Sentinel 1 imagery showing a slick visible with Synthetic Aperture Radar that appears to be emanating from the stricken vessel on July 17.

Signs of oil from the SSL Kolkata

Followers of our work will recall the merchant vessel SSL Kolkata that was being towed by the Indian Navy after catching fire on June 13th off the Sundarbans in the Bay of Bengal.  The Indian Navy had to abandon the ship after a series of explosions and it has been stuck in shallow water ever since. There have been concerns that the 400 tonnes of heavy fuel oil might start leaking as the ship is listing and cracks are developing. The Sundarbans are the world’s largest collection of mangrove forests and a Unesco World Heritage site (https://whc.unesco.org/en/list/452), and a major oil spill here could be devastating. We see indications in this Sentinel 1 radar satellite image from July 17 that this is a legitimate concern: there appears to be a 17km slick coming from the vessel, being pushed by the strong currents from the Ganges Delta.

Sentinel 1 imagery showing a slick visible with Synthetic Aperture Radar that appears to be emanating from the stricken vessel on July 17.

Sentinel 1 imagery showing a slick visible with Synthetic Aperture Radar that appears to be emanating from the stricken vessel on July 17.

Considering the volume of oil onboard, the slick on July 17 is far smaller than what we would expect if there were a serious leak. This Sentinel 2 multispectral image from the 19th has also captured the slick. Though it doesn’t give us a complete image of the slick as a radar image would (due to interference from the clouds and cloud shadows), we do get an idea of how the slick is spreading not just south, but also north toward the Delta.

Oil slicks seen in Sentinel 2 imagery taken two days later on July 19.

Oil slicks seen in Sentinel 2 imagery taken two days later on July 19.

Attempts have been made to salvage the ship but were abandoned after cracks developed and the ship started listing. Now that the fuel tank is underwater, they will need to suck the oil out carefully using a method known as “hot tapping.” Although poor weather has delayed these plans, we have observed one tugboat, the Lewek Harrier, visiting the site as recently as the 19th according to its Automatic Identification System (AIS) signal. Though we couldn’t definitively identify the vessel visible in this image at the time it was collected, the Lewek Harrier was the only vessel that was broadcasting AIS in the area on that day. The MCS Elly II has also been operating in the area though we haven’t seen it in any images.

[ Image 3 ]
This vigilant tug, the Lewek Harrier, has been a regular visitor.

This vigilant tug, the Lewek Harrier, has been a regular visitor.

We hope this means an end to this leak and that the extent of the spill will be limited. We will continue to watch this area closely as there is still a real threat to the nearby Sundarbans.

You can find more info on the cleanup here. 

You can find more info from when the containers began slipping off the ship here

Rockwool — Industry comes to SkyTruth’s backyard

Rockwool, a multinational corporation based in Denmark, is planning to build a new insulation manufacturing plant in Jefferson County, West Virginia, 5 miles from SkyTruth’s front door. If built, the plant will feature a 21-story (~210 feet) tall smokestack that will spew chemicals including formaldehyde, sulfuric acid mist, and hydrochloric acid.  For the full list of pollutants they plan to emit, see page 428 of the Roxul application submitted to the WV DEP on Nov 20, 2017.

This PlanetScope image shows the locations of the four schools located within three miles of the Rockwool site, along with the route of the proposed Mountaineer Pipeline.

The concerns over this predicted air pollution from the Rockwool facility are compounded by its location. Four schools are within 3 miles of the site (the site here is defined by the latitude and longitude provided by this WV DEP report, see page 1): two elementary schools, one middle school, and one high school. The closest of these is North Jefferson Elementary School, which is located a mere 3,400 feet from the Rockwool site as shown in the WV DEP permit application.

This wind rose (generated by The Global Wind Atlas) shows the prevailing wind directions for the area near the Rockwool facility.

This wind rose (generated using data from a weather station at the Eastern West Virginia Regional Airport) shows the prevailing wind directions for the area near the Rockwool facility from 2012-2016. This wind rose was included in the Air Modeling Report submitted by Roxul to the WV DEP (see page 30).

