Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Watershed production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Based on collinearity equations, images at nadir were corrected to adjust relief displacements and geometric errors, and the 0.5-m resolution digital orthorectified images DOM were obtained with the principle of digital differential rectification in Dayekou Basin.
0 2019-09-15
This data set includes the 2013 observation data of 10 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The 10 water net nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface infrared radiation temperature of the underlying surface. The time and frequency of conventional observation is 10 minutes. In order to ensure the accurate synchronization of si-111 and remote sensing, one minute intensive observation is conducted at 00:00-04:30, 08:00-18:00 and 21:00-24:00 every day. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research. For details, please refer to "2013 middle reaches of Heihe River waternet data document 20141231. Docx"
0 2020-03-14
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.
0 2020-06-11
The data are from 2011 to 2012. A 30m×30m Picea crassifolia canopy interception sample plot was set up in the Picea crassifolia sample plot at an altitude of 2800m m. A siphon raingauge model DSJ2 (Tianjin Meteorological Instrument Factory) was set up on the open land of the river about 50m from the sample plot to observe the rainfall outside the forest and its characteristics. Penetrating rain in the forest adopts a combination of manual observation and automatic observation. Automatic observation is mainly realized through a penetrating rain collection system arranged in the interception sample plot, which consists of a water collecting tank and an automatic recorder. Two 400cm×20cm water collecting tanks are connected with DSJ2 siphon rain gauge, and the change characteristics of penetrating rain under the forest are continuously recorded by an automatic recorder. Due to the spatial variability of the canopy structure of Picea crassifolia forest in the sample plot, a standard rainfall tube for manual observation is also arranged in the sample plot to observe the penetrating rain in the forest. Ninety rainfall tubes with a diameter of 20cm are arranged in the sample plot at intervals of 3m. After each precipitation event ends and the penetrating rain in the forest stops, the amount of water in the rain barrel will be emptied and the penetrating rain in the barrel will be measured with the rain cup.
0 2020-03-14
The vegetation sensor, sponsored by the European Commission, was launched by SPOT-4 in March 1998. It has received the spotvgt data for global vegetation cover monitoring since April 1998. The data is received by Kiruna ground station in Sweden, and the image quality monitoring center in Toulouse in France is responsible for image quality and provides relevant parameters (such as calibration coefficient). Finally, Belgium is responsible for image quality monitoring The Flemish Institute for technical research (Vito) vegetation processing center (ctiv) is responsible for preprocessing the data into 1km global data day by day. Preprocessing includes atmospheric correction, radiometric correction, geometric correction, and 10 day production to maximize the synthesized NDVI data, and set the value of - 1 to - 0.1 to - 0.1, and then convert to the DN value of 0-250 through the formula DN = (NDVI + 0.1) / 0.004. The dataset is a subset of China, which contains four bands of spectra synthesized every 10 days. Spot measurement (VGT) data is downloaded from the vegetation data website of Vito Institute in Belgium (http://free.vgt.vito.be), which includes the following: Spot vegation NDVI data and four band data, 10 days maximum synthesis, spatial resolution of 1km, effective time of 1998-2008, data naming specification is coverage + product type + year + month + day. Spot vector BRDF data, 10 days maximum synthesis, spatial resolution of 8km, effective time of 2001-2008, data naming specification is coverage + product type + year + month + day. Spot vectorization NPP data, 10 day maximum synthesis, spatial resolution of 8km, effective time of 1998-2006, data naming standard of "Heihe ﹣ NPP ﹣ VGT" + [1 or 2] + [year + month + day].
0 2020-03-08
Leaf area index, also known as leaf area coefficient, refers to the multiple of the total area of plant leaves in the land area per unit land area. Leaf area index is an important structural parameter of ecosystem, which is used to reflect the number of plant leaves, the change of canopy structure, the life activity of plant community and its environmental effect, to provide structured quantitative information for the description of material and energy exchange on the canopy surface, and to balance the energy of carbon accumulation, vegetation productivity and the interaction between soil, plant and atmosphere, Vegetation remote sensing plays an important role. Plant canopy imager CI - 110 was used to measure the alpine shrub and spruce leaf area index in hulugou watershed. The measurement period is July 22, 2014. It includes the main shrub types and Picea crassifolia forest in hulugou watershed. The data set mainly includes the original data of CI-110 measurement, including image and leaf area analysis image.
0 2020-03-11
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 |}
0 2019-05-23
On August 20, 2018, a DJI Elf 4 UAV camera was used to take aerial photographs of the alpine meadow sample in Qumali County, which is located in the source area of the Yangtze River. A total of 31 routes were set up. The flight altitude was 100 m, and the overlap degree of adjacent photographs was not less than 70%. A total of 664 aerial photographs were obtained and stored in the Drone Photoes of Qumalai (2018) folder.
0 2019-05-22
Three artificial rainfall events were performed on the shady grassland at the altitude of 2700m in the Pailugou watershed of the Qilian Mountains. The times were July 15, 2011, July 16, and July 22, 2011, respectively. Runoff rate, data is recorded every half an hour. Two rainfall simulations were also performed on the sun-slope grassland at the same altitude. As a comparative experiment, the time was July 24 and 25, 2011.
0 2020-03-15
Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).
0 2020-03-27
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