一种基于卫星遥感的森林生物量动态监测方法、系统、设备及存储介质

By constructing a multi-source remote sensing dataset, supplementing missing information with convolutional neural networks and synthetic aperture radar data, and combining it with the random forest algorithm, high-precision dynamic monitoring of forest biomass in complex environments was achieved. This solved the problem of insufficient data fusion in existing technologies and improved monitoring accuracy and real-time performance.

CN121366357BActive Publication Date: 2026-07-17CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF GEOSCIENCES (WUHAN)
Filing Date
2025-11-05
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies struggle to efficiently integrate multi-source remote sensing data, such as optical and radar data, in complex terrains and variable environments. This results in insufficient accuracy and real-time performance in monitoring forest biomass dynamics, failing to reflect the true state and changing trends of forests in a timely manner.

Method used

Optical images and radar signals were collected by satellite platform to construct a multi-source remote sensing dataset. Convolutional neural network was used to extract vegetation texture and height features. Synthetic aperture radar data was combined to supplement the missing cloud cover. A biomass distribution map was generated using the random forest algorithm, and the biomass change trend was calculated through time series analysis.

Benefits of technology

It enables high-precision, real-time dynamic monitoring of forest biomass in complex environments, improving data accuracy and coverage, and supporting ecological protection and resource management.

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Abstract

本发明提供了一种基于卫星遥感的森林生物量动态监测方法、系统、设备及存储介质。方法包括：通过卫星平台采集待监测森林的光学影像和雷达信号，获取多源遥感数据集并进行预处理；基于预处理后的多源遥感数据集，采用卷积神经网络提取植被纹理和高度特征获得初始生物量分布图；基于初始生物量分布图中的光学影像区域云层覆盖率，通过合成孔径雷达数据补充缺失部分获得完整生物量分布图；基于完整生物量分布图获取光学影像和雷达数据的谱段信息，结合随机森林算法获得融合生物量估计值；基于融合生物量估计值，在连续时间序列中计算差分图像获得生物量变化趋势，实现待监测森林生物量的动态监测。本发明实现了高精度、实时的森林生物量动态分析。
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Citation Information

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