PlumX Metrics
Embed PlumX Metrics

Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations

Remote Sensing, ISSN: 2072-4292, Vol: 15, Issue: 4
2023
  • 10
    Citations
  • 0
    Usage
  • 7
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    10
  • Captures
    7
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Sapienza University of Rome Researcher Provides New Study Findings on Machine Learning (Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations)

2023 FEB 17 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- Investigators publish new report

Article Description

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 10 kg (MLE method) and 1.32 ± 0.47 × 10 kg (NN method) for Kelud; 4.48 ± 1.61 × 10 kg (MLE method) and 4.32 ± 1.56 × 10 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.

Bibliographic Details

Francesco Romeo; Mario Papa; Frank Silvio Marzano; Luigi Mereu; Simona Scollo; Stefano Corradini; Luca Merucci

MDPI AG

Earth and Planetary Sciences

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know