Remote sensing in coastal water monitoring: Applications in the eastern Mediterranean Sea (IUPAC Technical Report)

2011 ◽  
Vol 84 (2) ◽  
pp. 335-375 ◽  
Author(s):  
Manos Dassenakis ◽  
Vasiliki Paraskevopoulou ◽  
Constantinos Cartalis ◽  
Nektaria Adaktilou ◽  
Katerina Katsiabani
Keyword(s):  
Remote Sensing ◽  
Mediterranean Sea ◽  
Forest Fires ◽  
Land Surface ◽  
Oil Spills ◽  
Environmental Concern ◽  
Eastern Mediterranean ◽  
Satellite Observation ◽  
Habitat Destruction ◽  
Remote Sensing Satellite

Remote sensing/satellite observation of land and oceans is a field of research that was developed during the second half of the 20th century, and its importance is widely recognised because of the amount of information it can provide to the scientific community and the general public. The outcomes of remote sensing/satellite observation can be used to address and study significant aspects of environmental concern, such as habitat destruction, environmental degradation, forest fires, oil spills, and climate change. There is continuous improvement of the methods and means of remote sensing observations in order to achieve more accurate and useful information. The main advantage is the possibility of observing large areas, and the main disadvantage is that it can observe only the water and land surface. The present paper is an effort to review the technologies used in remote sensing and the general applications in a comprehensive manner addressed to scientists who do not specialize in this area of research. Furthermore, this paper reviews case studies/applications in the Mediterranean Sea, an area affected by various polluting activities (industrial cities, agriculture, shipping, etc.) that should be continuously monitored so that the coastal countries are able to successfully manage this sensitive environment.

scholarly journals Comparison of Hydrocarbon-Degrading Consortia from Surface and Deep Waters of the Eastern Mediterranean Sea: Characterization and Degradation Potential

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2246
Author(s):  
Georgia Charalampous ◽  
Efsevia Fragkou ◽  
Konstantinos A. Kormas ◽  
Alexandre B. De Menezes ◽  
Paraskevi N. Polymenakou ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Oil Spills ◽  
Eastern Mediterranean ◽  
Sole Source ◽  
Eastern Mediterranean Sea ◽  
Deep Waters ◽  
Degradation Rates ◽  
Polycyclic Aromatic ◽  
Set Up

The diversity and degradation capacity of hydrocarbon-degrading consortia from surface and deep waters of the Eastern Mediterranean Sea were studied in time-series experiments. Microcosms were set up in ONR7a medium at in situ temperatures of 25 °C and 14 °C for the Surface and Deep consortia, respectively, and crude oil as the sole source of carbon. The Deep consortium was additionally investigated at 25 °C to allow the direct comparison of the degradation rates to the Surface consortium. In total, ~50% of the alkanes and ~15% of the polycyclic aromatic hydrocarbons were degraded in all treatments by Day 24. Approximately ~95% of the total biodegradation by the Deep consortium took place within 6 days regardless of temperature, whereas comparable levels of degradation were reached on Day 12 by the Surface consortium. Both consortia were dominated by well-known hydrocarbon-degrading taxa. Temperature played a significant role in shaping the Deep consortia communities with Pseudomonas and Pseudoalteromonas dominating at 25 °C and Alcanivorax at 14 °C. Overall, the Deep consortium showed a higher efficiency for hydrocarbon degradation within the first week following contamination, which is critical in the case of oil spills, and thus merits further investigation for its exploitation in bioremediation technologies tailored to the Eastern Mediterranean Sea.

scholarly journals Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

2018 ◽  
Vol 10 (11) ◽  
pp. 1777 ◽  
Author(s):  
Carmine Maffei ◽  
Silvia Alfieri ◽  
Massimo Menenti
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Forest Fires ◽  
Land Surface ◽  
Fire Danger ◽  
Parametric Models ◽  
Burned Area ◽  
Informative Content

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

scholarly journals Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

2019 ◽  
Vol 11 (18) ◽  
pp. 2101 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Land Surface ◽  
Vegetation Index ◽  
Significant Risk ◽  
Fire Danger ◽  
Area Of Interest ◽  
Forest Fire Danger ◽  
Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

scholarly journals Remote sensing and modelling analysis of the extreme dust storm hitting the Middle East and eastern Mediterranean in September 2015

