Remote Sensing for Assessing Landslides and Associated Hazards
- PDF / 3,092,747 Bytes
- 45 Pages / 439.37 x 666.142 pts Page_size
- 30 Downloads / 202 Views
Remote Sensing for Assessing Landslides and Associated Hazards Candide Lissak1 · Annett Bartsch2,3 · Marcello De Michele4 · Christopher Gomez5,6 · Olivier Maquaire1 · Daniel Raucoules4 · Thomas Roulland1 Received: 6 December 2019 / Accepted: 6 August 2020 © Springer Nature B.V. 2020
Abstract Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems. Keywords Landslide · Remote sensing · Machine learning · Monitoring · Inventory
* Candide Lissak [email protected] Extended author information available on the last page of the article
13
Vol.:(0123456789)
Surveys in Geophysics
1 Introduction Despite several decades of research, landslide detection is still a challenging task due to the wide variety of sizes, shapes and morphologies that those events can take and due to the variability of the area they trigger. Consequently, a broad array of methods has been tested in remote-sensing with lately combinations of those at different-scales using multi-platform methodologies. For decades, Remote-Sensing techniques are thus widely employed for landslides studies (e.g., Petley et al. 2002; Delacourt et al. 2007; Jaboyedoff et al. 2012, 2019; Tofani et al. 2013; Casagli e
Data Loading...
