Landslides Analysis and Management: From Data Acquisition to Modelling and Monitoring II
Landslides, debris flows, rock falls, rock avalanches, and lahars are gravitational processes affecting different-sized areas and operate at different speeds depending on the geological and geomorphological context (tectonic setting, lithology, terrain morphology, hydrology and hydrogeology). They represent a dynamic response to a set of triggering factors mainly heavy rainfall, seismicity, volcanism, and human activities. The risk they represent for human life and economic activity is increasing due to the constantly increasing population, land-use changes, and climate change. Their socioeconomic repercussions include the cost to individuals, local communities, national services, and industry.
Different approaches are available to analyze landslide scenarios in order to assess, mitigate, and manage the related risks: laboratory and field investigations, susceptibility mapping, physical and numerical modelling, monitoring techniques, early warning system design, and so on. This Topic focuses on i) recent enhancements and trends in data acquisition technologies and landslide monitoring techniques, such as the use of UAVs (unmanned aerial vehicles) for tracking and monitoring the movements of landslides or WSN (wireless sensor network) applications for real-time monitoring purposes, SFM (structure-from-motion) photogrammetry applications, and so on; and ii) studies devoted to physical and numerical modelling of landslides aiming to explore recent advances and future challenges.
Contributions may cover a broad range of topics ranging from remote sensing applications and susceptibility mapping to physical and numerical modelling, utilization of sensor technology in landslide monitoring, the Internet of Things (IoT) for landslide monitoring, machine learning, and deep learning. Reviews of the state of the art on the mentioned topics are also encouraged, as well as case studies on landslide risk management.
We look forward to receiving your contributions.
Dr. Irene Manzella
Dr. Bouchra Haddad
- data acquisition
- GIS, remote sensing and machine learning
- susceptibility mapping
- physical and numerical modelling
- monitoring techniques
- early warning system
|First Decision (median)
Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.
MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:
- Immediately share your ideas ahead of publication and establish your research priority;
- Protect your idea from being stolen with this time-stamped preprint article;
- Enhance the exposure and impact of your research;
- Receive feedback from your peers in advance;
- Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.