Hyperspectral inversion method of tree species leaf area index

Abstract

The invention discloses a hyperspectral inversion method of a tree species leaf area index. The method comprises the steps that leaf blade spectrum reflectivity and leaf blade leaf area index of subtropical evergreen broad-leaf forest tree species are collected; regression analysis is conducted on the basis of a vegetation index method of an experiential model, and the change rule of the leaf blade spectrum reflectivity and the leaf blade leaf area index and a quantitative relation of the leaf blade spectrum reflectivity and the leaf blade leaf area index are analyzed; a corresponding model simulation value is obtained on the basis of an inversion model; a root-mean-square error is adopted for inspecting the degree of fitting between the model simulation value and a measured value; the optimal subtropical evergreen broad-leaf forest tree species hyperspectral vegetation characteristic parameter inversion module is determined on the basis of the root-mean-square error value. Accordingly, regression analysis is conducted by means of the vegetation index method of the experiential model, hyperspectral data is built to obtain the inversion model of the optimal regional representative mixed tree species of LAI, LAI of evergreen broad-leaf forest in South China is inverted, and scientific basis is provided for operating management in southern subtropical forest.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a hyperspectral inversion method for tree species leaf area index. Background technique [0002] Accurate quantitative estimation of vegetation biochemical and physiological parameters is very useful for agricultural, ecological, and meteorological applications. As an important model input parameter, its temporal and spatial distribution is often used to quantify the material and energy transformation between the land surface and the atmosphere. Leaf area index (LAI), as one of the important characteristic parameters of community structure, is one of the most basic parameters to characterize the vegetation canopy structure, which controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, etc. , carbon cycle, and precipitation interception. In recent years, hyperspectral remote sensing technology has provided a new meth...

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Problems solved by technology

[0009] Multiple regression analysis involves many factors, and it is necessary to measure which factors are used to express. There are deficiencies in evaluating the model with multiple correlation coefficients. Even if the parameters added to the model are not statistically significant, the R value will still increase; another On the one hand, due to the complexity of the physical model inversion algorithm, there are too many factors to consider, and many input parameters are difficult to obtain, which limits the inversion accuracy.

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