2017 ◽  
Vol 17 (6) ◽  
pp. 4063-4079 ◽  
Author(s):  
Stavros Solomos ◽  
Albert Ansmann ◽  
Rodanthi-Elisavet Mamouri ◽  
Ioannis Binietoglou ◽  
Platon Patlakas ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Middle East ◽  
Dust Storm ◽  
Eastern Mediterranean ◽  
Dust Cloud ◽  
Remote Sensing Data ◽  
Satellite Observation ◽  
Atmospheric Modeling ◽  
Atmospheric Conditions ◽  
Northern Iraq

Abstract. The extreme dust storm that affected the Middle East and the eastern Mediterranean in September 2015 resulted in record-breaking dust loads over Cyprus with aerosol optical depth exceeding 5.0 at 550 nm. We analyse this event using profiles from the European Aerosol Research Lidar Network (EARLINET) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), geostationary observations from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and high-resolution simulations from the Regional Atmospheric Modeling System (RAMS). The analysis of modelling and remote sensing data reveals the main mechanisms that resulted in the generation and persistence of the dust cloud over the Middle East and Cyprus. A combination of meteorological and surface processes is found, including (a) the development of a thermal low in the area of Syria that results in unstable atmospheric conditions and dust mobilization in this area, (b) the convective activity over northern Iraq that triggers the formation of westward-moving haboobs that merge with the previously elevated dust layer, and (c) the changes in land use due to war in the areas of northern Iraq and Syria that enhance dust erodibility.

scholarly journals Modelling of oil spills in confined maritime basins: The case for early response in the Eastern Mediterranean Sea

2015 ◽  
Vol 206 ◽  
pp. 390-399 ◽  
Author(s):  
Tiago M. Alves ◽  
Eleni Kokinou ◽  
George Zodiatis ◽  
Robin Lardner ◽  
Costas Panagiotakis ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Oil Spills ◽  
Eastern Mediterranean ◽  
Early Response ◽  
Eastern Mediterranean Sea

scholarly journals Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions—A Review

2019 ◽  
Vol 11 (17) ◽  
pp. 1985 ◽  
Author(s):  
Kuenzer ◽  
Heimhuber ◽  
Huth ◽  
Dech
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Large River ◽  
Habitat Destruction ◽  
Anthropogenic Activity ◽  
Surface Dynamics ◽  
River Deltas ◽  
Wide Range

River deltas and estuaries belong to the most significant coastal landforms on our planet and are usually very densely populated. Nearly 600 million people live in river deltas, benefiting from the large variety of locational advantages and rich resources. Deltas are highly dynamic and vulnerable environments that are exposed to a wide range of natural and manmade threats. Sustainable management of river deltas therefore requires a holistic assessment of historic and recent ongoing changes and the dynamics in settlement sprawl, land cover and land use change, ecosystem development, as well as river and coastline geomorphology, all of which is difficult to achieve solely with traditional land-based surveying techniques. This review paper presents the potential of Earth Observation for analyses and quantification of land surface dynamics in the large river deltas globally, emphasizing the different geo-information products that can be derived from medium resolution, high resolution and highest resolution optical, multispectral, thermal and SAR data. Over 200 journal papers on remote sensing related studies for large river deltas and estuaries have been analyzed and categorized into thematic fields such as river course morphology, coastline changes, erosion and accretion processes, flood and inundation dynamics, regional land cover and land use dynamics, as well as the monitoring of compliance with respect to anthropogenic activity such as industry expansion-related habitat destruction. Additionally, our own exemplary analyses are interwoven into the review to visualize related delta work.

Remote Sensing in Coastal Water Monitoring: Applications in the Eastern Mediterranean Sea

2016 ◽  
Author(s):  
Manos Dassenakis ◽  
Vasiliki Paraskevopoulou ◽  
Constantinos Cartalis ◽  
Nektaria Adaktilou ◽  
Katerina Katsiabani
Keyword(s):  
Remote Sensing ◽  
Mediterranean Sea ◽  
Coastal Water ◽  
Eastern Mediterranean ◽  
Water Monitoring ◽  
Eastern Mediterranean Sea ◽  
Monitoring Applications ◽  
Coastal Water Monitoring
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