This map, created by SkyTruth (www.skytruth.org), shows the current boundaries of Bears Ears and Grand Staircase-Escalante National Monuments in green, and the proposed, reduced boundaries in red. Data was provided by The Wilderness Society and the Bureau of Land Management. Aerial images were provided by EcoFlight (www.ecoflight.org)

Our Shrinking National Monuments

The President announced sizeable reductions of several National Monuments earlier this week.  To help people see and understand the significance of this action, we produced an interactive map showing two of the most highly impacted Monuments, Bears Ears and Grand Staircase – Escalante, both in Utah.  Users of the map can zoom in and explore the places that the Trump administration wants to remove from protection.

Vigorous public opposition and lawsuits by companies such as Patagonia make it likely the fate of the monuments will be tied up in court for many months. In the meantime, our friends at EcoFlight tell us the reduced monuments are considered “de facto” until the courts decide the inevitable legal challenges.

Thanks to The Wilderness Society for providing the proposed new boundaries, based on maps that were leaked last week; and to EcoFlight for sharing geotagged photos from their many flyovers, to help us illustrate what’s in jeopardy.

This map, created by SkyTruth (www.skytruth.org), shows the current boundaries of Bears Ears and Grand Staircase-Escalante National Monuments in green, and the proposed, reduced boundaries in red. Data was provided by The Wilderness Society and the Bureau of Land Management. Aerial images were provided by EcoFlight (www.ecoflight.org)

This map shows the original boundaries of the Bears Ears and Grand Staircase – Escalante National Monuments in green, and the reduced boundaries announced by the President on December 4 in red. Click on the camera icons to see aerial photographs of those locations.  Data provided by The Wilderness Society and the US Bureau of Land Management. Aerial photographs provided by EcoFlight.

We’ll add more photos and info to this map as we get it.  View the map here, and please share this link with interested friends:  http://bit.ly/2AxdRrv

Update on Our Efforts to Map Surface Mining in Appalachia

Some time has passed since we’ve written about our work mapping surface mining in central Appalachia, but rest assured, we’re still actively monitoring this devastating practice. Our mining work to date has focused on mapping the locations of these operations.

Researchers, some of whom are using our data, are beginning to draw troubling connections between coal mining and the health of people living in communities near those operations. We are working to refine our mapping processes and enable new types of analysis to help understand the environmental and public health consequences of mountaintop removal mining.

The process we used to create our annual maps of surface mining from 1985-2015, relies on the use of a Normalized Difference Vegetation Index (NDVI). NDVI essentially measures a ratio of reflected red and near-infrared light and is particularly useful for detecting changes in vegetation. When areas within the scope of our study experience a change from forest to bare earth, this registers as mineland. The analysis is available here: skytruthmtr.appspot.com

This NDVI image shows the Hobet 21 Coal Mine in West Virginia. Vegetated areas are visualized in white, while bare earth is seen as dark grey or black.

We are working with Dr. Matt Ross, an ecosystem scientist from the University of North Carolina at Chapel Hill, to improve our mining identification algorithm, and add the capacity to evaluate how landscapes affected by surface mining recover over time. This algorithm is an integral step in assessing the efficacy of the reclamation efforts undertaken by mine operators. We expect our mapping will allow researchers to conduct more robust studies on the long-term environmental and health impacts of surface mining, which in turn will help mining-impacted communities hold industry and government accountable for repairing the damage done to Appalachian landscapes, ecosystems and public health. We also hope the work will stimulate government investment as coal mining declines throughout the region, enabling a just transition to a new economy.

The following slider compares one of the new indexes we are incorporating into our work, a Normalized Difference Moisture Index (NDWI), with NDVI at the Hobet 21 Coal Mine. NDWI measures the relative amounts of moisture present in landscapes, densely vegetated areas have high NDMI values, while sparsely vegetated areas or bare earth have lower values. By incorporating new indices we are gaining a better understanding of how the land is affected by these operations. It is worth noting, therefore, the low amount of moisture present across the mine, even in those areas which appear to be recovering in the NDVI.

Coal mining in Black Thunder coal mine, WY from 1985 (in green) to 2015 (in red) overlain on 2015 aerial survey photography (NAIP).

