Photo of flooding aftermath in West Virginia

Come Hell & High Water: Flooding in West Virginia

In late June devastating flooding hit many communities across southern West Virginia resulting in over 20 fatalities and complete destruction of homes and businesses across the Mountain State. Because we are located in West Virginia and have been studying mountaintop removal (MTR) coal mining across Appalachia, we’ve received a number of questions about what role MTR mining may have played in this recent disaster.

Depending on the amount of mining in the impacted watersheds, the quality of existing baseline data, and the number of measurements taken during and after the flood, scientists may not find a “smoking gun” directly linking the severity of this flood event with MTR mining. But let us take a look at what we do know about the relationship between flooding and MTR mining.

Drainage Sketches

 

If you are familiar with stormwater runoff issues then you have probably seen a diagram like the one above. Soil and vegetation absorb water. Impervious surfaces, like rock and pavement, do not. Since blasting off ridge tops to reach seams of buried coal strips the mountains of soil and vegetation, it seems logical that MTR mining would contribute to more intense flash floods. But even after decades of study there are a surprising number of gaps in our understanding of exactly how mining alters flooding.

Photo of flooding aftermath around Clendenin, W.Va.

Debris and mud are strewn around Clendenin, W.Va., after flood waters receded. Photo by Sam Owens, courtesy Charleston Gazette-Mail.

Research conducted so far suggests that MTR mining can contribute to greater flooding during intense rainfall events, but some studies actually found less severe flooding in watersheds with mining. Several of these studies suggested that valley-fills and underground mine workings have the ability to retain water, which may account for less severe “peaks” during moderately severe storms. If you want to dig into the details, I recommend starting with the summary of hydrological studies on MTR contained in Table 1 of this paper by Dr. Nicholas Zegre and Andrew Miller from West Virginia University.

What most of these studies have in common is that the researchers must at least know where mining occurred and how much surface area was impacted by said mining. This is where our work here at SkyTruth comes into play because we’ve been mapping the when, where, and how much of MTR mining for over forty years.

Thanks to a satellite record going back to the 1970’s, SkyTruth can look back in time to measure the footprint of mining in Appalachia. We continue to make this data freely available for research, and so far our decade-by-decade analysis has been cited in at least six peer-reviewed studies on the environmental and public health impacts of MTR. These studies investigate everything from the increased risk of birth defects and depression to impacts on biodiversity and hydrology. But clearly there are still many unanswered questions left to research.

Finally, it is worth noting that much of the rainfall (left) was concentrated on Greenbrier County, a part of the state with relatively little MTR mining. Neighboring Nicholas County, however, does have some large mines so it may be possible for hydrologists to diagnose and measure the difference in flooding between mined and unmined watersheds which received equivalent rainfall. But that will take time to decipher and analyze.

In the meantime, SkyTruth and our partners at Appalachian Voices and Duke University are working this summer  to update and refine our data about the spread of MTR mining in Appalachia. The resulting data will allow more comprehensive and more accurate research on the effects of MTR mining. Our vision is for this research and resulting studies on the impacts of MTR to lead to better decision-making about flood hazards, future mine permits, and mine reclamation.

Scientists develop precise methods to identify and measure three very different types of fishing activity

Scientists develop precise methods to identify and measure three very different types of fishing activity

Scientists develop precise methods to identify and measure three very different types of fishing activity

On dry land, ecologists and conservationists can map our human footprints on the landscape. We can see deforestation, mountaintop removal, river damming and development, and it is relatively easy to recognize our impacts on an ecosystem and the plants and animals that live there.

In the ocean, our impacts are less tangible. Water covers more than 70 percent of the surface of the globe and its resources are exploited as vigorously as those on land. Yet our footprints are lost to the ripples and waves. The effects of our exploits lie beneath the surface in a three-dimensional, liquid “landscape” that remains out of sight and far from reach.

