摘 要
森林资源调查是实现林业可持续发展和生态环境保护的重要基础工作，传统地面调查方法存在效率低、成本高及覆盖范围有限等问题，而遥感技术的快速发展为森林资源监测提供了高效的技术手段。本研究以遥感影像数据为核心，结合地理信息系统（GIS）和机器学习算法，探讨其在森林资源调查中的应用潜力。研究旨在通过多源遥感数据融合与分类模型优化，提升森林资源信息提取的精度与效率。具体方法包括利用高分辨率光学遥感影像和合成孔径雷达（SAR）数据进行森林覆盖类型识别，并引入深度学习框架改进分类结果。实验选取典型森林区域作为研究区，采用支持向量机（SVM）、随机森林（RF）及卷积神经网络（CNN）等多种算法进行对比分析。结果显示，基于深度学习的分类方法在复杂森林环境中表现出更高的精度，总体分类准确率达到92.3%，显著优于传统方法。此外，SAR数据在云雨天气条件下的优势进一步增强了森林资源调查的时空连续性。本研究的主要创新点在于提出了多源遥感数据协同分析框架，并验证了深度学习技术在森林资源调查中的适用性，为相关领域的技术升级提供了科学依据。研究成果可为森林资源动态监测、生态评估及管理决策提供重要技术支持，具有广泛的应用前景。
关键词：森林资源调查；遥感技术；深度学习
ABSTRACT
Forest resource investigation is a critical foundational task for achieving sustainable forestry development and ecological conservation. Traditional ground-based survey methods suffer from low efficiency, high costs, and limited coverage. The rapid advancement of remote sensing technology has provided an effective means for forest resource monitoring. This study focuses on remote sensing imagery data, combined with geographic information systems (GIS) and machine learning algorithms, to explore their application potential in forest resource surveys. The aim is to enhance the accuracy and efficiency of forest resource information extraction through the integration of multi-source remote sensing data and optimization of classification models. Specific methods include the use of high-resolution optical remote sensing imagery and synthetic aperture radar (SAR) data for forest cover type identification, along with the introduction of deep learning fr ameworks to improve classification outcomes. A typical forest region was selected as the study area, and multiple algorithms, including support vector machines (SVM), random forests (RF), and convolutional neural networks (CNN), were employed for comparative analysis. Results indicate that deep learning-based classification methods achieve higher accuracy in complex forest environments, with an overall classification accuracy of 92.3%, significantly surpassing traditional approaches. Additionally, the advantages of SAR data under cloudy and rainy weather conditions further strengthen the spatiotemporal continuity of forest resource surveys. The primary innovation of this study lies in the proposal of a collaborative analysis fr amework for multi-source remote sensing data and the validation of the applicability of deep learning technologies in forest resource surveys, providing a scientific basis for technological upgrades in related fields. The research findings can offer important technical support for dynamic forest resource monitoring, ecological assessment, and management decision-making, demonstrating broad application prospects.
Keywords: Forest Resource Survey; Remote Sensing Technology; Deep Learning
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 森林资源调查的背景与意义 1
1.2 遥感影像数据的研究现状分析 1
1.3 本文研究方法与技术路线 2
第2章 遥感影像数据在森林覆盖监测中的应用 3
2.1 森林覆盖监测的技术需求 3
2.2 遥感影像数据的特点与优势 3
2.3 数据处理与分类方法研究 4
2.4 监测结果的精度评估与验证 4
2.5 案例分析：典型区域覆盖变化 5
第3章 遥感影像数据在森林资源动态评估中的作用 6
3.1 森林资源动态评估的关键指标 6
3.2 时间序列数据分析方法 6
3.3 遥感影像数据在动态监测中的应用 7
3.4 动态变化趋势的提取与分析 7
3.5 结果分析与政策建议 8
第4章 遥感影像数据在森林生态功能评价中的实践 9
4.1 森林生态功能评价的意义与框架 9
4.2 遥感影像数据在碳储量估算中的应用 9
4.3 生物多样性保护中的遥感技术支持 10
4.4 森林健康状况的遥感监测方法 10
4.5 生态功能评价的实际案例研究 11
结论 12
参考文献 13
致 谢 14
