Oil Spill Detection Analyzing “Sentinel 2“ Satellite Images: A Persian Gulf Case Study

Oil spills near exploitation areas and oil loading ports are often related to the ambitions of governments to get more oil market share and the negligence at the time of the loading in large tankers or ships. The present study investigates one oil spill event using multi sensor satellite images in the Al Khafji (between Kuwait and Saudi Arabia) zone. Oil slicks have been characterized with multi sensor satellite images over the Persian Gulf and then analyzed in order to detect and classify oil spills in this zone. In particular this paper discusses oil pollution detection in the Persian Gulf by using multi sensor satellite images data. Oil spill images have been selected by using Sentinel 2 images pinpointing oil spill zones. ENVI software for analysing satellite images and ADIOS (Automated Data Inquiry for Oil Spills) for oil weathering modelling have been used. The obtained results in Al Khafji zone show that the oil spill moves towards the coastline firstly increasing its surface and then decreasing it until reaching the coastline

Oil spill monitoring on water surfaces by radar L, C and X band sar imagery: a comparison of relevant characteristics

During last years, several studies related to remote sensing technologies analyzed the processes to extract and classify slicks from SAR imagery. These images are used, among other purposes, for monitoring coastal and marine waters pollution where oil floating on the surface becomes visible because it damps the short gravity-capillary waves that are responsible for the radar backscattering [14]. Nowadays an important number of SAR images are available and this number will increase in coming years thanks the launch of Cosmo-Skymed 2nd generation, recent availability of Sentinel-1, ALOS Palsar-2 products and future SAOCOM launch. That will provide information suitable to support decision makers in managing emergencies or potential disasters. The present study show the results obtained from 190 regions of interest extracted from a set of X, C and L Band images, where a database related to spatial, textural, spectral and contextual characteristics of the features detected was ingested into a neural network algorithm. The classification process reached percentages of up to 95% of cases of oil spills and look-alikes correctly classified depending on the wavelength, the polarization and incidence angle

A Novel Sinergy Between Remote Sensing and GIS for Oil Spill Detection on Satellite Imagery

The large amount of images available today, thanks to the increasing of the number of orbiting EO satellites (Earth Observation Systems), which are able to provide information of every region of the Earth, represents an indispensable instrument for monitoring any terrestrial ecosystem. EO systems allow to detect and follow fast changing phenomena (like natural and anthropic disasters) providing the needed information for planning the necessary measures and reduce the impact of these events. This paper aims at showing the results obtained through the transformation of Mathematical Morphology algorithms (which have already shown their effectiveness) in IDL based algorithms (using ENVI EX rule sets) that can provide identical performances. The direct execution of these algorithms in a GIS environment, taking into account topology restrictions, allows to directly generate maps containing potential oil spills isolated on satellite images (exploiting the geometrical and physical characteristics of oil spills) together with contextual information on the same maps (typical of the GIS environment, as the wind regime, presence of vessels, etc) in order to assign a probability to candidate spots and create a much more accurate oil spill detection process

Urban growth assessment around Winam Gulf of Kenya based on satellite imagery

Urban growth and population dynamics are among the most critical information needed for future economic development planning, natural resources allocation and environmental management. In the present work, two methods, the first based on night-time images produced by NOAA and population maps provided by Oak Ridge National Laboratory's (ORNL) LandScan, and the second one on SAR imagery, were used in order to assess the expansion of urban areas surrounding the Winam Gulf (Lake Victoria, Kenya) at different scales. In the time covered by night-time lights imagery, the study highlighted a period of constant growth rate between 2002 and 2006 and a negative trend after 2006 and 2008. This decrease may be related to two main events occurring in the study area between 2006 and 2007: the decline of the Lake Victoria level and the abnormal proliferation of the floating weeds within the Winam Gulf. Meanwhile, the urban feature extraction obtained at a different scale within a particular district from 1997 up to 2008 results in a constant growth rate. Population movements around this zone explain different dynamics that should be studied in detail in order to understand their particular roots

Satellite-based products for supporting forest fires prevention and recovery in Europe

The main purpose of the FP7 PREFER project is to set up a space-based service infrastructure and up-to-date cartographic products, based on remote sensing data, to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region. This region is particularly affected by uncontrolled forest fires, with negative consequences on ecosystems, such as desertification and soil erosion, as well as on the local economy and, in extreme situations, causing also the loss of human lives. The present paper aims at illustrating the potential improvement in the forest fires fighting that may result from the use of information obtained by exploiting satellite imagery. The potentiality of satellite based information products will be demonstrated by reporting the results of the demonstration activity carried out during the 2015 summer season

Are the PREFER project products devoted to support fire prevention and recovery suitable to South-America ?

