Monitoring wetlands is necessary in order to understand and protect their ecohydrological balance. In Ireland, traditionally wetland-monitoring is carried out by manual field visits which can be very time-consuming. To automate the process, this study extends the ability of remote sensing-based monitoring of wetlands by combining RGB image processing, machine learning algorithms, and satellite data analysis to create seasonal maps of vegetation communities within the wetlands. The methodology matches multispectral and broad coverage of open-source Sentinel-2 (S2) imagery with the high spatial granularity of Unmanned Aerial Vehicles (UAV) or drone images. Single sensor drone imagery was captured, colour corrected and classified using random forest (RF) classifier for a subset of the wetland. The classified imagery was upsampled to satellite imagery scale to create training data for vegetation-segmentation in the entire wetland. The process was repeated for multiple seasons, and an annual map was created utilising the majority voting. The proposed framework has been evaluated on various wetlands across Ireland, with results presented herein for an ombrotrophic peatland complex, Clara Bog. The accuracy of the maps was checked utilising a set of area-based performance metric. The application of this method thereby reduces the number of field surveys typically required to assess the long-term ecological change of such wetland habitats. The performance of the proposed method demonstrates that the technique is a robust, quick, and cost-effective way to map wetland habitats seasonally and to explore their ecohydrological synergies.

为了了解和保护其生态水文学平衡，必须对湿地进行监测。在爱尔兰，传统上，湿地监测是通过人工实地考察来进行的，这非常耗时。为了使过程自动化，本研究通过结合RGB图像处理，机器学习算法和卫星数据分析来创建湿地内植被群落的季节性图，从而扩展了基于遥感的湿地监测能力。该方法将开放源Sentinel-2（S2）图像的多光谱和广泛覆盖与无人飞行器（UAV）或无人机图像的高空间粒度相匹配。使用随机森林（RF）分类器捕获了一部分湿地的单传感器无人机图像，对其进行了色彩校正和分类。将分类后的图像上采样到卫星图像比例，以创建用于整个湿地中的植被分段的训练数据。此过程重复了多个季节，并使用多数投票创建了年度地图。拟议的框架已在爱尔兰的各种湿地上进行了评估，此处给出了营养营养泥炭地综合体Clara Bog的结果。使用一组基于区域的性能指标来检查地图的准确性。因此，该方法的应用减少了评估此类湿地生境的长期生态变化通常所需的实地调查数量。所提出方法的性能表明，该技术是一种可靠，快速且经济高效的方法，可按季节绘制湿地栖息地图并探索其生态水文协同作用。