Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
2022-04-18
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
2021-01-27
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects. Resolution 1m
2020-07-29
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects.
2020-07-28
The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (GCC), phenological phase and fractional cover (FC). Please refer to Liu et al. (2018) for sites information in the Citation section.
2020-07-25
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