Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time-space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.
Keywords: ash fall-out; ash granulometry; core-characteristics estimation; vision-based paradigm; volcanic ash.