Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models
Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models
复制标题
DOI：
10.3390/rs13030451
复制
发表时间：
2021-02-01
期刊：
影响因子：
5
通讯作者：
Ratschbacher, Lothar
中科院分区：
文献类型：
--
作者：
Barbosa, Natalie;Andreani, Louis;Ratschbacher, Lothar
确定易发生山体滑坡的地区对于减轻相关风险至关重要。这通常是使用滑坡敏感性模型来实现的，该模型根据当地地形条件和已知过去事件的位置来估计滑坡的可能性。涵盖不同条件因素的详细数据库在制作可靠的易感性图方面至关重要。然而，来自发展中国家的专题数据很少。因此，磁化率模型往往依赖于从广泛可用的数字高程模型中获得的形态参数。在大多数情况下，使用使用3 x 3像素的移动窗口计算的简单参数，例如斜率、纵横比和曲率。最近，使用基于窗口的形态指标作为额外的输入已经增加。这些依赖于用户定义的观察窗口大小。在这方面的贡献，我们研究的影响，观察窗口大小时，使用基于窗口的形态指标作为核心预测变量的滑坡敏感性评估。我们计算了各种模型，包括用不同窗口大小计算的形态测量指数，并比较了预测能力和所得预测的可靠性。所有的模型都是基于随机森林算法。当使用不同的有意义的观察窗计算每个基于窗口的形态测量指数时，结果显著改善（AUC-ROC为0.89，AUC-PR为0.87）。敏感性分析突出了高信息量的观察窗口及其选择对模型性能的影响。我们还强调了评估滑坡敏感性结果的重要性，同时使用不同的适应指标的预测性能和可靠性。
The identification of areas that are prone to landslides is essential in mitigating associated risks. This is usually achieved using landslide susceptibility models, which estimate landslide likelihood given local terrain conditions and the location of known past events. Detailed databases covering different conditioning factors are paramount in producing reliable susceptibility maps. However, thematic data from developing countries are scarce. As a result, susceptibility models often rely on morphometric parameters that are derived from widely-available digital elevation models. In most cases, simple parameters, such as slope, aspect, and curvature, computed using a moving window of 3 x 3 pixels, are used. Recently, the use of window-based morphometric indices as an additional input has increased. These rely on a user-defined observation window size. In this contribution, we examine the influence of observation window size when using window-based morphometric indices as core predictive variables for landslide susceptibility assessment. We computed a variety of models that include morphometric indices that are calculated with different window sizes, and compared the predictive capabilities and reliability of the resulting predictions. All of the models are based on the random forest algorithm. The results improved significantly when each window-based morphometric index was calculated with a different and meaningful observation window (AUC-ROC of 0.89 and AUC-PR of 0.87). The sensitivity analysis highlights both the highly-informative observation windows and the impact of their selection on the model performance. We also stress the importance of evaluating landslide susceptibility results while using different adapted metrics for predictive performance and reliability.