc. with higher accuracy. Data format and naming: It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: YYYMMDDHH.grid, where YY is the year (2002), MM is the month, DD is the day, HH is the hour,. grid and. flux are file extensions, the former is the state variable output result and the latter is the flux output result. The file format is a binary FLOAT value, that is, every 4 bytes represents a value.
LI Xin, HUANG Chunlin
1. The data is digitized in the map of the development degree of desertification in daqintara (1974) from the drawing. The specific information of the map is as follows: * chief editor: zhu zhenda, qiu xingmin * editor: wang yimou * drawing: feng yu-sun, yao fa-fen, wu wei, wang jianhua, wang zhou-long * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house, unified isbn: 12461.26 二. The data is stored in ESRI Shapefile format, including the following layers: 1, * desertification development degree map (1974) : desertification1974.shp 2, * double river: river_double-shp 3, * single river: river_single-shp 4, Road: SHP 5, Lake: lake.shp 6, street: Stree. SHP 7, Railway: Railway. SHP 8, forest belt: Tree_networks 9. Residential land: residential. SHP 10. Map: map_margin.shp 三, desertification development degree figure property fields and encoding attribute: (1) desertification degree (Type) : a flow of sand (Semi - shifting Sandy Land), sand form class (Shapes), grass (Grassland), forest Land, Woodland and forest density (W_density), the cultivated Land (Farmland) (2) sand Shapes: Barchan Dunes, Flat Sandy Land, undulated Sandy Land, Vegetated Dunes (3) the grass (Grassland) (4) Woodland: Woodland. (5) woodland density (W_density): Sparse Woodlot (6) Farmland: Dryfarming and Abandoned Farmland, Irrigated Fields
WANG Jianhua, ZHU Zhenda, QIU Xingmin, FENG Yusun, YAO Fafen
1. The data is digitized in the map of the development degree of desertification in daqintara (1958) from the drawing. The specific information of the map is as follows: * chief editor: zhu zhenda, qiu xingmin * editor: wang yimou * drawing: feng yu-sun, yao fa-fen, wu wei, wang jianhua, wang zhou-long * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house, unified isbn: 12461.26 二. The data is stored in ESRI Shapefile format, including the following layers: 1, * desertification development degree map (1958) : desertification1958.shp 2, * double river: river_double-shp 3, * single river: river_single-shp 4, Road: SHP 5, Lake: lake.shp 6, street: Stree. SHP 7, Railway: Railway. SHP 8, forest belt: Tree_networks 9. Residential land: residential. SHP 10. Map: map_margin.shp 三, desertification development degree figure property fields and encoding attribute: (1) desertification degree (Type) : a flow of sand (Semi - shifting Sandy Land), sand form class (Shapes), grass (Grassland), forest Land, Woodland and forest density (W_density), the cultivated Land (Farmland) (2) sand Shapes: Barchan Dunes, Flat Sandy Land, undulated Sandy Land, Vegetated Dunes (3) the grass (Grassland) (4) Woodland: Woodland. (5) woodland density (W_density): Sparse Woodlot (6) Farmland: Dryfarming and Abandoned Farmland, Irrigated Fields
WANG Jianhua, ZHU Zhenda, QIU Xingmin, YAO Fafen, FENG Yusun
GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. The dataset includes the global vegetation index changes from 1981 to 2006, the format is ENVI standard format, the projection is ALBERS, and its time resolution is 15 days and its spatial resolution is 8km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data. 1. File format: The GIMMS-NDVI dataset contains all rar compressed files with a 15-day interval from July 1981 to 2006. After decompression, it includes an XML file, an .HDR header file, an .IMG file, and a .JPG image file. 2. File naming: The naming rules for compressed files in the NOAA / AVHRR-NDVI data set are: YYMMM15a (b) .n **-VIg_data_envi.rar, where YY-year, MMM-abbreviated English month letters, 15a-synthesized in the first half of the month, 15b-synthesized in the second half of the month, **-Satellite. After decompression, there are 4 files with the same file name, and the attributes are: XML document, header file (suffix: .HDF), remote sensing image file (suffix: .IMG), and JPEG image file. In this data set, the user uses the remote sensing image file with the suffix .IMG to analyze the vegetation index. Remote sensing image files with suffix of .IMG and .HDF used by users to analyze vegetation indices can be opened in ENVI and ERDAS software. 3. The data header file information is as follows: Coordinate System is: 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 ["Meter", 1]] Pixel Size = (8000.000000000000000, -8000.000000000000000) Corner Coordinates: Upper Left (-3922260.739, 6100362.950) (51d20'23.06 "E, 46d21'21.43" N) Lower Left (-3922260.739, 1540362.950) (71d16'1.22 "E, 8d41'42.21" N) Upper Right (3277739.261, 6100362.950) (151d 8'57.22 "E, 49d 9'35.37" N) Lower Right (3277739.261, 1540362.950) (133d30'58.46 "E, 10d37'13.35" N) Center (-322260.739, 3820362.950) (101d22'21.08 "E, 35d42'18.02" N) Band 1 Block = 900x1 Type = Int16, ColorInterp = Undefined Computed Min / Max = -16066.000,11231.000 4. Conversion relationship between DN value and NDVI NDVI = DN / 1000, divided by 10000 after 2003 The NDVI value should be between [-1,1]. Data outside this interval represent other features, such as water bodies.
