Articles | Volume 23, issue 7
Research article
24 Jul 2023
Research article |  | 24 Jul 2023

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

Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan

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Cited articles

ARPA Piemonte: Gli eventi alluvionali in Piemonte – Evento del 21–25 novembre 2016, Turin, (last access: 28 April 2023), 2018. 
Bellugi, D. G., Milledge, D. G., Cuffey, K. M., Dietrich, W. E., and Larsen, L. G.: Controls on the size distributions of shallow landslides, P. Natl. Acad. Sci. USA, 118, e2021855118,, 2021. 
Benesty, J., Chen, J., Huang, Y., and Cohen, I.: Pearson Correlation Coefficient, in: Noise Reduction in Speech Processing, edited by: Cohen, I., Huang, Y., Chen, J., and Benesty, J., Springer Berlin Heidelberg, Berlin, Heidelberg, 1–4,, 2009. 
Bhuyan, K., Tanyaş, H., Nava, L., Puliero, S., Meena, S. R., Floris, M., van Westen, C., and Catani, F.: Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data, Sci. Rep.​​​​​​​, 13, 162,, 2023. 
Bordoni, M., Meisina, C., Valentino, R., Lu, N., Bittelli, M., and Chersich, S.: Hydrological factors affecting rainfall-induced shallow landslides: from the field monitoring to a simplified slope stability analysis, Eng. Geol., 193, 19–37,, 2015. 
Short summary
We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Final-revised paper