Drilling Detection with Machine Learning Part 1: Getting to Know The Training Data

Intern Sasha Bylsma explains the first steps in teaching computers how to detect oil and gas well pads from satellite imagery.

[This blog post is the first in a three-part series describing SkyTruth’s effort to automate the detection of oil and gas well pads around the world using machine learning. This tool will allow local communities, conservationists, researchers, policymakers, and journalists to see for themselves the growth of drilling in the areas they care about. By the end of the series, we will have demystified the basic principles of machine learning applied to satellite imagery, and will have a technical step-by-step guide for others to follow. At SkyTruth, we aim to inspire people to protect the environment as well as educate those who want to learn from our work to develop applications themselves that protect people and the planet.]

Detecting environmental disturbances and land use change requires two things: the right set of technological tools to observe the Earth and a dedicated team to discover, analyze, and report these changes. SkyTruth has both. It is this process of discovery, analysis, and publication — this form of indisputable transparency that SkyTruth offers by bringing to light the when, the where, and hopefully the who of environmental wrongdoings — that appealed to me most about this organization, and what ultimately led me to apply for their internship program this summer.

In my first weeks as an intern, I was tasked with analyzing dozens of ocean satellite images, searching for oil slicks on the sea surface left behind by vessels, which show up as long black streaks. As a student with an emerging passion for Geographic Information System science (GIS), I was eager to find a more efficient way to scan the oceans for pollution. I wished I could simply press a “Next” button and have tiles of imagery presented to me, so I could search for patterns of oil quickly. It was a relief to find out that my coworkers were developing such a solution. Instead of relying on me and others to scan imagery and recognize the patterns of oil, they were training a computer to do it for us, a project called Cerulean. They were using machine learning and computer vision to teach a model to learn the visual characteristics of oil spills in images, pixel by pixel, after giving it many examples. I was really interested in getting involved in this work, so I asked if I could join a different project using machine learning: detecting new oil and gas drilling sites being built in important habitat areas in Argentina. 

Creating the training data

One of my first tasks was to organize a handful of existing polygons that we would use to create training data, which is what we call the information that is used to teach the model. Using Google Earth Engine, I placed the polygons over imagery collected from the European Space Agency’s Sentinel-2 satellites. The imagery is pretty coarse – these satellites collect images with a 10 meter spatial resolution. This means that every pixel in the image covers 100 square meters on the ground. The imagery can also be pretty cloudy, so one of the first things that I had to do was remove the clouds by creating cloud-free composite images. Basically, this combines several images of the same place, but at different times, and only uses the pixels that aren’t cloudy. This allowed me to create a single, cloud-free image of each of our sample areas. Once we’d done that, we were ready to make examples for the model to take in. 

Figure 1 shows a visual representation of the process that my colleagues and I developed to create the training data. On the left, we have a view of two well pads in Colorado from the default satellite base map in Google Earth Engine. This is the same imagery that you would see in the “Satellite” view of Google Maps; it’s very high resolution commercial satellite imagery, so it’s easy to see objects like these drilling sites in great detail. In the middle is a Sentinel-2 image of the same well pads. Sentinel-2 imagery is publicly available for free, and it is the imagery source that we use for our model. On the right, we have the Sentinel-2 image overlain with the well pad polygons that I’ve manually drawn.

Figure 1: Overlaying well pad polygons onto Sentinel-2 images

From here, we want to be able to select an area that captures each well pad and its surroundings. To accomplish this, we take the center of each blue well pad polygon, create a buffer of 200 meters, and then select a random spot within that circular buffer zone to drop a point, which appears below as a red dot. Figure 2 illustrates this step.

Figure 2: Area capturing well pads

These red dots are then used as the center of a 256 pixel by 256 pixel square – what we call a patch – that will house the well pad and its surroundings. I’ve illustrated what this box would look like in Figure 3, just using the left well pad for simplicity.

Figure 3: A “patch” housing a well pad and its surroundings

Next, we need to classify the image into “well pad” and “no well pad” labels. We create a binary mask with white representing well pad areas and black representing no well pad areas. This mask covers the entire image, and Figure 4 is a closeup of the two well pads we’ve been looking at with the boundary box visible as well.