Examining Mining Operations in the Powder River Basin Using Google Earth Engine

When you hear of coal production in America, what comes to mind? Perhaps you imagine a rugged man with a miner’s lamp on his helmet descending into a tunnel several hundred feet below the ground. Or maybe you picture giant machines removing topsoil and bedrock from a forested West Virginia mountain. But what if I told you most of the coal produced in America is mined from the arid grasslands of Wyoming and Montana?

According to the 2017 Federal Coal Program, “85% of production occurs in the arid region of Wyoming and Montana known as the Powder River Basin”. The Gillette coalfield in Wyoming contains the largest deposits of low-sulfur sub-bituminous coal. The area is flat, and the coal seams are very thick and close to the surface, making it much easier (and cheaper) to extract from open-pit mines, compared to the cost and effort of removing Appalachian mountaintops.

Mountaintop removal mining (MTR) is reshaping the Appalachian landscape. In the spring of 2016, Duke University and SkyTruth created a Google Earth Engine script to process satellite imagery and derive an accurate, annually updated map and GIS dataset of MTR operations across Appalachia. Google Earth Engine is a cloud-based geospatial processing platform with access to satellite imagery archives. For this work, we used Landsat imagery from 1985 to 2015. A band ratio was used on the imagery to identify active mining operations and to discriminate bare surfaces from vegetated land. A normalized difference vegetation index (NDVI) is a ratio of the red band to the infrared band. We chose this band ratio because vegetation will use red light but reflect infrared, while bare rock and soil strongly reflect both. The script determines an NDVI threshold based on testing the results against thousands of manually classified control points randomly scattered throughout the project area. If the NDVI value of a given pixel falls below the automatically determined threshold, it is classified as active mining.

Part of my summer internship was devoted to adapting this process for mining operations in the Powder River Basin. The first step in applying this script was to create a mask. Its purpose is to mask out everything that could be misclassified as mining because it’s a bare surface, like lakes, streams, roads, railroads, urban areas, etc. This data was collected from US Census TIGER shapefiles and merged to generate a raster mask. However, unlike Appalachian MTR operations, Powder River Basin coal mining is also surrounded by natural gas and oil drilling sites. To mask out these fracking pads, well permits were downloaded from the Wyoming Oil & Gas Conservation Commission, then added to the mask. Variables such as coal mining permits, and county boundaries (Converse and Campbell) were added for Wyoming.  

The vastly different climate proved difficult in this adaptation. While Appalachia is mostly mountainous deciduous forest, the high plains of eastern Wyoming are flat and semi-arid. There are naturally many small barren areas or badlands in this region that mimic mining operations, at least from the satellite’s perspective. My solution was to filter the results by eliminating any area classified as active mining that was less than 300,000 square meters (m2) in size. This threshold was determined during some post-process editing, where I examined all of the areas classified as mining that fell outside the boundaries of mining permits, and the largest was 300,000 m2. The resulting output only retained the larger vectors located within the permits and can be seen below.

Coal mining in Black Thunder coal mine, WY from 1985 (in green) to 2015 (in red) overlain on 2015 aerial survey photography (NAIP).

Coal mining in Black Thunder coal mine, WY from 1985 (in green) to 2015 (in red) overlain on 2015 aerial survey photography (NAIP).  

As you can see, this approach yields reliable results. I’m confident the methodology we demonstrated in Appalachia can work for coal mining out West. It is worth experimenting with changing the NDVI threshold to see if we can come up with a better tradeoff between identifying the active mining areas, and misclassifying badlands and other non-mining barren areas.  

 

Infrastructure Drives Development in the Brazilian Amazon: Highway –> Hydroelectric Plant –> Gold Mine

Big changes are happening in the Brazilian Amazon along a stretch of the Xingu River known as the Volta Grande (Big Bend), where it takes a detour to the south before turning back north to flow into the Amazon River. The region has experienced rapid growth and deforestation following the construction of the Trans-Amazonian Highway (BR 230 ) in 1972, as this pair of images illustrates:

1988:  Satellite imagery showing the Volta Grande region along the Xingu River in Brazil’s Para state. Tendrils of deforestation reveal settlement reaching out into the rainforest along the Trans-Amazonian Highway, built in 1972. Site of the future Belo Monte hydroelectric project is marked for reference. Compare with 2016 image below of the same area.