Tucker, C.J., J.E.Pinzon, M.E.Brown
The experimental project of vegetation degradation mechanism and reconstruction in Yuanjiang dry-hot valley in Yunnan belongs to the major research program of "Environmental and Ecological Science in Western China" of the National Natural Science Foundation. The principal is researcher Cao Kunfang of Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. The project runs from January 2004 to December 2007. Data collected for this project include: 1. Excel table of multi-year average temperature and rainfall in Yuanjiang dry-hot valley (1961-2004), with attribute fields including monthly average temperature and monthly average rainfall. 2. excel table of annual average temperature (1750-2006) in the middle of Hengduan Mountain in China based on tree ring, with attribute fields including year and reconstructed average temperature. 3. excel table of summer temperatures (1750-2006) in the central Hengduan Mountains in southern China based on tree rings. The attribute fields include the year and the reconstructed average temperature in summer (April-September). 4. excel table of drought index (1655-2005) in central Hengduan Mountains of China based on tree rotation, with attribute fields including year and reconstruction of drought index in spring (March-May). 5. pdf file of growth dynamic graph of leaves and branches. it records the growth dynamic trend line and leaf dynamic trend graph of plants with s-type, f-type, intermediate-type and S+SD-type branches from March 22, 2004 to April 8, 2005. 6.32 Phenological Summary Tables of Woody Plants (word Document: Specific Name, Number of Observed Plants/Branches, Type of Branch Extension, Leaf Phenology, Length of Current Year Branches (cm), Total Leaves on Branches, Leaf Area (cm2), Non-leaf Period (Months), Flowering Period, Fruit Ripening Period and Fruit Type) 7. Seasonal Changes of Relative Water Content of Plant Leaves in Yuanjiang Dry-hot Valley (March 2003-February 2004) Excel Table 8. Seasonal Changes of Photosynthesis of 6 Representative Plants in Yuanjiang Dry-hot Valley (Maximum Photosynthetic Rate, Stomatal Conductance, Water Use Efficiency, Maximum Subefficiency of photosystem II) excle Table (2003-2005) 9. excle Table of Long-term Water Use Efficiency (Isotope) Data of Representative Plants in Yuanjiang Dry-hot Valley (Water Use Efficiency in Dry and Wet Seasons of Shrimp Flower, Red-skin Water Brocade Tree, Three-leaf Lacquer, Phyllanthus emblica, Pearl Tree, Dried Sky Fruit, Cyclobalanopsis glauca, West China Small Stone Accumulation, Geranium, Tiger thorn, Willow and Pigexcrement Bean) 10. word Document of List of Plants in Mandan Qianshan, Yuanjiang
CAO Kunfang
Ⅰ. Overview Landsat5 was launched in April 1999. As a supplement and enhancement to the Landsat series, it carries an EMT+ sensor. The parameters of each band are close to that of Landsat5, but the panchromatic band with a resolution of 15 m is added, and the resolution of thermal infrared band is increased to 60 m.This dataset was collected in 1999-2010. There were 97 scenes of TM data in the upper reaches of the Yellow River. Due to sensor damage, there were bands in the images. Ⅱ. Data processing description Product level is L1 and has been geometrically corrected. Ⅲ. Data content description The naming method is L5 and row number and column number _ column number and date (yyyymmdd), such as L75129032_03220040816. Ⅳ. Data usage description The main applications are soil use/cover and desertification monitoring.
