A Framework for Integrating Remote Sensing and Geophysical Data in Landslide Prediction

Thesis Abstract

Effective landslide prediction remains a critical challenge in geomorphology and disaster risk management, particularly in regions characterized by steep terrain, active tectonics, and variable climatic conditions. Traditional methods relying solely on either remote sensing or geophysical techniques often fall short in capturing the complex, multi-scale processes that contribute to slope failure. This study aims to develop an integrated framework that synergizes remote sensing imagery and geophysical data to enhance the accuracy, reliability, and timeliness of landslide prediction models. Specifically, the research objectives include (1) identifying key remote sensing indicators and geophysical parameters associated with landslide-prone areas; (2) designing and validating an integrative model that combines these datasets; and (3) evaluating the predictive performance of the framework in a case study region in the Pacific northwest, covering an area of approximately 1,500 square kilometers with diverse geomorphological features. The methodological approach adopts a mixed-methods research design, combining quantitative data analysis with qualitative insights. The target population comprises satellite-derived datasets, including multispectral and LiDAR imagery, along with geophysical surveys such as electrical resistivity tomography (ERT) and seismic refraction measurements collected from 150 sites distributed across the study region. These data sources are obtained from satellite agencies, governmental geological surveys, and field instrumentation. The sample size of 150 geophysical measurement points was determined via stratified random sampling to ensure representation across various slopes, soil types, and land use patterns. Data collection instruments include high-resolution satellite imagery (from Sentinel-2 and LiDAR datasets), ERT and seismic equipment, and field observation checklists for ground truth validation. Data analysis employs a combination of advanced statistical and machine learning techniques. Remote sensing data are processed through spectral indices such as the Normalized Difference Vegetation Index (NDVI) and slope stability models derived from Digital Elevation Models (DEMs). Geophysical data are interpreted via inversion algorithms to extract subsurface resistivity and seismic velocity distributions. These datasets are integrated into a Geographic Information System (GIS) platform, followed by the application of multivariate regression analysis, Random Forest classification, and Support Vector Machines (SVM) to develop a predictive model. The framework also incorporates the Theory of Landslide Susceptibility Modeling and the Geomechanical Model of Hjulström to underpin the analysis. Expected findings suggest that the integrated model significantly outperforms traditional single-method approaches, achieving higher predictive accuracy with an area under the receiver operating characteristic curve (AUC) of approximately 0.89. Key indicators such as low resistivity zones, steep slope gradients, and reduced vegetation cover are identified as critical precursors in the model. The results are validated through cross-validation techniques and ground truth data, demonstrating the framework’s potential as a robust tool for early warning systems. This research contributes novel insights into the multifaceted mechanisms of landslide initiation by formalizing a comprehensive integration framework grounded in geomorphological theory, remote sensing, and geophysical principles. It advances the scientific understanding of how combined datasets can better capture subsurface and surface conditions relevant to slope failure. The study’s primary conclusion affirms that integrated remote sensing and geophysical data significantly improve landslide predictive capabilities. Recommendations include adopting the framework in regional disaster management strategies, extending its application to other hazard-prone terrains, and exploring real-time sensor integration for operational early warning systems. Ultimately, this study lays foundational groundwork for future research aimed at refining multi-sensor data fusion techniques and enhancing predictive models in landslide-prone regions worldwide.