Figure 4: A mask with white representing well pad, and black representing everything else

Finally, let’s zoom into the extent of the white boundary and put it all together.

This pair of small pictures in Figure 5 – the image patch on the left and the image’s label on the right – is what the model sees. Well, almost. Let’s break it down. Every image is made up of pixels that have numerical values for the amount of red, green, and blue in that pixel — three colors. If you can imagine each pixel as being three numbers deep, you can then imagine that the colored image on the left is a matrix with the dimensions 256 x 256 x 3. The right image is a matrix with the dimensions 256 x 256 x 1, since it only has one channel storing the label: 0’s for black pixels and 1’s for white pixels. One of these pairs – an image and its label – constitutes a single example that will go into the model.   

Thousands of examples

In order for the model to learn, we needed to create thousands of examples. So, we mapped well pads in Colorado, New Mexico, Utah, Nevada, Texas, Pennsylvania, West Virginia, and Argentina to use for training examples. My team and I tried to keep a couple of additional things in mind. First, we created a dataset of “look-alike” well pads, meaning that we found areas that the model could easily mistake for a well pad (such as square parking lots, plots of farmland, housing developments, etc.) and made labels for them. This indicates to the model that although these examples share similar features, they are not well pads, and this strengthens the neural connections of the model by refining its definition of what a well pad is, by showing it what a well pad isn’t

Second, we made sure to capture some variation in the appearance of well pads. While some are very bright in contrast to their landscape, others were darker than their surroundings, and some, especially in the American West, are essentially a dirt patch on desert land. By collecting training data of both the obvious well pads and the harder-to-distinguish ones, we added variance and complexity to the model. Since in the real world some well pads are old, some have been overgrown by vegetation, and some are covered with equipment, it’s important to include several examples of these special cases in the training data so that the model can recognize well pads regardless of the condition that they might be in. 

Prepping the training data for the model

To complete the process of preparing the training data, I packaged up the examples as TFRecords, a data format that is ideal for working with TensorFlow, a popular machine learning platform. These TFRecords will be fed into the model so that it can learn the visual characteristics of well pads well enough to be able to detect drilling sites in previously unseen imagery. 

Now that we’ve discussed how to develop the training data, Geospatial Analyst Brendan Jarrell will explain how we developed our model in the second post in this series. 

 

 

SkyTruth Visualization and App of Drilling Near Chaco Canyon Available to Activists and Others

The Bureau of Land Management has permitted intensive oil and gas drilling around Chaco Culture National Historical Park, threatening a landscape that supports one of the most important cultural sites in the world.

[This discussion of the threats to Chaco Culture National Historical Park, and SkyTruth tools highlighting that threat, was written as a collaborative effort between SkyTruth team members Matthew Ibarra and Amy Mathews.]

Reminders of an ancient civilization dominate the desert landscape in northwestern New Mexico. Ruins of massive stone “Great Houses,” once several stories high with hundreds of rooms, remain at Chaco Culture National Historical Park. Their complexity and numbers reveal that a sophisticated culture thrived in this place a thousand years ago. Descendants of those native peoples — today’s Pueblo tribes and several Navajo clans — say that Chaco was a central gathering place where people shared ceremonies, traditions, and knowledge. Yet much about Chaco remains a mystery. During the late 1200s, construction of buildings and monuments slowed and the Chacoan people moved from the area. However, Chaco is still considered to be a spiritual and sacred place by many Native Americans. 

Parts of Chaco were first designated as a national monument by President Theodore Roosevelt in 1907. Eighty years later the United Nations recognized the monument as a World Heritage Site because of its unique cultural significance. Despite these protections, the area surrounding the park is now threatened. 

Over the past two decades, the federal Bureau of Land Management (BLM) has allowed oil and gas companies to drill hundreds of wells within 15 miles of the park using the technique known as hydraulic fracturing, or fracking. Fracking typically creates air and noise pollution, threatens water supplies, increases truck traffic on local roads, and harms communities with toxic chemicals. SkyTruth’s data on fracking in Pennsylvania has been used by scientists at Johns Hopkins University to demonstrate some of the harmful health effects associated with fracking. 

