The data set contains the rare animal survey data for the Sanjiangyuan area from 2016 to 2017, including the latitude and longitude of the survey site, the length of the sample line, animal discovery time, animal names, quantity, location of the occurrence, type of habitat, affiliated families, etc.
0 2021-04-18
This dataset is the FPAR observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 19 July, 2012 (UTC+8). Measurement instruments: AccuPAR (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: For corn, to measure the incoming PAR on the canopy, transmission PAR under the canopy, reflected PAR on the canopy, reflected PAR under the canopy. For orchard and white poplar forest, to measure the incoming PAR outside of the canopy, transmission PAR under the canopy. Corresponding data: Land cover, plant height, crop rows identification
0 2021-07-26
This dataset includes one scene acquired on (yy-mm-dd) 2012-07-25, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 0.6 m and 2.4 m, respectively. The data product level of this image is Level 2A. QuickBird dataset was acquired through purchase.
0 2021-07-27
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.
0 2020-06-05
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.
0 2021-10-26
The Tibetan Plateau is known as “The World’s Third Pole” and “The Water Tower of Asia”. A relatively accurate map of the frozen soil in the Tibetan Plateau is therefore significant for local cold region engineering and environmental construction. Thus, to meet the engineering and environmental needs, a decision tree was established based on multi-source remote sensing data (elevation, MODIS surface temperature, vegetation index and soil moisture) to divide the permafrost and seasonally frozen soil of the Tibetan Plateau. The data are in grid format, DN=1 stands for permafrost, and DN=2 stands for seasonally frozen soil. The elevation data are from the 1 km x 1 km China DEM (digital elevation model) data set (http://westdc.westgis.ac.cn); the surface temperature is the yearly average data based on daily data estimated by Bin Ouyang and others using the Sin-Linear method. The estimation of the daily average surface temperature was based on the application of the Sin-Linear method to MODIS surface products, and to reduce the time difference with existing frozen soil maps, the surface temperature of the study area in 2003 was used as the information source for the classification of frozen soil. Vegetation information was extracted from the 16-day synthetic product data of Aqua and Terra (MYD13A1 and MOD13A1) in 2003. Soil moisture values were obtained from relatively high-quality ascending pass data collected by AMSR-E in May 2003. Therefore, based on the above data, the classification threshold of the decision tree was obtained using the Map of Frozen Soil in the Tibetan Plateau (1:3000000) and Map of the Glaciers, Frozen Soil and Deserts in China (1:4000000) as the a priori information. Based on the prosed method, the frozen soil types on the Tibetan Plateau were classified. The classification results were then verified and compared with the surveyed maps of frozen soil in the West Kunlun Mountains, revised maps, maps of hot springs and other existing frozen soil maps related to the Tibetan Plateau. Based on the Tibetan Plateau frozen soil map generated from the multi-source remote sensing information, the permafrost area accounts for 42.5% (111.3 × 104 km²), and the seasonally frozen soil area accounts for 53.8% (140.9 × 104 km²) of the total area of the Tibetan Plateau. This result is relatively consistent with the prior map (the 1:3000000 Map of Frozen Soil in the Tibetan Plateau). In addition, the overall accuracy and Kappa coefficient of the different frozen soil maps show that the frozen soil maps compiled or simulated by different methods are basically consistent in terms of the spatial distribution pattern, and the inconsistencies are mainly in the boundary areas between permafrost areas and seasonally frozen soil areas.
0 2021-10-18
From 1982 to 2015, the NDVI change data sets of different types of permafrost regions in the northern hemisphere have a temporal resolution of once every five years, covering the entire Arctic countries with a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, the regulation and service functions of Permafrost on Ecosystem in the northern hemisphere are quantified by using GIS and ecological methods, All the data are under quality control.
0 2021-10-18
This data set is the plant collection and distribution site information of Three-River-Source National Park investigated by Northwest Plateau Biology Institute of Chinese Academy of Sciences. The data set covers the period from 2008 to 2017, and the survey covers theThree-River-Source National Park. The survey contents include information such as collection date, number, family, genus, species, survey date, collection place, collector, longitude, latitude, altitude, habitat, appraiser, etc. Three parks of the national park were investigated respectively. 88 species of vegetation belonging to 56 genera and 24 families were investigated in the Yangtze River Source Park, with 116 records in total. Vegetation of 110 species in 64 genera and 26 families was investigated in the Yellow River Source Park, with 159 records in total. The vegetation of 30 species in 22 genera and 12 families was investigated in Lancang River Source Park, with a total of 33 records.
0 2021-10-13
This data set contains statistical tables on the community situation of each county in Three-River-Source National Park. The specific contents include: Table 1 includes: number of administrative villages, number of natural villages, number of households, population, number of rural labor force, total value of primary and secondary industries, net income per capita, and number of livestock. Table 2 includes: the ethnic composition of the population (population of each ethnic group), education-related statistics (number of primary and secondary schools and number of students), health-related statistics (number of hospitals, health rooms and medical personnel), and statistics on the education level of the population (number of people with different education levels); Table 3 includes: the grassland (total grassland area, usable grassland area, moderately degraded area and grassland vegetation coverage), woodland (total area, arbor forest area, shrub forest area and sparse forest area), water area (total area, river area, lake area, glacier area, snowy mountain area and wetland area). A total of four counties were designed: Maduo, Qumalai, Zaduo and Zhiduo. This data comes from statistics of government departments.
0 2021-04-20
The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
0 2021-03-29
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