Modelling and mapping the suitability of European forest formations at 1-km resolution
Modelling and mapping the suitability of European forest formations at 1-km resolution
复制标题
DOI：
10.1007/s10342-011-0480-x
复制
发表时间：
2011-11-01
影响因子：
2.8
通讯作者：
Pekkarinen, Anssi
中科院分区：
文献类型：
--
作者：
Casalegno, Stefano;Amatulli, Giuseppe;Pekkarinen, Anssi
积极主动的森林养护规划需要关于树种潜在分布的准确的空间信息。获得这一信息的最具成本效益的方法是生境适宜性建模，即预测生物群的潜在分布随环境因素的变化。在这里，我们使用引导聚合机器学习集成分类器随机森林（RF）来推导出1公里分辨率的欧洲森林形成适宜性地图。该统计模型使用6 000多个实地数据森林清查样地和大量环境变量作为输入。使用欧洲环境署的森林类别分类方案，将实地数据地块划分为不同的森林形态。选择了10个最主要的森林类别（不包括人工林）进行分析。模型结果的总体准确率为76%。类别之间的分数是不平衡的，中隐层落叶林被认为是最不正确的森林分类类别。该模型的变量排名分数被用来讨论森林类别/环境因素之间的关系，并深入了解模型的局限性和地图适用性的优势。欧洲森林适宜性地图现已可供进一步应用于森林养护和气候变化问题。
Proactive forest conservation planning requires spatially accurate information about the potential distribution of tree species. The most cost-efficient way to obtain this information is habitat suitability modelling i.e. predicting the potential distribution of biota as a function of environmental factors. Here, we used the bootstrap-aggregating machine-learning ensemble classifier Random Forest (RF) to derive a 1-km resolution European forest formation suitability map. The statistical model use as inputs more than 6,000 field data forest inventory plots and a large set of environmental variables. The field data plots were classified into different forest formations using the forest category classification scheme of the European Environmental Agency. The ten most dominant forest categories excluding plantations were chosen for the analysis. Model results have an overall accuracy of 76%. Between categories scores were unbalanced and Mesophitic deciduous forests were found to be the least correctly classified forest category. The model's variable ranking scores are used to discuss relationship between forest category/environmental factors and to gain insight into the model's limits and strengths for map applicability. The European forest suitability map is now available for further applications in forest conservation and climate change issues.