NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. The SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model. From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

中文翻译：

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调整突发滑坡识别产品 (SLIP) 和检测实时增加的降水 (DRIP) 算法来绘制喀麦隆西部高地（中非）降雨引发的滑坡地图

NASA 的开发人员最近提出了突发滑坡识别产品 (SLIP) 和检测实时增加的降水 (DRIP) 算法。这种双重方法使用 Landsat 8 卫星图像和每日降雨数据来实时绘制该地质灾害的地图。本研究调整处理以解决喀麦隆西部高地近期滑坡事件绘图的数据质量和不可用/差距问题。SLIP 算法经过调整，通过整合反归一化差异植被指数 (NDVI) 来评估土壤裸露度、修正归一化多波段干旱指数 (MNMDI) 结合热液指数评估土壤水分，以及坡度倾斜度以绘制地图最近的山体滑坡。更远，DRIP 算法使用平均日降雨量来评估与最近的滑坡事件对应的阈值。它们的概率密度函数 (PDF) 曲线叠加在一起，它们的交点用于提出 2019 年 10 月 28 日滑坡事件之前（1948-2018 年）和之后的二分变量集。此外，以喀麦隆的第一个已知事件为起点，并使用 Cox 模型，进行了生存分析以将滑坡的发生与降雨相关联。根据 SLIP 模型，滑坡危险区 (LHZ) 地图的总体准确度为 96%。此外，DRIP 模型指出 6/9 范围的概率是 99.99% 的降雨触发滑坡，在 6 月和 10 月之间，而 3/9 范围仅显示相同间隔的 4.88% 风险。最后，已知站点的生存概率高达 0。68 表示最佳值，0.38 和 0.1 之间表示随时间推移的最低值。提议的方法是基于数据（不）可用性的替代方法，由站点的生命周期分析完成，以便在观察和预测阈值方面具有更大的灵活性。