To read these wind roses, the outer edge indicates the direction from which the wind blows. With the dominant wind direction from the northwest, all four of the schools will typically be downwind from this facility, frequently exposing students, faculty and staff to the pollutants Rockwool says they plan to emit.

Last August, SkyTruth worked with the Eastern Panhandle Protectors to produce a map of the Mountaineer Pipeline Eastern Panhandle Expansion.  What’s the connection? As it turns out, natural gas delivered via this pipeline will feed the Rockwool plant.  One thing leads to another….

This PlanetScope image, collected on August 6, 2018, of the Rockwool site shows recent construction activity. Less than a mile from the site is the North Jefferson Elementary School.

The concerned citizens of Jefferson County are making their voices heard, and are actively opposing the final permits and approvals needed for construction of the Rockwool facility. As a nonprofit that makes its home in West Virginia, SkyTruth is pleased to offer access to our maps (including an interactive web map, which will be updated as we learn more), to the citizens of Jefferson County, in the hopes that these resources will help raise awareness and engage the community on this potentially serious public health issue.

Updating SkyTruth’s mountaintop removal mining dataset

In Appalachia, it is not uncommon to see the effects that mountaintop removal mining have on the surrounding ecosystems.  As someone living in the great Mountain State of West Virginia, one of the projects that caught my interest when I started my internship at SkyTruth was their work mapping the extent of surface mines in central Appalachia.  There are many risks that come with this type of mining. Lung cancer, kidney disease, and birth defects are more likely in areas with high exposure to the toxins produced by mountaintop mining. There are currently no government funded long-term health studies in progress.  The last one, started during the Obama administration, was shut down in August 2017.

SkyTruth has been using the Normalized Difference Vegetation Index (NDVI) to detect surface mines.  Basically, this algorithm measures vegetation intensity over an area. So, areas with low NDVI scores – or low vegetation levels – show up as mines.  Seems simple enough. But the challenge to using NDVI to detect surface mines is that roads, parking lots, lakes, and buildings also have very low levels of vegetation, so they can confuse the algorithm into believing that they’re actually mines.  The two NDVI images below – one showing Charleston, West Virginia, and another showing a surface mine in central Appalachia – show how difficult it can be for the algorithm to tell things apart.

An NDVI image of Charleston, West Virginia.

NDVI image of Charleston, WV.

Compare that image with an image of surface mines in central Appalachia:

An NDVI image of surface mines in central Appalachia.

NDVI image of surface mines in central Appalachia.

Both of these images show areas with low NDVI scores (or low vegetation intensity).  Low NDVI scores are indicated by the darker shades in each of the images. The areas that have a high NDVI score are colored in lighter/whiter shades.  Using these images as an example, it’s easy to see how the algorithm that we’re using can get confused. Since things can look alike, we needed to figure out a way to help the algorithm determine which things were roads and buildings and which things were actually mines.  

SkyTruth’s surface mine mapping work relies on the use of a data mask to separate out the region’s urban areas, water features, and roads.  The mask is used to block out areas from the analysis which have a similar spectral signature to mines (basically roads, buildings, water, parking lots, etc.).  Since the surface mine mapping project is updated annually, the mask needs to be updated annually, too. First, I downloaded the area files, which are provided by US Census Bureau, and then I buffered the roads and water features by 60 meters. The buffer is to ensure that these areas are not picked up by the algorithm. The image below shows the mask that will cover the features that have a low vegetation index that could potentially be incorrectly identified as a surface mine.

An NDVI image of SkyTruth’s mountaintop mining study area with roads, water, and urban areas masked.

NDVI image of SkyTruth’s mountaintop mining study area with roads, water, and urban areas masked.

This next image shows how the mask covers an area like Charleston, West Virginia and blocks the algorithm from detecting it as a surface mine.  The other features in the image that have low vegetation intensity – like potential mines – are still visible as darkly areas in the image.

An NDVI image of Charleston, West Virginia with the mask applied.

NDVI image of Charleston, West Virginia with the mask applied.

