Multi-Sensor Remote Sensing Time Series Analysis of Landslide Processes

authored by

Wandi Wang

supervised by

Mahdi Motagh

Abstract

Landslides are among the most devastating natural hazards globally, threatening lives, infrastructure, and ecosystems, particularly in mountainous and tectonically active regions. The increasing frequency and intensity of extreme weather events, driven by climate change, have further intensified landslide risks. Effective landslide monitoring and risk assessment are thus critical for disaster mitigation.
Conventional ground-based techniques, such as GNSS, geotechnical instrumentation,
and field surveys, provide high accuracy but are limited by their spatial coverage, cost,
and accessibility, especially in remote or high-risk areas. In contrast, satellite-based remote sensing, particularly Synthetic Aperture Radar (SAR) and optical imagery, provides wide-area, high-frequency, and all-weather capabilities, making it a powerful tool for landslide detection and analysis. However, remote sensing applications in landslide research remain challenged by the evolving spatiotemporal complexity of slope kinematics, characterized by stochastic magnitudes, directional variability, and temporally distinct responses to heterogeneous external triggers. To effectively utilize remote sensing technologies for landslide detection and monitoring, it is essential to not only ensure data availability but also to implement appropriate strategies for characterizing the spatiotemporal slope kinematics.

Organisation(s)

Institute of Photogrammetry and GeoInformation (IPI)

External Organisation(s)

GFZ Helmholtz Centre for Geosciences

Type

Doctoral thesis

No. of pages

139

Publication date

17.12.2025

Publication status

Published

Sustainable Development Goals

SDG 13 - Climate Action

Electronic version(s)

https://doi.org/10.15488/20210 (Access: Open)