Satellite tracking technology and big-data processing are helping solve that problem by allowing us to see and record the tracks of ships on the ocean. This week, a new study released in the journal PLoS ONE brings finer resolution to our newly developing view of how humans are using the seas. Researchers from Dalhousie University in Halifax, Nova Scotia have developed distinct methods for identifying the activity of vessels fishing with three different types of gear.

Commercial fishing vessels regularly broadcast their positions to satellites via an Automatic Identification System (AIS). By plotting these signals on a map of the ocean we can recreate their tracks and identify movement or behavior consistent with fishing. Until now, remote sensing methods have provided only a broad or incomplete view of fishing behavior.

The broad view, which tries to capture all fishing activity without considering the type of gear being used, is somewhat like trying to quantify land-based farming for a given area without distinguishing between livestock, row-crops or orchard farming.

There has also been work to develop more fine-scale views that focus in on a specific type of fishing. Previous studies have looked at trawling, for instance. While this work is significant, it doesn’t allow for a comprehensive view of fishing activity. Again, to take an analogy from the land, analyzing wheat farms doesn’t allow us to make conclusions about land use relative to all farming.

This new work allows researchers to take a comprehensive look at how fishers use the oceans by combining fine-scale analyses of three of the most common types of fishing, trawling, longlining and purse seining. It also allows them to see the amount of time spent actually fishing as opposed to something else such as transiting or hanging at anchor.

“We’re very much aware of the differences of the gear types, and we’ve tailor made our algorithms for that so we can really tell what is happening out there,” says Kristina Boerder, one of our academic partners and an author on the paper. “Because all three algorithms were developed in one place to fit into the same framework, it is the first opportunity to run these analyses all together.”

According to Elizabeth Madin, researcher from Macquarie University in New South Wales Australia, this work helps to fill critical gaps in the scientific understanding of how and where fishing is occurring on the high seas. “It’s something that’s been notoriously difficult to quantify over large scales with any accuracy in the past,” she says. “Perhaps equally importantly, this study improves our ability to harness the full power of the vast dataset of fishing patterns globally that has emerged through the use of satellite AIS technology. Marine scientists and ocean resource managers will find this incredibly valuable.

To develop their tools, Kristina and her team analyzed satellite-based AIS tracks from 2011 to Oct 2015. They looked at characteristics such as speed, changes in direction, how a vessel moves, and how long it engages in certain types of movement. Some characteristics were more important than others in identifying each type of gear. So in order to automate the identification process, they had to take a different approach for each fishing method.

For trawlers, they applied a machine learning approach. They fed a computer thousands of examples of trawling vessel tracks (millions of individual AIS signals) and asked the computer to identify patterns among those tracks. Having established the set of rules to define trawler patterns, the computer could then apply those rules to unidentified tracks and pick out trawling behavior.

Purse seiner behavior is distinct in that vessels move very quickly in a circle around a school of fish to set the net, then move very slowly for a period of time, drifting as they haul up their nets. For these vessels, the researchers applied a filtering process by which, step-by-step, the computer eliminated behavior that did not fit with the behavior of pulling up the net. By re-evaluating after each filter, and applying the next level of elimination, the algorithm narrowed in on purse seine fishing events with 97 percent accuracy.

Longliners posed a slightly different challenge because speed was not as relevant to longlining as it was to the other two gear types. In this case, the researchers used a data mining technique and applied methodology from land-based ecologists who study animal movements. Other work has shown that human fishing activity resembles that of animals searching for and hunting prey. “If an animal spends a lot of time in one confined area,” Kristina says, “there has to be something of interest there—they could be sleeping, or foraging, or hiding.” Longliners offer the same clues by spending a lot of time traveling back and forth over the same territory as they set and retrieve their lines and hooks.