 

2016:  The same area as shown above in 1988. Considerable deforestation has occurred in the 18-year interval.

Small-scale gold mining has also occurred in this area over the past few decades, peaking in the 1980s. But now a major hydroelectric project, that became operational in 2015 and is still under construction, may be paving the way for a multinational mining company, Belo Sun of Canada, to propose a massive open-pit gold-mining operation.  Some local residents, already negatively impacted by the hydro project, are wary of the gold mining proposal: “I have seen mining companies elsewhere, they take all the wealth and leave craters. We have to think about it ten times over before accepting their projects.”

The mining operation is temporarily on hold, so there’s nothing yet to see.  But Google Earth does have high-resolution satellite imagery showing the construction of the hydroelectric project that may be a key part of the business plan for this mining project.

2014: High-resolution panchromatic (black and white) satellite imagery of the Belo Monte hydroelectric project under construction on Brazil’s Xingu River. Project became operational in 2015. Compare with 2010 image below of the same area.

 

2010: High-resolution satellite imagery showing the site of the future Belo Monte hydroelectric project. Compare with 2016 image above of same area.

As we can see from the detail below, showing a line of trucks at work on the dam in 2014, this is a huge project. And the development sequence illustrated so clearly in this area shows that one big project begets another — from highway, to hydro, to mine.

Detail from 2014 satellite imagery showing trucks at work on part of the Belo Monte hydroelectric project.

The influx of people that results is inexorably transforming the Amazon rainforest.

Into… Ohio?

Mountain Top Removal in Appalachia: What’s in your backyard?

SkyTruth’s new Mountain Top Removal visualization tool is now available to inspect active mining data for 74 counties in Kentucky, West Virginia, Tennessee and Virginia. The website can be accessed at SkyTruthMTR.appspot.com.

The maps leverage tens of thousands of mining footprints, the result of SkyTruth’s mountain top removal (MTR) research. Before expanding to 74 counties, the orginal work included 59 counties and identified an estimated 445,792 acres of new mining over a 30 year period. This data has already allowed outside organizations and research institutions to directly link MTR to downstream water pollution and related environmental destruction, as well as provide input into numerous health studies and predict where coal companies might go next.

By visiting SkyTruthMTR.appspot.com, you can:

  • Click anywhere on the map to see its active mining history.
  • Visualize a timeline of active mining from 1985 through 2015, with zooming available right down to the rooftop.
  • Draw a rectangle or polygon on the map, then see a breakdown of mining in that area by year and as a percentage of the total selected area. Once drawn, shapes can be edited.
  • Click on one of the 74 counties and see the total active mining for that county by year.
  • Use standard Google maps for a baseline, then overlay with one of SkyTruth’s composite images for any year from 1985 through 2015. These images combine the best satellite photos for each year into a single layer.

