What About the Oceans? Mapping Offshore Infrastructure

Mapping stationary structures in the ocean helps us track fishing vessels and monitor pollution more effectively.

We’re all accustomed to seeing maps of the terrestrial spaces we occupy. We expect to see cities, roads and more well labeled, whether in an atlas on our coffee table or Google Maps on our smartphone. SkyTruthers even expect to access information about where coal mines are located or where forests are experiencing regrowth. We can now see incredibly detailed satellite imagery of our planet. Try looking for your house in Google Earth. Can you see your car in the driveway?

In comparison, our oceans are much more mysterious places. Over seventy percent of our planet is ocean, yet vast areas are described with only a handful of labels: the Pacific Ocean, Coral Sea, Strait of Hormuz, or Chukchi Sea for example. And while we do have imagery of our oceans, its resolution decreases drastically the farther out from shore you look. It can be easy to forget that humans have a permanent and substantial footprint across the waters of our planet. At SkyTruth, we’re working to change that.

Former SkyTruth senior intern Brian Wong and I are working to create a dataset of offshore infrastructure to help SkyTruth and others more effectively monitor our oceans. If we know where oil platforms, aquaculture facilities, wind farms and more are located, we can keep an eye on them more easily. As technological improvements fuel the growth of the ocean economy, allowing industry to extract resources far out at sea, this dataset will become increasingly valuable. It can help researchers examine the effects of humanity’s expanding presence in marine spaces, and allow activists, the media, and other watchdogs to hold industry accountable for activities taking place beyond the horizon.

What We’re Doing

Brian is now an employee at the Marine Geospatial Ecology Lab (MGEL) at Duke University. But nearly two years ago, at a Global Fishing Watch research workshop in Oakland, he and I discussed the feasibility of creating an algorithm that could identify vessel locations using Synthetic Aperture Radar (SAR) imagery. It was something I’d been working on on-and-off for a few weeks, and the approach seemed fairly simple.

Image 1. SkyTruth and Global Fishing Watch team members meet for a brainstorming session at the Global Fishing Watch Research Workshop, September 2017. Photo credit: David Kroodsma, Global Fishing Watch.

Readers who have been following SkyTruth’s work are probably used to seeing SAR images from the European Space Agency’s Sentinel-1 satellites in our posts. They are our go-to tools for monitoring marine pollution events, thanks to SAR’s ability to pierce clouds and provide high contrast between slicks and sea water. SAR imagery provides data about the relative roughness of surfaces. With radar imagery, the satellite sends pulses to the earth’s surface. Flat surfaces, like calm water (or oil slicks), reflect less of this data back to the satellite sensor than vessels or structures do, and appear dark. Vessels and infrastructure appear bright in SAR imagery because they experience a double-bounce effect. This means that — because such structures are three-dimensional — they typically reflect back to the satellite more than once as the radar pulse bounces off multiple surfaces. If you’re interested in reading more about how to interpret SAR imagery this tutorial is an excellent starting point.

Image 2. The long, dark line bisecting this image is a likely bilge dump from a vessel captured by Sentinel-1 on July 2, 2019. The bright point at its end is the suspected source. Read more here.

Image 3. The bright area located in the center of this Sentinel-1 image is Neft Daşları, a massive collection of offshore oil platforms and related infrastructure in the Caspian Sea.

Given the high contrast between water and the bright areas that correspond to land, vessels, and structures (see the vessel at the end of the slick in Image 2 and Neft Daşları in Image 3), we thought that if we could mask out the land, picking out the bright spots should be relatively straightforward. But in order to determine which points were vessels, we first needed to identify the location of all the world’s stationary offshore infrastructure, since it is virtually impossible to differentiate structures from vessels when looking at a single SAR image. Our simple task was turning out to be not so simple.

While the United States has publicly available data detailing the locations of offshore oil platforms (see Image 4), this is not the case for other countries around the world. Even when data is available, it is often hosted across multiple webpages, hidden behind paywalls, or provided in formats which are not broadly accessible or useable. To our knowledge, no one has ever published a comprehensive, global dataset of offshore infrastructure that is publicly available (or affordable).

