Oil Spill Monitoring Using Sentinel-1

Summary

This tutorial, led by Mohammad Ramadan, introduces the process of monitoring oil spills using Sentinel-1 satellite data. The video covers essential steps, including data collection, GIS processing, and oil spill detection using open-source software like SNAP and QGIS. It highlights the advantages of Synthetic Aperture Radar (SAR) data over optical sensors in tracking oil spills, especially in challenging weather conditions. Through case studies from Egypt, viewers learn how to identify and map oil spills, calculate affected areas, and use online tools for further analysis. The training aims to enhance the capabilities of African countries in environmental monitoring.

Takeaways

• 😀 The video introduces a tutorial on how to monitor oil spills using Sentinel-1 satellite data.
• 😀 It explains how to process satellite data using open-source software like SNAP and QGIS for oil spill detection and analysis.
• 😀 The tutorial is part of the NAV COST project, funded by the European Union and African Union Commission, aimed at providing coastal and marine services to North African countries.
• 😀 Oil spills are a significant environmental hazard, and detecting them early is crucial for minimizing damage to marine life and fisheries.
• 😀 Sentinel-1 radar data is preferred for oil spill monitoring because it is unaffected by weather conditions and works day and night.
• 😀 The video demonstrates how radar sensors detect oil spills by identifying dark areas in the satellite images, which indicate a reduction in backscatter caused by oil films on the water surface.
• 😀 A case study from Borsaid, Egypt, is used to demonstrate the oil spill detection process, including image processing, speckle filtering, and contrast enhancement.
• 😀 The SNAP software is used for processing Sentinel-1 data, including filtering, thresholding, and identifying oil spills based on radar reflections.
• 😀 In QGIS, the oil spill mask is converted into a shapefile, and the area of the oil spill is calculated, providing valuable information for monitoring and decision-making.
• 😀 The tutorial emphasizes the importance of accurate geospatial data for oil spill detection, GIS calculations, and providing decision-makers with actionable information.
• 😀 The video concludes by showcasing the Sentinel Earth Observation online data hub for browsing and downloading Sentinel-1 data for oil spill analysis, helping to expand the reach of the project and support future monitoring efforts.

Q & A

What is the main objective of this training video?

The main objective is to introduce viewers to the NAVOST project services, particularly oil spill monitoring using Sentinel-1 satellite images, and to guide viewers in processing this data using open-source software like SNAP and QGIS.

How does Sentinel-1 radar data contribute to oil spill monitoring?

Sentinel-1 radar data is preferred for oil spill monitoring because it is not affected by cloud cover or weather conditions, and it works day and night. Additionally, it offers wide spatial coverage, which makes it suitable for monitoring oil spills.

Why is optical sensor data not as effective for oil spill detection as Sentinel-1 radar data?

Optical sensors can be affected by weather conditions like clouds, and they may miss detecting oil spills at night. In contrast, Sentinel-1 radar data operates in the microwave range, making it effective under all weather conditions and during both day and night.

What is the significance of the 'dark pixels' in the radar images for oil spill detection?

The dark pixels in radar images indicate areas where oil is present. Oil, being a viscous material, dampens capillary waves on the water surface, which reduces the radar backscatter, resulting in dark areas in the image, helping to identify oil spills.

How do you process Sentinel-1 radar data in SNAP for oil spill detection?

In SNAP, you can process Sentinel-1 data by first selecting the appropriate radar bands, applying a speckle filter to reduce noise, enhancing the contrast using a decibel scale, and then using the oil spill detection tool. The tool extracts dark areas from the image, which correspond to potential oil spills.

What is the role of geometric correction in oil spill detection?

Geometric correction is necessary to correct the reverse distortion that occurs in radar images due to the sensor's orientation. This correction ensures that the final output image aligns properly with geographical coordinates, making it suitable for GIS analysis.

How is the area of an oil spill calculated using QGIS?

In QGIS, after converting the raster data to polygons, you extract non-zero pixel values representing the oil spill. Then, you use the field calculator to compute the area, typically converting the units to square kilometers for clarity.

What does the 'threshold shift' parameter do in the oil spill detection tool in SNAP?

The 'threshold shift' parameter helps refine the oil spill detection process by setting a limit for identifying dark areas in the radar image. Adjusting this threshold changes how sensitive the detection is, which can impact the size and accuracy of the detected oil spill.

What kind of oil spill data can be accessed from the Sentinel Earth Observation Data Hub?

The Sentinel Earth Observation Data Hub provides access to a wide range of satellite images, including Sentinel-1 data, which can be used to detect oil spills. Users can browse data by location and time range, visualize it, and download it for further analysis.

How does the NAVOST project support oil spill detection in North African countries?

The NAVOST project provides essential coastal and marine services, including oil spill mapping, to North African countries. The project also focuses on building human capacity to use Earth observation data for oil spill monitoring and other applications, helping local authorities manage marine pollution more effectively.