Active Mining by County, 1985-2015

West Virginia

County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015
Boone 504 322658 6022 7291 12395 18663 24094 26392 24520
1.87% 2.26% 3.84% 5.78% 7.47% 8.18% 7.60%
Braxton 517 330904 1 1 2 1 1 1 0
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Cabell 289 184699 296 232 331 401 346 385 296
0.16% 0.13% 0.18% 0.22% 0.19% 0.21% 0.16%
Clay 344 220460 838 717 1869 3667 4538 4617 3261
0.38% 0.33% 0.85% 1.66% 2.06% 2.09% 1.48%
Fayette 669 428327 3507 4590 5532 4788 5256 5851 5214
0.82% 1.07% 1.29% 1.12% 1.23% 1.37% 1.22%
Greenbrier 1026 656722 1687 2667 2728 2082 1672 2002 2706
0.26% 0.41% 0.42% 0.32% 0.25% 0.30% 0.41%
Kanawha 913 584066 5319 6591 7363 7589 8932 8845 8746
0.91% 1.13% 1.26% 1.30% 1.53% 1.51% 1.50%
Lincoln 440 281358 608 535 1231 3131 3900 5227 5012
0.22% 0.19% 0.44% 1.11% 1.39% 1.86% 1.78%
Logan 456 291882 4711 7502 11617 15342 15911 13389 12469
1.61% 2.57% 3.98% 5.26% 5.45% 4.59% 4.27%
Mason 445 284957 473 700 630 639 611 746 754
0.17% 0.25% 0.22% 0.22% 0.21% 0.26% 0.26%
McDowell 536 342848 3561 3204 3228 5603 5472 6349 6753
1.04% 0.93% 0.94% 1.63% 1.60% 1.85% 1.97%
Mercer 422 269789 557 479 650 722 702 701 771
0.21% 0.18% 0.24% 0.27% 0.26% 0.26% 0.29%
Mingo 425 271892 3574 6425 9440 13173 13310 13020 10881
1.31% 2.36% 3.47% 4.84% 4.90% 4.79% 4.00%
Nicholas 655 419323 3041 6693 6525 7486 9105 11080 7835
0.73% 1.60% 1.56% 1.79% 2.17% 2.64% 1.87%
Pocahontas 943 603797 619 930 829 1570 841 1940 2015
0.10% 0.15% 0.14% 0.26% 0.14% 0.32% 0.33%
Putnam 351 224799 540 522 674 743 784 1006 989
0.24% 0.23% 0.30% 0.33% 0.35% 0.45% 0.44%
Raleigh 610 390716 2365 2310 2752 3888 5452 7500 7619
0.61% 0.59% 0.70% 1.00% 1.40% 1.92% 1.95%
Summers 368 235806 356 308 445 395 520 537 424
0.15% 0.13% 0.19% 0.17% 0.22% 0.23% 0.18%
Webster 557 356552 1258 1388 3519 3976 4811 6348 4986
0.35% 0.39% 0.99% 1.12% 1.35% 1.78% 1.40%
Wyoming 503 321717 2762 2982 2283 5239 6625 6896 5327
0.86% 0.93% 0.71% 1.63% 2.06% 2.14% 1.66%
County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015

