Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping
Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping
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大型触发事件后滑坡位置的自动确定：与人工制图相比的优点和缺点
DOI：
10.5194/nhess-22-481-2022
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发表时间：
2022-02
影响因子：
4.6
通讯作者：
D. Milledge;D. Bellugi;Jack Watt;A. Densmore
中科院分区：
文献类型：
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作者：
D. Milledge;D. Bellugi;Jack Watt;A. Densmore
摘要。山区的地震可能引发数千次同震滑坡，造成重大破坏，阻碍救援工作，并迅速重新分配沉积物。了解对这些滑坡的控制的努力在很大程度上依赖于人工绘制的滑坡清单，但这些清单的收集成本高，耗时长，而且它们的可重复性通常没有很好的限制。本文基于谷歌Earth Engine中Landsat时间序列的逐像素归一化植被指数（NDVI）差异，考虑季节性因素，提出了一种新的滑坡自动检测指数（ALDI）算法。我们将分类清单与最近五次地震的手工绘制的清单进行了比较：2005年的克什米尔地震、2007年的阿伊萨姆、2008年的汶川地震、2010年的海地地震和2015年的廓尔喀地震。我们测试了ALDI恢复滑坡位置（使用接收器工作特征- ROC曲线）和滑坡大小（根据滑坡面积-频率统计）的能力。我们发现，在ALDI局部优化的14个案例中，有10个案例的ALDI比公布的库存更熟练地识别滑坡位置，在ALDI全局优化和阻力测试的14个案例中，有8个案例的ALDI更熟练地识别滑坡位置。这些结果不仅反映了自动化方法的良好性能，而且还反映了手动映射的令人惊讶的糟糕性能，这对未来的分类器如何测试以及基于这些清单的解释都有影响。我们发现，通常使用更精细分辨率图像的手动制图更熟练地捕捉到滑坡区域频率统计数据，这可能是由于相对于ALDI，减少了对单个小滑坡的审查和滑坡集群的合并。我们的结论是，就识别滑坡影响位置的能力而言，ALDI是人工测绘的可行替代方案，但不太适合检测小型孤立滑坡或精确的滑坡几何形状。其快速的运行时间、免费的图像要求和接近全球的覆盖范围表明，有可能显著提高滑坡库存的覆盖范围和数量。此外，它的简单性（仅对像素进行分析）和输入的简洁性（仅对光学图像进行分析）意味着还可以进行相当大的改进。
Abstract. Earthquakes in mountainous areas can trigger thousands of co-seismic landslides, causing significant damage, hampering relief efforts, and rapidly redistributing sediment across the landscape. Efforts to understand the controls on these landslides rely heavily on manually mapped landslide inventories, but these are costly and time-consuming to collect, and their reproducibility is not typically well constrained. Here we develop a new automated landslide detection index (ALDI) algorithm based on pixel-wise normalised difference vegetation index (NDVI) differencing of Landsat time series within Google Earth Engine accounting for seasonality. We compare classified inventories to manually mapped inventories from five recent earthquakes: Kashmir in 2005, Aysén in 2007, Wenchuan in 2008, Haiti in 2010, and Gorkha in 2015. We test the ability of ALDI to recover landslide locations (using receiver operating characteristic – ROC – curves) and landslide sizes (in terms of landslide area–frequency statistics). We find that ALDI more skilfully identifies landslide locations than published inventories in 10 of 14 cases when ALDI is locally optimised and in 8 of 14 cases both when ALDI is globally optimised and in holdback testing. These results reflect not only good performance of the automated approach but also surprisingly poor performance of manual mapping, which has implications both for how future classifiers are tested and for the interpretations that are based on these inventories. We find that manual mapping, which typically uses finer-resolution imagery, more skilfully captures the landslide area–frequency statistics, likely due to reductions in both the censoring of individual small landslides and amalgamation of landslide clusters relative to ALDI. We conclude that ALDI is a viable alternative to manual mapping in terms of its ability to identify landslide-affected locations but is less suitable for detecting small isolated landslides or precise landslide geometry. Its fast run time, cost-free image requirements, and near-global coverage suggest the potential to significantly improve the coverage and quantity of landslide inventories. Furthermore, its simplicity (pixel-wise analysis only) and parsimony of inputs (optical imagery only) mean that considerable further improvement should be possible.