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.
0 2020-08-20
Based on the meteorological data of 105 meteorological stations in and around the Qinghai Tibet Plateau from 1980 to 2019, the National Meteorological Science Data Center of China Meteorological Administration (CMA) was established. By calculating the oxygen content, it is found that there is a significant linear correlation between oxygen content and altitude, y = - 0.0263x + 283.8, R2 = 0.9819. Therefore, the oxygen content distribution map can be calculated based on DEM data grid. Due to the limitation of the natural environment in the Qinghai Tibet Plateau, there are few related fixed-point observation institutions. This data can reflect the distribution of oxygen content in the Qinghai Tibet Plateau to a certain extent, and has certain reference significance for the research of human living environment in the Qinghai Tibet Plateau.
0 2021-01-25
Canopy conductance (mm s-1) is a sensitive index of forest transpiration response to environmental factors, and is a key parameter in water and carbon exchange model. The data is obtained by expanding the water consumption scale measured by stem sap flow technology to the stand scale to obtain the water consumption of the stand, and then using penman equation to calculate. This data mainly provides basic data for some eco hydrological models.
0 2020-03-10
1. Data overview: This data set is the scale meteorological gradient data of qilian station from January 1, 2012 to December 31, 2012 (installed at the end of September 2011).VG1000 gradient observation system carries out long-term monitoring of wind speed, wind direction, air temperature, humidity, radiation and other conventional meteorological elements, and carries out data storage and processing analysis in combination with the data collector with high precision and high scanning frequency. 2. Data content: The main observation factors include four layers of air temperature, humidity and two-dimensional ultrasonic wind, rain and snow volume meter, eight layers of ground temperature, soil moisture content, etc. 3. Space and time range: Geographical coordinates: longitude: longitude: 99° 52’e;Latitude: 38°15 'N;Height: 3232.3 m
0 2020-03-10
The dataset of soil moisture observations (VWC%) was obtained at the super site (100m×100m) around the Dayekou Guantan forest station on Jun. 5, 2008. The super site was divided into 16 subplots (25m×25m). 10 points were measured by TDR 300 (with the probe 20cm long) at random location in each subplot. The serial number and the cover type of the subplot, the number of the sample points and soil moisture (%) were recorded. Those provide reliable data for the construction of the 3D structure of the forest scene, and for the modeling of active and passive remote sensing mechanisms and the simulation of remote sensing images.
0 2019-05-23
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
0 2019-09-12
It is of great significance to carry out the quantitative study on the evapotranspiration of forest vegetation in Qilian Mountain, to correctly understand the hydrological function of the forest ecosystem in Qilian Mountain, to understand the water cycle process and to develop the hydrological model of the watershed, and to make a reasonable forest management plan. Forest evapotranspiration is mainly composed of soil surface evaporation, vegetation transpiration and canopy interception water evaporation. Traditional evapotranspiration research methods can be divided into two categories: actual measurement and estimation. The actual measurement methods include hydrology method, micro meteorology method and plant physiology method; the estimation method is to calculate Evapotranspiration by model, mainly including analysis model and empirical model. However, none of these methods can effectively distinguish forest transpiration from evaporation. The trunk liquid flow method can effectively calculate the transpiration of forest land by measuring the transpiration water consumption of trees. The trunk liquid flow method can effectively calculate the transpiration of forest land by measuring the transpiration water consumption of trees. The transpiration water consumption of Picea crassifolia forest was measured by thermal pulse technique, and the scale was extended to the stand scale to indicate the transpiration water consumption of Picea crassifolia forest.
0 2020-03-10
The dataset of snow depth measured by the elevation-graduated snow sticks was obtained in the Binggou watershed foci experimental area from Nov. 11 to 16, 2007, during the pre-observation period. 51 snow-stakes (2m long) were arranged according to different topographic landscapes, such as the flat, ubac, tailo and partial shade, and the length above the ground was recorded. From Mar. 2 to Apr. 6, 2008, the intensive observation period, ten measurements (Mar. 2, Mar. 4, Mar. 9, Mar. 16, Mar. 19, Mar. 21, Mar. 23, Mar. 29, Apr. 1 and Apr. 6) were carried out both manually and additionally by the telescope for the snow depth around the snow-stakes. Two files including raw data and preprocessed snow depth data were archived. Those provide reliable data for snow spatial heterogeneity study and snow accumulation and melt monitoring in the Binggou watershed.
0 2019-05-23
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Xiyinghe Station from January 1 to December 31, 2018. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan,Qinghai Province. The elevation is 3639 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, and Ta_8 m; RH_2 m, RH_4 m, and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s/m^2)), soil heat flux (Gs_5 cm, Gs_10cm) (W/m^2), soil temperature (Ts_20 cm, Ts_40 cm) (℃), soil moisture (Ms_20 cm, Ms_40 cm) (%, volumetric water content), soil water potential (SWP_20cm , SWP_40cm)(kpa) , soil conductivity (Ec_20cm, Ec_40cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The meteorological data were missing during Aug. 29 to Oct.18 because of unstable power supply due to battery box flooding; The wind speed and direction profile data were rejected because of sensor failure; The precipitation data were rejected because of program error; The air humidity data before Mar. 2 were rejected due to program error; (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
0 2019-09-15
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 data set is the Shule River long-time series vegetation index data set, which is mainly aimed at normalized difference vegetation index (NDVI). It includes spectral reflectance of four bands synthesized every 10 days and maximum NDVI for 10 days from 1998 to 2008. The spatial resolution is 1km and the temporal resolution is ten days.
0 2020-04-01
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