Kristina says the team fed AIS signals into their computer and asked the computer to mine the data and pull out tracks that met a certain set of expectations for what longliner fishing behavior over time and space looked like. Evaluating each vessel track, the computer took note when a vessel began to behave according to the expectations given. It then applied the next level of expectations, highlighting tracks that met those, and re-evaluated again. This is the first time an automated process has been developed to identify fishing activity of longliners.

The new Dalhousie algorithms can be a game changer for fisheries management and conservation, especially in combination with Global Fishing Watch’s list of individual fishing vessels, which helps identify the species of fish being harvested. Knowing where and when a given species is being taken from the ocean allows for a much better assessment of fisheries management on a global scale.

Researchers will be able to study more precisely how human activity overlaps with such things as migration patterns of tuna, nursery areas for sharks, or ecosystems surrounding marine protected areas. “It is a tailor-made approach that can be used to search and evaluate fishing effort for any fleet anywhere in the world,” says Kristina. “We hope that other researchers and ocean managers will use our tools to further their work. It opens the door to a whole host of research questions that couldn’t be asked before.”

Impact Story: Global Fishing Watch

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Global Fishing Watch is the product of a technology partnership between SkyTruth, Oceana, and Google, designed to enable anyone to see and understand apparent fishing effort worldwide. This, in turn, will help reduce overfishing and illegal fishing and help restore the ocean to sustainability and abundance.

The story of Global Fishing Watch is really the story of a team coming together over the vision of what might be possible with satellite data on a global scale.

More than a decade after its founding, SkyTruth had become known as the small nonprofit with a big-picture view of the world. Environmental organizations had been coming to us for help solving challenging problems with remote sensing. We had become a trusted source for unbiased analysis and indisputable imagery that revealed what was once invisible. So when we were asked to turn our analysis to the issue of commercial fishing far out at sea, it was a natural fit.

In 2012, Pew Charitable Trust’s Global Ocean Legacy program was encouraging conservation in the rich and diverse waters of Easter Island Province, a remote territory of Chile located in the southeastern Pacific Ocean, about 2,500 west of the mainland. Hoping to demonstrate the need for protection and the feasibility of monitoring, they looked to us for a solution.

Satellite photographs of illegal fishing in the area would have easily made the point, but such photos don’t exist. Contrary to common belief, no one is actually taking high resolution, fine-scale images of the entire world at all times. So we had to come up with a new method of looking at fishing behavior far over the horizon.

Using low-resolution satellite radar images, we detected the presence of ships in the water based on the radar reflectivity of their metal hulls. Then we learned to work with radio signals broadcast via the Automatic Identification System (AIS) used by many ships to avoid collisions at sea. Combining the data, our analysis showed that fishing was occurring in the open ocean right up to the edge of Chile’s territorial waters. It also revealed that not all fishing vessels were broadcasting their presence with AIS. That was enough to demonstrate that Chilean waters could be vulnerable to unscrupulous fishing behavior, and the Chilean government subsequently stationed a long-range reconnaissance airplane on Easter Island to monitor activity in the area. With that project, we quickly realized the power of AIS data to identify and track fishing activity over the horizon and out of sight. And that’s where the vision began.

Impact Story: Chevron Spill May Have Reset the Tone for Oil Boom in Brazil

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2011 turned out to be both a banner year for Brazilian oil exploration and a big eye-opener for the people of Brazil. Fueled by the discovery of 19 new oil and gas reserves and hungry for the spoils, big multi-national companies poured billions of new investment dollars into the South American nation.

Most Brazilians expressed little concern over the potential safety risks of the offshore boom. But then SkyTruth president John Amos noticed an inconspicuous report of a seemingly insignificant oil leak buried in the daily cycle of business news.

On November 8, 2011, Reuters reported that Brazil’s oil regulator, the National Petroleum Agency (ANP), was investigating an offshore oil leak near Chevron’s Frade field, 230 miles from the coast of Rio de Janeiro. According to the report, Chevron was checking to see if oil was leaking from a crack in the seafloor.

