森林资源监测中的多式联运数据融合研究进展
Ming Wang1, Qian Zhang1, Xin Liu1
1Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Frontiers in plant science
|February 4, 2026
概括
多模式数据融合通过整合卫星,无人机和地面传感器数据来增强森林监测. 这种"空中空间-地面"方法解决了数据采集和数据融合方面的挑战,以实现智能,数据驱动的林业管理.
科学领域:
- 林业科学 林业科学
- 遥感 遥感 遥感 遥感
- 数据融合技术数据融合技术
背景情况:
- 动态森林资源监测对于生态安全至关重要.
- 使用单一数据源的传统方法对于精细的森林管理是不够的.
- 到2024年,全球森林损失将大幅增加80%,这凸显了技术进步的必要性.
研究的目的:
- 从2020-2025年开始,系统地审查用于森林资源监测的多式联络数据融合技术.
- 建立一个全面的技术链,从数据采集到融合战略.
- 确定和解决关键的技术挑战,并提出未来的研究方向.
主要方法:
- 建立一个"空中空间-地面"协作网络,集成卫星遥感,无人机超频谱和地面传感器.
- 使用多模式数据融合 (光学,雷达,LiDAR) 与机器学习和深度学习相结合.
- 根据技术特征,场景应用和挑战突破,审查进展.
主要成果:
- 建立了一个完整的技术链,用于数据采集,预处理和多策略融合.
- 在树种分类,土地监测和灾害评估方面有显著的应用.
- 在LiDAR精度,时空记录,融合战略和边缘部署方面发现了关键瓶.
结论:
- 该研究提供了一个标准化的"空中空间-地面"数据技术链和一个适应场景的融合框架.
- 未来的方向包括模型轻量化和边缘计算,以加强林业监测.
- 该研究支持向智能,数据驱动的林业管理过渡.
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