Abstract
Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides are playing an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extend of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The landslide inventory can act as a starting point for a more comprehensive hazard and risk reduction framework for Denmark. Furthermore, machine-learning algorithms can use the dataset as a training dataset to improve future automated mapping approaches. The complete landslide inventory is made freely available for download at https://doi.org/10.6084/m9.figshare.16965439.v1 (Svennevig and Luetzenburg, 2021) or as web map (https://data.geus.dk/landskred/) for further investigations.
Highlights
Landslides can be a serious natural hazard, existing worldwide causing high numbers of fatalities and damage to property every year (Froude and Petley, 2018)
Landslides play an important role in the evolution of landscapes by mobilizing and transporting sediment downslope (Moon et al, 2015)
Under the generic term ‘landslide’ a variety of types can be distinguished based on the process and the material involved (Cruden and Varnes, 1996)
Summary
Landslides can be a serious natural hazard, existing worldwide causing high numbers of fatalities and damage to property every year (Froude and Petley, 2018). The study of landslides reaches from site-specific field investigations to global datasets of landslides and from event-based inspections to long-term monitoring for several years (Alberti et al, 2020; Coe, 2020; Mateos et al, 2020; Svennevig et al, 35 2020b). Among the different spatial and temporal approaches of landslide studies, landslide inventory mapping is a common method to investigate the spatial occurrence of landslides (Guzzetti et al, 2012; Galli et al, 2008; Hao et al, 2020). The combination of machine learning algorithms and remote sensing data is expected to greatly improve the quality of landslide mapping datasets (Zhong et al, 2020). The limited availability of training datasets and the complexity of automated landslide mapping methods lead to still mostly human efforts in creating landslide inventories (Prakash et al, 2020). With this paper and dataset, we present the first comprehensive landslide inventory for Denmark
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.