SkyTruth, Appalachian Voices, and scholars at Duke University recently published the first-ever annual footprints of mountaintop mining in central Appalachia between 1985 and 2015.  You can learn more about it from SkyTruth’s lead author, Christian Thomas, or you can read the whole paper in PLOS ONE. The updated map will give SkyTruth, Appalachian Voices, and scholars at Duke University, the most current information about the footprint of surface mining in Appalachia, and it will allow them to update their annual footprints with 2016 and 2017.  I hope that this map will help inform the public about where surface mines in Appalachia are located, and that it will show people just how much of the Appalachian region is affected.

Welcome Amy McCormick

Amy McCormick

Amy McCormick

We are thrilled to announce that Amy McCormick has joined the SkyTruth team as our new Development Director. Amy brings with her a wealth of fundraising experience and grant writing savvy, as well as boundless enthusiasm and a passion for environmental protection.

Amy’s most recent career chapter has been spent in Portland, Oregon, working for the Columbia Land Trust managing foundation relations and the Trust’s annual campaign. Before that, she spent 8 years securing funds for Appalachian Trail Conservancy, based out of their headquarters down the road from SkyTruth in Harpers Ferry, WV. Captivated by the Pacific Northwest, Amy will remain in Portland and work remotely for SkyTruth as a new and integral part of our distributed, international team. Having lived in Shepherdstown for many years, Amy looks forward to work trips back to our tiny town to visit with her friends and family here and in other parts of her native West Virginia.

In addition to overseeing all aspects of our foundation and individual donor relations, Amy will steer our strategic communications, growing our audience and base of support. She holds a Masters in Corporate and Organizational Communications from West Virginia University. When she’s not working, Amy can be found hiking or camping in the East Cascade Mountains or eating and biking her way through Portland’s amazing food scene.

All of us at SkyTruth hope you will join us in giving Amy a hearty welcome as she helps create a strong and vibrant future for our organization.

Satellite analysis shows steep increase in amount of land destroyed to mine a ton of coal in Appalachia

Today at 2:00 pm EST, the Open Access journal PLOS ONE published “Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine”. This paper, the product of a partnership between researchers at SkyTruth, Duke, and Appalachian Voices, provides the first comprehensive map of annual surface coal mining extent in Central Appalachia.

Mountaintop removal coal mining (MTM) is an immensely damaging practice which involves removing rock and soil which overlay coal seams using a combination of explosives and heavy machinery. The removed material is often deposited into valleys, in a practice known as valley fill. Mountaintop removal coal mining is responsible for disturbing thousands of square kilometers of land in Central Appalachia and is the region’s single largest source of land use change.

This animation shows the expansion of surface mining’s footprint (displayed in yellow) from 1985 to 2015 for a 31,000 square kilometer sub-region of the study area in West Virginia and Kentucky, and has county boundaries visible.

In addition to substantial disturbances directly visible on the surface of the land, MTM and subsequent valley fills have been shown to adversely impact stream health as seen by decreases in salamander abundance and aquatic macroinvertebrate diversity. MTM has also been associated with risks to public health in nearby communities, including higher rates of cancer and heart disease.

This study, published today, improves upon earlier SkyTruth work, and creates the first ever dataset of yearly active mining in Central Appalachia. Between 1985 and 2015 our study finds that 2,900 square kilometers (~720,000 acres) of this typically forested region has been cleared as a result of MTM. Coupling our results with our earlier work which dates back to 1976 we find that 5,900 square kilometers (~1.5 million acres) of Central Appalachian forest has been cleared. This is an area 18% larger than the state of Delaware, and roughly 3 times larger than the Great Smoky Mountains National Park.

This image shows a snippet of code used to create the annual mining footprint data, which is visualized in the bottom section of the image. Older mining in this visual is red; newer mining is yellow.

In addition to determining the spatial extent of MTM in Central Appalachia, the study also examined the relationship between the area of land being mined to coal production as reported to the United States Mine Health and Safety Administration (MSHA). Over the course of this study period (1985-2015) we see a threefold increase in the amount of land cleared to extract an identical quantity of coal (from 10 square meters of land per metric ton in in the 1980’s to 30 square meters in 2015).