Many tribal groups have voiced concerns about the spiritual, cultural, physical and health impacts from drilling in the area. In September of 2019, the U.S. House of Representatives approved the Chaco Cultural Heritage Area Protection Act that would create a 10-mile buffer zone on federal lands around the park to prevent any future leasing for oil and natural gas drilling. Although the entire New Mexico congressional delegation supports this legislation, the Senate has not taken action on this bill. Reportedly, the bill does not have Republican support in the Senate, which substantially reduces its chances of becoming law under the current majority.

To illustrate the extent of drilling in recent years, SkyTruth created an animation of wells surrounding Chaco Culture National Historical Park by illustrating data from New Mexico’s Oil Conservation Division as well as using the most current imagery from  the U.S. Department of Agriculture’s National Agriculture Imagery Program (NAIP) as a backdrop. (See “About the Data” below to learn more about how we did this.) The visualization shows the growth of wells throughout the region surrounding the park, with distances from the park boundary delineated. New wells have emerged throughout the region in this time period, from the park boundary to 15 miles and beyond. The region within 15 miles of the park now contains 33% more oil and gas wells than it did in 2000 — an increase of 367 wells. 

The growth of oil and gas wells within a 15-mile radius of Chaco Culture National Historical Park from 2000 – 2018

Despite local opposition and congressional action, the BLM currently is proposing additional leasing for drilling around the park. The public comment period for input on this leasing plan has been extended to September 25th, 2020. (Click here for information on how to submit comments.) 

In addition to the animation of drilling build out, SkyTruth has also created still images showing the changes around Chaco Canyon from 2000-2018. Each image highlights change in drilling activity for the year and features the most recent NAIP imagery from 2018 as a backdrop.

Still images for each year in the animation

SkyTruth also has developed an interactive app that allows users to view a map of the Chaco Culture National Historical Park and its surrounding area with all the surrounding oil and gas wells. Users will be able to click on a well pad to see more information such as the well pad identification or the status of the well, such as whether it is being plugged, or is still fully operational. (See “About the App” section below.) This app can be viewed here.

Chaco Culture National Historical Park is home to the largest and best preserved ancient architectural structures in all of North America. It was home to communities throughout the 1000s and remains important to Native Americans and others. Today, this magnificent region is becoming an industrialized area cluttered by oil and gas wells and threatens to harm the people who honor this place of heritage. SkyTruth hopes the visualizations and tools we’ve created will help arm activists, draw attention to the leasing process, and support congressional action to protect a remarkable place.

About the Data

The data used to identify wells comes from New Mexico’s Oil Conservation Division. The link for this dataset can be found here, labeled Public FTP Site. This large dataset was analyzed to create buffer zones based on the distance to Chaco Culture National Historical Park. The dataset was used in QGIS — a geographic information system tool — alongside NAIP imagery exported from Google Earth Engine to create an accurate map of the data and wells. We used TimeManager, a plugin for QGIS, to create this visualization. TimeManager allows users to easily add data to the working map based on time. Wells were added to the working map by month starting from January of 2000 through September of 2019, creating over 200 still images. TimeManager also allows users to export these still images as frames to create an MP4 file. We then used Final Cut Pro to add an overlay over this MP4 and create a visualization with a legend, scale bar, and other necessary features. 

About the App  

The Chaco Canyon Well Inspector app allows users to pan and zoom around an interactive map and inspect each individual well around the Chaco Canyon area. Upon clicking an individual well point, data such as the well identification number (API number) and status becomes visible to the user. Users will be able to inspect the area surrounding Chaco Culture National Historical Park to see how the growing number of wells has impacted the surrounding area and gain a better understanding of the status of oil and gas wells in the area.

Update 9/9/20: The animation, photos, and Chaco Canyon Well Inspector app were updated to reflect the spud date at each site; that is, the date when wells officially broke ground for drilling.

Alice Foster’s Internship Triggered New Excitement About Her Career Possibilities

Before her internship, Alice felt burnt out at school. After applying new skills and technologies to environmental projects at SkyTruth, she’s looking forward to her remaining classes and a fulfilling career.

