Big slopes, little data: data-driven nowcasting of deep-seated landslide deformation

Landslides are a major geohazard in hilly and mountainous environments. We focus on slow-moving, deep-seated landslides that are characterized by gradual, non-catastrophic deformations of millimeters to decimeters per year and cause extensive economic damage. To assess their potential impact and for the design of mitigation solutions, a detailed...

Probabilistic vegetation transitions in dunes by combining spectral and lidar data

Monitoring the status of the vegetation is required for nature conservation. This monitoring task is time consuming as kilometers of area have to be investigated and classified. To make this task more manageable, remote sensing is used. The acquisition of airplane remote sensing data is dependent on weather conditions and permission to fly in...

World-wide InSAR sensitivity index for landslide deformation tracking

Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture...

Integrated Monitoring of a Slowly Moving Landslide Based on Total Station Measurements, Multi-Temporal Terrestrial Laser Scanning and Space-Borne Interferometric Synthetic Aperture Radar

Continuous landslide monitoring is a crucial task for the management of natural hazards for identifying suitable mitigation measures, including nature-based solutions. In the present study, three monitoring techniques including (i) an automated tracking total station (ATTS), (ii) multi-temporal terrestrial laser scanning (TLS) and (iii) space...

Documenting Impacts of Hydro-Meteorological Events Using Earth Observation

The ambition of H2020 OPERANDUM project is to develop and document Nature Based Solutions (NBS) to mitigate risks associated with hydro-meteorological (HM) hazards. NBS mitigate risks by reducing the vulnerability of a particular system. The aim of this work is to demonstrate the use of multisource remote sensing data in documenting the impact...

Machine learning: New potential for local and regional deep-seated landslide nowcasting

Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated...