## 1 Introduction  
Remote sensing research has used multispectral remote sensing imagery to provide additional data, proving to be a valuable source of spatio-temporal data for some applications. Applications that are widely used are: land cover classification, detection catch archeology, extracting spatial features and classification in a residential area, the extraction of the street, the estimation of urban sprawl automatic mapping feature flow, classification and feature extraction transport, mapping snow cover and the evaluation of geomorphological features [1,2].  
Land cover assessment (LC) is very important in planning, monitoring and sustaining the utilization of natural resources. LC has a direct impact on water, atmospheric and soil erosion and is therefore directly related to many globally-important environmental issues [3]. Appropriate knowledge, updated and temporal, about LC is very important to address the issue of unplanned development, environmental degradation, loss of wildlife habitat and depletion of primary agricultural and forest land [4]. Therefore, it is important to evaluate and monitor the LC dynamics resulting from anthropogenic activities and natural phenomena to plan, monitor and sustain the utilization of natural resources [5].  
In this study, land cover assessment is used to see the distribution of volcanic eruptions' deposits that may be dangerous to the surrounding area when the eruption occurs. The volcano is a naturally-formed entity on the surface of the Earth that occupies an area and displays volcanism. An eruption is the discharge of magma from within the Earth and can be divided into three types: explosive, effusive and hot spot eruptions. The type of eruption that occurs is influenced by many factors such as magma viscosity, the gas content of the magma, the influence of groundwater and the depth of the magma chamber [6]. The products ejected by volcanic eruptions, which are often catastrophic, can be captured by satellite, optical and radar sensors. Remote sensing images can be utilized to detect the spread of eruptive fumes associated with volcanic eruptions that spread in the atmosphere, pyroclastic deposits, incandescent lava, lahar distribution and dome deformation [7].  
We analyze the evaluation of volcano hazard, especially pyroclastic deposit, using land classification with Landsat imagery data and the artificial neural network approach. Several different approaches to the evaluation of volcano hazards can be found in the current literature, including direct and indirect heuristic approaches and deterministic, probabilistic and statistical approaches. [PERSON] et al. (2015) summarized the many analyses of volcano activity based on remote sensing and GIS techniques [8]. Recently, studies on volcano activity assessment made use of remote sensing, and many applied probabilistic models such as satellite imagery. We used land classification in satellite imagery using an artificial neural network method to analyze the area affected by an eruption. [PERSON] at al. (2003) showed that the most frequently-used neural network method is the backpropagation learning algorithm, a learning algorithm of a multilayered neural network that consists of an input layer, hidden layers and an output layer [9]. Many experiments have shown that multilayered neural networks are more accurate for land cover classification than traditional statistical methods [10; 11; 12; 13].