Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%.

中文翻译：

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使用来自自动无人机观测的高分辨率RGB图像数据进行基于CNN的树种分类

森林管理者，清查机构，林务员以及私人和市政森林所有者经常要求提供有关树种分布的数据。然而，基于航测的被动遥感数据对树木种类的自动检测仍未得到充分发展，无法获得可靠的结果，而与物候阶段，一天中的时间，季节，树木的生命力和主要的大气条件无关。在这里，我们介绍了一种新颖的树种分类方法，该方法基于克服了这些不足的自动无人机飞行期间收集的高分辨率RGB图像数据。对于分类任务，设计了计算轻量级的卷积神经网络（CNN）。我们证明，通过选择的CNN模型架构，与四种不同树种的光照条件和物候阶段无关，平均分类准确率可达到92％。我们还表明，分类模型需要最小的地面采样密度为1.6 cm / px，以便能够利用数据中的空间结构信息。最后，为了证明所提出的方法在导出空间上明确的树种信息方面的适用性，生成了网格产品，其平均分类精度为88％。