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首页> 外文期刊>Earth Surface Dynamics Discussions >Measuring river planform changes from remotely sensed data – a Monte Carlo approach to assessing the impact of spatially variable error
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Measuring river planform changes from remotely sensed data – a Monte Carlo approach to assessing the impact of spatially variable error

机译:测量从远程感测数据的河流普通变化 - 一种评估空间变量误差影响的蒙特卡罗方法

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Remotely sensed data from fluvial systems are extensively used to document historical planform changes. However, geometric and delineation errors inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially rates of channel lateral migration. It is thus imperative to take into account a spatially variable (SV) error affecting the remotely sensed data. In the wake of recent key studies using this SV error as a level of detection, we introduce a new framework to evaluate the significance of measured channel migration. Going beyond linear metrics (i.e. migration vectors between diachronic river centrelines), we assess significance through a channel polygon method yielding a surficial metric (i.e. quantification of eroded, deposited, or eroded-then-deposited surfaces). Our study area is a mid-sized active wandering river: the lower Bruche, a ~20m wide tributary of the Rhine in eastern France. Within our four test sub-reaches, the active channel is digitised using diachronic orthophotos (1950 and 1964), and the SV error affecting the data is interpolated with an inverse-distance weighting (IDW) technique. The novelty of our approach arises from then running Monte Carlo (MC) simulations to randomly translate active channels and propagate geometric and delineation errors according to the SV error. This eventually leads to the computation of percentage of uncertainties associated with each of the measured planform changes, which allows us to evaluate the significance of the planform changes. In the lower Bruche, the uncertainty associated with the documented changes ranges from 15.8% to 52.9%. Our results show that (i) orthophotos are affected by a significant SV error; (ii) the latter strongly affects the uncertainty of measured changes; and (iii) the significance of changes is dependent on both the magnitude and the shape of the surficial changes. Taking the SV error into account is strongly recommended even in orthorectified aerial photos, especially in the case of mid-sized rivers (30m width) and/or low-amplitude river planform changes (1m2m-1yr-1). In addition to allowing detection of low-magnitude planform changes, our approach is also transferable as we use well-established tools (IDW and MC): this opens new perspectives in the fluvial context (e.g. multi-thread river channels) for robustly assessing surficial channel changes.
机译:来自河流系统的远程感测数据广泛用于记录历史平面变化。然而,与这些数据固有相关的几何和描绘误差可能导致对测量变化的差或均匀的误导性解释,尤其是通道横向迁移的速率。因此,必须考虑影响远程感测数据的空间变量(SV)误差。在最近使用该SV误差作为检测水平的关键研究之后,我们介绍了一个新的框架来评估测量信道迁移的重要性。超越线性度量(即,Diagronic River Centrelines之间的迁移向量),我们通过产生曲面度量的通道多边形方法评估显着性(即侵蚀,沉积或腐蚀的然后沉积的表面的定量)。我们的研究区是一个中型活跃的徘徊河:较低的Bruche,在法国东部的莱茵河宽阔的支流。在我们的四个测试子到达中,有源通道使用DiaCronic Orthophotos(1950和1964)来数字化,并且影响数据的SV误差与反向距离加权(IDW)技术插值。我们的方法的新颖性来自于随后运行Monte Carlo(MC)模拟,以随机翻译有源通道并根据SV错误传播几何和描绘误差。这最终导致计算与每个测量的平面变换变化相关的不确定性百分比,这使我们能够评估平面变化变化的重要性。在较低的Bruche中,与记录的变化相关的不确定性范围为15.8%至52.9%。我们的研究结果表明,(i)原耳osp受到显着的SV误差的影响; (ii)后者强烈影响测量变化的不确定性; (iii)变化的重要性取决于表层变化的幅度和形状。即使在矫正型航空照片中,强烈推荐考虑SV错误,特别是在中尺寸的河流(30米宽)和/或低幅度河流的情况下发生变化(1m2m-1yr-1)。除了允许检测到低幅度的平面变化的变化外,我们的方法也可以转让,因为我们使用良好的工具(IDW和MC):这在河流背景(例如多线河道)中打开了用于鲁棒性评估的新观点频道更改。

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