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Volcanic ash cloud detection from MODIS image based on CPIWS method

Liu L., Li Ch., Lei Y., Yin J., Zhao J.

Acta Geophysica

2017

Vol. 65, no. 1

151--163

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.

Remote Sensing Monitoring of Volcanic Ash Clouds Based on PCA Method

Li Ch., Dai Y., Zhao J., Zhou S., Yin J., Xue D.

Acta Geophysica

2015

Vol. 63, no. 2

432--450

Volcanic ash clouds threaten the aviation safety and cause global environmental effects. It is possible to effectively monitor the volcanic ash cloud with the aid of thermal infrared remote sensing technology. Principal component analysis (PCA) is able to remove the inter-band correlation and eliminate the data redundancy of remote sensing data. Taking the Eyjafjallajokull volcanic ash clouds formed on 15 and 19 April 2010 as an example, in this paper, the PCA method is used to monitor the volcanic ash cloud based on MODIS bands selection; the USGS standard spectral database and the volcanic absorbing aerosol index (AAI) are applied as contrasts to the monitoring result. The results indicate that: the PCA method is much simpler; its spectral matching rates reach 74.65 and 76.35%, respectively; and the PCA method has higher consistency with volcanic AAI distribution.