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SANA: SUB-PIXEL AUTOMATIC NAVIGATION OF AVHRR IMAGERY
No full textAn automatic method (SANA) for sub-pixel navigation of Advanced Very High Resolution Radiometer (AVHRR) imagery is proposed. It progressively corrects satellite attitude and reduces navigation errors all over the scene by using an iterative approach. Tests performed on more than 400 AVHRR passes over Europe, demonstrate the above mentioned method capability to obtain, with no human intervention, a final navigation accuracy within 1 pixel. Main characteristics of such a method are its processing speed as well as its full exportability to other satellite packages
Two years of operational use of SANA (sub-pixel automatic navigation of AVHRR) scheme: accuracy assessment and validation.
No full textAutomated techniques for satellite imagery navigation and co-location are especially required for environmental monitoring activities
intensively using satellite data. In this work are presented the results obtained after 2 years of operational use of the Subpixel Automatic
Navigation of AVHRR (SANA) scheme. An automatic method for accuracy assessment of satellite navigation techniques, which permits a
preliminary evaluation of their performances, dealing with a large collection of test images is also proposed. The navigation accuracy
assessment, performed by using a selection of small islands as reference points, is discussed.
Results achieved over more than 400 Advanced Very-High-Resolution Radiometer (AVHRR) scenes confirm that the SANA scheme is a
very accurate one (computed mean navigation error is generally about one AVHRR pixel). Furthermore, because of its high processing speed,
it can be considered a suitable tool for intensive satellite data processing in multitemporal analyses, especially required for environmental
studies as well as for operational monitoring purposes
Automated detection of thermal features of active volcanoes by means of Infrared AVHRR records.
No full textAn innovative, Advanced Very High Resolution Radiometer (AVHRR)-based technique for improved automatic detection of volcanic hotspots and thermal anomalies is proposed in this paper. It is mainly based on a multitemporal analysis of historical, long-term satellite records. Such a technique basically rests on the Robust AVHRR Techniques (RAT) approach, which has been already successfully applied to several natural and environmental emergencies (e.g., fires, floods, earthquakes). In this work, the proposed technique has been tested on an extended set of eruptive events of Mt. Etna and Stromboli volcanoes. Results achieved, in terms of reliability (low false alarm rate) as well as of effectiveness (detection sensitivity), are described in detail. Moreover, the potential in low-level thermal anomaly detection, as possible pre-eruptive thermal signs, is also addressed and preliminary results obtained for a couple of events, discussed. The study cases here presented show the benefits of such a technique especially when different observational conditions (time/season of pass, atmospheric moisture content, solar illumination, satellite angles of view, etc.) are considered, making such a method globally applicable.
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 (such as SEVIRI aboard Meteosat Second Generation) are also discussed
A google earth engine tool to investigate, map and monitor volcanic thermal anomalies at global scale by means of mid-high spatial resolution satellite data
Full text linkSeveral satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed
A ROBUST SATELLITE TECHNIQUE (RST) FOR DUST STORM DETECTION AND MONITORING: THE CASE OF 2009 AUSTRALIAN EVENT
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A Robust Satellite Technique (RST) for dust storm detection and monitoring: The case of 2009 Australian event
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Tramutoli, V. ; Filizzola, C. ; Marchese, F. ; Mazzeo, G. ; Paciello, R. ; Pergola, N. ; Pietrapertosa, C. ; Sannazzaro, F. ;
Dept. of Eng. & Phys. of the Environ., Univ. of Basilicata, Potenza, Italy
This paper appears in: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Issue Date : 25-30 July 2010
On page(s): 1707 - 1709
ISSN : 2153-6996
E-ISBN : 978-1-4244-9564-1
Print ISBN: 978-1-4244-9565-8
References Cited: 25
INSPEC Accession Number: 11686826
Digital Object Identifier : 10.1109/IGARSS.2010.5650621
Date of Current Version : 03 dicembre 2010
Abstract
