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.
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.
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.
This data set is the acquisition of the super-site forest 3D structure of the scanning point cloud data and other ancillary data based on the ground-based lidar (LiDAR) . Data acquisition time is from June 4, 2008 to June 12, 2008. Riegl LMS-Z360i ground-based LiDAR was used. The super site is divided into 16 sub-samples of 25m×25m, LiDAR base station points are set in each sub-sample, and LiDAR acquisition 3D full coverage LiDAR point metadata is set at each base station point. The content of the data set: total station measurement coordinates (x, y, z) for each LiDAR data acquisition base station point, the instrument attitude measured by a digital slope meter and an angle meter when each station collects data, and the laser radar scanning point cloud data at each station. This data set can provide realistic 3D forest scenes, provide detailed ground observation data for the development and correction of various 3D forest remote sensing models, and provide ground verification data for airborne and spaceborne remote sensing data.
The dataset of evaportranspiration measured by micro-lysimeter was obtained at the super site (100m×100m, pure Qinghai spruce) around the Dayekou Guantan forest station. Observation items included the ground-based lidar scanning, the total station measuring, DGPS, tally investigation, LAI, canopy spectrum, camera observations of the canopy, soil evapotranspiration, the soil frozen tube observations, surface roughness, precipitation interception, soil moisture and dry-wet weight of the forest component. Observation time was 18:00 every day from Jun. 1 to Dec. 31, 2008. 20 rain gauges, 4 self-made Lysimeter (diameter: 20cm) and the electronic balance were used. Those provide reliable data for retrieval of evapotranspiration from remote sensing data.
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.
The dataset of forest canopy gap fraction above the rain gauges observed by the camera (PENTAX K100D, 2400×1600) was obtained at the super site (100m×100m, Qinghai spruce) around the Dayekou Guantan forest station from 9:00-10:40 on Jun. 4, 2008. Observation items included the ground-based LiDAR scanning, the total station measuring, DGPS, tally investigation, LAI, canopy spectrum, camera observations of the canopy, soil evapotranspiration, the soil frozen tube observations, surface roughness, precipitation interception, soil moisture and dry-wet weight of the forest component. A subplot (25m×25m) was chosen for precipitation interception observations with different canopy density, and 32 sets of photos were taken 1m above the ground. Through studying those photos, the number and location of rain gauges could be determined; and then the canopy density could also be further developed.
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.
The dataset of water content of forest canopy components (the twig and the leaf) measurements was obtained at the super site (100m×100m) around the Dayekou Guantan forest station on Jun. 5, 2008. The sample tree was selected according to different diameters at breast height. 5 diameter classes were divided and in each class, 10 trees were selected and altogether 30 trees were selected as sampling trees. Branches in different parts were picked by the tree pruner and the twig and the leaf were separated manually, whose green weight was measured by the scales on the scene and dry weight by oven drying in the lab. Those provide reliable data for the reconstruction of the 3D structure of the forest scene, and for modelling active and passive remote sensing mechanisms and the simulation of remote sensing images.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center