A grassland degradation monitoring method based on remote sensing monitoring

By integrating multi-source, multi-temporal remote sensing data and using a convolutional neural network model for spatiotemporal feature fusion, the data integration challenge in grassland degradation monitoring has been solved, enabling accurate assessment and dynamic monitoring of grassland ecosystems and supporting scientific management and protection.

CN120913071BActive Publication Date: 2026-03-24INSTITUTE OF GRASSLAND RESEARCH OF CAAS
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Patent Information

Application Number
CN202511024986.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-24
Publication Date
2026-03-24
Estimated Expiration
2045-07-24

AI Technical Summary

Technical Problem

Existing methods are unable to effectively integrate multi-source remote sensing data in grassland degradation monitoring, and cannot accurately capture the complex dynamic changes of grassland ecosystems, resulting in insufficient monitoring accuracy and spatiotemporal coverage, making it difficult to meet the needs of scientific management and precise restoration.

Method used

By collecting multi-source, multi-temporal remote sensing data, performing radiometric and terrain-adaptive corrections, extracting multi-dimensional feature parameters, and combining time series analysis and convolutional neural network models to fuse spatiotemporal features, a degradation level classification map is generated. This map is then validated in multiple dimensions using historical data and environmental factors, ultimately outputting a refined grassland degradation assessment report.

Benefits of technology

It has enabled precise monitoring of grassland degradation in complex terrain areas, providing a scientific basis for grassland ecological protection and sustainable utilization, and improving the accuracy and comprehensiveness of monitoring.

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Abstract

The application discloses a grassland degradation monitoring method based on remote sensing monitoring, comprising: acquiring multi-source remote sensing data; preprocessing the multi-source remote sensing data to obtain preprocessed remote sensing data; extracting multi-dimensional feature parameters based on the preprocessed remote sensing data to obtain a multi-dimensional feature parameter set; performing time sequence analysis on vegetation dynamic characteristics, soil dynamic characteristics and human disturbance characteristics by combining multi-time observation data through the multi-dimensional feature parameter set to construct a dynamic characteristic time sequence data set; performing deep fusion on spatial features and time sequence changes in the time sequence by using a convolutional neural network model according to the dynamic characteristic time sequence data set to generate a spatio-temporal fusion feature set; obtaining a degradation grade division result map according to the spatio-temporal fusion feature set; integrating multi-source remote sensing data based on the degradation grade division result map to obtain a refined grassland degradation evaluation result, and monitoring grassland degradation according to the evaluation result.
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