Vegetation index re-synthesis algorithm

Abstract

The invention discloses a vegetation index re-synthesis algorithm, and belongs to the technical field of vegetation coverage change research. According to the method, EVI and NDVI are resynthesized interms of time and space, and the method specifically comprises the following steps: extracting monthly NDVI and monthly EVI of a to-be-detected region within nearly 5-10 years; synthesizing a monthlyNDVI value and an EVI value by using a maximum value synthesis method and a mean value synthesis method, and determining an EVIMax value, an EVIMean value, an NDVIMax value and an NDVIMean value as basic data of the synthesized VI through correlation analysis; carrying out nonlinear normalization processing on the EVIMax value, the EVIMean value, the NDVIMax value and the NDVIMean data, and calculating a synthetic vegetation index VI. The vegetation cover change analyzed by the synthetic vegetation index has better research effect than a single vegetation index.

Description

technical field [0001] The invention relates to a vegetation index resynthesis algorithm, which belongs to the technical field of vegetation coverage change research. Background technique [0002] As an important part of the natural geographical environment, vegetation affects the energy balance of the natural ecological environment, and plays an important role in soil erosion and soil erosion. Since the 21st century, remote sensing technology has been widely used in agricultural research, ecological monitoring, regional planning, etc., providing effective means for large-scale vegetation detection and quantification. Many domestic and foreign scholars have studied vegetation cover changes through vegetation indices, and have achieved a large number of results. Benewinde J-B et al. used the normalized difference vegetation index (Normalized Difference Vegetation Index, NDVI) change trend and the land use change score (LandUse / Land Cover Change, LULCC) to study the reasons f...

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Problems solved by technology

[0003] Previous studies mostly used NDVI and EVI alone to study and analyze vegetation cover changes. NDVI has the characteristics of being able to accurately reflect the growth of vegetation in a wide range, which is of great significance to the study of seasonal and annual changes. However, when the ground vegetation is dense , the NDVI vegetation index will appear saturated; EVI can better solve the saturation problem of NDVI, but at the same time it will also extract pixels similar to the spectral characteristics of vegetation, which will eventually affect the results of vegetation change analysis

Generally speaking, when NDVI and EVI are used alone, they are not effective in studying changes in vegetation coverage.

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