Preprints
https://doi.org/10.5194/nhess-2022-189
https://doi.org/10.5194/nhess-2022-189
 
19 Jul 2022
19 Jul 2022
Status: this preprint is currently under review for the journal NHESS.

Semi-automatic mapping of shallow landslides using free Sentinel-2 and Google Earth Engine

Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan Davide Notti et al.
  • Institute for Geo-Hydrological Protection (IRPI), Italian National Research Council (CNR), Torino, Strada Delle Cacce 73, 10135, Italy

Abstract. 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.

Davide Notti et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-189', Anonymous Referee #1, 14 Aug 2022 reply
    • AC1: 'Reply on RC1', Davide Notti, 23 Sep 2022 reply

Davide Notti et al.

Data sets

Semi-automatic and manual shallow landslide inventories of two extreme rainfall events. Notti Davide, Cignetti Martina, Godone Danilo, Giordan Daniele. https://doi.org/10.5281/zenodo.6617194

Davide Notti et al.

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Short summary
We present a methodology for semi-automatic shallow landslide mapping based on free-cost satellite data. The method is aimed to create a low-cost and user-friendly instrument to detect the areas potentially affected by shallow landslides using the pre- post- event NDVI variation. We also provide Google Earth Engine scripts to evaluate the effects of the shallow landslide on different land uses. Datasets and codes are public and open to be improved by the scientific community.
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