Image 4. Two versions of a single Sentinel-1 image collected over the Gulf of Mexico, in which both oil platforms and vessels are visible. On the left, an unlabelled version which illustrates how similar infrastructure and vessels appear. On the right, oil platforms have been identified using the BOEM Platform dataset.

As we began to explore the potential of SAR imagery for automated vessel and infrastructure detection, we quickly realized that methods existed to create the data we desired. The Constant False Alarm Rate algorithm has been used to detect vessels in SAR imagery since at least 1988, but thanks to Google Earth Engine we are able to scale up the analysis and run it across every Sentinel-1 scene collected to date (something which simply would not have been possible even 10 years ago). To apply the algorithm to our dataset, we, among other things, had to mask out the land, and then set the threshold level of brightness that indicated the presence of a structure or vessel. Both structures and vessels will have high levels of reflectance. So we then had to separate the stationary structures from vessels. We did this by compiling a composite of all images for the year 2017. Infrastructure remains stationary throughout the year, while vessels move. This allowed us to clearly identify the infrastructure.

Image 5. An early version of our workflow for processing radar imagery to identify vessel locations. While the project shifted to focus on infrastructure detection first, many of the processing steps remained.

Where We Are Now

Our next step in creating the infrastructure dataset was testing the approach in areas where infrastructure locations were known. We tested the algorithm’s ability to detect oil platforms in the Gulf of Mexico, where the US Bureau of Ocean Energy Management (BOEM) maintains a dataset. We also tested the algorithm’s ability to identify wind turbines. We used a wind farm boundary dataset provided by the United Kingdom Hydrographic Office to validate our dataset, as well as information about offshore wind farms in Chinese waters verified in media reports, with their latitude and longitude available on Wikipedia.

Image 6. Wind farms in the Irish Sea, west of Liverpool.

Our results in these test areas have been very promising, with an overall accuracy of 96.1%. The methodology and data have been published by the journal Remote Sensing of Environment. Moving beyond these areas, we are continuing to work with our colleagues at MGEL to develop a full global dataset. What started as a project to identify vessels for GFW has turned into an entirely different, yet complementary, project identifying offshore infrastructure around the world.

Image 7. This animated map shows the output of our offshore infrastructure detection algorithm results (red) compared to the publicly available BOEM Platform dataset (yellow).

In addition to helping our partners at Global Fishing Watch identify fishing vessels, mapping the world’s offshore infrastructure will help SkyTruth more effectively target our daily oil pollution monitoring work on areas throughout the ocean that are at high risk for pollution events from oil and gas drilling and shipping (such as bilge dumping). This is also the first step towards one of SkyTruth’s major multi-year goals: automating the detection of marine oil pollution, so we can create and publish a global map of offshore pollution events, updated on a routine basis.

Be sure to keep an eye out for more updates, as we will be publishing the full datasets once we complete the publication cycles.

SkyTruth appears on Netflix!

Last week, SkyTruth made an appearance on Netflix when their show Patriot Act with Hasan Minhaj used our interactive map of oil spills reported in the Gulf of Mexico in the years following the BP / Deepwater Horizon disaster.

To learn more about the ongoing 14-year Taylor Energy leak that was the focus of this episode, check out our chronology of spill reports and observations at the site, as well as our most recent estimate of the cumulative oil spill based on those reports.

This appearance follows a recent front page article in the Washington Post on the “oil spill you have never heard of” that also referenced reports and data generated by SkyTruth. These are two great examples of how the work we do helps raise awareness of incidents of oil pollution and other types of environmental degradation across the globe.

Port Aransas

Oil Spill Off Port Aransas, Texas

Around 4:30 am on October 20, a barge filled with nearly 5-½ million gallons of crude oil exploded off the coast of Port Aransas, Texas. Two crewmen lost their lives, and although the cargo holds reportedly were not breached, the crippled vessel began leaking oil into the Gulf. The U.S. Coast Guard reported a spill roughly two miles long and a quarter mile wide, and response crews were seen setting up oil booms by late afternoon. By the end of the weekend, more than 6,000 feet of containment booms had been placed to protect essential habitat areas along Mustang and North Padre islands.

