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The right releve in the right vegetation unit: a new typicality index to reproduce expert judgement with an automatic classification programme

机译:正确的植被单位中的正确选择:一种新的典型指数,可通过自动分类程序重现专家的判断

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Question: How to classify forest vegetation releves automatically in the traditional phytosociological system and thus distinguish typical releves - easier to classify - fromreleves with non- diagnostic species and intermediate releves? Material: A data set of 11 324 forest vegetation releves, including 4880 releves classified a priori by experts in phytosociology down to the association level ( 100 associations in 30 alliances and ten classes, covering all forest vegetation and environmental conditions encountered in France). Methods: A new typicality index was formalized to quantify the probability of automatically classifying a given relevein the same vegetation unit as would do an expert in phytosociology. Computation of the typicality index is based on two parameters: a level of affinity linked to the number of diagnostic species of the vegetation unit present in the releve ', and a level of differentiation that is greater when the risk of confusion in classification of the relevebetween different units is smaller. Results: The automatic classification was identical to expert judgement for 60% of the 4080 calibration releves. The typicality index isolated atypical releves that are more difficult to classify. The model was successfully transferred to classify an independent data set of 6444 releves from the French National Forest Inventory performed in 2008 and to distinguish 1114 ( 17%) typical releves of phytosociological associations. Conclusions: Interest in this new typicality index is manifold. It is: ( 1) operational for the current phytosociological system that forms the basis of the Natura 2000 system; ( 2) easy to implement from characteristics of species and communities; and ( 3) based on criteria of uncertainty used by phytosociological experts. This study establishes a clear bridge between recent works on collecting, storing and analysing vegetation releves, on one hand, and the traditional phytosociological approach, on the other. It should trigger further studies on the spatial and temporal distribution of European habitats.
机译:问题:如何在传统的植物社会学系统中自动对森林植被分类进行分类,从而区分具有易于诊断性的典型分类与非诊断物种和中间分类的分类?资料:11 324个森林植被种群的数据集,包括由植物社会学专家按先验分类为协会级别的4880个物种(30个联盟和10类的100个协会,涵盖了法国遇到的所有森林植被和环境条件)。方法:对新的典型指数进行了形式化,以量化与植物学社会学专家在同一植被单位中自动对给定松树进行分类的可能性。典型性指数的计算基于两个参数:亲和度与存在于植被中的植被单元的诊断物种数量相关联的水平,以及分化程度,当在相互之间的分类中存在混淆的风险时,分化程度更大不同的单位较小。结果:4080个校准标样中的60%的自动分类与专家判断相同。典型性指数隔离了较难分类的非典型序列。该模型已成功转移,以对来自2008年法国国家森林清单的6444个版本的独立数据集进行分类,并区分了1114个(17%)典型的植物社会学关联。结论:对这种新的典型指数的兴趣是多方面的。它是:(1)构成Natura 2000系统基础的当前植物社会学系统可操作; (2)易于根据物种和群落特征实施; (3)基于植物社会学专家使用的不确定性标准。这项研究在一方面收集,存储和分析植被遗迹的最新研究与另一方面的传统植物社会学方法之间建立了明确的桥梁。它应该引发对欧洲生境的时空分布的进一步研究。

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