Working with Landslide Inventories and Susceptibility Maps in Lower Austria

Schweigl, Joachim; Straka, Wolfgang

doi:10.1007/978-3-642-31325-7_5

Cited by 1 publication

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“…Even using a single satellite image frame, some studies performed multi-scale methodologies and observed higher landslide extraction accuracies compared to single-scale performances [17]. Scientific progress toward pixel-based automatic identification of landslides was made using remote sensing imageries with different resolutions [19]. Considerable progress toward pixel-based automatic identification of landslides was achieved using deep-learning convolution neural networks (CNN) [1,14].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Meena

Piralilou

2019

Geosciences

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Despite landslide inventories being compiled throughout the world every year at different scales, limited efforts have been made to critically compare them using various techniques or by different investigators. Event-based landslide inventories indicate the location, distribution, and detected boundaries of landslides caused by a single event, such as an earthquake or a rainstorm. Event-based landslide inventories are essential for landslide susceptibility mapping, hazard modeling, and further management of risk mitigation. In Nepal, there were several attempts to map landslides in detail after the Gorkha earthquake. Particularly after the main event on 25 April 2015, researchers around the world mapped the landslides induced by this earthquake. In this research, we compared four of these published inventories qualitatively and quantitatively using different techniques. Two principal methodologies, namely the cartographical degree of matching and frequency area distribution (FAD), were optimized and applied to evaluate inventory maps. We also showed the impact of using satellite imagery with different spatial resolutions on the landslide inventory generation by analyzing matches and mismatches between the inventories. The results of our work give an overview of the impact of methodology selection and outline the limitations and advantages of different remote sensing and mapping techniques for landslide inventorying.

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