Thermal monitoring of volcanic effusive activity: the uncertainties and outlier detection

Abstract

Thermal observations of volcanic activity when the volcano is partially covered by clouds or observed under a wide-scan angle are often removed from further analyses. In the event of a volcanic crisis, such a reduced set of data is not adequate. Even when the observation conditions are favourable, the full observation set is still required to provide decision-makers with quality information about the data. Automatic quality estimation and outlier detection was not estimated in the past. We propose to analytically define the uncertainty for individual observations based on the measurement circumstances. To additionally reduce the temporal noise of the radiant power (RP) time series we apply a Kalman Filter (KF). The KF is able to recursively analyse an unevenly sampled time series. Based on some proposed rules, it can also detect outliers. We apply the proposed methodology to the 2008-09 Etna eruption monitored by MODIS (Moderate Resolution Imaging Spectroradiometer). The analysis of the results shows that the topography has a greater influence on RP than previously considered.

Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano

“…The detected thermal anomalies are usually quantified by their temperature, area, volcanic radiant power (VRP), or time averaged lava discharge rate (TADR) [14][15][16][17][18][19][20][21][22][23][24]. The uncertainty of single measured spectral radiances depends on numerous factors such as different sensor's point spread function and spectral response functions, different time of overpass, orbit geometry, and spatial resolution [25,26].…”

“…In this case, the signal may be dispersed over several pixels although a typical lava flow is no more than a pixel wide in most satellite instruments. This effect is a result of the imperfect point spread function of the optics on satellite instruments [26,38]. In the case of TET-1, this is a very important step as its spatial sampling is 160 m, but the effective pixel size is twice as large (Table 1).…”

“…These are most likely the consequence of cloud coverage. In our previous work [25,26] we were dealing with uncertainties in VRP's time series. However, we cannot use the methodology based on the Kalman filter to filter out the outliers, as we have only used the MODVOLC database, which is more convenient than processing a large number of MODIS images.…”

Risk evaluation, detection and simulation during effusive eruption disasters

“…In addition, all contributions were invited as reviewstyle submissions that focus on entirely effusive themes so as to build a manual, inventory and directory as to the current state of the art in operational lava flow tracking and modelling. Part 1 begins with a review of three wellestablished hot spot detection algorithms: MOD-VOLC (Wright 2015), RST-Volc (Pergola et al 2015) and AVHotRR (Lombardo 2015), plus a new algorithm type based on application of the Kalman Filter (Zakšek et al 2015). We also review means of near-real-time multiple sensor targeting to obtain high spectral, spatial and temporal resolution event coverage (Ramsey 2015) plus the use of a sensor web-based networking approach to target all available tracking devices on an effusive crisis .…”