High mountain forest line extraction method based on Otsu and edge detection algorithm of GEE

Abstract

The invention discloses a high mountain forest line extraction method based on an Otsu and edge detection algorithm of GEE, and the method comprises the steps: S1, selecting a data source: employing a multispectral land remote sensing satellite Landsat8 image with the resolution of 30 meters, or employing a Landsat7 or Landsat5 image, wherein the selected image is in the early growth season of vegetation from April to June, and the target to be extracted is an area with a high mountain forest line; s2, preprocessing the remote sensing image of the target area; s3, extracting a vegetation region in the remote sensing image by adopting a normalized vegetation index NDVI; s4, image segmentation and edge detection: introducing an Otsu algorithm in a GEE platform to realize image segmentation of an NDVI index, and further extracting a high mountain forest line by using an edge detection algorithm; and S5, carrying out post-processing on the image, and finally obtaining a distribution diagram of the high mountain forest line. The method for extracting the high mountain forest line is high in technological process automation, rapid, simple and good in extraction effect, batch operation can be achieved, and the problems that traditional field investigation is time-consuming, labor-consuming and unsafe are solved.

Description

technical field [0001] The invention relates to the technical field of ecological remote sensing, in particular to an alpine timberline extraction method based on GEE's Otsu and edge detection algorithms. Background technique [0002] The alpine timberline is a transition zone where forests, shrubs, and grasslands alternate along the elevation gradient. The dynamic changes of alpine treelines in different regions also showed different rules. However, especially in the fragile ecosystems of the Tibetan Plateau, the spatial pattern of multi-year dynamic changes in alpine treelines remains undetermined. Previous studies mainly obtained location information of alpine timberlines through historical data and field surveys. Although the field survey has good accuracy and reliability, the survey scale is small and the safety is not high, which limits the operation efficiency and applicability. At the same time, field surveys are not conducive to long-term and large-scale monitori...

AI Technical Summary

Problems solved by technology

However, supervised classification requires selection of training samples and human intervention, which brings uncertainty to long-term monitoring of alpine timberlines

At the same time, there is little difference in the spectral attributes of the remote sensing images of the vegetation near the alpine timberline, and the use of supervised classification will cause a large

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