Mapping Recent Lava Flows at Mount Etna Using Multispectral Sentinel-2 Images and Machine Learning Techniques

Accurate mapping of recent lava flows can provide significant insight into the development
of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both
intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues
(e.g., the di culty to survey a spatially extended lava flow with either aerial or ground instruments
while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral
satellite data currently provides new opportunities for monitoring of extreme thermal events, such as
eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward,
problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here,
we present a new supervised classifier based on machine learning techniques to discriminate recent
lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification
evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number,
reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m
(bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number
of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow
field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in
2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision,
and sensitivity.