| Autor: |
Notti, Davide, Cignetti, Martina, Godone, Danilo, Giordan, Daniele |
| Předmět: |
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| Zdroj: |
Natural Hazards & Earth System Sciences Discussions; 7/19/2022, p1-34, 34p |
| Abstrakt: |
The global availability of Sentinel-2 data and the widespread coverage of free-cost and high-resolution images nowadays give opportunities to map, at low-cost, shallow landslides triggered by extreme events (e.g. rainfall, earthquake). A rapid and low-cost shallow landslides mapping could improve damages estimations, susceptibility models or land management. This work presents a semi-automatic methodology to map potential landslides (PL) using Sentinel-2 images, and it is the first step toward more detailed mapping. We create a GIS-based and user-friendly methodology to extract PL based on pre- post- event NDVI variation and geomorphological filtering. The semi-automatic inventory was compared with benchmark landslides inventory drawn on high-resolution images. We also used the Google Earth Engine scripts to extract the NDVI time series and make a multi-temporal analysis. We apply this to two study areas in NW Italy hatted in 2016 and 2019 by extreme rainfall events. The results show that the semi-automatic mapping based on Sentinel-2 allows detecting the majority of shallow landslides larger than satellite ground pixel (100 m²). PL density and distribution well match with the benchmark. However, the false positives (30% to 50% of cases) are challenging to filter, especially when they correspond to river bank erosions or cultivated land. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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