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首页> 外文期刊>South-East European Forestry >Modelling Stand Variables of Beech Coppice Forest Using Spectral Sentinel-2A Data and the Machine Learning Approach
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Modelling Stand Variables of Beech Coppice Forest Using Spectral Sentinel-2A Data and the Machine Learning Approach

机译：利用光谱前哨2A数据和机器学习方法对山毛榉小林的林分变量建模

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Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p0.01). It seems that additional information is needed to increase prediction accuracy, so stand information (management classes, site class, soil type, canopy closure and others), new sampling strategy and new spectral products could be integrated and examined in further more complex modelling of forest attributes.
机译：背景和目的：矮林在林业和环境管理中具有特殊的社会经济和生态作用。它们的生产可持续性和空间稳定性对于林业部门以及本地和全球社区而言至关重要。最近，综合的森林资源清查和使用非参数统计方法分析的遥感数据已使人们能够更详细地了解森林的结构特征。这项研究的目的是通过使用机器学习方法对清单和Sentinel S2A卫星数据进行整合，以评估萨拉热窝州的山毛榉小林林的森林属性。材料和方法：基础面积，平均林分直径，生长蓄积量和总体积数据是从为代表的小灌木林设计的森林清单中确定的。从Sentinel S2A卫星图像的波段，植被指数（差异，归一化差异和比率植被指数）和生物物理变量（吸收的光合有效辐射的分数，叶面积指数，植被覆盖率，叶中的叶绿素含量）收集光谱数据。冠层含水量）。基于机器学习规则的M5模型树（M5P）和随机森林（RF）方法用于森林属性估计。预测子集选择基于假设M5P和RF学习方案的包装。在训练数据子集（402个样地）上开发模型，并在验证数据子集（207个样地）上进行评估。模型的性能通过均方根误差对平均值（rRMSE）的百分比以及观察值和估计值之间的相关系数的平方来评估。结果与结论：预测子集的选择导致森林属性和方法的预测因子数量不同，它们对RF的贡献更大（介于8和11之间）。光谱生物物理变量在子集中占主导地位。与M5P相比，RF导致所有属性的训练集误差较小，而两种方法的验证集误差都很高（rRMSE高于50％）。林分基础面积的最低rRMSE为50％。 M5P和RF模型在训练子集中观察到的变异性分别约为30％和95％，但在测试子集中这些值较低（低于12％），但仍然很显着。样本和模型森林属性均值的差异不显着，而所有森林属性的模型变异性均显着较低（p <0.01）。似乎需要更多信息来提高预测准确性，因此可以在更复杂的森林建模中集成和检查林分信息（管理类别，站点类别，土壤类型，林冠封闭等），新的采样策略和新的光谱产品。属性。

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