1,720,981 research outputs found
On the potential of the RST-FLARE algorithm for gas flaring characterization from space
No full textAn effective characterization of gas flaring is hampered by the lack of systematic, complete and reliable data on its magnitude and spatial distribution. In the last years, a few satellite methods have been developed to provide independent information on gas flaring activity at global, national and local scale. Among these, a MODIS-based method, aimed at the computation of gas flared volumes by an Italian plant, was proposed. In this work, a more general version of this approach, named RST-FLARE, has been developed to provide reliable information on flaring sites localization and gas emitted volumes over a long time period for the Niger Delta region, one of the top five gas flaring areas in the world. Achieved results showed a good level of accuracy, in terms of flaring sites localization (95% of spatial match) and volume estimates (mean bias between in 16% and 20%, at annual scale and 2–9% in the long period) when compared to independent data, provided both by other satellite techniques and national/international organizations. Outcomes of this work seem to indicate that RST-FLARE can be used to provide, at different geographic scales, quite accurate data on gas flaring, suitable for monitoring purposes for governments and local authorities
Landslides Detection and Mapping with an Advanced Multi-Temporal Satellite Optical Technique
Get PDFLandslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident
Monitoring turbidity in the Ionical coast during extreme events by applying a Robust Satellite Technique (RST) to MODIS Imagery
No full textMonitoring of Suspended Sediment Concentrations in plumes (SSC) at river mouths is particularly important for a correct sediment balance. Such evaluation requires the knowledge of many hydraulic and hydro-geological factors, highly variable in the space-time domain and not easily measurable through conventional techniques. Mainly because of this, satellite techniques have been proposed to allow the monitoring of SSC space-time dynamics with spatial resolution ranging between 250 and 3000 meters and time repetition from a few hours up to a few minutes.
This paper presents a first attempt to apply the general Robust Satellite Technique (RST) approach, already used to investigate several natural and environmental hazards, for SS detection and mapping. Preliminary achieved results seem to confirm that such approach is suitable for automatically identifying the spectral signatures of different suspended particle mixtures, taking into account bathymetric effects, which might mask the presence of sediments near the coasts. A preliminary, qualitative integration of ground-based data and measurements (e.g. water levels and discharges) with satellite-based information is also proposed as a first step toward a further methodology suggested for a full data integration strategy.
Ground-based and satellite derived information, based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, are combined in the cases of the flood events occurred on 28 March 2007 and 11 March 2010 along the Ionical coast of Basilicata Region, in Southern Italy
Analyzing the December 2013 Metaponto Plain (Southern Italy) Flood Event by Integrating Optical Sensors Satellite Data
Get PDFTimely and continuous information about flood dynamics are fundamental to ensure
an effective implementation of the relief and rescue operations. Satellite data provided by optical
sensors onboard meteorological satellites could have great potential in this framework, offering
an adequate trade-off between spatial and temporal resolution. The latest would benefit from the
integration of observations coming from different satellite systems, also helping to increase the
probability of finding cloud free images over the investigated region. The Robust Satellite Techniques
for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at
detecting flooded areas which has already been applied with good results on different polar orbiting
optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution
Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership
(SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata
and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved
results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas
regardless of the sensor used. Flooded areas have indeed been detected with similar performance by
the two sensors, allowing for their continuous and near-real time monitoring
Assessing Performance of the RSTVOLC Multi-Temporal Algorithm in Detecting Subtle Hot Spots at Oldoinyo Lengai (Tanzania, Africa) for Comparison with MODLEN
Get PDFThe identification of subtle thermal anomalies (i.e., of low-temperature and/or spatial
extent) at volcanoes by satellite is of great interest for scientists, especially because minor changes
in surface temperature might reveal an unrest phase or impending activity. A good test case for
assessing the sensitivity level of satellite-based methods is to study the thermal activity of Oldoinyo
Lengai (OL) (Africa, Tanzania), which is the only volcano on Earth emitting natrocarbonatite lavas