XUE Xian, DU Heqiang
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
China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.
LIU Shiyin, GUO Wanqin, XU Junli
This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).
HUANG Xiaodong
一. An overview This data set is a 1:100,000 distribution map of China's deserts as the data source, and it is tailored according to the river basin boundary. It mainly reflects the geographical distribution, area size, mobility and fixation degree of deserts, sandy land and gobi in the upper reaches of the Yellow River.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out. 二. Data processing instructions This data set takes the 1:100,000 distribution map of China's deserts as the data source and is tailored according to the basin boundary.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out.According to the system design requirements and related standards, the input data is standardized and uniformly converted into various data input standard formats. 三. data content description This data set is divided into desert and non-desert category, the non-desert code is 999. The desert is divided into three categories, namely desert (land), gobi and saline-alkali land, and the classification code is 23410, 2342000 and 2343000 respectively.Among them, deserts (land) are divided into four categories, namely mobile desert (land), semi-mobile desert (land), semi-fixed desert (land) and fixed desert (land). The classification codes are 2341010, 2341020, 2341030 and 2341040. 四. Data usage instructions It can make the resources, environment and other related workers understand the desert type, area and distribution in the upper reaches of the Yellow River, and make the classification and evaluation of the wind and sand hazards in ningmeng river section.
XUE Xian, DU Heqiang
The data set mainly includes observation data of each tree in the super site, and the observation time is from June 2, 2008 to June 10, 2008. The super site is set around the Dayekou Guantan Forest Station. Since the size of the super site is 100m×100m, in order to facilitate the forest structure parameter survey, the super site is divided into 16 sub-sample sites, and tally forest measurement is performed in units of sub-samples. The tally forest measurement factors include: diameter, tree height, height under branch, crown width in transversal slope direction, crown width in up and down slope direction, and tindividual tree growth status. The measuring instruments are mainly: tape, diameter scale, laser altimeter, ultrasonic altimeter, range pole and compass. The data set also records the center point latitude and longitude coordinates of 16 sub-samples (measured by Z-MAX DGPS). The data set can be used for verification of remote sensing forest structure parameter extraction algorithm. The data set, together with other observation data of the super site, can be used for reconstruction of forest 3D scenes, establishment of active and passive remote sensing mechanism models, and simulation of remote sensing images,etc.
CHEN Erxue, BAI Lina, WANG Bengyu, TIAN Xin, LIU Qingwang, CAO Bin, Yang Yongtian, Zhihai Gao, Bingxiang Tan, GUO Zhifeng, WANG Xinyun, FU Anmin, ZHANG Zhiyu, NI Wenjian, WANG Qiang, BAO Yunfei, WANG Dianzhong, ZHANG Yang, ZHAO Liqiong, LIANG Dashuang, WANG Shunli, ZHAO Ming, LEI Jun, NIU Yun, LUO Longfa
Observation time: 2008-06-05 ~ 2008-06-15.A sample strip with a length of 1Km and a width of 20m was set up to cross the super sample plot from the starting point of the super sample plot at the geantan forest station in ohnoguchi.The compass was used to determine the direction of the sample, and the azimuth was 115 degrees north by east, which was basically consistent with the flight route.20 meters ×20 meters of sample land shall be arranged every 50 meters in the sample belt, a total of 20 pieces of sample land.There is some overlap between the sample belt and the super sample land. The center of the no.1 sample land of the sample belt is located at the center of the super sample land. The observation data is shown in the measurement data set per wood of the super sample land.This data set records the observation data of sample 2 ~ 20.These data include the following three parts: 1) tree data of sample plots: each wood of 2 ~ 20 plots was measured: chest diameter, tree height, crown width and undershoot height.Laser altimeter and ultrasonic altimeter were used to measure the height of big trees and under branches, flower rod was used to measure the height of small trees and under branches, chest diameter was used to measure the chest diameter of trees, and crown width was measured with a leather tape measure. 2) sample location data: the sample location is roughly determined by using a tape measure and compass. The coordinates of the center point of the sample are accurately measured using the French THALES DGPS measurement system (model z-max).The observation method is to use two GPS receivers to conduct synchronous static measurement, one in the reference station and the other in the mobile station. The observation lasts 30 minutes. The data processing software provided by the system is used for post-processing difference. 3) LAI observation data: LAI area index (LAI) of each sample plot was measured by lai-2000 and HemiView.