As I wrap up my four-month internship at SkyTruth, I would like to share some highlights and takeaways from my experience. During my internship I explored the field of geospatial technology for the first time, which allowed me to learn new skills and gave me insight into my career goals. I learned about global environmental issues that I hadn’t known existed. And I got to work with a kind, dedicated, creative group of people. I contributed to SkyTruth’s mountaintop mining research and Project Inambari, which will create an early alert system for tropical forest mining. I also spent time identifying oil and gas well pads, collecting images of oil slicks, and creating annotated maps in QGIS, a geographic information system application that can be used to analyze and visualize geospatial data such as satellite imagery or a ship’s track across the ocean.

On just my first day of orientation at SkyTruth, the high level of support and guidance I received from the staff surprised me. My advisers Brendan Jarrell and Christian Thomas spent lots of time introducing me to concepts and technologies (like Google Earth Engine and QGIS) that I would use in my work. One of the first skills I learned was recognizing oil slicks on satellite imagery — most likely from vessels dumping oily bilge water at sea — and creating an annotated map to reveal the slicks to the public. Brendan patiently guided me through the steps to making a map twice. The team congratulated me when I found my first slick, even though I did not think it merited attention. This encouragement made me feel welcomed and excited about my work. 

The search for oil slicks allowed me to virtually explore oceans and coastlines across the globe. With time, it revealed to me more than how to use geospatial technology, but how little geography I knew. I would toggle past a country or island and wonder what it was like there, realizing I did not even know its name. And so I started exploring a geography trivia website in my free time to teach myself the countries of the world. I am now learning capital cities in Europe, which I tend to forget.

After getting practice with Google Earth Engine — a tool for analyzing and mapping satellite imagery and change around the world —  during my first couple of weeks at SkyTruth, I became involved in some mining-related projects. In one project, I adapted code from SkyTruth’s mountaintop mining research to incorporate satellite imagery from the European Space Agency’s Sentinel-2 satellite. This imagery provides us additional data, which could improve our ability to detect surface mining throughout Central Appalachia. Working with the code in Earth Engine allowed me to better understand SkyTruth’s process for identifying mines. First, we produce a greenest pixel composite image from a collection of images. Making a composite in Earth Engine means combining multiple overlapping images to create a single image. Images can be combined in different ways; in this case, the greenest pixel composite selects pixels with the highest Normalized Difference Vegetation Index (NDVI) values compared with corresponding pixels in the image collection. NDVI is an indicator of plant health in a given area. To provide a more concrete example, suppose we want to make a greenest pixel composite from three images, all showing a part of West Virginia at different times of summer. Say we look at one pixel in one of the images, which covers a small square of forest. We then compare this pixel with the pixels covering the same bit of forest in the other two images, and we choose the greenest of the three (or, the pixel with the highest NDVI value). If we repeat this process for every pixel in the image, we get one image with all the greenest pixels selected from the collection. 

A second script uses the greenest pixel composite to approximate the lowest NDVI value for each county, producing a threshold image. Again, say we have the greenest pixel composite of West Virginia that we just made. Now we look at forested areas within one county and find the pixels that are least green, or have the lowest NDVI values, and then take the average of these NDVI values. This is the threshold for that county; if a pixel is less green than the threshold, it is likely a mine. Our output image contains these values for every county. As a final step, we compare the greenest pixels with the NDVI thresholds to determine likely mine areas. 

Figure 1. Mining data overlying a Sentinel-2 greenest composite image. The image covers counties in West Virginia, Virginia, and Kentucky.

SkyTruth’s surface mining expert, Christian Thomas, also had me experiment with two different techniques for masking clouds in Sentinel-2 imagery. Clouds obstruct necessary data in images, so clearing them out improves analyses. The standard approach uses a built-in “cloud mask” band. The other approach is an adapted “FMasking” method. This takes advantage of the  arrangement of sensors on Sentinel-2 satellites, which creates a displacement effect in the imagery that is more pronounced for objects at altitude. The FMask uses this effect to distinguish low altitude clouds from human-made infrastructure on land. Though the two methods had similar results, the FMask seemed slightly more accurate.

Working on technical projects like this, I learned how much I enjoy using imagery and geospatial data. I had found analyzing data interesting in the past, but something about being able to visualize the information on a map was even more appealing. I loved how a satellite image could be reduced to numbers and assessed quantitatively, or understood visually, almost as a piece of art. 

