Marine conservation and management require detailed and accurate habitat mapping, which is usually produced by collecting data using remote sensing methods. In recent years, unmanned aerial systems (UAS) are used for marine data acquisition, as they provide detailed and reliable information through very high-resolution orthophoto-maps. However, as for all remotely sensed data, it is important to study and understand the accuracy and reliability of the produced maps. In this study, the effect of different environmental conditions on the quality of UAS orthophoto-maps was examined through a positional and thematic accuracy assessment. Selected objects on the orthophoto-maps were also assessed as to their position, shape, and extent. The accuracy assessment results showed significant errors in the different maps and objects. The accuracy of the classified images varied between 2.1% and 27%. Seagrasses were under-classified, while the mixed substrate class was overclassified when environmental conditions were not optimal. The highest misclassifications were caused due to sunglint presence in combination with a rough sea-surface. A change detection workflow resulted in detecting misclassifications of up to 45%, on orthophoto-maps that had been generated under non-optimal environmental conditions. The results confirmed the importance of optimal conditions for the acquisition of reliable marine information using UAS.

中文翻译：

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沿海环境中环境条件对UAS正射影像质量的影响

海洋保护和管理需要详尽而准确的栖息地地图，通常是通过使用遥感方法收集数据来绘制的。近年来，无人航空系统（UAS）用于海洋数据采集，因为它们可以通过非常高分辨率的正射影像图提供详细而可靠的信息。但是，对于所有遥感数据，重要的是研究和了解所生成地图的准确性和可靠性。在这项研究中，通过位置和主题准确性评估研究了不同环境条件对UAS正射影像质量的影响。还评估了正射影像图上的选定对象的位置，形状和范围。准确性评估结果表明，不同的地图和对象存在明显的误差。分类图像的准确性在2.1％和27％之间变化。当环境条件不是最佳条件时，海草分类不足，而混合底物分类则分类过多。归类最多的是归因于日照的存在以及粗糙的海面。变更检测工作流程导致在非最佳环境条件下生成的正射影像图上检测到错误分类的可能性高达45％。结果证实了使用无人机系统获取可靠海洋信息的最佳条件的重要性。归类最多的是归因于日照的存在以及粗糙的海面。变更检测工作流程导致在非最佳环境条件下生成的正射影像图上检测到错误分类的可能性高达45％。结果证实了使用无人机系统获取可靠海洋信息的最佳条件的重要性。归类最多的是归因于日照的存在以及粗糙的海面。变更检测工作流程导致在非最佳环境条件下生成的正射影像图上检测到错误分类的可能性高达45％。结果证实了使用无人机系统获取可靠海洋信息的最佳条件的重要性。