Cao Yu, Mo Li-jiang, Li Yan, Zhang Wen-mei
Department of Land Management, Zhejiang University, Hangzhou 310029, China.
Ying Yong Sheng Tai Xue Bao. 2009 Dec;20(12):3084-92.
Wetland landscape ecological classification, as a basis for the studies of wetland landscape ecology, directly affects the precision and effectiveness of wetland-related research. Based on the history, current status, and latest progress in the studies on the theories, indicators, and methods of wetland landscape classification, some scientific wetland classification systems, e.g., NWI, Ramsar, and HGM, were introduced and discussed in this paper. It was suggested that a comprehensive classification method based on HGM and on the integral consideration of wetlands spatial structure, ecological function, ecological process, topography, soil, vegetation, hydrology, and human disturbance intensity should be the major future direction in this research field. Furthermore, the integration of 3S technologies, quantitative mathematics, landscape modeling, knowledge engineering, and artificial intelligence to enhance the automatization and precision of wetland landscape ecological classification would be the key issues and difficult topics in the studies of wetland landscape ecological classification.
湿地景观生态分类作为湿地景观生态学研究的基础，直接影响湿地相关研究的精度和有效性。基于湿地景观分类的理论、指标和方法的研究历史、现状及最新进展，本文介绍并讨论了一些科学的湿地分类系统，如美国国家湿地清单（NWI）、《拉姆萨尔公约》湿地分类系统以及水文地貌分类法（HGM）。研究认为，基于水文地貌分类法并综合考虑湿地空间结构、生态功能、生态过程、地形、土壤、植被、水文和人类干扰强度的综合分类方法应是该研究领域未来的主要方向。此外，整合3S技术、定量数学、景观建模、知识工程和人工智能以提高湿地景观生态分类的自动化程度和精度，将是湿地景观生态分类研究中的关键问题和难点。
