Method for determining reasonable sample number in forest aboveground biomass remote sensing estimation

Abstract

The invention discloses a method for determining the number of reasonable samples in forest aboveground biomass remote sensing estimation. The method comprises steps of taking two tree species, i.e.,pinus alpine and fir yunnanensis, as research objects, combining a Landsat 8 image and ground survey sample plot data, taking a k-NN remote sensing estimation model as a case and taking a root-mean-square error as an evaluation index based on a geostatistics semi-variance function theory; and calculating to obtain the number of samples when the precision of the pinus alpine and fir clovershanensismodels is optimal, and determining a reasonable sample number range. In order to discuss uncertainty of the number of samples in quantitative remote sensing inversion, the uncertainty influence of the number of samples in forest aboveground biomass estimation is analyzed based on a geostatistics semi-variation function theory and a k-NN model, and a reference basis is provided for a problem thatthe number of samples in traditional quantitative remote sensing inversion is difficult to determine.

Description

technical field [0001] The invention relates to an uncertainty analysis technique for sample quantity in forest biomass remote sensing estimation, in particular to a method for determining reasonable sample number in forest biomass remote sensing estimation. Background technique [0002] Forest biomass is one of the important indicators to evaluate forest ecosystem productivity, terrestrial ecosystem function and sustainability. With the rapid development of remote sensing technology, the use of multi-source remote sensing data instead of traditional survey methods to quantify aboveground forest biomass can not only obtain information on the quantity, spatial distribution and dynamic changes of forest resources, but also combine various models and plot surveys. Realize the quantitative inversion of forest parameters (He Xingyuan et al., 2018), and save survey costs while meeting the needs of monitoring and analysis of forest resources and ecological processes at different sc...

AI Technical Summary

Problems solved by technology

It can be seen that the larger the sample size, the better the representativeness of the sample, and the higher the accuracy of the modeling, but the more manpower and material resources are consumed, and it is even difficult to complete

On the contrary, if the sample size is too small, its modeling accuracy cannot meet the demand

The invention aims to analyze the influence of the sample number on the uncertainty of forest aboveground biomass estimation, and has important scientific value for solving the problem that the sample number is difficult to determine in the traditional quantitative remote sensing inversion

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