Surface Temperature Patterns as Indicators of Slope Instability: the Montaguto Earthflow and the Melendugno Rockfall Case Studies, Southern Italy

Landslide monitoring plays an important role to reduce risk for communities and infrastructures within areas affected by slope instability. In this study, we utilize high resolution thermal and RGB data acquired by using drone camera to detect surface temperature variations and identify potential precursory indicators of landslide. The proposed approach is applied to two different landslide case studies, the Montaguto earthflow in the Apennines and the Melendugno rockfall along the Apulian coastline, in southern Italy. Seasonal surveys were conducted to capture temporal changes in surface thermal patterns, enabling the detection of anomalous temperature zones that may indicate early slope movement. The primary tool to detect such surface temperature anomalies is thermal imagery, whereas an RGB image is used to validate thermal observations and provide more detailed data on slope morphology, cracks, vegetation and other topographic features. The combination of thermal and RGB data allows for a comprehensive analysis, correlating surface thermal anomalies with geomorphological features to enhance the reliability of detected precursors. Repeated thermal surveys, both in the summer and winter seasons, provide an insight to interpret the conditions and morphology of the surface to evaluate landslide susceptibility in the study areas. This technique, therefore, provides high resolution thermal information that can improve the ability to monitor landslide risk zones and can be used as an effective tool for an early warning system in landslide-prone regions.