Feature Articles—September 2009 Issue
Coastal Zone Mapping For Oil Spill Emergency ManagementA Geomatics Approach to Situational, Risk and Damage Analysis And Emergency Response Applications for Oil Spill Management
By Dr. Hamid Assilzadeh
Research Associate
Dr. Yang Gao
Professor
Department
of Geomatics Engineering
Schulich School of Engineering
University of Calgary
Calgary, Canada
and
Dr. Jason K. Levy
Associate Professor
L. Douglas Wilder School
of Government and Public Affairs
Virginia Commonwealth University
Richmond, Virginia
The Canadian coastal and marine environments contain many sensitive species, habitats and resources that could be severely affected by oil pollution. Accordingly, protection of the marine environment from oil spills is a high priority for Canada. Since it is not always possible to prevent these spills, it is important to address their consequences.
In order to combat pollution from marine oil spills successfully, Canada must have an effective response strategy. A successful operation to combat a marine oil spill depends on a rapid response, from the time the oil spill is reported until it has been fully combated. The use of modeling, spatial analysis and a near-real-time system can assist decision makers in making better informed judgments that will affect the governance and management of the ocean environment during an oil spill.
A successful plan for oil spill management should combine required information from before, during and after a disaster. There is not currently an adequate operational contingency planning tool to help government and local authorities monitor oil spills, simulate trajectories and make informed decisions about the resources at risk and mechanisms to combat this risk.
Although there are several existing models already available for the Canadian coastal area, such as the NOAA Emergency Response Division’s (ERD) computer-aided management of emergency operations (CAMEO) and general NOAA operational modeling environment (GNOME), there is not a system with a strong focus on the environmental resources at risk. The biggest problem in combating oil spills is the lack of adequate information to help decision makers and coastal communities protect sensitive coastal resources. In this regard, baseline information about the coastal resources and their sensitivity is critical.
The diversity of databases involved and differently formatted data make it difficult to connect various systems into a unique, organized system for oil spill contingency planning. Therefore, oil spill contingency plans are currently inefficient due to the incompatibility of platforms, database formats and systems configuration.
Employing geomatic systems like remote sensing and geographic information systems (GIS) mitigates the problems of information availability and timeliness, data management and synoptic inventory of natural resources when an oil spill is caused by accident or human error.
This article demonstrates the development of multiple models in GIS and remote sensing to provide the required information for disaster mitigation and relief. The schema includes an oil spill detection model using synthetic aperture radar (SAR) image data and applying the output results to generate an oil-spill-extent map. It also includes the integration of spatial data and disaster models in a GIS platform for generating essential oil spill emergency response information in map format, including risk, trajectory, emergency response and affected area maps. These products are required for an oil spill mitigation plan. Remote sensing and GIS have been employed to provide adequate information and decision support to reduce response time and facilitate the decision-making process for an oil spill accident.
Methodology
Detecting Oil Spills from SAR Data. SAR imagery in real-time oil spill detection systems is associated with attempts to more fully automate oil spill detection and identification. The system combines hardware, software, remote sensing technologies, geoinformation and communication subsystems and provides key information for further analysis and decision making.
In SAR images, the physical mechanism that allows responders to detect oil spills is the dampening of capillary waves present on the ocean surface. These capillary waves produce backscattering of the radar incident pulse due to a Bragg scattering mechanism.
As a result, ocean regions containing oil are dark in contrast with the background radar signal.
A group of application programs in PCI Geomatics’ (Toronto, Canada) Geomatica®image analysis software were employed to provide extensive digital image processing functions and automate processing for oil spill detection in the SAR data.
Processes for oil spill analysis include texture analysis scaling, thresholding, gamma filtering, unsupervised classification and creating contours. The model performs a homogeneity texture analysis algorithm on a SAR image of 10 by 10 windows. This reflects the homogeneity/uniformity of the SAR image.
Oil film has a higher surface tension, which decreases the roughness of the sea surface in the polluted area. This can cause homogeneous dark patches to appear in a SAR image with low speckle noise, and the oil spill and seawater can be easily discriminated.
The output radar image after texture analysis would be at 32-bit channel resolution. The typical use of a scaling program in processing is to scale/quantize imagery at a high resolution (32-bit) channel down to fewer numbers of gray levels to fit into a low resolution (eight-bit) channel.
The thresholding function differentiates the gray values of radar data based on a predefined range of brightness for oil spills.
A gamma filtering process is used for image noise reduction. After gamma filtering, the scaling program is used again to shift the dynamic range of gray values over a masked area into a limited range of 25 gray values. K-means (minimum distance) unsupervised classification is performed to classify image data into different clusters. Only areas under the masked area are classified, and the rest of the image is not processed. A contour program creates a vector segment containing contour lines from a classified SAR image. The contour generation of possible oil slicks is based on a number of classes generated over the spilled area. The classes are made through contrast, based on the slick’s type, surroundings, shapes and adages.