This dataset is freely available for the public to download, visualize, and analyze the footprint of mining in Central Appalachia. To access all the data, please visit: https://www.skytruth.org/mtr-data-files/. To explore a subset of the data via an interactive web map, please visit: http://skytruthmtr.appspot.com/.

Illegal transshipment of fish between Saly Reefer and Flipper 4 fishing vessel. (Photo courtesy of Greenpeace.)

Machine learning and satellite data provide the first global view of transshipment activity

[This post originally appeared on the Global Fishing Watch blog.]
Illegal transshipment of fish between Saly Reefer and Flipper 4 fishing vessel. (Photo courtesy of Greenpeace.)

Illegal transshipment of fish between Saly Reefer and Flipper 4 fishing vessel. (Photo courtesy of Greenpeace.)

This week marks the publication of the first-ever global assessment of transshipment in a scientific journal. Researchers at Global Fishing Watch and SkyTruth, in the journal Frontiers of Marine Science, published “Identifying Global Patterns of Transshipment Behavior.”

What is transshipment? Why does it matter? What have we learned and what remains unknown? Read on to find out.

Vessels may meet at sea for a number of reasons, such as to refuel, to exchange crew, or to deliver supplies. In the commercial fishing industry, vessels also meet to transfer catch in a process known as transshipment. Huge vessels with refrigerated holds – some large enough to hold over 100 US school buses – collect catch from multiple fishing boats at sea to carry back to port.

By enabling fishing vessels to remain on the fishing grounds, transshipment reduces fuel costs and ensures faster delivery of catch to port. As a result, many vessels that fish in the high seas or in waters far from their home ports engage in the practice. Unfortunately, it also leaves the door open for mixing illegal catch with legitimate catch, drug smuggling, forced labor and human rights abuses. Fishing vessels can remain at sea for months or even years at a time, enabling captains to keep their crew at sea indefinitely and, in some cases, resulting in de facto slavery. As a pathway for illegal catch to enter the global market (an estimated $23.5 billion worth of fish annually worldwide is illegal, unreported and unregulated (IUU)), transshipment prevents an accurate measurement of the amount of marine life being taken from the sea. It obscures the seafood supply chain from hook to port and hobbles efforts to manage fisheries sustainably. Occurring far from shore and out of sight, transshipment activities have traditionally been hard to manage and relatively invisible. Data on transshipment has been virtually nonexistent, proprietary, and rarely shared publicly – until now.

With generous support from the Walton Family Foundation, Global Fishing Watch and SkyTruth are applying machine learning and satellite data to study global transshipment patterns and shine a light on what has historically been an opaque practice. Previously, no public, global database of transshipment vessels existed. So, as a first step to understand global transshipment activity, we developed one, combining data from vessel registries, hard-nosed internet investigations, and applying machine learning techniques to identify potential transshipment vessels. This first public, carrier vessel database includes roughly 680 vessels, predominated by large vessels operating within Russian waters or the high seas tuna/squid fleets.

In the Indian Ocean, off the remote Saya de Malha bank, the refrigerated cargo vessel (reefer) Leelawadee was seen with two unidentified likely fishing vessels tied alongside. Image Captured by DigitalGlobe on Nov. 30, 2016. Credit: DigitalGlobe © 2017. Image by DigitalGlobe via SkyTruth.

In the Indian Ocean, off the remote Saya de Malha bank, the refrigerated cargo vessel (reefer) Leelawadee was seen with two unidentified likely fishing vessels tied alongside. Image Captured by DigitalGlobe on Nov. 30, 2016. Credit: DigitalGlobe © 2017. Image by DigitalGlobe via SkyTruth.

With databases of fishing and transshipment vessels sorted, the next challenge was to identify where these vessels met at sea. To do this, the team analyzed over 30 billion vessel tracking signals (Automatic Identification System (AIS) messages) to identify potential transshipment encounters. AIS is a collision avoidance system that transmits a vessel’s location at sea and these transmissions are collected by land and satellite-based receivers and delivered to Global Fishing Watch for automated processing. Nearly all large transshipment vessels carry AIS making it possible to identify all locations where they loiter at sea long enough to receive a transshipment, or locations where two vessels (a transshipment vessel and a fishing vessel) are in close proximity long enough to transfer catch, crew or supplies.