Kentucky

County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015
Bell 362 231484 3998 4958 4116 5440 5150 6148 5096
1.73% 2.14% 1.78% 2.35% 2.22% 2.66% 2.20%
Boyd 162 103859 1179 930 829 868 870 816 786
1.14% 0.90% 0.80% 0.84% 0.84% 0.79% 0.76%
Breathitt 496 317413 7938 8100 9495 11239 594 8985 7355
2.50% 2.55% 2.99% 3.54% 0.19% 2.83% 2.32%
Carter 413 264298 1140 723 642 963 929 957 809
0.43% 0.27% 0.24% 0.36% 0.35% 0.36% 0.31%
Clay 472 302157 4169 2050 2267 2582 2091 2222 2140
1.38% 0.68% 0.75% 0.85% 0.69% 0.74% 0.71%
Clinton 206 131821 112 142 168 360 384 399 427
0.08% 0.11% 0.13% 0.27% 0.29% 0.30% 0.32%
Elliott 236 150845 860 404 244 220 361 432 371
0.57% 0.27% 0.16% 0.15% 0.24% 0.29% 0.25%
Estill 256 164010 525 654 637 648 629 631 639
0.32% 0.40% 0.39% 0.40% 0.38% 0.38% 0.39%
Floyd 396 253732 4101 4999 5267 5582 5091 5568 5037
1.62% 1.97% 2.08% 2.20% 2.01% 2.19% 1.99%
Greenup 355 227162 1017 1477 1203 1115 1419 1080 863
0.45% 0.65% 0.53% 0.49% 0.62% 0.48% 0.38%
Harlan 469 300134 3243 4571 3754 5489 6060 7879 7102
1.08% 1.52% 1.25% 1.83% 2.02% 2.63% 2.37%
Jackson 347 222163 1223 741 515 546 634 730 490
0.55% 0.33% 0.23% 0.25% 0.29% 0.33% 0.22%
Johnson 265 169528 1274 1169 924 1209 1692 1902 1671
0.75% 0.69% 0.55% 0.71% 1.00% 1.12% 0.99%
Knott 353 226208 6049 6880 9521 12541 7056 13019 11285
2.67% 3.04% 4.21% 5.54% 3.12% 5.76% 4.99%
Knox 389 248682 1586 1187 962 1317 1278 1968 1574
0.64% 0.48% 0.39% 0.53% 0.51% 0.79% 0.63%
Laurel 444 284452 3448 1269 1084 1232 1284 1243 1019
1.21% 0.45% 0.38% 0.43% 0.45% 0.44% 0.36%
Lawrence 421 269377 2290 1458 1055 1222 2333 2298 1997
0.85% 0.54% 0.39% 0.45% 0.87% 0.85% 0.74%
Lee 212 135435 630 640 701 564 525 375 353
0.47% 0.47% 0.52% 0.42% 0.39% 0.28% 0.26%
Leslie 405 259372 4544 3850 5475 8136 8551 10205 8857
1.75% 1.48% 2.11% 3.14% 3.30% 3.93% 3.41%
Letcher 340 217405 4761 5154 5305 7991 7012 5517 4465
2.19% 2.37% 2.44% 3.68% 3.23% 2.54% 2.05%
Lewis 496 317299 495 558 365 481 487 517 493
0.16% 0.18% 0.12% 0.15% 0.15% 0.16% 0.16%
Magoffin 310 198142 3032 1465 1495 1869 1691 3730 2984
1.53% 0.74% 0.75% 0.94% 0.85% 1.88% 1.51%
Martin 231 147784 5799 6221 6463 10294 10289 7667 3939
3.92% 4.21% 4.37% 6.97% 6.96% 5.19% 2.67%
McCreary 432 276630 1774 1358 1055 1012 1058 1071 892
0.64% 0.49% 0.38% 0.37% 0.38% 0.39% 0.32%
Menifee 206 132045 171 267 242 215 321 299 257
0.13% 0.20% 0.18% 0.16% 0.24% 0.23% 0.19%
Morgan 384 246009 1374 614 520 512 680 913 726
0.56% 0.25% 0.21% 0.21% 0.28% 0.37% 0.30%
Owsley 199 127155 709 741 542 627 580 803 576
0.56% 0.58% 0.43% 0.49% 0.46% 0.63% 0.45%
Perry 343 219685 8067 10388 11931 15369 1596 16104 11658
3.67% 4.73% 5.43% 7.00% 0.73% 7.33% 5.31%
Pike 790 505304 8470 8679 13987 24058 25448 20373 13869
1.68% 1.72% 2.77% 4.76% 5.04% 4.03% 2.74%
Powell 180 115512 178 342 338 371 406 385 345
0.15% 0.30% 0.29% 0.32% 0.35% 0.33% 0.30%
Pulaski 678 434030 1542 1531 1223 1114 1111 1086 1056
0.36% 0.35% 0.28% 0.26% 0.26% 0.25% 0.24%
Rockcastle 319 204021 1481 810 431 479 388 424 415
0.73% 0.40% 0.21% 0.23% 0.19% 0.21% 0.20%
Rowan 287 183556 111 121 193 302 440 462 355
0.06% 0.07% 0.11% 0.16% 0.24% 0.25% 0.19%
Wayne 485 310457 561 156 173 234 287 325 313
0.18% 0.05% 0.06% 0.08% 0.09% 0.10% 0.10%
Whitley 446 285375 2502 2336 1599 1617 1256 1340 1778
0.88% 0.82% 0.56% 0.57% 0.44% 0.47% 0.62%
Wolfe 223 142802 950 800 1742 1232 904 513 356
0.67% 0.56% 1.22% 0.86% 0.63% 0.36% 0.25%
County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015

