Aussenac Raphaël, Monnet Jean-Matthieu, Klopčič Matija, Hawryło Paweł, Socha Jarosław, Mahnken Mats, Gutsch Martin, Cordonnier Thomas, Vallet Patrick
Université Grenoble Alpes, INRAE, LESSEM, 2 rue de la Papeterie-BP 76, F-38402 St-Martin-d'Hères, France.
Forêts et Sociétés, Université de Montpellier, CIRAD, Montpellier, France.
Open Res Eur. 2023 Dec 5;3:32. doi: 10.12688/openreseurope.15373.2. eCollection 2023.
Ecology and forestry sciences are using an increasing amount of data to address a wide variety of technical and research questions at the local, continental and global scales. However, one type of data remains rare: fine-grain descriptions of large landscapes. Yet, this type of data could help address the scaling issues in ecology and could prove useful for testing forest management strategies and accurately predicting the dynamics of ecosystem services. Here we present three datasets describing three large European landscapes in France, Poland and Slovenia down to the tree level. Tree diameter, height and species data were generated combining field data, vegetation maps and airborne laser scanning (ALS) data following an area-based approach. Together, these landscapes cover more than 100 000 ha and consist of more than 42 million trees of 51 different species. Alongside the data, we provide here a simple method to produce high-resolution descriptions of large landscapes using increasingly available data: inventory and ALS data. We carried out an in-depth evaluation of our workflow including, among other analyses, a leave-one-out cross validation. Overall, the landscapes we generated are in good agreement with the landscapes they aim to reproduce. In the most favourable conditions, the root mean square error (RMSE) of stand basal area (BA) and mean quadratic diameter (Dg) predictions were respectively 5.4 m .ha and 3.9 cm, and the generated main species corresponded to the observed main species in 76.2% of cases.
生态学和林业科学正在使用越来越多的数据来解决地方、大陆和全球尺度上的各种技术和研究问题。然而,有一种数据仍然很少见:大尺度景观的精细描述。然而,这类数据有助于解决生态学中的尺度问题,并可能对测试森林管理策略和准确预测生态系统服务动态有用。在这里,我们展示了三个数据集,它们描述了法国、波兰和斯洛文尼亚的三个欧洲大尺度景观,细化到树木层面。树木的直径、高度和物种数据是通过基于区域的方法,结合实地数据、植被图和机载激光扫描(ALS)数据生成的。这些景观总面积超过10万公顷,由51个不同物种的4200多万棵树组成。除了数据之外,我们还提供了一种简单的方法,利用越来越容易获得的数据——清查数据和ALS数据,来生成大尺度景观的高分辨率描述。我们对工作流程进行了深入评估,包括留一法交叉验证等分析。总体而言,我们生成的景观与它们旨在再现的景观高度吻合。在最有利的条件下,林分断面积(BA)和平均二次直径(Dg)预测的均方根误差(RMSE)分别为5.4平方米·公顷和3.9厘米,并且在76.2%的情况下,生成的主要物种与观测到的主要物种一致。