Grassland biomass remote sensing inversion method considering grassland height factor

Abstract

The invention discloses a grassland biomass remote sensing inversion method considering a grassland height factor. According to the method, a statistical model among the grassland height, the vegetation index and the grassland biomass is established with a multi-element stepwise linear regression analysis method according to research area ground survey point data, inversion calculation is performed on research area remote sensing image data on the basis, and the grassland height variable is acquired through the grassland type, NDVI (normalized differential vegetation index) and DEM (digital elevation model) inversion. The method has the advantages that the grassland height factor is added to a grassland biomass inversion model, so that the grassland biomass remote sensing inversion accuracy can be increased, the defect that traditional vegetation index model methods lack consistency and stability for grassland in different forms can be overcome, and the method is reliable and has the practical value.

Description

technical field [0001] The invention relates to the technical field of quantitative remote sensing applications, in particular to a grassland biomass remote sensing inversion method considering grassland height factors, which is used for remote sensing quantitative inversion of grassland aboveground biomass. Background technique [0002] Grassland is the largest terrestrial ecosystem in my country. It not only has ecological functions such as water conservation and soil and water conservation, but also is the material basis for the development of grassland animal husbandry. Aboveground biomass of grassland refers to the weight of living plant material contained in the unit area above the ground surface, which is one of the important parameters of grassland ecosystem. Real-time dynamic detection of grassland growth and estimation of its yield not only helps to grasp the feedback between climate change and grassland ecosystem, but also has important significance for guiding he...

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Problems solved by technology

[0004] However, one of the biggest problems in the vegetation index model is the lack of consistency and stability between different forms of vegetation.

Li Deren pointed out that when remote sensing data are used to estimate areas with relatively high biomass, there is a problem of remote sensing information saturation, which cannot accurately reflect changes in biomass

Liu Zhanyu et al. also pointed out that the vegetation index model has a good fit for grasslands with small biomass, but a poor fit for grasslands with large biomass.

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