Constructing method for topographic correction vegetation index

Abstract

The invention relates to a constructing method for topographic correction vegetation index, comprising the following steps of: (1) carrying out radiative correction with different degrees according toobtained optical remote sensing image data source; (2) carrying out judgment and processing to nonvegetated dark matters; and (3) carrying out calculation to topographic correction vegetation index (TCVI). The method is adopted to construct the topographic correction vegetation index, thus making up the defect that the existing vegetation index can not eliminate topographic influence and eliminating the dependence to the topographic correction method based on DEM data. The invention can quantitatively and fast reflect the vegetable information of mountain areas only by needing two wave bandsof data, namely an infrared wave band and a near-infrared wave band.

Description

technical field [0001] The invention relates to a method for constructing terrain correction vegetation index. Background technique [0002] Due to the influence of terrain in mountainous and hilly areas, there is a huge contrast in the band radiance information between the shady and sunny slopes of the mountain in optical remote sensing images, which seriously affects the accuracy of remote sensing quantitative inversion of vegetation information in mountainous areas. [0003] The current inversion of vegetation information from optical remote sensing images mainly uses the method of vegetation index. The most commonly used vegetation indices are Normalized Difference Vegetation Index NDVI, Ratio Vegetation Index RVI, etc.; indices proposed to eliminate atmospheric influences include anti-atmospheric vegetation index ARVI, greenness anti-atmospheric vegetation index GARVI, visible light anti-atmospheric index VARI, etc. ; There are also indices proposed to eliminate the in...

AI Technical Summary

Problems solved by technology

This method does not require additional input data, but because terrain changes have different effects on different bands, this model can only suppress the effect of terrain, but cannot achieve the ideal terrain correction effect. In recent years, there have been few related studies

There are also studies that use the grayscale linear matching method to correct the hillshade, but because the selected samples cannot cover the entire image, only a linear function derived from this method for band correction has great limitations

However, there is no relevant research on whether the method of removing clouds and building shadows in urban high-resolution remote sensing images can improve the retrieval accuracy of vegetation index information in mountainous areas

[0006] In summary, both the existing vegetation index and the terrain correction method have obvious deficiencies in the remote sensing quantitative inversion of vegetation information in mountainous areas.

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