In this paper, an original method of satellite data analysis named RST (Robust Satellite Technique), already successfully used to study and monitor several natural and environmental hazards, is applied for the first time to a recent dust storm occurred in Australia in September 2009. This event was analyzed implementing RST on MTSAT-1R (Multi-functional Transport Satellite-1Replacement) Japanese geostationary satellite data. Some preliminary results of this study are presented, discussing RST performances even in comparison with traditional split window satellite techniques
On the potential of multi-temporal analysis of MODIS and AVHRR data for near real time mapping and monitoring of flooded areas
No full textFloods are devastating natural disasters which may cause very high costs in lives and damages. Among the other potential contributes, satellite remote sensing may help for mapping and monitoring flooded areas. In order to support flood risk management, information coming from satellite, especially during crisis phases, need to be timely provided. Besides, suitable techniques, able to reliably detect flooded areas with a low rate of false alarms, are requested. In this context, a new technique using visible AVHRR data has been recently proposed. In this work, in order to further confirm the reliability and the sensitivity of the proposed approach we analyze the August 2002 flood which hit wide territories of Germany. Afterwards, we exported
this
approach on MODIS data, in order to exploit the higher spatial resolution in the visible and near-infrared channels offered by such a sensor, for increasing the accuracy in both near real time flooded area mapping and monitorin
REAL TIME MONITORING OF FLOODED AREAS BY A MULTI-TEMPORAL ANALYSIS OF OPTICAL SATELLITE DATA
No full textOptical sensors aboard meteorological satellites are an excellent tool to monitor floods and support the flood risk management cycle, mainly thanks to their high temporal resolution, which allow us to obtain real time and frequently updated information on environmental changes. The RST (Robust Satellite Techniques) approach, an automatic change detection scheme, has been already applied using AVHRR (Advanced very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) data to detect and monitor flooded areas. Results achieved have shown its capability in automatically identify flooded areas with a low rate of false alarms, also discriminating permanent water from actual inundated areas. In this paper, in order to further assess the reliability and the sensitivity of the proposed approach in different conditions of observation, the RST methodology has been used to analyze the July 2007 and October 2008 floods occurred in the South Africa and Algeria regions
A MODIS-based Robust Satellite Techniques for near real time oil spill detection and monitoring.
No full textThe accidental release of oil into the oceans from
tankers may have remarkable ecological impact on maritime and coastal environments. Satellite remote sensing may be an useful tool for the monitoring of such a kind of marine hazards. In particular, MODIS sensor allows us a good combination of spectral-spatial-temporal resolution to provide frequent and detailed mapping of oil affected areas. Using data acquired in the first two MODIS channels, a new techniques for the near real time oil spill detection and monitoring has been recently proposed and applied, with encouraging results, to analyze several spill events. First results have, in fact, shown the potential of such an approach for a timely and continuous detection of these events. In this paper, we verify the sensitivity of such a technique in detecting also the presence of oil spill of low intensity and/or small extent, like those often related to illicit vessel discharges
Near real time oil spill detection and monitoring using satellite optical data
No full textTimely detection and continuously updated information are fundamental in reducing the ecological impact of the different sources of sea pollution. Satellite remote sensing, especially from meteorological platforms having a high temporal resolution and an easy data delivery, can be profitably used for a near real time sea monitoring. Recently, a new methodology for oil spill detection and monitoring, based on the general Robust Satellite Technique (RST) approach, has been proposed. This technique has shown, by using AVHRR Thermal Infrared (TIR) data, a good capability in automatically detect, with high level of reliability, oil spill presence. In this paper, such an approach has been exported for the first time to MODIS TIR data. Preliminary results obtained for an oil spill event occurred during Lebanon war in 2006, are shown and discussed
Investigating Phases of Thermal Unrest at Ambrym (Vanuatu) Volcano through the Normalized Hot Spot Indices Tool and the Integration with the MIROVA System
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