A method for artificially promoting the regeneration of natural secondary fir forests

By employing hyperspectral remote sensing technology and a multi-level collaborative search method, the challenge of identifying structural variations in natural fir secondary forests was solved, enabling precise ecological restoration and enhancement of ecological functions in natural fir secondary forests, thus meeting the needs of efficient ecological restoration.

CN122223565BActive Publication Date: 2026-07-17SICHUAN FORESTRY RES INST (SICHUAN FORESTRY IND RES & DESIGN INST)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SICHUAN FORESTRY RES INST (SICHUAN FORESTRY IND RES & DESIGN INST)
Filing Date
2026-05-20
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional methods are insufficient to accurately identify structural variations within natural fir secondary forests, resulting in poor ecological function restoration. Furthermore, large-scale forest stand surveys are time-consuming and labor-intensive, failing to meet the needs of efficient ecological restoration.

Method used

Using hyperspectral remote sensing technology, a training sample set and a multi-level collaborative search method are constructed to automatically optimize the weights and classification thresholds of spectral features, simulate swarm intelligence division of labor and cooperation, and conduct statistical analysis and ecological evaluation of the mixed ratio of natural fir secondary forests.

Benefits of technology

It has achieved precise ecological restoration of natural secondary fir forests, enhanced ecological functions, shortened succession time, and met the needs of large-scale and efficient ecological restoration.

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Abstract

This invention discloses a method for artificially promoting the regeneration of natural secondary fir forests, relating to the field of ecological restoration technology. The method includes the following steps: S1, acquiring original hyperspectral images of the target secondary forest, dividing them into blocks, constructing a training sample set for classification, and determining the mixing ratio of each image block; S2, extracting several ecological indicators from each image block and determining the optimal reference block; S3, performing ecological regeneration based on the mixing ratio of the optimal reference block. This invention does not use a fixed artificial ratio as the restoration target, but rather selects the patches whose functions are closest to the original forest from naturally existing local patches within the secondary forest through grey relational analysis of multidimensional ecological function indicators, using their mixing ratio as the adjustment target. This invention fully considers the spatial heterogeneity of forest stands, making the restoration target ecologically reasonable and automatically adjustable according to different regions, effectively promoting the natural regeneration and ecological function enhancement of secondary forests.
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