Landslide Information from Earth Observation to Support Humanitarian Aid

Geohazards such as landslides cause numerous fatalities and significant infrastructure damage, leading to enormous individual and economic losses each year. Due to climate change and its associated effects, the frequency and magnitude of landslides, rock falls, and mudflows are expected to increase. Humanitarian organisations, such as Médecins Sans Frontières (MSF), are at the forefront of disaster response in these situations. Timely, effective, reliable, and up-to-date information as input for hazard and damage assessments is vital to both rescue operations and humanitarian relief efforts. Unfortunately, conventional approaches to landslide recognition, such as ground surveys and visual image interpretation, are resource- and time-intensive. Earth observation (EO) data and technologies have demonstrated their value in aiding emergency response and disaster risk management following landslide events. However, even though state-of-the-art EO techniques are constantly being improved and further developed within academic research, they have seldom been tested in working conditions, for instance, to directly support humanitarian aid with relevant landslide information during emergency situations. Furthermore, existing solutions are rarely in line with the specific user requirements and information needs of humanitarian organisations that appear during different phases of the disaster cycle. Hence, the LEONA (Landslide Information from Earth Observation to Support Humanitarian Aid) project aims to create targeted landslide information from EO data to support humanitarian aid. To achieve this goal, LEONA proposes the establishment of a framework to effectively create, provide, and communicate EO-based landslide information to the humanitarian community in emergency situations when time pressure, data scarcity, and unsafe field conditions are to be expected. We will apply a range of different methods using both optical and synthetic aperture radar (SAR) data, identify the approaches that are most suitable for producing targeted landslide information, and customise them to the requirements of our user, MSF. Such a framework dedicated to support humanitarian aid constitutes an innovative basis for comprehensive landslide mapping using EO data and established technologies, which will be further complemented by products on landslide susceptibility, vulnerability, and risk, targeted at disaster response. To achieve effective communication, we will combine the knowledge gained through EO with essentials of risk communication to empower humanitarian aid staff in the response to landslide events. By doing so, the project will facilitate the transition of knowledge from EO scientific research to practical applications in humanitarian aid.