A Alpine Timberline Extraction Method Based on GEE's Otsu and Edge Detection Algorithm

Abstract

The invention discloses a method for extracting alpine timberlines based on GEE's Otsu and edge detection algorithms, comprising steps: S1, selecting a data source: using Landsat8 images of multi-spectral land remote sensing satellites with a resolution of 30 meters, or using Landsat7 or Landsat5 Image; the selected image is in April-June, the early growth season of vegetation, and the target to be extracted is the area with alpine timberline; S2, preprocessing the remote sensing image of the target area; S3, using the normalized difference vegetation index NDVI to extract the remote sensing Vegetation area in the image; S4, image segmentation and edge detection: introduce the Otsu algorithm in the GEE platform to realize the image segmentation of the NDVI index, and further use the edge detection algorithm to extract the alpine treeline; S5, post-process the image, and finally get the alpine Treeline distribution map. The technical process of extracting alpine timberline by the method of the present invention is highly automated, fast and simple, has good extraction effect, can realize batch operation, and avoids the time-consuming, laborious and unsafe problems of traditional field surveys.

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 number of pixel confusion

In addition, the alpine timberline obtained from the vegetation type boundary is not accurate, which violates the definition of alpine timberline as a vegetation transition zone

However, using the empirical NDVI index to extract alpine treelines is not adaptable to different imaging times, different research areas and different satellite products.

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