摘 要
湿地生态系统作为地球上重要的自然资源，具有调节气候、净化水质和维持生物多样性等关键功能，然而其动态变化受人类活动与自然因素的双重影响，亟需科学监测与评估。本研究以遥感影像解译技术为核心，结合地理信息系统（GIS）分析方法，探讨其在湿地生态系统研究中的应用潜力。研究选取典型湿地区域为案例，利用多源遥感数据（包括高分辨率光学影像与合成孔径雷达影像），通过机器学习算法与深度学习模型实现湿地分类与变化检测。结果表明，基于卷积神经网络（CNN）的分类方法能够显著提高湿地类型识别的精度，相较于传统监督分类方法，总体精度提升超过10%；同时，时间序列分析揭示了研究区湿地面积在过去十年间的显著缩减趋势，年均减少速率为2.3%。此外，本研究创新性地引入了不确定性分析框架，量化了遥感解译过程中的误差来源及其对结果的影响，为提高湿地监测的可靠性提供了理论支持。研究表明，遥感影像解译技术不仅能够高效获取湿地空间分布与动态变化信息，还为湿地保护政策制定提供了科学依据。主要贡献在于提出了适用于复杂湿地环境的智能化解译方案，并验证了其在实际应用中的可行性和优越性，为未来湿地生态系统的精细化管理奠定了技术基础。
关键词：湿地生态系统；遥感影像解译；卷积神经网络
ABSTRACT
Wetland ecosystems, as vital natural resources on Earth, play crucial roles in climate regulation, water quality purification, and biodiversity maintenance. However, their dynamic changes are influenced by both human activities and natural factors, necessitating scientific monitoring and evaluation. This study focuses on remote sensing image interpretation techniques combined with Geographic Information System (GIS) analysis methods to explore their application potential in wetland ecosystem research. A typical wetland area was selected as a case study, utilizing multi-source remote sensing data, including high-resolution optical images and Synthetic Aperture Radar (SAR) images. Machine learning algorithms and deep learning models were employed for wetland classification and change detection. The results indicate that the classification method based on Convolutional Neural Networks (CNN) significantly improves the accuracy of wetland type identification, with an overall accuracy increase of over 10% compared to traditional supervised classification methods. Meanwhile, time-series analysis reveals a significant reduction trend in the wetland area of the study region over the past decade, with an average annual decrease rate of 2.3%. Additionally, this study innovatively introduces an uncertainty analysis fr amework to quantify error sources during the remote sensing interpretation process and their impacts on the results, providing theoretical support for enhancing the reliability of wetland monitoring. The study demonstrates that remote sensing image interpretation techniques can efficiently acquire spatial distribution and dynamic change information of wetlands and offer scientific evidence for wetland protection policy formulation. The primary contribution lies in proposing an intelligent interpretation scheme suitable for complex wetland environments and validating its feasibility and superiority in practical applications, laying a technical foundation for the refined management of future wetland ecosystems.
Keywords: Wetland Ecosystem; Remote Sensing Image Interpretation; Convolutional Neural Network
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 湿地生态系统研究的背景与意义 1
1.2 遥感影像解译技术的研究现状 1
1.3 本文研究方法与技术路线 2
第2章 遥感影像解译技术的基础理论 3
2.1 遥感影像的基本原理与特性 3
2.2 常用遥感影像解译方法概述 3
2.3 解译技术在湿地研究中的适配性分析 4
2.4 遥感数据获取与预处理的关键步骤 4
第3章 遥感影像解译在湿地动态监测中的应用 6
3.1 湿地变化检测的技术框架 6
3.2 不同时相影像的对比分析方法 6
3.3 湿地面积变化的定量评估 7
3.4 动态监测结果的验证与误差分析 7
第4章 遥感影像解译在湿地生态评估中的实践 9
4.1 湿地生态健康指标体系构建 9
4.2 基于遥感影像的植被覆盖度分析 9
4.3 土地利用类型对湿地生态的影响评估 10
4.4 解译结果在生态管理中的应用前景 10
结论 12
参考文献 13
致 谢 14
