Landslide risk reduction through close partnership between research, industry, and public entities in Norway: Pilots and case studies

publishedVersio

Structural instrumentation and monitoring of the Block Island Offshore Wind Farm

publishedVersio

Changes in the Statistics of Ambient Excitations in the Performance of Two Damage Detection Schemes

International audienc

Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape

publishedVersio

Landslide risk reduction through close partnership between research, industry, and public entities in Norway: Pilots and case studies

Cross-sectorial and cross-disciplinary collaboration, as well as public-private partnerships are necessary to handle the complexity of climate adaptation. The Research Council of Norway has established the Centres for Research-based Innovation (CRI) in which research- and education organizations, public entities and private enterprises join forces in 8-year long collaborations. CRI-Klima 2050 focuses on climate adaptation of buildings and infrastructure and runs several pilot projects to innovate new solutions for building resilience, stormwater- and landslide risk management. Several of the major infrastructure owners in Norway are partners in the centre. Norway is increasingly affected by precipitation triggered landslides. Klima 2050 pilot projects on landslide risk reduction include a web-based toolbox for prioritizing and choosing optimal mitigation measures, including Nature-Based Solutions, improved early warning systems and mitigation measures for slope instability, and improved local warning for hazardous weather systems, all developed in close collaboration between centre partners from different sectors and disciplines. The results of these projects can all be upscaled and are transferable to other infrastructure elements

Håndtering av skredrisiko i et endret klima

acceptedVersio

Landslide risk reduction through close partnership between research, industry, and public entities in Norway: Pilots and case studies

publishedVersio

Håndtering av skredrisiko i et endret klima

acceptedVersio

Implementing nature-based solutions in rural landscapes: Barriers experienced in the phusicos project

publishedVersio

Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape

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 Jølster (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 Jølster, 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