{"title":"Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine","authors":"D. Notti, M. Cignetti, D. Godone, D. Giordan","doi":"10.5194/nhess-23-2625-2023","DOIUrl":null,"url":null,"abstract":"Abstract. 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. This work presents a two-phase procedure to detect and map shallow\nlandslides. The first is a semi-automatic methodology allowing for mapping\npotential shallow landslides (PLs) using Sentinel-2 images. The PL aims to\ndetect 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\ngeomorphological filtering. In the second phase, the semi-automatic\ninventory was compared with a benchmark landslide inventory drawn on\nhigh-resolution images. We also used Google Earth Engine scripts to\nextract the NDVI time series and to make a multi-temporal analysis. 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 m2). 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\ncorrespond to riverbank erosions or cultivated land.\n","PeriodicalId":18922,"journal":{"name":"Natural Hazards and Earth System Sciences","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Hazards and Earth System Sciences","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/nhess-23-2625-2023","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract. 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. 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. 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 m2). 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.
期刊介绍:
Natural Hazards and Earth System Sciences (NHESS) is an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences. Embracing a holistic Earth system science approach, NHESS serves a wide and diverse community of research scientists, practitioners, and decision makers concerned with detection of natural hazards, monitoring and modelling, vulnerability and risk assessment, and the design and implementation of mitigation and adaptation strategies, including economical, societal, and educational aspects.