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In this paper use is made of fractal models for the development of a processing chain devoted to volcano monitoring. In particular, we present new models for the characterization of microwave images of fractal surfaces and we show how these models can account for the presence of different types of lava flows on these images.The imaged surfaces are modeled as fractional Brownian motion (fBm) stochastic processes. First of all, we show how the radar image relevant to an fBm can be linked to an associated fractional Gaussian noise (fGn) process. Different types of lava flow surfaces are simulated and their image spectra are analyzed and compared. Finally, a case study is presented. The area of interest is the Vesuvio volcano close to Naples, Italy. Simulated results, showing the possibility to discriminate different types of lava, are provided.