Unmanned aerial vehicles (UAVs) provide high-spatial-resolution imagery and allow the collection of data in locations or periods of time where field-based data collection is challenging or impossible, such as in wetlands and floodplains. Computational deep learning techniques are transforming the way in which remotely sensed imagery and data can be used and are having an increasing role in remote sensing. Here, we describe a method using UAV and machine learning technique convolutional neural networks (CNNs) to estimate the cover of wetland features Phragmites australis reeds, leaf litter, water, bareground, and other vegetation in a large inland floodplain wetland in Western New South Wales (NSW), Australia. We firstly describe the process we took to train, validate, and test the model. We describe the model's performance by calculating a range of performance indicators and provide density maps and results from individual sites. The model had an overall accuracy of 0.947 and recognized and estimated Phragmites australis reeds to a very high accuracy (>98%). Here, we show an effective, accurate, and reproducible way to estimate the cover of Phragmites australis reeds and other wetland features using UAV and CNNs in a semi-arid wetland.

中文翻译：

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使用无人机和神经网络估计半干旱湿地芦苇的覆盖率

无人机 (UAV) 提供高空间分辨率图像，并允许在基于现场的数据收集具有挑战性或不可能的位置或时间段收集数据，例如在湿地和洪泛区。计算深度学习技术正在改变遥感图像和数据的使用方式，并且在遥感中发挥着越来越大的作用。在这里，我们描述了使用UAV和机器学习技术的卷积神经网络（细胞神经网络）来估计湿地的盖的方法的特征芦苇澳大利亚新南威尔士州西部 (NSW) 的一个大型内陆漫滩湿地中的芦苇、落叶、水、裸地和其他植被。我们首先描述了我们训练、验证和测试模型的过程。我们通过计算一系列性能指标来描述模型的性能，并提供来自各个站点的密度图和结果。该模型的整体准确度为 0.947，识别和估计芦苇芦苇的准确度非常高 (>98%)。在这里，我们展示了一种有效、准确和可重复的方法，在半干旱湿地中使用无人机和 CNN估计芦苇芦苇和其他湿地特征的覆盖。