Volcanic ash cloud detection from remote sensing images using principal component analysis

• A novel volcanic ash cloud detection method for remote sensing images is proposed.
• Principal component analysis method is introduced into MODIS images.
• The proposed method reduces the inter-band correlation and data redundancy.
• Advantages: novelty, simpleness, good consistency with the real distribution.

Thermal infrared remote sensing can quickly and accurately detect the volcanic ash cloud. However, remote sensing data have pretty strong inter-band correlation and data redundancy, both of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Principal component analysis (PCA) can compress a large number of complex information into a few principal components and overcome the correlation and redundancy. Taking the Eyjafjallajokull volcanic ash cloud formed on April 19, 2010 for example, in this paper, the PCA is used to detect the volcanic ash cloud based on moderate resolution imaging spectroradiometer (MODIS) remote sensing image. The results show that: the PCA can successfully acquire the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the spatial distribution, SO2 concentration and volcanic absorbing aerosol index (AAI).

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