Tennessee

County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015
Anderson 345 221107 316 455 487 490 610 648 521
0.14% 0.21% 0.22% 0.22% 0.28% 0.29% 0.24%
Campbell 499 319473 1206 1142 702 779 955 1295 825
0.38% 0.36% 0.22% 0.24% 0.30% 0.41% 0.26%
Claiborne 442 283094 435 432 448 946 1223 1094 718
0.15% 0.15% 0.16% 0.33% 0.43% 0.39% 0.25%
Cumberland 686 439282 1350 1442 1173 1713 1526 1641 1418
0.31% 0.33% 0.27% 0.39% 0.35% 0.37% 0.32%
Fentress 500 319842 1118 1360 1332 1602 1690 1290 1173
0.35% 0.43% 0.42% 0.50% 0.53% 0.40% 0.37%
Morgan 524 335123 1046 1200 1226 1514 1102 940 1191
0.31% 0.36% 0.37% 0.45% 0.33% 0.28% 0.36%
Overton 436 278734 393 509 591 638 682 831 802
0.14% 0.18% 0.21% 0.23% 0.24% 0.30% 0.29%
Pickett 175 111996 101 87 137 162 197 179 157
0.09% 0.08% 0.12% 0.14% 0.18% 0.16% 0.14%
Putnam 403 258163 329 325 407 490 641 694 727
0.13% 0.13% 0.16% 0.19% 0.25% 0.27% 0.28%
Roane 396 253538 546 583 514 615 711 720 665
0.22% 0.23% 0.20% 0.24% 0.28% 0.28% 0.26%
Scott 534 341784 1922 1608 1081 1244 1260 1262 1119
0.56% 0.47% 0.32% 0.36% 0.37% 0.37% 0.33%
County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015

Virginia

County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015
Buchanan 505 323299 3609 3320 2946 4818 6497 8034 7206
1.12% 1.03% 0.91% 1.49% 2.01% 2.49% 2.23%
Dickenson 334 213957 1562 1613 1574 2620 2508 2432 2638
0.73% 0.75% 0.74% 1.22% 1.17% 1.14% 1.23%
Lee 438 280567 625 1030 1099 1506 1419 1614 1331
0.22% 0.37% 0.39% 0.54% 0.51% 0.58% 0.47%
Russell 478 305659 1313 1024 1309 2032 1831 1814 1395
0.43% 0.34% 0.43% 0.66% 0.60% 0.59% 0.46%
Scott 540 345410 531 292 642 696 774 688 537
0.15% 0.08% 0.19% 0.20% 0.22% 0.20% 0.16%
Tazewell 521 333400 1135 912 866 1147 1205 1841 1836
0.34% 0.27% 0.26% 0.34% 0.36% 0.55% 0.55%
Wise 413 264483 3122 3557 6484 10749 15587 15944 11851
1.18% 1.34% 2.45% 4.06% 5.89% 6.03% 4.48%
County Sq Miles Acreage 1985 1990 1995 2000 2005 2010 2015

 

Fracking, Mountaintop Mining, and More…My Summer at SkyTruth

 Hi, my name is Jerrilyn Goldberg.  Over the course of  two months last summer I worked as an intern at SkyTruth. In September I started my junior year at Carleton College in Northfield, Minnesota, majoring in environmental studies and physics. Over the course of my internship I contributed to SkyTruth’s Mountaintop Removal (MTR) research by creating a mask to block out rivers, roads, and urban areas that could be confused with mining activity by our analytical model. I also helped classify many of the ~1.1 million control points that allow us assess the accuracy of our MTR results.

To analyze the accuracy of the MTR results we obtained through our Earth Engine analysis, we dropped 5,000 randomly distributed points at each of 10 sample areas for each year between 1984 and 2016. These points were manually classified as being `mine` (if it overlapped a user IDed mine location) or `non-mine` (if it overlapped anything other than a mine). A subset of those manually classified points were then used to assess the accuracy of the output from our Earth Engine analysis

In addition to the MTR project, I created a story map illustrating the development of Marcellus Shale gas drilling and hydraulic fracturing (fracking) in Pennsylvania, and discussing the environmental and public health consequences fracking is having on some rural Pennsylvania communities. Check it out here. Through my research for the story map, I learned about the hydraulic fracturing process. I also learned about many of the political and social complexities surrounding the fracking industry in Pennsylvania, including conflicts between economic and community interests. Our goal with this story map is to present an accessible and accurate narrative about the fracking industry in Pennsylvania, which begins with understanding what’s actually going on now.

Click the image above to visit Jerrilyn’s interactive story map.