Oil Spill Trajectory Simulation. An essential aspect of any oil spill model is the ability to accurately represent the environment into which oil may be spilled. After extracting vectors over the spill region, these vectors were transferred to an oil spill information system (OSIS), with online connection to weather and sea state data, to perform a real-time trajectory simulation. OSIS is one of BMT Cordah’s (London, England) technology system models, tools to support oil spill response operations by giving information on the trajectory and spreading of marine spills. In an operational capacity, the models become a primary source of information to guide the response operation. OSIS trajectory simulation outputs vectors compatible with the existing commercial GIS environment.
Creating Oil Spill Products in GIS. Disaster models were developed in GIS to provide specific products from each analysis. The outputs from the oil spill detection and trajectory simulation were transferred to ESRI’s (Redlands, California) ArcGIS and overlaid on other spatial data to create predefined output products, including location, risk, oil affected area and emergency response maps.
Results and Discussions
Oil Spill Detection. The oil spill detection model was used on various SAR images recording oil spill accidents in marine and coastal areas.
Two SAR images taken from the Strait of Malacca, situated on the west coast of Peninsular Malaysia, were analyzed. The SAR data from Radarsat-1 on C band (beam mode: Wide 1, horizontally transmitted, horizontally received polarization with 12.5-meter spatial resolution) were selected for analysis, taken on October 26, 1997, over this area, covering about 110 by 150 kilometers of the region. The image covers a huge amount of the area of an oil spill two weeks after the accidental collision of the tankers MV Evoikos and MT Orapin Global in the coastal area of Singapore.
Around 25,000 metric tons of marine fuel oil spewed into the sea from the accident. The weather conditions were determined from ground stations over the study area. Wind velocity, taken at 10 meters above the water surface, was seven meters per second.
Analysis of the SAR image using the developed model shows successful oil spill detection and classification. Oil spill image classification results discriminated three distinct regions, with a high concentration area in the center, a medium concentration area surrounding it and a low concentration area at the edge of the spill region. Backscattering measurements over the SAR image clearly show maximum dampening of pixel brightness in the center of the oil spill compared to the high and low-concentration areas.
Another spill occurred in a nearby area in 2005. The processed SAR image from that event clearly shows different levels of pollution at several locations.
Oil Spill Active Disaster Map. The purpose of the oil spill active disaster map is to show the oil spill’s location and the surrounding area. Auxiliary data such as infrastructure and the locations of population centers will be added to also show the easiest ways to access the area. The map shows all information about the spill’s location, variations in thickness and region.
Oil Spill Trajectory Map. Using the output from the trajectory model, an oil spill trajectory map can be produced. Data on land use, bathymetry, location of response equipment, the oil spill source and spill response units are also used. In this model, the trajectory is forecast at 20-minute intervals, since the spill region is close to the coastal area. The interval for trajectory simulation is variable, according to the oil spill’s distance from the coastline.
Oil Spill Risk Map. A risk map demarcates the areas where the spill could potentially affect human life, having economic effects or causing environmental changes, for instance. The oil spill risk map shows the risk profile of the coastal area based on the environmental sensitivity index (ESI) map. The ESI map contains the socioeconomic and biological ESI of the shoreline. The ESI’s rank is based on the standard ESI ranking for coastal areas. In order to produce an oil spill risk map, the ESI layers (shoreline, socioeconomic and biological) were combined with other auxiliary data layers, such as land use, bathymetry and settlements. Without trajectory simulation data, the result will create a long-term risk map that signifies the areas most vulnerable to spilled oil, and with trajectory simulation data layers, the results will present an early warning for oil spill risk.
Oil Spill Emergency Response Map. The oil spill emergency response map provides the base map and other information related to emergency response and mitigation. The emergency re-sponse map is generated by overlaying the oil spill trajectory on a map of emergency resources, such as equipment bases, access sites, support centers and infrastructure. The map helps coordinate the oil spill cleanup operation. The layers required to produce this map include the oil spill source, settlements, trajectory simulation output, access to site, equipment bases, land use, high-resolution satellite images (e.g., from SPOT-5) and transportation networks, such as roads and ports.
Oil Spill Affected Area Map. The oil spill affected area map shows the extent of the spill and the area affected. It is created by overlaying the ESI map on reports from other organizations involved with the disaster emergency response as well as the trajectory simulation results. Other data layers involved with this product include land use, bathymetry and high-resolution satellite images.
Acknowledgments
The authors would like to recognize the financial support of Geomatics for Informed Decisions of the Networks of Centres of Excellence and the Natural Sciences and Engineering Research Council of Canada. The authors also thank the Malaysian Centre for Remote Sensing for the provision of required data provided under the National Disaster Data and Information Management Program.
References
For a full list of references, please contact Hamid Assilzadeh at hassilza@ucalgary.ca.
James Gates is a writer living in Vancouver, Canada. He writes about issues that affect the health of British Columbia’s ocean waters.
Geoff Gilliard is the communications manager for the Living Oceans Society. He has been writing for several years on issues of sustainability.
Sea Technology is read worldwide in more than 110 countries by management, engineers, scientists and technical personnel working in industry, government and educational research institutions. Readers are involved with oceanographic research, fisheries management, offshore oil and gas exploration and production, undersea defense including antisubmarine warfare, ocean mining and commercial diving.