Applying these two methods, we have presented the first open-source and global view of transshipment. We found that over half of transshipment behavior identified using AIS may occur in the high seas and these are generally associated with regions of reduced management and oversight. This lax oversight extends to the vessels involved in potential transshipments, with nearly half of the transshipment vessels we have identified registered to flags of convenience (countries with reduced oversight and limited connection to the vessel, if you’re interested this blog post has more details). As regulations for transshipment vary widely, the data alone do not suggest illegality, but reveal patterns and hotspots of activity, the vessels involved, and provides a new perspective which can further investigations around specific incidents and inform general policy discussions.

Global Fishing Watch’s new encounters layer reveals for the first time where and when thousands of vessels are involved in close encounters at sea. 

We are only just beginning to see the true impact of this unprecedented dataset, but already it has been used to identify vessels potentially involved in catching sharks that were illegally transported through the Galapagos (described here) and in an upcoming scientific paper by research collaborators at Dalhousie University, identifying those fisheries that most heavily utilize transshipment. Our partner, Oceana also analyzed the data in their report that identified patterns of likely transshipping, top ports visited by these vessels and vessels at sea for more than 500 days. Additionally, our models have been incorporated into recent efforts to estimate the costs and profitability of high seas fishing (described here), a set of potential transshipments have been incorporated as a layer within the Global Fishing Watch public map (here) and our work has supported investigations into human right abuses within fishing fleets (Greenpeace, 2018).

Our next steps involve extending these analyses to include “bunker” vessels which provide fuel to fishing vessels at sea, which along with transshipment vessels, play a critical role in supporting high seas, distant water fishing. Combining bunkering (refueling) and transshipment events, with vessel identities (owners/operators and flag states) and additional vessel events including port visits, we will identify the social network at sea. With generous support from the Walmart Foundation, over the coming years we will also explore transshipment in tuna fisheries, analysing and mapping activities that enable global tuna fleets to stay at sea for long periods without oversight. We hope this work will help global efforts to combat illegal and unsustainable tuna fishing.

The publication of this unprecedented dataset provide the first view of the global patterns of transshipment and is the first step towards greater transparency in a previously difficult to track activity. By making the underlying data freely available it can be used by governments, NGOs and academia to support both regional and global efforts to strengthen monitoring and enforcement to eliminate IUU fishing.

Sediment or Oil?

You may recall we posted about a slick emerging from an unidentified platform off the coast of the Democratic Republic of the Congo on June 4th. At the time, we noted that the slick was most likely directed by the strong currents from the nearby entrance to the Congo River as it wasn’t in line with the wind direction. In this image from June 28, we now see a second slick alongside the first.

Sentinel 1 imagery showing the slicks visible with Synthetic Aperture Radar.

This could be a sign of new construction in the area. We also noticed a slick closer to shore which led us to check Sentinel 2 imagery which allows us to see in the visual spectrum. In the inset image, from June 8th, we can see that there are long, brown trails coming from the platforms, usually a sign of sediment being kicked up by wake turbulence from strong currents hitting the structures.

Detailed view of one of the trails in Sentinel 2 imagery.

This raises the possibility that the slicks we are seeing on the radar images are not from oil but from sediment plumes. Turbidity and sediment in the water can dampen wind-driven wavelets, just like an oil slick, making a dark slick on a radar image. The fact that the wind was very low in these images, between 0-5 knots, could possibly emphasize the sediment plumes against the slack water, making them more visible than usual.

The original slick we reported on in June.

However, the way that these slicks remain coherent over 50km lends weight to them being comprised of an oily substance, especially the feathering pattern seen in the middle. This is consistent with what we expect from wind and currents pulling an oily slick in different directions.  So another possibility is that we’re seeing the intentional discharge of drilling fluids and/or “produced water” that includes residual amounts of oil.

In the end, we cannot say with certainty what we are seeing in these images. There is evidence supporting chronic leaking or discharge from the platforms, but there is also support for these being trails of sediment, kicked up by the strong currents coming from the Congo River. It’s times like this that we need some ground truth to help solve the mystery.