Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/16389
Authors: | Corradino, Claudia* Amato, Eleonora* Torrisi, Federica* Del Negro, Ciro* |
Title: | Data-Driven Random Forest Models for Detecting Volcanic Hot Spots in Sentinel-2 MSI Images | Journal: | Remote Sensing | Series/Report no.: | /14 (2022) | Publisher: | MDPI | Issue Date: | 2022 | DOI: | 10.3390/rs14174370 | URL: | https://www.mdpi.com/2072-4292/14/17/4370 | Abstract: | Volcanic thermal anomalies are monitored with an increased application of optical satellite sensors to improve the ability to identify renewed volcanic activity. Hotspot detection algorithms adopting a fixed threshold are widely used to detect thermal anomalies with a minimal occurrence of false alerts. However, when used on a global scale, these algorithms miss some subtle thermal anomalies that occur. Analyzing satellite data sources with machine learning (ML) algorithms has been shown to be efficient in extracting volcanic thermal features. Here, a data-driven algorithm is developed in Google Earth Engine (GEE) to map thermal anomalies associated with lava flows that erupted recently at different volcanoes around the world (e.g., Etna, Cumbre Vieja, Geldingadalir, Pacaya, and Stromboli). We used high spatial resolution images acquired by a Sentinel-2 MultiSpectral Instrument (MSI) and a random forest model, which avoids the setting of fixed a priori thresholds. The results indicate that the model achieves better performance than traditional approaches with good generalization capabilities and high sensitivity to less intense volcanic thermal anomalies. We found that this model is sufficiently robust to be successfully used with new eruptive scenes never seen before on a global scale. |
Appears in Collections: | Article published / in press |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
remotesensing-14-04370-v3.pdf | Open Access published article | 3.59 MB | Adobe PDF | View/Open |
Page view(s)
90
checked on Apr 27, 2024
Download(s)
24
checked on Apr 27, 2024