Vegetation change occurrence time detection method based on time series similarity

Abstract

The invention relates to a vegetation change occurrence time detection method based on time series similarity, comprising: first, establishing multi-year time-space continuous vegetation index time series data of a research area, calculating JM distance between vegetation index time series curves of each past year and the initial year pixel by pixel, year by year, and generating a time series curve for the JM distance of each past year and the original year; fitting the time series curve for the JM distance of each past pear and the original year by using a logistic model, and acquiring time parameter from logistic model parameters so as to automatically extract vegetation change time. In the method, the JM distances of vegetation index time series curves between the past years and the initial year are used to indicate time series similarity, and vegetation change time is acquired from change law of yearly time series similarity. The method is effective in detecting changes of time series curves in terms of amplitude, frequency and the like, the complex step of decomposing original spectral index time series data is avoided, and the problem that it is difficult to extract indexes directly from original spectral index time series data to provide comprehensive characterization of vegetation changes is solved.

Description

technical field [0001] The technical field of data mining of the present invention relates in particular to a method for detecting the occurrence time of vegetation change based on time series similarity. Background technique [0002] Vegetation plays a very important role in the balance of the ecosystem by providing us with oxygen and food. Vegetation change is closely related to global change, so it has attracted much attention. Currently vegetation change monitoring focuses on forest vegetation change. In terms of vegetation remote sensing dynamic monitoring, the commonly used methods are LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) and BFAST (Break Detection For Additive and Trend) methods. These change monitoring methods based on the time series data of remote sensing images provide a new development orientation for the continuous monitoring of vegetation change in time and space. However, these methods are generally based on spectral in...

AI Technical Summary

Problems solved by technology

Since the original band reflectance data of remote sensing images are often affected by various factors such as atmospheric conditions and changes in the sun's altitude angle, there is inevitably a certain degree of uncertainty in the spectral index data calculated on this basis.

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