PREFER (Space-based information support for the Prevention and Recovery of Forest Fires Emergency in the Mediterranean Area) is a three years project devoted to develop a satellite based service infrastructure capable to provide up-to-date information to support the preparedness, prevention, recovery and reconstruction phases of the Forest Fires emergency cycle in the European Mediterranean Region. The project has been successfully completed at the end of 2015. This paper aims at illustrating some of the project achievements and discuss their applicability to manage forest fires in South-America

development and validation of fire damage-severity indices in the framework of the PREFER project

PREFER (space-based information support for prevention and recovery of forest fires emergency in the Mediterranean area) is one of the Copernicus FP7 Emergency projects funded in 2012. It is uniquely devoted to forest pre- and post-fire management. The overall goal of the project is to develop and demonstrate a preoperational portfolio of products, based on Earth observation data for helping fire management on a Mediterranean scale. Samples of the PREFER information products are available to stakeholders through the project Geoserver (prefer.cgspace.it). The project foresees the utilization of satellite images' optical and SAR at medium (MODIS-moderate resolution imaging spectroradiometer), high (Landsat, Spot-Satellite Pour l'Observation de la Terre), and very high (KOMPSAT-Korea Multi-Purpose Satellite, RapidEye, Pleiades, COSMO-SkyMed-constellation of small satellites for Mediterranean basin observation, and TanDEM-X-TerraSAR-X add on for digital elevation measurement) spatial resolution, and a refresh rate of the products varying from high (days) to low (twice a month) to very low (once a year). The present paper is devoted to introducing the methodology developed for computing one of the project product, i.e., the damage-severity map. These maps provide the level of damage caused in vegetated areas by fires. Further, the paper aims at presenting the results of the validation of such product carried out during the first semester of 2015. The methodology is based on the utilization of Landsat8/OLI images

The PREFER FP7 project: damage severity maps validation results

PREFER is one of the Copernicus FP7 Emergency projects funded in 2012. It is uniquely devoted to forest pre-and post-fire management. The overall goal of the project is to develop and demonstrate a pre-operational portfolio of products, based on Earth Observation data for helping fires management at Mediterranean scale. Samples of the PREFER (Space-based Information Support for Prevention and REcovery of Forest Fires Emergency in the MediteRranean Area) information products are available to stakeholders through the project Geoserver (prefer.cgspace.it). The project foresees the utilization of satellite images optical and SAR at low (MODIS), medium (Landsat, Spot) and high (Kompsat, RapidEye, Pleiades, Cosmo-SkyMed, TanDEM-X, etc.) spatial resolution and a refresh rate of the products varying from high (days) to low (twice a month) to very low (once a year). The present paper is devoted to introduce the methodology developed for computing the maps of the level of damage caused in vegetated areas by fires and to present the results of the validation process just starte

Oxidant generation by particulate matter: from biologically effective dose to a promising, novel metric

Areas where urban and wildland intermingle, known as wildland-urban interface (WUI), are increasing worldwide over the last decades (Theobald and Romme 2007; Montiel and Herrero 2010). These WUI areas are of particular concern in forest fire risk management because the presence of housing developments in contact with forestlands increases the likelihood of a fire starting as a consequence of human activities. In Spain, for example, there is increasing evidence that the wildland-urban interface constitutes a highly risk prone area (Herrero et al. 2012; Chas-Amil et al. 2013). Given the recognised role of land cover distribution in fire risk (Bajocco and Ricotta 2008; Oliveira et al. 2013), this paper evaluates recent fire activity across different land cover categories, and the causes and motivations, comparing WUI and non-WUI areas. Fire data were collected in Galicia, Spain, where fires are mostly due to deliberately-caused ignitions. We show that arsonist are more likely to ignited a fire in WUI areas than in non-WUI; and the same seems to be true for fires ignited by agricultural activities. Moreover, land cover types only have a significant impact on the patterns of fire occurrence in WUI areas

Synergistic Use of Radar and Optical Data for Agricultural Data Products Assimilation: a case Study in Central Italy

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve bio-physical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering