Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique

Abstract

Landslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident.

Vegetation-based Approach for Rotational Landslide Activity Classification: Case Study of Kundasang, Sabah

“…However, using tree rings to study landslide behavior has certain limitations; for example, some trees need to be felled, which is impossible in places such as forest nature reserves. Many academic investigations also explore the impact of diverse vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), as a means of assessing and monitoring the state of landslide activity (Zhong et al, 2021, Xun et al, 2022, Satriano et al, 2023. Overall, the existing studies have focused on single landslide types and activity, while a limited number of indicators were used.…”