I started by learning about SkyTruth’s FrackFinder Pennsylvania data and methodology from the 2013 project. I read through our GitHub repository and figured out why the FrackFinder team chose their methodology and what the results represented. (While I was familiar with the general concept of the project, I did not know much about the specifics beforehand.) With this in mind, I set out to update the dataset with well pads built after 2013.

 

I quickly realized that this task presented many questions such as, which of the many state oil and gas datasets actually contained the information I sought. I selected the Spud Data, which contains all of the individual locations where operators have reported a drilling start-date for a permitted well. I filtered to include only unconventional horizontal wells drilling for natural gas and excluded those reported as ‘not drilled.’ To account for some missing drilling locations which I noticed while reviewing the latest Google base map imagery, I also download the Well Inventory Dataset which includes all permitted oil and gas wells along with their status. From here I filtered out all the spuds and wells not listed as drilled in 2014, 2015, or 2016 and joined the files. After joining the layers, I formed a well pad dataset by creating a 150 meter buffer around the wells, dissolving overlapping areas, then locating the centers of each buffer. This step effectively says ‘create a 150 m radius circle around each point, but when these overlap, clump them into one circle, then find the center of that new circle.’ Finally, I found all the buffers that overlapped with FrackFinder drilling locations from 2013 and earlier, and eliminated all of those centroids.

A quick note about the imagery: USDA collects high resolution aerial imagery as part of the National Agriculture Imagery Program (NAIP), which at the time of my project was last collected for Pennsylvania in 2015. While I worked hard to eliminate inaccurate points, I was unable to verify all of these with the existing NAIP imagery. That said, I found that the other points accurately represented the general well pad locations and thus chose to include the points for the first half of 2016, even though I obviously couldn’t verify the existence of those recent drilling locations on the mid-summer 2015 NAIP imagery.

 

At the same time I found The Nature Conservancy’s (TNC’s) 2010 Energy Impact Analysis, which looked at the predicted development of wind, shale gas, and wood fuel usage in Pennsylvania. Part of TNC’s study identified three construction scenarios for how many wells and well pads could be built in Pennsylvania by 2030. With an assumption that 60,000 new wells would be drilled between 2010 and 2030, the study predicted between 6000 and 15000 new well pads would be built to host those wells. Each scenario featured a different distance between pads and a different number of wells per pad (because that number stays constant at 60,000 new wells). I found some data from TNC’s study hidden on an old SkyTruth backup with help from Christian and David. With the FrackFinder data, my update, and the ‘informed scenarios’ in hand, I started trying to figure out an appropriate way to synthesize the three datasets, to identify which TNC drilling scenario best fits what is actually happening..

 

One roadblock in conducting a thorough analysis and comparison was that TNC’s research makes a quantitative prediction about the possible volume of infrastructure development instead of a more tangible spatial prediction. The study distributes the predicted numbers of new well pads across the counties of Pennsylvania, which overlay the region of Marcellus Shale with ideal conditions for hydraulic fracturing for natural gas. All of the included counties now contain at least one well pad. I did notice that since 2010, about 1/3 of the well pads estimated by the low impact scenario (6000 well pads) have already been constructed. If the rate of development between 2010 and 2016 remains constant, Pennsylvania will surpass TNC’s low impact scenario.

An example of The Nature Conservancy’s “low” impact scenario for fracking well construction across a section of Pennsylvania.

The Nature Conservancy’s medium impact scenario for future fracking well construction across a section of Pennsylvania.

The Nature Conservancy’s high impact scenario for future fracking well construction over a section of Pennsylvania.

 

Fracking Pennsylvania” uses maps and other media to create a narrative of hydraulic fracturing and its consequences. While originally intended for the community members we work with in southern Pennsylvania, I hope this story map becomes a useful tool for many different communities grappling with fracking.

 

While I have my time in the Watchdog spotlight, I want to publicly thank everyone here for welcoming me into the awesome world of SkyTruth. I’m so grateful for the learning opportunities I had last summer and for all of the support I received. Special thanks to Christian for introducing me to SkyTruth and to John for helping me improve my Story Map even though he is definitely one of the busiest people in the office. I look forward to sharing my experience through the Carleton Internship Ambassador program this year.  

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.