In another project, I had the opportunity to develop my writing skills by contributing to an  application for the Artisanal Mining Grand Challenge, a global competition to provide solutions for small-scale, low-tech, and/or informal mining. Researching artisanal gold mining was illuminating, as I knew almost nothing about the subject beforehand. I learned that illegal gold mining in Venezuela and Peru has often involved brutal violence and exploitation. In recent decades, labor and sex trafficking have plagued remote mining regions like Madre de Dios. Small-scale mining practices are also particularly damaging to the biodiverse Amazon ecosystem. To extract a small amount of gold, miners must dig up massive amounts of sediment, denuding the landscape in the process. The use of mercury in artisanal gold mining is incredibly detrimental to water quality and human health.

I was also able to be involved in the technical side of this project, building a tool to detect mines in the Peruvian Amazon. I created a mask that removes water from satellite images so that water areas could not be mistaken for mine areas or vice versa. Mines are often near water or can look like water in imagery. To make the mask, I used the European Commission’s Joint Research Centre global surface water dataset. This dataset contains information about where and when surface water occurred around the world over the past thirty years. In Google Earth Engine, the data is stored in an image with bands representing different measures of surface water. I used the “occurrence,” “seasonality,” and “recurrence” bands to create the mask. “Occurrence” refers to how often water was present at a location; “seasonality” means the number of months during which water was present; and “recurrence” is the frequency with which water returned from one year to the next. I tried to find a combination of band values that would do the best job getting rid of water without masking mines or forest. For example, using an occurrence value of twenty, (that is, masking pixels where water was present twenty percent or more of the time), ended up masking mine areas as well. Christian also suggested using a buffer, which meant that pixels adjacent to a masked pixel also got masked. Since the mask often did not capture all of the pixels in a body of water, the buffer filled in the gaps. Masked pixels dotting a river became a continuous thread. The buffer also helped eliminate river banks, which look similar to mines. We applied the finished water mask to the area of interest in Madre de Dios, Peru.

Figure 2: Water mask in the Madre de Dios region of Peru. White pixels have value 1, while black pixels (water) have value 0. When the mask is applied to a satellite image, all pixels in the black areas appear transparent and are not included in analyses. When identifying potential mines in the image, the masked areas are ignored.

Researching issues related to artisanal gold mining left me unsure of how countermeasures can fully promote the welfare of mine workers and others involved in the long term. The problem of illegal gold mining seems entrenched in broader economic and social issues and therefore cannot be addressed simply by identifying and eradicating mines. Nevertheless, understanding the great damage that this type of mining can do to humans and their environment made clear to me the importance of the project. 

Not only did working at SkyTruth teach me a variety of technical and professional skills, it also helped reveal to me what I want to learn about and pursue in the future. In school last fall, I felt burnt out to the point that I just wanted to get through my remaining semesters and be done. Now I feel the excitement about academics I had as a freshman, motivated and informed by my experience at SkyTruth. With my interest in geology and climate issues renewed, I feel like there is barely enough time left to take all the classes I want to. I hope to improve on skills like writing and computer programming so that I can contribute my best work in the future. Being part of an amazing team has motivated me in that way. I also know that I would like to use the geospatial technologies and approaches I learned at SkyTruth moving forward. I feel excited about future career possibilities; before my internship, I felt confused.

I want to give a huge thank you to Bruce and Carolyn Thomas for hosting me in Shepherdstown. I want to thank Christian for introducing me to SkyTruth and for including me in his Dungeons and Dragons game! And I want to thank everyone on the SkyTruth team for their guidance and for being wonderful.

Figure 3: Team Hike, Harpers Ferry, West Virginia. Photo by Amy Emert.

SkyTruth Board Member Mary Anne Hitt: Activist Extraordinaire

Mary Anne Hitt has led Sierra Club’s Beyond Coal Campaign to extraordinary national success. But she honed her skills in Appalachia, with a little help from SkyTruth.