CHEN Erxue, GUO Zhifeng, LIU Qingwang, WANG Bengyu, TIAN Xin, WANG Xinyun, FU Anmin, ZHANG Zhiyu, NI Wenjian, WANG Qiang, CAO Bin, Yang Yongtian, Zhihai Gao, Bingxiang Tan, WANG Dianzhong, ZHANG Yang, ZHAO Liqiong, LIANG Dashuang
The dataset of airborne WiDAS mission was obtained in the Linze station-Linze grassland flight zone on Jun. 29, 2008. Data available for general users include Level-2C data (after geometric, radiometric and atmospheric corrections), Level-1B browse image (after intra-band matchingintra-band) and Level-2B browse image (intra-bandafter registration). The raw data, Level-1A, and data processing parameters were filed; applications would be evaluated prior to access. Data processing started in Aug. 2008 and ended in Apr. 2009, and in Nov. 2009, CCD data were reprocessed to adjust radiometric calibration. The flying time of each route was as follows: {| ! id ! flight ! relative height ! starttime ! endtime ! data size ! data state ! data quality ! ground targets |- | 1 || 1#13 || 1500m || 11:44:35 || 11:50:31 || 90 || processed;complete || good || Pingchuan reservoir |- | 2 || 1#11 || 1500m || 11:55:55 || 12:01:55 || 91 || processed;complete || good || Linze grass station |- | 3 || 1#9_1 || 1500m || 12:06:27 || 12:12:27 || 91 || incomplete || incomplete || Pingchuan reservoir |- | 4 || 1#9_2 || 1500m || 13:01:35 || 13:07:43 || 93 || processed;complete || good || Pingchuan reservoir |- | 5 || 1#7 || 1500m || 12:17:59 || 12:23:59 || 91 || processed;complete || good || desert transit plot |- | 6 || 1#5 || 1500m || 12:28:35 || 12:34:31 || 90 || processed;complete || good || North-south desert strip |- | 7 || 1#3 || 1500m || 12:39:11 || 12:45:03 || 89 || processed;complete || good || Pingchuan reservoir |- | 8 || 1#1 || 1500m || 12:50:55 || 12:56:51 || 90 || processed;complete || good || Linze station |}
Liu Qiang, XIAO Qing, Wen Jianguang, FANG Li, WANG Heshun, LI Bo, LIU Zhigang, LI Xin, MA Mingguo
The main contents of this data set are forest, shrub and grassland sample plot survey data.The fixed samples are located in the drainage ditch valley of qilian mountain and the dayaokou valley where the hydrology observation and test site of the water source conservation forest research institute of gansu province is located. The information of the sample is as follows: Number elevation quadrat size longitude latitude surface type G1 2715 20 × 20 100 ° 17 '12 "38 ° 33' 29" qinghai spruce forest G2 2800 20×36 100°17 '07 "38°33' 27" moss spruce forest G3 2840 20×20 100°17 '37 "38°33' 05" moss spruce forest G4 2952 20 × 20 100 ° 17 '59 "38 ° 32' 47" qinghai spruce forest G5 3015 20 × 20 100 ° 18 '06 "38 ° 32' 42" qinghai spruce forest G6 3100 20 × 20 100 ° 18 '13 "38 ° 32' 31" thicket qinghai spruce forest G7 3300 23.5 × 20 thickets qinghai spruce forest G8 2800 20×20 100°13 '30 "38°33' 29" moss spruce forest B1 2700 12.8×25 moss spruce forest B2 2800 20×20 100°17 '38 "38°32' 59" moss spruce forest B3 2900 20×20 100°17 '59 "38°32' 51" grass spruce forest B4 3028 20×20 100°17 '59 "38°32' 39" moss spruce forest B5 3097 20×20 100°18 '02 "38°32' 32" moss spruce forest B6 3195 20 × 20 100 ° 18 '06 "38 ° 32' 25" qinghai spruce forest B7 2762 20 × 20 100 ° 17 '08 "38 ° 33' 21" qinghai spruce forest B8 2730 20×20 100°17 '06 "38°33' 27" moss spruce forest GM1 3690 5×5 100°18 '02 "38°32' 02" caragana scrub (middle) GM2 3690 5×5 100°18 '02 "38°32' 02" caragana scrub (rare) GM3 3700 5×5 100°18 '03 "38°32' 03" caragana + jilaliu shrub (dense) GM4 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow thicket (middle) GM5 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow shrub (sparse) GM6 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow thicket (dense) GM7 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (middle) GM8 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (dense) GM9 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (rare) GM10 3400 5×5 100°18 '18 "38°32' 12" golden pheasant scrub (rare) GM11 3400 5×5 100°18 '18 "38°32' 12" golden pheasant + golden raspberry shrub (dense) GM12 3400 5×5 100°18 '18 "38°32' 12" golden pheasant scrub (rare) GM13 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" giraliu thicket GM14 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" caragana + jila shrub GM15 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" caragana + jila shrub