Abstract Floodplain areas belong to the most diverse, dynamic and complex ecological habitats of the terrestrial portion of the Earth. Spatial and temporal quantification of floodplain dynamics is needed for assessing the impacts of hydromorphological controls on river ecosystems. However, estimation of land cover dynamics in a post-classification setting is hindered by a high contribution of classification errors. A possible solution relies on the selection of specific information of the change map, instead of increasing the overall classification accuracy. In this study, we analyze the capabilities of Unmanned Aerial Systems (UAS), the associated classification processes and their respective accuracies to extract a robust estimate of floodplain dynamics. We show that an estimation of dynamics should be built on specific land cover interfaces to be robust against classification errors and should include specific features depending on the season-sensor coupling. We use five different sets of features and determine the optimal combination to use information largely based on blue and infrared bands with the support of texture and point cloud metrics at leaf-off conditions. In this post-classification setting, the best observation of dynamics can be achieved by focusing on the gravel-water interface. The semi-supervised approach generated error of 10% of observed changes along highly dynamic reaches using these two land cover classes. The results show that a robust quantification of floodplain land cover dynamics can be achieved by high-resolution remote sensing.

中文翻译：

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通过高分辨率遥感对河流土地覆盖动态进行稳健量化

摘要 洪泛区属于地球陆地部分最多样化、最具活力和最复杂的生态栖息地。为了评估水文形态控制对河流生态系统的影响，需要对洪泛区动态进行时空量化。然而，分类错误的高贡献阻碍了分类后环境中土地覆盖动态的估计。一种可能的解决方案依赖于选择变化图的特定信息，而不是提高整体分类精度。在这项研究中，我们分析了无人机系统 (UAS) 的能力、相关的分类过程及其各自的准确性，以提取对洪泛区动态的可靠估计。我们表明，动态估计应建立在特定的土地覆盖界面上，以便对分类错误具有鲁棒性，并且应包括取决于季节-传感器耦合的特定特征。我们使用五组不同的特征并确定最佳组合以使用主要基于蓝色和红外波段的信息，并在叶子脱落条件下支持纹理和点云指标。在这种分类后设置中，通过关注砾石-水界面可以实现对动力学的最佳观察。使用这两个土地覆盖类别，半监督方法沿高度动态的河段产生了 10% 的观测变化误差。结果表明，高分辨率遥感可以实现对漫滩土地覆盖动态的稳健量化。