The data set is based on the geodetic coordinate data and other auxiliary data of the corner points of 16 subsamples of super sample plots, the setting points of lidar base station of the foundation and the base points of each tree trunk measured by the total station. The data acquisition time of total station is from June 3, 2008 to June 12, 2008, which is divided into two groups. One total station is used respectively, with the models of topcon602 and topcon7002. A total of 1468 Picea crassifolia trees in the super sample plot were measured, and all the corner points of the sub sample plot and the top points of the stake set on the base station of lidar were located. These positioning results are the main data content of the dataset. In addition, on June 3, 2008, June 4, 2008, June 6, 2011, the differential GPS z-max was used to locate all the stake vertices. By manually measuring the height of each stake, the height of the surface under the stake was calculated, and finally the three-dimensional coordinate position of the surface of each tree and the topographic map of super sample plot were generated. These data constitute the secondary data of the dataset. This data set can provide detailed ground observation data for the establishment of real three-dimensional forest scene, the development and correction of various three-dimensional forest remote sensing models, and ground validation data for the extraction of airborne lidar forest parameters.
2020-03-10
The super sample plot is composed of 16 sub samples. In order to locate each tree in the sample plot and facilitate the location of the base station point for ground-based radar observation, it is necessary to measure the geodetic coordinates of the sub sample plot corner point and the preset base station point for ground-based radar. The location of these points and each tree is measured by total station. Because the total station measures relative coordinates, in order to obtain geodetic coordinates, it is necessary to use differential GPS (DGPS) to measure at least one reference point around the super sample plot with high precision. In addition, we also use DGPS to observe the geodetic coordinates of all corner points of the subsample, and the measurement results can form the verification of the total station measurement results. The data set is based on all the positioning results measured by DGPS, excluding the positioning results of total station. The measurement time is from June 1 to 13, 2008, using the French Thales differential GPS measurement system, model z-max. The observation method is to use two GPS receivers for synchronous static measurement, one is the base station, which is set next to Gansu Water Conservation Forest Research Institute (the WGS geodetic coordinate of the base station is a first-class benchmark introduced from Zhangye City through multi station observation using z-max). The other is the mobile station, which is placed on the observation point of super sample plot. The observation time of each point varies from 10, 15, 20, 25, 30 minutes. The specific time depends on the satellite signal. The signal difference time is measured for several minutes more. Finally, the final positioning result is obtained by using the processing software of the instrument. WGS geodetic coordinate system is used for the positioning results. Firstly, six temporary control points were measured in the open area next to the super sample plot, providing reference points for the total station to measure the position of trees in the super sample plot. Then, flow stations were set up on each corner of 16 sub plots of super plot, and the coordinates of corner points were measured, and 41 observation points were obtained. The dataset stores the positioning results of these 47 points. This data is only for project use and not for external sharing.
2020-03-09
The dataset of ground truth measurement synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained in the Biandukou foci experimental area on May 25, 2008. Observation items included: (1) the soil temperature in L1, L2, L3, L4, L5, L6 and L7; (2) roughness measured by the roughness grid board and collected by the digital camera. Files with "result" field were processed data, in which the first row was RMS height (cm; one value), the second row was distance (cm), and the third row was correlation function (cm; changed into correlation length when it is 1/e). (3) GPR and TDR data. Five files were included, roughness photos and preprocessed data, the soil temperature, coordinates of quadrates and sampling lines, GPR and microwave radiometer data. All were archived as Excel and .txt files. Those provide reliable ground data for development and validation of soil moisture and freeze/thaw algorithms from active remote sensing approaches.
2019-05-23
The dataset of surface roughness was obtained at the super site (100m×100m, pure Qinghai spruce) around the Dayekou Guantan forest station. 25 corner points and 16 center points were collected and each point was measured twice and photos were taken. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. The photos were processed using ArcView software; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 radius is also included for further checking. Those provide reliable ground data for improving and verifying the remote sensing algorithms.
2019-05-23
The dataset of surface roughness measurements by phototaking was obtained in the Huazhaizi desert steppe foci experimental area. Observation items included: (1) Surface roughness synchronizing with ASAR and MODIS in Huazhaizi desert No. 2 plot on May 24, 2008. (2) Surface roughness synchronizing with WiDAS in Huazhaizi desert No. 1 plot on May 30, 2008. The self-made roughness reference board (Cold and Arid Regions Environmental and Engineering Research Institute, CAS), the digital camera and the compass were used. Sample points were selected at equal intervals along the diagonals and marked in the photos.
2019-05-23
The dataset of airborne L-band microwave radiometer and thermal imager mission was obtained in the Binggou-A'rou flight zone in the afternoon of Apr. 1, 2008. The frequency of L bands was 1.4 GHz with back sight of 35 degree and dual polarization (H&V) was acquired. The plane took off at Zhangye airport at 12:48 (BJT) and landed at 16:35 along the scheduled lines at the altitude about 5000m and speed about 260km/hr.. The raw data include microwave radiometer (L) data, thermal imager data (7.5-13 um; FOV: 24×18º) and GPS data; the first were instantaneous non-imaging observation recorded in text, which could be converted into brightness temperatures according to the caliberation coefficients (filed with raw data together), and the third are aircraft longitude, latitude and attitude. Moreover, based on the respective real-time clock log, observations by the microwave radiometer and GPS can be integrated to offer coordinates matching for the former. Yaw, flip, and pitch motions of aircraft were ignored due to the low resolution of microwave radiometer observations. Observation information can also be rasterized, as required, after calibration and coordinates matching. L band resolution (x) and footprint can be approximately estimated as x=0.3H (H is relative flight height). The thermal imager was 320*240 pixels and with FOV of 24×18º. The thermal imager data were stored in binary format with a text header file. The recorded value was brightness temperature at sensor with scale and gain parameter recorded in the header file. And the thermal images were not geometrically corrected because there were gaps between sequential images.
2019-05-23
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