Grassland aboveground biomass estimation method and system based on vegetation classification

By acquiring multi-source data and using machine learning models for vegetation classification and feature extraction, the problem of low accuracy in grassland biomass estimation in arid areas was solved, and high-precision biomass prediction was achieved.

CN122244506APending Publication Date: 2026-06-19XINJIANG UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XINJIANG UNIVERSITY
Filing Date
2026-03-10
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies have low accuracy in estimating aboveground biomass in grasslands in arid regions, especially when vegetation cover is sparse and the non-vegetation background is significant, making it difficult to achieve efficient dynamic monitoring.

Method used

By acquiring aerial imagery, satellite remote sensing imagery, and environmental factor data, vegetation is classified using a pre-trained machine learning classification model. Vegetation patch features and multi-dimensional feature variables are extracted, and biomass is estimated using a machine learning regression model, eliminating noise interference from non-vegetation-covered areas.

Benefits of technology

It significantly improves the accuracy of grassland biomass estimation in arid regions, overcomes the impact of mixed vegetation and non-vegetation disturbances, and provides high-precision biomass predictions.

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Abstract

This application discloses a method and system for estimating grassland aboveground biomass based on vegetation classification. The method includes: acquiring aerial image data, satellite remote sensing image data, and environmental factor data of vegetation in a target area; classifying the aerial image data based on a pre-trained machine learning classification model to obtain classification results; the classification results include vegetation cover area data; extracting vegetation patch features based on the vegetation cover area data; determining multi-dimensional feature variables based on the vegetation patch features, satellite remote sensing image data, and environmental factor data; and estimating the multi-dimensional feature variables based on a pre-trained machine learning regression model to obtain the predicted value of grassland aboveground biomass in the target area.
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