Worldwide landslides correspond to one of the most dangerous geological events due to their destructive power and unpredictable nature. In the Chilean Patagonia, the SERNAGEOMIN (Chilean Geological Survey Service) has detected 2533 landslide events in the Northern Patagonia (42. 7^∘S, 72. 4^∘W) alone, a small area compared to the whole Chilean Patagonia. However, only 11 evens have known date. Consequently, it is not possible to associate temporal triggers and mechanisms that control such events, resulting in a lack of understanding of the factors that enable landslides. This work aims to detect landslides and identify the main environmental variables (climatic and geomorphological) that explain their occurrence using machine learning methods. We will address the following research questions: 1) How can a temporal landslide dataset be built using Landsat images and Google Earth Engine in Northern Patagonia? 2) Once the landslides and their timing have been detected, what are the main variables that condition the landslide processes? In our work, we developed a temporal dataset of landslides for the northern Patagonia of Chile. We used three machine learning approaches, where it was possible to identify the main environmental variables that allow us to predict their generation. Statistical models show that during the last 19 years, there has been complex interaction between different environmental variables that have influenced the activity of landslides. Climatic indices, indicators of extreme events, have a high incidence in the events’ predictive capacity. However, the most important are those linked to Patagonia’s tectonic context. In particular, the time elapsed after the eruptive event of the Chaitén volcano. Finally, the Liquiñe-Ofqui Fault System’s presence extending throughout the entire north of Patagonia has generated discontinuities at a general level, causing significant geomorphological instability. The study area has a relief in evolution and reactive to climatic conditions. Therefore, we highlight the need to understand better the interaction between geological and climatic processes and the future impact of these natural hazards. We emphasize the importance of analyzing landslide controls, considering both geomorphological variables and sporadic geological events.

由于其破坏力和不可预测的性质，全球滑坡是最危险的地质事件之一。在智利巴塔哥尼亚，所述SERNAGEOMIN（智利地质调查服务）已检测到在北巴塔哥尼亚（42 7 2533个滑坡事件^∘小号，72 4 ^∘ W¯¯），相比整个智利巴塔哥尼亚，这是一个很小的区域。但是，只有11个偶数已知日期。因此，不可能将时间触发因素和控制此类事件的机制联系起来，导致对导致滑坡的因素缺乏了解。这项工作旨在检测滑坡并识别使用机器学习方法解释滑坡发生的主要环境变量（气候和地貌）。我们将解决以下研究问题：1）如何使用Landsat图像和北巴塔哥尼亚北部的Google Earth Engine构建时空滑坡数据集？2）一旦检测到滑坡及其时机，影响滑坡过程的主要变量是什么？在我们的工作中，我们为智利北部的巴塔哥尼亚开发了一个滑坡的时态数据集。我们使用了三种机器学习方法，可以识别出主要的环境变量，从而使我们能够预测它们的产生。统计模型表明，在过去的19年中，影响滑坡活动的不同环境变量之间存在复杂的相互作用。气候指数是极端事件的指标，在​​事件的预测能力中具有很高的发生率。但是，最重要的是那些与巴塔哥尼亚的构造背景有关的东西。特别是柴滕火山爆发后经过的时间。最终，Liquiñe-Ofqui断层系统的存在遍及巴塔哥尼亚的整个北部，在一般水平上产生了不连续性，从而导致了严重的地貌不稳定。研究区域在进化方面具有缓解作用，并且对气候条件有反应。因此，我们强调需要更好地了解地质和气候过程之间的相互作用以及这些自然灾害的未来影响。我们强调分析滑坡控制的重要性，同时考虑地貌变量和零星的地质事件。