Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.

中文翻译：

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卷积神经网络使用多光谱无人机图像绘制半天然草原植物群落图

半天然草原 (SNG) 是欧洲文化景观的重要组成部分。它们是许多动植物物种的重要栖息地，并提供多种生态功能。根据草原的环境和管理因素，不同的植物群落随着时间的推移而演变。这些不同的植物群落提供多种生态系统服务，也会影响家畜饲料的草料价值。然而，随着农业集约化程度的提高和 SNG 的丧失，草原生物多样性持续下降。在本文中，我们提出了一种对草原植物群落进行空间分类的方法，以便识别和绘制出现在半天然草甸中的植物群落和杂草物种。为了这，高分辨率多光谱遥感数据由无人机 (UAV) 定期捕获，并由卷积神经网络 (CNN) 进行分类。选择具有第一和第二生长植被的异质半天然干草草甸作为研究区域。固定地块的植物相关性被用作基本事实和独立测试数据。这些独立测试数据的准确度高达 88%，显示了在生态和农业应用的高分辨率无人机数据中使用 CNN 进行植物群落绘图的巨大潜力。固定地块的植物相关性被用作基本事实和独立测试数据。这些独立测试数据的准确度高达 88%，显示了在生态和农业应用的高分辨率无人机数据中使用 CNN 进行植物群落绘图的巨大潜力。固定地块的植物相关性被用作基本事实和独立测试数据。这些独立测试数据的准确度高达 88%，显示了在生态和农业应用的高分辨率无人机数据中使用 CNN 进行植物群落绘图的巨大潜力。