{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T11:38:17Z","timestamp":1776425897191,"version":"3.51.2"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Council of Norway","award":["237859"],"award-info":[{"award-number":["237859"]}]},{"name":"Centre for Research-based Innovation Klima 2050","award":["237859"],"award-info":[{"award-number":["237859"]}]},{"name":"NORADAPT (Norwegian Research Centre on Sustainable Climate Change Adaption)","award":["237859"],"award-info":[{"award-number":["237859"]}]},{"name":"NGU (Norges Geologiske Unders\u00f8kelse)","award":["237859"],"award-info":[{"award-number":["237859"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides triggered by a summer rainstorm in J\u00f8lster (Norway), based on changes in the normalised difference vegetation index (NDVI) between pre- and post-event images. Pre- and post-event multi-temporal images were then created by reducing across all available images within one month before and after the landslide events, from which final change detection image composites were produced. We used the mean of backscatter intensity in co- (VV) and cross-polarisations (VH) for Sentinel-1 synthetic aperture radar (SAR) data and maximum NDVI for Sentinel-2. The NDVI-based mapping increased the number of registered events from 14 to 120, while spatial bias was decreased, from 100% of events located within 500 m of a road to 30% close to roads in the new inventory. Of the 120 landslides, 43% were also detectable in the multi-temporal SAR image composite in VV polarisation, while only the east-facing landslides were clearly visible in VH. Noise, from clouds and agriculture in Sentinel-2, and speckle in Sentinel-1, was reduced using the multi-temporal composite approaches, improving landslide visibility without compromising spatial resolution. Our results indicate that manual or automated landslide detection could be significantly improved with multi-temporal image composites using freely available earth observation images and Google Earth Engine, with valuable potential for improving spatial bias in landslide inventories. Using the multi-temporal satellite image composites, we observed significant improvements in landslide visibility in J\u00f8lster, compared with conventional bi-temporal change detection methods, and applied this for the first time using VV-polarised SAR data. The GEE scripts allow this procedure to be quickly repeated in new areas, which can be helpful for reducing spatial bias in landslide databases.<\/jats:p>","DOI":"10.3390\/rs14102301","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"2301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape"],"prefix":"10.3390","volume":"14","author":[{"given":"Erin","family":"Lindsay","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway"}]},{"given":"Regula","family":"Frauenfelder","sequence":"additional","affiliation":[{"name":"Norwegian Geotechnical Institute (NGI), 0806 Oslo, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-5708","authenticated-orcid":false,"given":"Denise","family":"R\u00fcther","sequence":"additional","affiliation":[{"name":"Department Environmental Sciences, Western Norway University of Applied Sciences (HVL), 5063 Bergen, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2327-8721","authenticated-orcid":false,"given":"Lorenzo","family":"Nava","sequence":"additional","affiliation":[{"name":"Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, 35129 Padua, Italy"}]},{"given":"Lena","family":"Rubensdotter","sequence":"additional","affiliation":[{"name":"Geohazard and Earth Observation, Norwegian Geological Survey (NGU), 7040 Trondheim, Norway"},{"name":"Department of Arctic Geology, University Centre of Svalbard (UNIS), 9171 Longyearbyen, Norway"}]},{"given":"James","family":"Strout","sequence":"additional","affiliation":[{"name":"Norwegian Geotechnical Institute (NGI), 0806 Oslo, Norway"}]},{"given":"Steinar","family":"Nordal","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Field, C.B., Barros, V., Stocker, T.F., and Dahe, Q. 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