Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
<p>The global availability of Sentinel-2 data and the widespread coverage of cost-free and high-resolution images nowadays give opportunities to map, at a low cost, shallow landslides triggered by extreme events (e.g. rainfall, earthquakes). Rapid and low-cost shallow landslide mapping could i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-07-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/23/2625/2023/nhess-23-2625-2023.pdf |
| _version_ | 1827894484345552896 |
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| author | D. Notti M. Cignetti D. Godone D. Giordan |
| author_facet | D. Notti M. Cignetti D. Godone D. Giordan |
| author_sort | D. Notti |
| collection | DOAJ |
| description | <p>The global availability of Sentinel-2 data and the widespread coverage of cost-free and high-resolution images nowadays give opportunities to map, at a low cost, shallow landslides triggered by extreme events (e.g. rainfall, earthquakes). Rapid and low-cost shallow landslide mapping could improve damage estimations, susceptibility models and land management.</p>
<p>This work presents a two-phase procedure to detect and map shallow
landslides. The first is a semi-automatic methodology allowing for mapping
potential shallow landslides (PLs) using Sentinel-2 images. The PL aims to
detect the most affected areas and to focus on them an high-resolution mapping and further investigations. We create a GIS-based and user-friendly methodology to extract PL based on pre- and post-event normalised difference vegetation index (NDVI) variation and
geomorphological filtering. In the second phase, the semi-automatic
inventory was compared with a benchmark landslide inventory drawn on
high-resolution images. We also used Google Earth Engine scripts to
extract the NDVI time series and to make a multi-temporal analysis.</p>
<p>We apply this procedure to two study areas in NW Italy, hit in 2016 and 2019 by extreme rainfall events. The results show that the semi-automatic mapping based on Sentinel-2 allows for detecting the majority of shallow landslides larger than satellite ground pixel (100 m<span class="inline-formula"><sup>2</sup></span>). PL density and distribution match well with the benchmark. However, the false positives (30 % to 50 % of cases) are challenging to filter, especially when they
correspond to riverbank erosions or cultivated land.</p> |
| first_indexed | 2024-03-12T22:07:16Z |
| format | Article |
| id | doaj.art-985a03a5145047c6ad3f8e29ec20551b |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-03-12T22:07:16Z |
| publishDate | 2023-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-985a03a5145047c6ad3f8e29ec20551b2023-07-24T10:04:13ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812023-07-01232625264810.5194/nhess-23-2625-2023Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth EngineD. NottiM. CignettiD. GodoneD. Giordan<p>The global availability of Sentinel-2 data and the widespread coverage of cost-free and high-resolution images nowadays give opportunities to map, at a low cost, shallow landslides triggered by extreme events (e.g. rainfall, earthquakes). Rapid and low-cost shallow landslide mapping could improve damage estimations, susceptibility models and land management.</p> <p>This work presents a two-phase procedure to detect and map shallow landslides. The first is a semi-automatic methodology allowing for mapping potential shallow landslides (PLs) using Sentinel-2 images. The PL aims to detect the most affected areas and to focus on them an high-resolution mapping and further investigations. We create a GIS-based and user-friendly methodology to extract PL based on pre- and post-event normalised difference vegetation index (NDVI) variation and geomorphological filtering. In the second phase, the semi-automatic inventory was compared with a benchmark landslide inventory drawn on high-resolution images. We also used Google Earth Engine scripts to extract the NDVI time series and to make a multi-temporal analysis.</p> <p>We apply this procedure to two study areas in NW Italy, hit in 2016 and 2019 by extreme rainfall events. The results show that the semi-automatic mapping based on Sentinel-2 allows for detecting the majority of shallow landslides larger than satellite ground pixel (100 m<span class="inline-formula"><sup>2</sup></span>). PL density and distribution match well with the benchmark. However, the false positives (30 % to 50 % of cases) are challenging to filter, especially when they correspond to riverbank erosions or cultivated land.</p>https://nhess.copernicus.org/articles/23/2625/2023/nhess-23-2625-2023.pdf |
| spellingShingle | D. Notti M. Cignetti D. Godone D. Giordan Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine Natural Hazards and Earth System Sciences |
| title | Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine |
| title_full | Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine |
| title_fullStr | Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine |
| title_full_unstemmed | Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine |
| title_short | Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine |
| title_sort | semi automatic mapping of shallow landslides using free sentinel 2 images and google earth engine |
| url | https://nhess.copernicus.org/articles/23/2625/2023/nhess-23-2625-2023.pdf |
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