Irina Coviello

Near real time oil spill detection and monitoring using satellite optical data

Timely 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.

Real time monitoring of flooded areas by a multi-temporal analysis of optical satellite data

Optical 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.

Advanced multi-temporal passive microwave data analysis for soil wetness monitoring and flood risk forecast

Microwave remote sensing offers emerging capabilities to monitor soil moisture variations at global scale. The Robust Satellite Techniques (RST), a general change detection methodology, has been applied using Advance Microwave Sounding Unit (AMSU) data to define a new Soil Wetness Variation Index (SWI), which has already demonstrated its capabilities in monitoring soil wetness variations in the space-time domain. In this work, the RST approach has been implemented also using AMSR-E (Advanced Microwave Scanning Radiometer — Earth Observing System) C-band data, trying to exploit its better spectral features in order to define a new advanced index able to give suitable information about soil moisture and its variation. In particular, the flooding event which hit the Algeria in October 2008 has been analyzed by such an index as well by using SWVI. Results of such a study will be shown and discussed in this work.

Thermal Monitoring of Eyjafjöll Volcano Eruptions by Means of Infrared MODIS Data

In the evening of 20 March 2010, after about two centuries of quiescence, an effusive eruption took place at Eyjafjöll (Iceland) volcano, from a small vent localized on the northeast flank (Fimmvörduháls Pass) of the volcano edifice. On 31 March, a new eruptive fissure opened on the same region emitting lava. About 2 weeks later, on 14 April, a strong explosive eruption took place under the Eyjafjallajökull glacier, injecting copious amounts of ash in the atmosphere and causing an unprecedented air traffic disruption in Northern and Central Europe. In this paper, the changes in thermal signals occurring at Eyjafjöll volcano during 1 March-20 April 2010 are investigated, testing the RSTVOLC algorithm for the first time in a subpolar environment. Outcomes of this retrospective study, performed by means of infrared Moderate Resolution Imaging Spectroradiometer (MODIS) data, show that both effusive and explosive eruptions of the Eyjafjöll volcano could be identified in a timely manner and well monitored from space. Moreover, in spite of a lack of pre-eruptive hot spots detection, this paper reveals a general increasing trend of the middle infrared signal at crater area, beginning 2 weeks before the explosion, stimulating and suggesting further investigations devoted to better characterize the thermal behavior of the monitored volcano.

A New RST‐Based Approach for Continuous Oil Spill Detection in TIR Range: The Case of the Deepwater Horizon Platform in the Gulf of Mexico

Monitoring and M A Record-Breakin Geophysical Mon Copyright 2011 b 10.1029/2011GM Oil pollution is a threat that increasingly concerns marine/coastal ecosystem. Timely detection and continuous update of information are fundamental to reduce oil spill environmental impact. EOSs, especially meteorological satellites, can be profitably used for a near real time sea monitoring thanks to their high temporal resolution and easy data delivery. In this paper, we present a new algorithm, based on the general Robust Satellite Technique (RST) approach, for automatic near-real-time oil spill detection and continuous monitoring (i.e., in both daytime and nighttime) by using optical data. The new RST scheme has been applied to the analysis of the recent oil spill disaster of the Deepwater Horizon Platform in the Gulf of Mexico. In particular, a dense temporal series of RST-based oil spill maps, obtained by using Moderate Resolution Imaging Spectroradiometer-thermal infrared records acquired in both daytime and nighttime during the 25–29 April 2010 period, are shown and commented. The results seem to confirm the good performance of the proposed approach in automatic detection of oil spill presence with a high level of reliability and sensitivity even in nighttime acquisitions. These achievements confirm the potential of optical data for oil spill detection and monitoring, thus suggesting their use in combination with radar acquisitions toward developing a multiplatform system that is able to furnish detailed and frequent information about oil spill presence and dynamics.

A Multi-Sensor Exportable Approach for Automatic Flooded Areas Detection and Monitoring by a Composite Satellite Constellation

Timely and frequently updated information about flood-affected areas and their space-time evolution are often crucial in order to correctly manage the emergency phases. In such a context, optical data provided by meteorological satellites, offering the highest available temporal resolution (from hours to minutes), could have a great potential. As cloud cover often occurs reducing the number of usable optical satellite images, an appropriate integration of observations coming from different satellite systems will surely improve the probability to find cloud-free images over the investigated region. To make this integration effective, appropriate satellite data analysis methodologies, suitable for providing congruent results, regardless of the used sensor, are envisaged. In this paper, a sensor-independent approach (RST, Robust Satellites Techniques-FLOOD) is presented and applied to data acquired by two different satellite systems (Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration platforms and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System satellites) at different spatial resolutions (from 1 km to 250 m) in the case of Elbe flood event occurred in Germany on August 2002. Results achieved demonstrated as the full integration of AVHRR and MODIS RST-FLOOD products allowed us to double the number of satellite passes daily available, improving continuity of monitoring over flood-affected regions. In addition, the application of RST-FLOOD to higher spatial resolution MODIS (250 m) data revealed to be crucial not only for mapping purposes but also for improving RST-FLOOD capability in identifying flooded areas not previously detected at lower spatial resolution.