When John reviewed satellite photos of the area, he saw a slick originating near an exploratory drilling site that extended for 35 miles and covered about 180 square kilometers. By his estimates the sheen on the water represented about 47,000 gallons of oil.

Three days later it had grown to 56 miles in length, and Chevron had declared it a natural seep unrelated to their drilling activities. “It is possible, but call us skeptical,” John posted on our blog. “From my previous years working as an exploration geologist I know there are natural seeps off Brazil. But I’ve never seen a natural seep create a slick this large on a satellite image.” What’s more, comparisons with historical satellite photos showed the slick had not been there before.

Over the following days we watched the spread of oil on the water’s surface. While Chevron maintained that it was natural and estimated a leak rate of 8,400 to 13,860 gallons (200 -330 barrels) per day, John posted satellite images that hinted at a much bigger problem. By his analysis the spill was leaking 157,000 gallons (3,700 barrels) per day. That was more than ten times the official estimate.

John’s reports and the indisputable images he posted gained international media attention,  spurred a vigorous discussion on our site, and led to a public outcry in Brazil.

Unable to hide the true nature of the spill, Chevron came under scrutiny from Brazilian legislators and state agencies, and the tone of their official story began to shift.

Under pressure for more transparency, the oil and gas giant eventually conceded they had lost control of a well. They claimed the pressure of the reservoir had exceeded their expectations and forced oil up through fissures in the seafloor.

Kerick Leite who was working for ANP in offshore inspections at the time reflects on the situation this way: “In my opinion, if were not for SkyTruth’s independent assessment of the spill existence and size, I believe the Chevron Spill would have been dismissed as a minor one,” says Leite, “maybe even a natural seep, as initially reported, and remain mostly unknown by the public even today.”

According to the New York Times, Brazil’s former environment minister, Marina Silva, said “This event is a three-dimensional alert to the problems that may occur.” She told the Times that the spill served as a warning just as Brazil was preparing to expand its oil production and exploit its tremendously rich presalt reserves—an extremely complicated process because the presalt lies in 10,000 feet of water beneath thick layers of sand, salt and rock.

As a result of the spill and Chevron’s misleading response, the ANP banned the company from all drilling activities in Brazil onshore and off, pending a full investigation. After lengthy court battles, the company ended up paying  24 violations, and the company paying $17 million in fines to the ANP, more than $18 million to the Brazilian Ministry of the Environment, and $42 million to settle civil lawsuits.

What’s more, it emphasized how small the playing field is in the deepwater oil and gas drilling industry. As we learned through our Twitter followers, the drilling contractor on the job had been Transocean—the same company involved in the disastrous BP / Deepwater Horizon spill in the Gulf of Mexico just a year earlier. Brazil dodged a bullet with this accident, but the new understanding of how bad it might have been made Brazilians pay attention.

“It was a wake-up call,” said John. “These are multi-national organizations. The same contractors are working for most of the major name-brand oil companies. This kind of thing can happen anywhere.” Chevron’s reluctance to claim culpability and their delayed response to the spill drove home the need for diligence in regulation and enforcement by Brazilian authorities.

Leite said the spill has led to increased public awareness and concern over safety in the oil and gas industry in Brazil that persists today. “I believe the issue of offshore safety now has more priority than before the chevron spill,” he says. “Back when I still worked at the ANP sector dedicated to environmental issues and operational safety, it had around 16 to 18 servants. Today there are around 40 servants dedicated to it.”

It was a full year before Chevron was allowed to resume doing business Brazil. During that time, a significant portion of the company’s global investments remained inaccessible to them. We hope the loss of profits, over and above the fines levied by Brazilian authorities, will provide incentives for Chevron to do a better job and will send a message to other oil and gas companies. Accidents can no longer be hidden or brushed aside. Chevron’s Frade field spill demonstrated that a satellite image can be worth a thousand words — and in this case, millions of dollars.