You might say Mary Anne Hitt has Appalachian activism in her blood. When she was growing up in Gatlinburg, Tennessee (where she attended Dolly Parton’s former high school), her father was Chief Scientist at Great Smoky Mountains National Park. Back then, acid rain was decimating high elevation forests in the East, fueled by pollution from coal-fired power plants. Her father watched as iconic places in the park turned into forests of skeleton trees. He knew the science pointed to nearby power plants run by the Tennessee Valley Authority, and wanted to stop the pollution. But his warnings triggered some resistance from those who didn’t want to rock the boat. “So right from the start,” says Mary Anne, she was “immersed in the beauty and the threats” of protecting Appalachian forests. And she knew the costs of speaking out.

Those costs have never stopped her. Mary Anne graduated from the University of Tennessee, creating her own environmental studies major and forming a student environmental group that continues today. Later, she obtained a graduate degree in advocacy at the University of Montana. Now, she leads the Sierra Club’s Beyond Coal Campaign; a national effort to retire all coal plants in the United States, moving towards 100% renewable energy by 2030, while supporting economic opportunities in communities affected by plant closures.

And she serves on SkyTruth’s board of directors. Her entre to SkyTruth is also steeped in Appalachian advocacy. In the early aughts, Mary Anne was Executive Director of Appalachian Voices, a nonprofit conservation group dedicated to fighting mountaintop mining, fracked-gas pipelines and other harmful activities in Appalachia, while advancing energy and economic alternatives that allow Appalachian communities to thrive. Appalachian Voices is one of SkyTruth’s conservation partners; a relationship that began under Mary Anne’s leadership.

As Mary Anne tells it, Appalachian Voices was fighting mountaintop mining and construction of a new coal plant in southwest Virginia. While fighting the plant, they discovered that 200 new power plants were planned across the country. In other words, a whole new generation of power plants was on the books to replace aging plants. A coalition of grassroots groups and local citizens, organized with help from the Sierra Club, worked to stop them, fighting permits at every stage, slowing the process down and making financial backers nervous.

Figure 1. Mary Anne Hitt

Appalachian Voices contacted SkyTruth to help them convey the vast extent of mountaintop mining in Appalachia as part of their work. In response, SkyTruth developed the first scientifically credible database on the extent of mountaintop mining in the region. (You can read more about this collaboration and what we found here.) SkyTruth continues to update this database every year, providing scientists and others valuable information that supports research on the ecological and human health effects of mountaintop mining.

SkyTruth’s database helped support the broader advocacy work Appalachian Voices was spearheading to fight coal mining and power plants in the region. Collectively, environmental, legal, and grassroots groups nationwide stopped almost all of the proposed power plants, according to Mary Anne. (Ironically, the one in southwest Virginia actually did get built.) “If these plants had been built it would have been doom for our climate,” Mary Anne says now. “There would have been no room for renewables…Grassroots people working in their communities made it happen. That’s what makes me most proud.”

Mary Anne took her successful experience fighting power plants in Appalachia and brought it to the Sierra Club as Deputy Director of the Beyond Coal Campaign in 2008, later becoming Director. The Sierra Club has built on those early lessons and applied them to shutting down all coal plants in the United States. Today, 312 of 530 plants that existed in 2010 have retired or announced their retirement. And according to Mary Anne, the United States reached a promising benchmark a year ago: last April marked the first time we obtained more energy from renewables than from coal. In fact, in 2019 the US consumed more power from renewable energy than from coal for the first time in 130 years. “Most of our arguments now are economic,” says Mary Anne. “The power from a coal plant is more expensive than renewable energy, so people don’t want it. People will keep demanding renewables.”

In April of this year, Mary Anne took on an even bigger responsibility at Sierra Club – the National Director of Campaigns, a new position in the organization where she oversees all the organization’s campaign work. It’s a big job, on top of being a mother to her ten-year old daughter. So why did she agree to join the SkyTruth Board? “Ever since my daughter was born,” says Mary Anne, “I had a policy of not being on any boards because I have a demanding job and serving on boards was more time away from her. But I really believe that SkyTruth’s work is foundational for the environmental movement. I think the ability to see for yourself what’s going on, especially in this age of misinformation, where people don’t know what to believe… the ability to show people with their own eyes what’s going on, I think is more important than ever.”

