Abstract Sparse vegetation such as riparian forests and trees outside forests (TOF) cover only small areas but present various ecological advantages. The detection of these vegetation types in semi-arid mountainous areas is challenging as trees are heavily mixed with other land cover types. Their mapping requires therefore high-resolution imagery. We propose to leverage the advantages and synergies of freely available Sentinel-2 data and a light-weight consumer-grade unmanned aerial vehicle (UAV) with a simple red–greenblue (RGB) camera to detect these vegetation types. In our approach, an object-based random forest land cover classification is first developed over smaller sites using very high-resolution UAV data. The resulting maps are afterwards used as training data for multi-temporal Sentinel-2 based classifications at regional scale. We tested the approach in five different riparian landscapes of a semi-arid mountainous area in Iran. For comparison, mono- and multi-temporal Sentinel-2 data were also used alone – without support from UAV data – to build pixel-based random forest classification models at regional scale. Our results show that compared to the best mono-temporal results, the multi-temporal classification approach improved the overall accuracy and Kappa values of Sentinel-2 classifications from 77.0% to 83.9% and 0.72 to 0.81, respectively. The producer’s and user’s accuracy of the riparian forest class were also improved from 64.0% to 70.0% and 57.1% to 73.7%, respectively. Combining UAV and Sentinel-2 data improved the overall accuracy only slightly, but enabled a much better detection of Persian oak stands – for this class, the producer’s accuracy increased by 13.0 percentage points. Overall, we recommend the combined use of UAV and multi-temporal Sentinel-2 data to detect Persian oak forest stands.

中文翻译：

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使用 Sentinel-2 和 UAV 数据对半干旱山区植被进行精细检测，重点是河岸景观

摘要 河岸林、林外树木（TOF）等稀疏植被覆盖面积小，但具有多种生态优势。由于树木与其他土地覆盖类型严重混合，因此在半干旱山区检测这些植被类型具有挑战性。因此，它们的映射需要高分辨率图像。我们建议利用免费提供的 Sentinel-2 数据和轻型消费级无人机 (UAV) 的优势和协同作用，使用简单的红绿蓝 (RGB) 相机来检测这些植被类型。在我们的方法中，首先使用非常高分辨率的无人机数据在较小的站点上开发了基于对象的随机林地覆盖分类。生成的地图随后用作区域尺度上基于 Sentinel-2 的多时态分类的训练数据。我们在伊朗半干旱山区的五个不同河岸景观中测试了该方法。为了进行比较，还单独使用了单时和多时 Sentinel-2 数据——没有无人机数据的支持——在区域尺度上构建基于像素的随机森林分类模型。我们的结果表明，与最好的单时间结果相比，多时间分类方法将 Sentinel-2 分类的整体准确度和 Kappa 值分别从 77.0% 提高到 83.9% 和 0.72 到 0.81。生产者和用户对河岸林分类的准确率也分别从 64.0% 提高到 70.0% 和 57.1% 到 73.7%。结合 UAV 和 Sentinel-2 数据仅略微提高了整体准确性，但可以更好地检测波斯橡树林——对于此类，生产者的准确率提高了 13.0 个百分点。总体而言，我们建议结合使用无人机和多时相 Sentinel-2 数据来检测波斯橡树林分。