A review of RSTVOLC, an original algorithm for automatic detection and near-real-time monitoring of volcanic hotspots from space

Abstract The observation of volcanic thermal activity from space dates back to the late 1960s. Several methods have been proposed to improve detection and monitoring capabilities of thermal volcanic features, and to characterize them to improve our understanding of volcanic processes, as well as to inform operational decisions. In this paper we review the RSTVOLC algorithm, which has been designed and implemented for automated detection and near-real-time monitoring of volcanic hotspots. The algorithm is based on the general Robust Satellite Techniques (RST) approach, representing an original strategy for satellite data analysis in the space–time domain. It has proven to be a useful tool for investigating volcanoes worldwide, by means of different satellite sensors, onboard polar orbiting and geostationary platforms. The RSTVOLC rationale, its requirements and main operational capabilities are described here, together with the advantages of the tool and the known limitations. Results achieved through the study of two past eruptive events are shown, together with some recent examples demonstrating the near-continuous monitoring capability offered by RSTVOLC. A summary is also made of the type products that the method is able to generate and provide. Lastly, the future perspectives, in terms of its possible implementation on the new generation of satellite systems, are briefly discussed.

A Multitemporal Investigation of AMSR-E C-Band Radio-Frequency Interference

Radio-frequency interference (RFI) is increasingly a severe problem for present and future microwave satellite missions. RFI at C- and X-bands can contaminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer (AMSR-E) and the WindSat sensor. In this work, the multitemporal Robust Satellite Techniques approach has been implemented on C-band AMSR-E data in order to identify areas systematically affected by different levels of RFI, trying to discriminate them from natural geophysical variability zones. To the scope, nine years of AMSR-E data have been investigated, allowing us also to better infer RFI impact on data acquired during ascending or descending passes, as well as in horizontal or vertical polarization. In detail, two analyses were carried out: one considering only measurements at C-band and another one taking into account a combination between C- and X-band measurements. The results of this study will be shown and discussed in this paper.

On the Potential of Robust Satellite Techniques Approach for SPM Monitoring in Coastal Waters: Implementation and Application over the Basilicata Ionian Coastal Waters Using MODIS‐Aqua

Monitoring river plume dynamics and variations in complex coastal areas can provide useful information to prevent marine environmental damage. In this work, the Robust Satellite Techniques (RST) approach has been implemented and tested on historical series of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor, for the first time, Suspended Particulate Matter (SPM) anomalies associated to river plumes. To this aim, MODIS-Aqua Level 1A data were processed using an atmospheric correction adequate for coastal waters, and SPM daily maps were generated applying an algorithm adapted from literature. The RST approach was then applied to these maps to assess the anomalous presence of SPM. The study area involves the Basilicata region coastal waters (Ionian Sea, South of Italy). A long-time analysis (2003–2015) conducted for the month of December allows us to find that the maximum SPM concentration value was registered in December 2013, when an extreme hydrological event occurred. A short-time analysis was then carried out applying RST to monitor the dynamics of anomalous SPM concentrations. Finally, the most exposed areas, in terms of SPM concentration, were identified. The results obtained in this work showed the RST high potential when used in combination with standard SPM daily maps to better characterize and monitor coastal waters.

On the potential of an RST-based analysis of the MODIS-derived chl-a product over Condor seamount and surrounding areas (Azores, NE Atlantic)

Oceanographic cruises have been conducted on the Condor seamount (SW Faial Island, Azores archipelago, NE Atlantic) since 2009 to collect in situ data and understand potential seamount effects on local biodiversity. Satellite data have been concurrently collected to infer the space-time upper-ocean optical property variability and the associated physical processes. The main limitation of this analysis is the persistent and significant cloud coverage above the region that, especially in some seasons, can significantly hinder satellite data availability. This study was meant to test the robust satellite technique (RST) over the Condor seamount, assess its capability to estimate multiyear trends and identify space-time anomalies. To this aim, 11-year MODIS/AQUA level 2-derived chlorophyll-a (chl-a) data were used. Results achieved for October 2010 show, within a large-scale analysis, the presence of well-defined areas of near-surface chl-a anomalies, highlighting the occurrence of a trapping effect due to flow-topography interaction processes. Regarding the Condor area, the chl-a anomalies detected along the eastern side of the seamount were linked to a strong vertical mixing that provided sufficient inorganic nutrients requested for productivity. The achieved results, whose accuracy was also tested through a comparison with in situ data, are consistent with those independently obtained by other authors who described the phytoplankton variability around the Condor seamount. This study shows the high potential of the RST approach to assess the chl-a variability in the space-time domain in oligotrophic regions such as the Azores, allowing the identification of the most important areas to be preserved and/or managed.