Abstract
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 difficulty 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.
Highlights
Lava fields are mapped usually through ground-based surveys [11,12] or more recently with Unmanned Aerial Vehicles (UAV) in the air [13,14], satellite remote sensing offers an overall view of active lava flows, while avoiding the difficulties of working in hazardous locations [15,16]
We introduced a new machine-learning approach to map recent lava flows that exploits the high spatial resolution and freely available information coming from Sentinel 2-MultiSpectral Imager (MSI) channels
Our approach relies on a k-medoids unsupervised classifier, able to separate the data points in n different clusters, depending on the correlation measure among the pixels belonging to the area of interest
Summary
Lava fields are mapped usually through ground-based surveys [11,12] or more recently with Unmanned Aerial Vehicles (UAV) in the air [13,14], satellite remote sensing offers an overall view of active lava flows, while avoiding the difficulties of working in hazardous locations [15,16]. Remote sensing techniques are considered a safer and more robust alternative approach capable of providing a more comprehensive survey of the whole lava flow field, which is essential when attempting to monitor lava flow hazards during volcanic eruptions [18,19]. The multispectral nature of the data and the repeated coverage of extensive volcanic terrains are major advantages of satellite remote sensing techniques [20]
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