Remote sensing monitoring method for regional forest volume

By using medium-resolution remote sensing data and machine learning algorithms, the spectral and topographic features of the forest area are extracted and the sample area is expanded, and the rapid and accurate monitoring of forest accumulation in large areas is achieved, which solves the problem of time-consuming and labor-consuming monitoring of traditional forest resources and improves monitoring efficiency and accuracy.

CN114624232BActive Publication Date: 2025-04-11AEROSPACE INFORMATION RES INST CAS
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Patent Information

Application Number
CN202011462620.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2020-12-14
Publication Date
2025-04-11
Estimated Expiration
2040-12-14

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Abstract

The present invention discloses a method for monitoring regional forest stock volume using LANDSAT‑8 medium-resolution remote sensing data, comprising the steps of: 1) extracting spectrum, greenness, humidity, brightness and principal component transformation features based on remote sensing data; 2) expanding measured sample plots based on the features of remote sensing images; 3) realizing optimal feature selection based on a random forest algorithm, and using three non-parametric machine learning algorithms and two groups of sample combinations to optimize model parameters and realize regional forest stock volume monitoring. The present invention uses openly accessible LANDSAT and SRTM DEM satellite remote sensing data to expand the measured limited samples, and then realizes the rapid inversion of regional forest stock volume through a variety of sample combination methods, which provides technical support for the dynamic monitoring of forest stock volume, and also changes the high-intensity and high-cost traditional operation mode of artificially establishing forest sample plots to obtain forest stock volume, which can provide technical support for the macro-control of forest resources.
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Citation Information

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