Assessing landslide activity at large scales has historically been a challenging problem. Here, we present a different approach on radar coherence and normalized difference vegetation index (NDVI) analyses – metrics that are typically used to map landslides post-failure – and leverage a time series analysis to characterize the pre-failure activity of the Mud Creek landslide in California. Our method computes the ratio of mean interferometric coherence or NDVI on the unstable slope relative to that of the surrounding hillslope. This approach has the advantage that it eliminates the negative impacts of long temporal baselines that can interfere with the analysis of interferometric synthetic aperture (InSAR) data, as well as interferences from atmospheric and environmental factors. We show that the coherence ratio of the Mud Creek landslide dropped by 50 % when the slide began to accelerate 5 months prior to its catastrophic failure in 2017. Coincidentally, the NDVI ratio began a near-linear decline. A similar behavior is visible during an earlier acceleration of the landslide in 2016. This suggests that radar coherence and NDVI ratios may be useful for assessing landslide activity. Our study demonstrates that data from the ascending track provide the more reliable coherence ratios, despite being poorly suited to measure the slope's precursory deformation. Combined, these insights suggest that this type of analysis may complement traditional InSAR analysis in useful ways and provide an opportunity to assess landslide activity at regional scales.

中文翻译：

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利用雷达相干和归一化差异植被指数比率的时间序列分析来表征加利福尼亚泥溪滑坡的破坏前活动

从历史上看，大规模评估滑坡活动一直是一个具有挑战性的问题。在这里，我们提出了一种不同的雷达相干性和归一化植被指数（NDVI）分析（通常用于绘制灾后滑坡图的指标）的方法，并利用时间序列分析来表征Mud Creek滑坡的灾前活动。在加利福尼亚。我们的方法计算了不稳定斜坡上相对于周围山坡的平均干涉相干或NDVI的比率。这种方法的优势在于，它消除了长时间基线的负面影响，该影响可能会干扰干涉式合成孔径（InSAR）数据的分析以及来自大气和环境因素的干扰。我们显示，在2017年灾难性破坏发生前5个月，当滑坡开始加速时，Mud Creek滑坡的相干性比率下降了50％。巧合的是，NDVI比率开始接近线性下降。在2016年更早的滑坡加速期间，可以看到类似的现象。这表明雷达相干性和NDVI比率可能有助于评估滑坡活动。我们的研究表明，尽管不太适合测量斜坡的先验变形，但来自上升轨道的数据仍提供了更可靠的相干比。综合起来，这些见解表明，这种类型的分析可能会以有用的方式补充传统的InSAR分析，并为评估区域规模的滑坡活动提供机会。