YC3 2700 1×1 100°17 '14 "38°33' 33" needle thatch field YC4 2750 1×1 100°17 '18 "38°33' 32" needle thatch field YC5 2800 1×1 100°17 '21 "38°33' 33" needle thatch field YC6 2850 1×1 100°17 '25 "38°33' 33" needle thatch field YC7 2900 1×1 100°17 '31 "38°33' 32" aster + needle thatch field YC8 2950 1×1 100°17 '44 "38°33' 23" needle thatch field YC9 2980 1×1 100°17 '48 "38°33' 25" needle thatch field The sample geodesic tree data were surveyed from July to August 2007.The survey included: 1. Basic survey of sample plots in drainage ditch basin: A) sample land setting: sample land number, elevation, slope direction, slope position, slope, soil layer thickness, sample land size, longitude and latitude, community type, soil type, operation status, age B) survey of each wood in the sample plots: sample plot number, tree number, tree species, tree classification, chest diameter, tree height, undershoot height, crown radius 2. Soil profile survey record sheet Including forest/vegetation status, major tree species, forest age, soil name, surface soil erosion, parent rock and material, drainage conditions, land use history, soil profile (soil layer, moisture, color, texture, structure, root system, gravel content) 3. Standard ground cover factor Standard land area, dominant tree species, stand/vegetation origin, elevation, slope direction, slope position, slope, cutting and utilization method, afforestation land preparation type, survey method, canopy coverage, living ground cover, dead cover cover, litter thickness (undivided strata, semi-decomposed layer, decomposed layer) 4. Canopy survey: 5. Draft quadrat (1m×1m) survey record sheet Including species name, number, coverage, average height 6. Results of determination of soil physical properties in source forest of qilian mountain (land sample survey) Contains the soil physical properties measurement process (+ wet mud weight aluminum box, aluminum box, soil moisture content, suddenly bulk density, etc.), bringing biomass measurement (total fresh weight of shrub and herb, fresh weight of sample, sample dry weight, etc.), litter dry weight (including mosses) layer and the largest capacity calculation process (of moss and litter thickness, total fresh weight, fresh weight of samples, the dry weight of the sample, soaking for 24 h after heavy, maximum water holding capacity, the largest water depth, the biggest hold water rate, maximum moisture capacity) 7. Bush sample survey: Including species name, number, coverage, average height 8. Standard sample land setting and questionnaire for each wooden inspection ruler Including tree species, tree classification, age, chest diameter, number of height, undershoot height, crown radius 9. Litter layer survey record sheet Including litter (decomposed layer, semi-decomposed layer, decomposed layer) thickness 10. Update survey records: Including tree species, natural regeneration (height <30cm, height 31-50cm, height >51cm), artificial regeneration (height <30cm, height 31-50cm, height >51cm) This data set can provide ground measured data for remote sensing inversion of forest structure parameters.
WANG Shunli, LUO Longfa, WANG Rongxin, CHE Zongxi, JING Wenmao
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 was compiled from "China's 1:100,000 wetland data". "China 1:100,000 wetland data" mainly reflects the information of marshes and wetlands throughout the country in the 2000s, and is represented by geographical coordinates in decimal scale. The main contents include: types of marshes and wetlands, types of water supply, types of soil, types of main vegetation, and geographical regions.The information classification and coding standard of China sustainable development information sharing system was implemented.Data source of this database: 1:20 swamp map (internal version), 1:500 000 swamp map (internal version) of qinghai-tibet plateau, 1:100 000 swamp survey data and 1:400 000 swamp map of China;The processing steps are as follows: data source selection, preprocessing, marshland element digitization and coding, data editing and processing, establishment of topological relationship, edge-to-edge processing, projection transformation, connection with attribute database such as geographical name and acquisition of 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
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