She also knows from her years in advocacy that having access to technical resources and expertise is challenging for nonprofits, especially small ones. “To provide this to groups in a way that’s technically sophisticated, but they can use it, is a real service,” she says. And SkyTruth has had significant impact on key issues, she notes, particularly given its small size. “To the extent that I can help, I want to do that. And I love that they are based in West Virginia and Shepherdstown – it’s a cool part of SkyTruth’s story.”

But a professional life of activism involves a lot of conflict, Mary Anne acknowledges. To balance it out, she and her husband Than Hitt, a stream ecologist, sing and play guitar at local fundraisers and other community events. Than is a 10th generation West Virginian and they live in Shepherdstown, where SkyTruth is based. The local singing is all for fun she says.

“It’s a way to connect with people you wouldn’t otherwise… And having a creative outlet helps keep me whole.” With activism, “you’re living in your head a lot. Music is in your heart. We all need that.”

 

SkyTruth’s West Virginia FrackFinder Datasets Updated

Oil and gas drilling activity in West Virginia continues to expand.

For more than a decade, SkyTruth has been tracking the footprint of oil and gas development in the Marcellus and Utica shale basins in West Virginia, Pennsylvania, and Ohio through our FrackFinder project. Initially, our FrackFinder project relied on volunteers to help us identify activity on the ground (thank you to all you SkyTruthers out there!). Since then, we’ve continued to update this database with help from SkyTruth interns and staff. Today, we’re excited to announce our latest updates to our West Virginia FrackFinder datasets. The updated data now include drilling sites and impoundments that appeared on the landscape through 2015–2016 (our 2016 update) and through 2017–2018 (our 2018 update). In 2016, 49 new drilling sites and 17 new impoundments appeared on the landscape. In 2018, 60 additional drilling sites and 20 new impoundments appeared; an 18% and 15% jump, respectively, from 2016.

With these additions, our West Virginia datasets track the footprint of oil and gas development in the state for more than decade, stretching from 2007 to 2018. 

Image 1. New drilling sites in Tyler County, near Wilbur and West Union, WV

We use high-resolution aerial photography collected as a part of the USDA’s National Agricultural Imaging Program (NAIP) to identify drilling sites and impoundments and make their locations available to the public. NAIP imagery is typically collected every two to three years, so once the imagery from each flight season is available, we  compare permit information from the West Virginia Department of Environmental Protection with NAIP imagery to find and map new drilling sites. Our datasets of what’s actually on the ground — not just what’s been permitted on paper — help landowners, public health researchers, nonprofits, and policymakers identify opportunities for better policies and commonsense regulations. And our data has resulted in real-world impacts. For example, researchers from Johns Hopkins University used our FrackFinder data in Pennsylvania to document the human health impacts of fracking. Their research found that living near an unconventional natural gas drilling site can lead to higher premature birth rates in expecting mothers and may also lead to a greater chance of suffering an asthma attack. Maryland Governor Larry Hogan cited this information in his decision to ban fracking in his state. 

We’ve shared the updated FrackFinder West Virginia data with research partners at Downstream Strategies and the University of California–Berkeley investigating the public health impacts of modern drilling and fracking, and with environmental advocacy groups like Appalachian Voices and FracTracker Alliance fighting the expansion of energy development in the mid-Atlantic.

We are also proud to roll out a Google Earth Engine app, which will be the new home for our  West Virginia FrackFinder data. Users can find all of our previous years’ data (2007–2014) as well as our new 2016 and 2018 datasets on this app. The interactive map allows you to zoom into locations and see exactly where we’ve found oil and gas drilling sites and wastewater impoundments. A simple click on one of the points will display the year in which we first detected drilling, along with the measured area of the site or impoundment (in square meters). Users can toggle different years of interest on and off using the left panel of the map. At the bottom of that same panel, uses can access the total number of drilling sites and impoundments identified during each year. Lastly, users can download SkyTruth’s entire FrackFinder dataset using the export button.

Image 2. Our Earth Engine app lets users track oil and gas development through time in WV.

We hope that the updates to our West Virginia FrackFinder datasets, and the new Earth Engine app that hosts them, will inform researchers, landowners, policymakers, and others, and help them bring about positive change. Feel free to take a look and send us feedback; we love to hear from people using our data.