深度学习的湿地生态资产清查与价值评估体系构建研究

Research on the Construction of Wetland Ecological Asset Inventory and Value Assessment System Based on Deep Learning

摘要: 该研究基于深度学习技术，构建了湿地生态资产清查与价值评估体系。通过遥感影像分析和生态学评估方法，深入探索了湿地生态系统的多样性与功能性，提出了一个有效的湿地生态资产评估模型。研究结果表明，不同类型湿地在生态服务功能上的价值存在显著差异，尤其是河流湿地、湖泊湿地及红树林湿地在碳汇、水源涵养和生物多样性保护等方面的贡献尤为突出。通过对多个湿地区域的实证研究，验证了模型的精度与可靠性，为湿地保护和生态补偿政策提供了科学依据。研究发现，尽管现有模型具有较高的精度，但仍需进一步优化以提高其适用性和精度，尤其是在不同湿地类型和区域的应用中。该研究为湿地的可持续管理与政策制定提供了重要支持，具有较高的理论与实践价值。

Abstract: This study constructs a wetland ecological asset inventory and value assessment system based on deep learning technology. Through remote sensing image analysis and ecological assessment methods, the diversity and functionality of wetland ecosystems were thoroughly explored, and an effective wetland ecological asset evaluation model was proposed. The results reveal significant differences in the value of ecological service functions among different wetland types, with river wetlands, lake wetlands, and mangrove wetlands demonstrating particularly prominent contributions in carbon sequestration, water conservation, and biodiversity protection. Empirical studies across multiple wetland regions verified the accuracy and reliability of the model, providing a scientific basis for wetland conservation and ecological compensation policies. The study found that while the current model achieves high accuracy, further optimization is needed to enhance its applicability and precision, especially across diverse wetland types and regions. This research offers critical support for sustainable wetland management and policy formulation, demonstrating significant theoretical and practical value.