at a lower temperature (i.e., in the range 500–600 C) than usual magmatic surfaces. In this work,
we assess the potential of the RSTVOLC multi-temporal algorithm in detecting subtle hot spots at OL
for comparison with MODLEN: A thermal anomaly detection method tailored to OL local conditions,
by using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Our results investigating
the eruptive events of 2000–2008 using RSTVOLC reveal the occurrence of several undocumented
thermal activities of OL, and may successfully integrate MODLEN observations. In spite of some
known limitations strongly affecting the identification of volcanic thermal anomalies from space
(e.g., cloud cover; occurrence of short-lived events), this work demonstrates that RSTVOLC may
provide a very important contribution for monitoring the OL, identifying subtle hot spots showing
values of the radiant flux even around 1 MW
On the Potential of RST-FLOOD on Visible Infrared Imaging Radiometer Suite Data for Flooded Areas Detection
No full textTimely and continuous information about flood spatiotemporal evolution are fundamental
to ensure an effective implementation of the relief and rescue operations in case of inundation
events. In this framework, satellite remote sensing may provide a valuable contribution provided
that robust data analysis methods are implemented and suitable data, in terms of spatial, spectral and
temporal resolutions, are employed. In this paper, the Robust Satellite Techniques (RST) approach,
a satellite-based differential approach, already applied at detecting flooded areas (and therefore
christened RST-FLOOD) with good results on different polar orbiting optical sensors (i.e., Advanced
Very High Resolution Radiometer – AVHRR – and Moderate Resolution Imaging Spectroradiometer –
MODIS), has been fully implemented on time series of Suomi National Polar-orbiting Partnership
(Suomi-NPP-SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. The flooding event
affecting the Metaponto Plain in Basilicata and Puglia regions (southern Italy) in December 2013
was selected as a case study and investigated by analysing five years (only December month) of
VIIRS Imagery bands at 375 m spatial resolution. The achieved results clearly indicate the potential
of the proposed approach, especially when compared with a satellite-based high resolution map of
flooded area, as well as with the official flood hazard map of the area and the outputs of a recent
published VIIRS-based method. Both flood extent and dynamics have been recognized with good
reliability during the investigated period, with only a residual 11.5% of possible false positives over
an inundated area extent of about 73 km2. In addition, a flooded area of about 18 km2 was found
outside the hazard map, suggesting it requires updating to better manage flood risk and prevent
future damages. Finally, the achieved results indicate that medium-resolution optical data, if analysed
with robust methodologies like RST-FLOOD, can be suitable for detecting and monitoring floods also
in case of small hydrological basins
Investigating the chlorophyll-a variability in the Gulf of Taranto (North-western Ionian Sea) by a multi-temporal analysis of MODIS-Aqua Level 3/Level 2 data
No full textCascading landslide–barrier dam–outburst flood hazard: A systematic study using rockfall analyst and HEC-RAS
Get PDFLandslide hazard chains pose significant threats in mountainous areas worldwide, yet their cascading effects remain insufficiently studied. This study proposes an integrated framework to systematically assess the landslide-landslide dam-outburst flood hazard chain in mountainous river systems. First, landslide susceptibility is assessed through a random forest model incorporating 11 static environmental and geological factors. The surface deformation rate derived from SABS-InSAR technology is incorporated as a dynamic factor to improve classification accuracy. Second, motion trajectories of rock masses in high-risk zones are identified by Rockfall Analyst model to predict potential river blockages by landslide dams, and key geometric parameters of the landslide dams are predicted using a predictive model. Third, the 2D HEC-RAS model is used to simulate outburst flood evolution. Results reveal that: (1) incorporating surface deformation rate as a dynamic factor significantly improves the predictive accuracy of landslide susceptibility assessment; (2) landslide-induced outburst floods exhibit greater destructive potential and more complex inundation dynamics than conventional mountain flash floods; and (3) the outburst flood propagation process exhibits three sequential phases defined by the Outburst Flood Arrival Time (FAT): initial rapid advancement phase, intermediate lateral diffusion phase, and mature floodplain development phase. These phases represent critical temporal thresholds for initiating timely downstream evacuation. This study contributes to the advancement of early warning systems aimed at protecting downstream communities from outburst floods triggered by landslide hazard chains. It enables researchers to better analyze the complex dynamics of such cascading events and to develop effective risk reduction strategies applicable in vulnerable regions
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