植被的分类和描述是认识植物与环境相互关系、开发利用植物资源、制定生态保护政策和开展生物多样性保护的基础，一直受到国内外研究者的重视和广泛研究。本项目选择石灰岩青冈林，通过历史植被样方资料收集及其环境信息补充调查、系统的野外植被样方调查，建立统一的植被样方数据库，采用EcoVeg、样方分类、TWINSPAN、COCKTAIL、DCA群落排序等分析方法进行基于样方的数量分类，使用GIS空间分析、利用便利性、专家分类、野外验证等方法对各分类结果进行综合权衡与优化，确定石灰岩青冈林群系与群丛的数量，揭示各群系和群丛间的关键鉴别特征，提供群丛和群系的描述规范与各群丛的具体描述，建立与国际分类系统兼容、与国内实际相适应的中国石灰岩青冈林分类系统。研究结果可为国内植被分类提供规范化样方数据获取与整合、基于样方的植被数量分类、植被分类系统修订等工作提供重要参考范例。

Vegetation classification and description are the basis for understanding the relationship between plants and environment, exploiting and utilizing plant resources, formulating ecological policies and biodiversity conservation strategies, and have always been the key research topics in ecology. In this project, forests of Cylobalanopsis glauca in limestone areas in Southern China were selected as a typical case study to demonstrate a plots-based, numerical classification. A plot database covering overall distribution of the forest type will be established by collecting historical plots and supplementary investigation for omitted environmental information, and by implementing systematic field surveys, according to the international standards for natural vegetation classification. EcoVeg approach, plot-based classification and quantitative methods such as two-way indicator species analysis (TWINSPAN), COCKTAIL and detrended correspondence analysis (DCA) will be used to distinguish plant associations and alliances. The classification results should be adjusted and optimized according to experts’ opinion, field validation and weighed on practical needs for restoration of rocky-desert karstification. The diagnose characteristics between associations and alliances will be revealed. A guideline for the descriptions of plant associations and alliances, and details of descriptions of them will be provided. A new classification system and hierarchy of Cyclobalanopsis glauca forests in limestone areas, which adapts to the characteristics of limestone vegetation and coordinates with national and international classification system, will finally be proposed. The results of this study will provide a powerful reference for vegetation classification revision in China.