Automated and reliable satellite-based techniques are strongly required for volcanic ash cloud detection and tracking. In fact, volcanic ash clouds pose a serious hazard for air traffic and the synoptic (and possibly frequent) coverage offered by satellites can provide exciting opportunities for monitoring activities as well as for risk mitigation purposes. A new, AVHRR-based technique for improved automatic detection of volcanic clouds by means of multi-temporal analysis of historical, long-term satellite records has been recently proposed. The technique basically rests on the Robust AVHRR Techniques (RAT) approach, which is an innovative strategy of satellite data analysis, devoted to a former characterisation of the measured signal, in terms of expected value and natural variability and a further recognition of signal anomalies by an automatic, unsupervised change detection step. In this work, an extension of this method to nighttime observations is presented, by using thermal infrared information coming from AVHRR bands centred approximately at 3.5, 11.0 and 12.0 μm. Results achieved for two recent eruptive events of Mount Etna (occurred in May 2000 and in July 2001) seem to be encouraging, showing clear improvements in terms of ash detection sensitivity as well as in terms of false alarms reduction. The technique performance is also evaluated by comparison with the traditional “split-window” brightness temperature difference method; this exercise revealed a general improvement obtained by the proposed approach, even though some common problems still remain unsolved. The main merits of such an approach are its intrinsic self-adaptability to different environmental/natural/observational conditions and its natural exportability also to different satellite sensors. The results here presented show the benefits of such a technique especially when different observational conditions (time of pass, seasonal period, atmospheric moisture, solar illumination, volcanic cloud composition, satellite angles of view, etc.) are considered. The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (like, for example, SEVIRI aboard Meteosat Second Generation platform) are also discussed.
Improving volcanic ash clouds detection by a robust satellite technique. Remote Sensing of Environment
TRAMUTOLI, Valerio;
2004-01-01
Abstract
Automated and reliable satellite-based techniques are strongly required for volcanic ash cloud detection and tracking. In fact, volcanic ash clouds pose a serious hazard for air traffic and the synoptic (and possibly frequent) coverage offered by satellites can provide exciting opportunities for monitoring activities as well as for risk mitigation purposes. A new, AVHRR-based technique for improved automatic detection of volcanic clouds by means of multi-temporal analysis of historical, long-term satellite records has been recently proposed. The technique basically rests on the Robust AVHRR Techniques (RAT) approach, which is an innovative strategy of satellite data analysis, devoted to a former characterisation of the measured signal, in terms of expected value and natural variability and a further recognition of signal anomalies by an automatic, unsupervised change detection step. In this work, an extension of this method to nighttime observations is presented, by using thermal infrared information coming from AVHRR bands centred approximately at 3.5, 11.0 and 12.0 μm. Results achieved for two recent eruptive events of Mount Etna (occurred in May 2000 and in July 2001) seem to be encouraging, showing clear improvements in terms of ash detection sensitivity as well as in terms of false alarms reduction. The technique performance is also evaluated by comparison with the traditional “split-window” brightness temperature difference method; this exercise revealed a general improvement obtained by the proposed approach, even though some common problems still remain unsolved. The main merits of such an approach are its intrinsic self-adaptability to different environmental/natural/observational conditions and its natural exportability also to different satellite sensors. The results here presented show the benefits of such a technique especially when different observational conditions (time of pass, seasonal period, atmospheric moisture, solar illumination, volcanic cloud composition, satellite angles of view, etc.) are considered. The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (like, for example, SEVIRI aboard Meteosat Second Generation platform) are also discussed.File | Dimensione | Formato | |
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