Forest information remote sensing and automatic extracting method based on vegetation index time series data dispersion measures

Abstract

The invention relates to a forest information remote sensing and automatic extracting method based on vegetation index time series data dispersion measures. According to the forest information remote sensing and automatic extracting method, based on vegetation index time series data of each day in a year of each grid pixel element in a research area, according to the overall distribution condition and the distribution condition in different value ranges of the index time series data, an overall dispersion measure index, an intermediate-high dispersion measure index, a growth peak period dispersion measure index and a high value continuity index are designed; based on the principle the vegetation index time series data dispersion of a forest is small, forest classification flow chart is established, forest information is remotely sensed and automatically extracted, and finally a forest distribution map of the research area is obtained. By the adoption of the forest information remote sensing and automatic extracting method, based on the process of fully extracting the changes of vegetation index data dispersion measures of different forest types on the whole within different value ranges and different time periods, multiple dispersion indexes are established and used for forest information remote sensing and automatic extracting, and the forest information remote sensing and automatic extracting method has the advantages that the robustness is good, the classification accuracy is high, the automation degree is high, and the disturbance resistance is high.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a remote sensing automatic extraction method of forest information based on the dispersion of vegetation index time series data. Background technique [0002] Rapid and accurate access to information on the distribution of different types of forests is crucial for the study of the global carbon cycle and ensuring ecosystem security. Traditional forest resource survey methods are difficult to obtain forest spatial distribution information quickly and efficiently, and cannot meet the needs of modern forest resource management. Since remote sensing data has the advantages of large scale, high timeliness, and increasingly abundant free data, it is an effective way to carry out large-scale forest resource monitoring based on remote sensing time series data. [0003] Time-series remote sensing data can effectively describe the changing characteristics of different ground object typ...

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Problems solved by technology

But its shortcoming is that since vegetation phenology is inevitably affected by factors such as altitude, topography, and climate, it is difficult to establish standard time series curves and ideal phenological parameter distribution intervals for different ground features, which directly affects the classification accuracy.

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