A land consolidation ecological environment dynamic monitoring method based on multi-source remote sensing data

CN121708458BActive Publication Date: 2026-08-28GUANGXI ZHUANG AUTONOMOUS REGION NATURAL RESOURCES ECOLOGICAL RESTORATION CENT
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Patent Information

Application Number
CN202511653642.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-12
Publication Date
2026-08-28
Estimated Expiration
2045-11-12

AI Technical Summary

Technical Problem

[0005]本发明提供一种基于多源遥感数据的土地整治生态环境动态监测方法,其主要目的在于解决多源遥感数据在土地整治生态环境动态监测中,存在时序缺失与模态差异带来的对齐困难和重建误差,难以保障监测的连续性与准确性的问题

Benefits of technology

[0015]本发明通过获取覆盖土地整治区域的时序遥感数据集,包括光学遥感图像和合成孔径雷达图像,对所述时序遥感数据集进行超分辨插值,得到标准遥感数据集,能够显著提高图像的空间分辨率和细节呈现,进而增强对土地整治区域的监测能力,为后续的动态监测和生态变化分析提供了更为精确的遥感数据基础,提取所述光学遥感图像的缺失光学图像以及所述缺失光学图像的相邻遥感图像,通过提取光学遥感图像的近红外波段和短波红外波段,并筛选出云层区域,可以有效识别出由于云层遮挡造成的图像缺失区域,根据所述相邻遥感图像和所述合成孔径雷达图像利用预设的时序重建配对模型对所述缺失光学图像进行重建,将得到的重建光学图像替换所述缺失光学图像,得到光学遥感重建图像,利用相邻遥感图像和合成孔径雷达图像,结合预设的时序重建配对模型,不仅恢复了缺失的光学图像,还充分考虑了时空信息的连续性和相互关系,提高了光学遥感重建图像的完整性,减小了监测误差,计算所述光学遥感重建图像和所述光学遥感图像的重建残差,并根据所述重建残差识别所述覆盖土地整治区域的整治真实生态变化区域,通过像素级别的差异计算和灰度直方图分析,能够准确识别图像中变化显著的区域,通过自动阈值选择和空间重叠分析,确保了变化区域的精准识别,并且只关注与土地整治相关的实际生态变化,将所述光学遥感重建图像对所述标准遥感数据集进行更新,得到更新遥感数据集,并根据所述更新遥感数据集生成所述整治真实生态变化区域的生态指数变化可视图,提取光谱特征并计算多个植被指数(如归一化植被指数、增强型植被指数和土壤调节植被指数)有助于全面反映生态状况的变化,生成的生态指数变化可视图直观展示了土地整治区域内的生态变化,利用所述生态指数变化可视图对生态环境的土地整治进行动态监测,有效降低了时序缺失与模态差异带来的对齐困难和重建误差,从而提高监测的连续性与准确性。

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Abstract

The present application relates to the technical field of environmental dynamic monitoring, and discloses a land consolidation ecological environment dynamic monitoring method based on multi-source remote sensing data, which comprises the following steps: performing super-resolution interpolation on a time-series remote sensing data set covering a land consolidation area to obtain a standard remote sensing data set; according to adjacent remote sensing images of the missing optical image in the optical remote sensing image and a synthetic aperture radar image, combining a preset time-series reconstruction pairing model to reconstruct the missing optical image, and replacing the optical remote sensing reconstructed image, calculating the reconstruction residual of the optical remote sensing reconstructed image and the optical remote sensing image, and identifying a real ecological change area of consolidation; generating an ecological index change visual map of the real ecological change area of consolidation according to an updated remote sensing data set; and dynamically monitoring the land consolidation of the ecological environment by using the ecological index change visual map. The present application effectively reduces the alignment difficulty and reconstruction error caused by time-series missing and modal difference, thereby improving the continuity and accuracy of monitoring.
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