摘 要
森林病虫害是威胁全球森林生态系统健康和可持续发展的重要因素，传统监测方法存在效率低、覆盖范围有限等问题。为此，本研究提出了一种基于多源数据融合技术的森林病虫害监测新方法，旨在通过整合遥感影像、地面传感器数据及无人机采集信息，构建高精度、全方位的监测体系。研究采用数据同化算法与深度学习模型相结合的技术路线，实现了异构数据的高效融合与病虫害早期识别。实验结果表明，该方法在病虫害识别准确率上较单一数据源提升了25%以上，同时显著降低了误报率。此外，本研究开发的实时监测平台能够为林业管理部门提供决策支持，提升病虫害防控的时效性和科学性。研究的主要创新点在于首次将多源数据融合技术系统性应用于森林病虫害监测领域，并提出了适用于复杂森林环境的数据处理框架，为相关研究提供了新的思路和技术参考。关键词：森林病虫害监测; 多源数据融合; 遥感影像; 深度学习模型; 数据同化算法
Abstract
Forest pests and diseases are significant factors threatening the health and sustainable development of global forest ecosystems, while traditional monitoring methods suffer from low efficiency and limited coverage. To address these issues, this study proposes a novel forest pest and disease monitoring method based on multi-source data fusion technology, which aims to construct a high-precision and comprehensive monitoring system by integrating remote sensing images, ground sensor data, and information collected by unmanned aerial vehicles. The study adopts a technical approach combining data assimilation algorithms with deep learning models, achieving efficient fusion of heterogeneous data and early identification of pests and diseases. Experimental results indicate that this method improves the accuracy of pest and disease recognition by more than 25% compared to single-data-source approaches, while significantly reducing false alarm rates. Furthermore, the real-time monitoring platform developed in this study provides decision support for forestry management departments, enhancing the timeliness and scientific nature of pest and disease control. The primary innovation of this research lies in its systematic application of multi-source data fusion technology to the field of forest pest and disease monitoring for the first time, as well as the proposal of a data processing fr amework suitable for complex forest environments, offering new ideas and technical references for related studies.Key words:Forest Pest Monitoring; Multi-Source Data Fusion; Remote Sensing Image; Deep Learning Model; Data Assimilation Algorithm
目 录
摘 要 I
Abstract II
引 言 1
第1章、多源数据融合技术概述 3
1.1、数据融合的基本概念 3
1.2、森林病虫害监测需求分析 3
1.3、多源数据融合技术的应用背景 4
第2章、数据采集与预处理方法 5
2.1、遥感数据的获取与处理 5
2.2、地面传感器数据的采集与校准 5
2.3、数据质量评估与清洗策略 6
第3章、数据融合算法及其优化 7
3.1、基于层次分析的数据融合方法 7
3.2、融合算法的性能评价指标 7
3.3、算法优化与实时性提升研究 8
第4章、监测系统设计与实践应用 9
4.1、系统架构设计与功能实现 9
4.2、实时监测中的多源数据融合案例 9
4.3、技术应用效果与改进方向 9
结 论 11
参考文献 12