Port Aransas Spill

Satellite imagery from Planet shows the spill at a resolution of three meters, just two days after the explosion. The spill spread out off Port Aransas and started drifting slowly south toward Mustang Island State Park and Padre Island National Seashore – critical wintering habitat for migratory birds including the red knot and the piping plover, both listed as threatened under the U.S. Endangered Species Act.

The Coast Guard issued a news release late on October 25 indicating the barge had been moved to shore. Beach cleanup teams continued to work on Mustang and North Padre islands, where more than 70 cubic yards of “oily solids” have been removed. Some shorebirds have been seen with oil on them, but wildlife teams have had difficulty catching and cleaning any of them. If oiled wildlife is rescued, they’re likely to go to the University of Texas Marine Science Institute’s Amos Rehabilitation Keep (ARK) for treatment.

Hurricane Harvey as seen by the GOES-16 satellite at 8:30 am CDT Friday, August 25, 2017. Image credit: NOAA/CIRA/RAMMB. NOAA’s GOES-16 satellite has not been declared operational and its data are preliminary and undergoing testing.

One-Third of U.S. Oil and Gas Reserves are Located in Harvey’s Path

Hurricane Harvey is anticipated to strengthen to a category 3 storm as it reaches the Texas coast tonight through early Saturday, bringing high winds, coastal flooding, and torrential rains. Some areas could see 30 inches or more of rain —  the amount these coastal cities normally get in a year.

After hurricanes Katrina and Rita, we saw leaks and spills from dozens of pipelines and platforms offshore, and from damaged coastal facilities, that cumulatively amounted to at least 9 million gallons of oil. After Ike and Isaac, we saw similar leaks from drilling sites, processing and storage facilities, and petrochemical facilities inundated by flood waters resulting from sustained heavy rainfall. Forecasts for Hurricane Harvey suggest we may see similar problems as it moves ashore.

Christian developed the following map using Carto to show just how much oil and gas infrastructure is in Harvey’s projected path (in red). The green points below represent offshore platforms. The gray lines are pipelines.

Map Legend: The black points on the map are Forecast center locations for Hurricane Harvey, from NOAA’s National Hurricane Center. The red area shows the potential track area, from NOAA’s National Hurricane Center, the red path is the forecast path, again from NOAA’s National Hurricane Center The green dots represent offshore platforms, and the gray lines are pipelines, data from BOEM.

The black points on the map are the forecast center locations for Hurricane Harvey for the next few days, from NOAA’s National Hurricane Center (data downloaded at 2pm ET on August 24).  The red path connecting those dots is the predicted track of the storm.  The larger area enclosed in red shows the potential track area, indicating a high degree of uncertainty as the storm is predicted to stall over the coast after making landfall late Friday.  The green dots show the locations of offshore oil and gas platforms, and the gray lines show seafloor oil and gas pipelines; data from BOEM. View more detail on our interactive map here.

We will be monitoring Hurricane Harvey over the weekend and will be sharing more information as it becomes available. In the meantime, follow the latest radar here.

 

More Oil Spotted at the Taylor Energy Site

We posted about a slick emanating from the Taylor Energy site on April 28th. And surprise, surprise a mere 12 days later, what should we see but yet another slick.

In 2008 Taylor Energy set aside over $600 million to pay for work related to the chronic leak that we have covered extensively since it came to our attention in 2010. As you can see in this image collected by the European Space Agency’s Sentinel 2 satellite, as well as in numerous other images we have collected, their work to date doesn’t seem to have stemmed the leak.

Sentinel 2 image collected of the Taylor Energy Site on May 8, 2017.

Which begs the question: why is Taylor suing the government to return the $432 million remaining in trust? That money was set aside for work that is yet to be finished. Why would they think they have earned it back?