Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google

15 Pages·2017·3.83 MB·English

RESEARCHARTICLE
Mapping the yearly extent of surface coal
mining in Central Appalachia using Landsat
and Google Earth Engine
AndrewA.Pericak1,ChristianJ.Thomas2*,DavidA.Kroodsma2,MatthewF.Wasson3,
MatthewR.V.Ross1,NicholasE.Clinton4,DavidJ.Campagna5,YolanditaFranklin2,Emily
S.Bernhardt1,JohnF.Amos2
1 DepartmentofBiology,DukeUniversity,Durham,NorthCarolina,UnitedStatesofAmerica,2 SkyTruth,
Shepherdstown,WestVirginia,UnitedStatesofAmerica,3 AppalachianVoices,Boone,NorthCarolina,
a1111111111
UnitedStatesofAmerica,4 GoogleEarthEngineTeam,GoogleInc.,MountainView,California,United
a1111111111
StatesofAmerica,5 DepartmentofGeology&Geography,WestVirginiaUniversity,Morgantown,West
a1111111111
Virginia,UnitedStatesofAmerica
a1111111111
a1111111111 *[email protected]
Abstract
OPENACCESS SurfaceminingforcoalhastakenplaceintheCentralAppalachianregionoftheUnited
Citation:PericakAA,ThomasCJ,KroodsmaDA, Statesforwelloveracentury,withanotableincreasesincethe1970s.Researchershave
WassonMF,RossMRV,ClintonNE,etal.(2018) quantifiedtheecosystemandhealthimpactsstemmingfrommining,relyinginpartona
Mappingtheyearlyextentofsurfacecoalminingin
geospatialdatasetdefiningsurfacemining’sextentatadecadalinterval.Thisdataset,how-
CentralAppalachiausingLandsatandGoogleEarth
Engine.PLoSONE13(7):e0197758.https://doi. ever,doesnotdeliverthetemporalresolutionnecessarytosupportresearchthatcould
org/10.1371/journal.pone.0197758 establishcausallinksbetweenminingactivityandenvironmentalorpublichealthandsafety
Editor:JuanA.Añel,UniversidadedeVigo,SPAIN outcomes,norhasitbeenupdatedsince2005.HereweuseGoogleEarthEngineand
LandsatimagerytomaptheyearlyextentofsurfacecoalmininginCentralAppalachiafrom
Received:August29,2017
1985through2015,makingourprocessingmodelsandoutputdatapubliclyavailable.We
Accepted:May8,2018
findthat2,900km2oflandhasbeennewlyminedoverthis31-yearperiod.Addingthis
Published:July25,2018 more-recentminingtosurfaceminesconstructedpriorto1985,wecalculateacumulative
Copyright:©2018Pericaketal.Thisisanopen miningfootprintof5,900km2.Overthestudyperiod,correlatingactivemineareawithhistor-
accessarticledistributedunderthetermsofthe icalsurfaceminecoalproductionshowsthateachmetrictonofcoalisassociatedwith12
CreativeCommonsAttributionLicense,which
m2ofactivelyminedland.Ourautomated,open-sourcemodelcanberegularlyupdatedas
permitsunrestricteduse,distribution,and
reproductioninanymedium,providedtheoriginal newsurfaceminingoccursintheregionandcanberefinedtocaptureminingreclamation
authorandsourcearecredited. activityintothefuture.Wefreelyandopenlyofferthedataforuseinarangeofenvironmen-
DataAvailabilityStatement:Dataunderlyingthe tal,health,andeconomicstudies;moreover,wedemonstratethecapabilityofusingtools
studyareavailableattheFigsharerepository.A likeEarthEnginetoanalyzeyearsofremotelysensedimageryoverspatiallylargeareasto
DOIforeachfileonFigshareislistedinthe
quantifylandusechange.
SupportingInformationfiles.
Funding:Wegratefullyacknowledgefinancial
supportfromtheFoundationfortheCarolinas
(https://www.fftc.org/)totheNicholasSchoolof
theEnvironment(AAP,MRVR,ESB);theNational
ScienceFoundationGraduateResearchFellowship
Program(https://www.nsfgrfp.org/)andNational
ScienceFoundationEarthSciencesHydrological
Sciences(https://www.nsf.gov/div/index.jsp?div=
PLOSONE|https://doi.org/10.1371/journal.pone.0197758 July25,2018 1/15
MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
EAR;grant1417405)(AAP,MRVR,ESB);the Introduction
CornellDouglasFoundation(http://www.
ImpactsofsurfacecoalmininginAppalachia
cornelldouglas.org/)(CJT,DAK,YF,JFA);andthe
WallaceGeneticFoundation(http://www.
Surfaceminingisabroadlyusedminingtechniquethathasincreasinglyreplacedunderground
wallacegenetic.org/)(CJT,DAK,YF,JFA).The
miningforavarietyofresources[1],especiallycoalintheUnitedStates[2].InCentralAppala-
fundershadnoroleinstudydesign,datacollection
chia,mostofthissurfaceminingforcoalisdoneinthesteep,dissectedlandscapesofKen-
andanalysis,decisiontopublish,orpreparationof
themanuscript.Variousauthorsareemployedby tucky,Tennessee,Virginia,andWestVirginia.Surfacemininginsuchsteeplandscapesis
non-profitorcommercialinstitutions;thespecific calledmountaintopremovalcoalminingwithvalleyfills(MTMVF).Toaccesscoalfromthe
rolesoftheseauthorsarearticulatedinthe‘author surface,MTMVFoperatorsharvestoverlyingforest,dismantlebedrockwithexplosivesand
contributions’section.ThefunderSkyTruth
heavymachinery,andextractcoalseamsrangingfrom0.25through1.5mthick[3].Thispro-
providedsupportintheformofsalariesforauthors
cessgenerateslargequantitiesofleftoverwasterock,orminespoils,whicharedepositedinto
CJT,DAK,YF,JFA,butdidnothaveanyadditional
headwatervalleys,buryingstreamsinasmuchas~200mofspoil[4].Unlikemanyothertypes
roleinthestudydesign,datacollectionand
analysis,decisiontopublish,orpreparationofthe ofsurfacemineoperations,whichmaybehundredsofmetersdeepbutoccuroverrelatively
manuscript.ThefunderAppalachianVoices smallspatialscales,MTMVFmineshavebeenconstructedacrossthousandsofsquarekilome-
providedsupportintheformofsalariesforauthor
tersofland,makingitthesinglelargestsourceoflandusechangeintheregion[5,6].
MFW,butdidnothaveanyadditionalroleinthe
MTMVFdramaticallyaltersvegetation,surfacetopography,andsubsurfacestructurein
studydesign,datacollectionandanalysis,decision
minedregions.NativeAppalachianforestsdonotreestablishonmostpost-mininglandscapes,
topublish,orpreparationofthemanuscript.The
funderGoogle,Inc.,providedsupportintheform causingashiftfromforesttograssland/shrublandecosystems[7,8].Thesenon-nativeecosys-
ofsalariesforauthorNEC,butdidnothaveany temsgrowonalandscapewheremininghasloweredthelocaltopographiccomplexity[9],
additionalroleinthestudydesign,datacollection loweredtheaverageslopebynearly10˚[4],andcreatednovelplateau-likelandscapes[4,9].
andanalysis,decisiontopublish,orpreparationof
ChangingAppalachianlandscapesfromsteep,shallow-soiledforeststoflatgrasslandsoverly-
themanuscript.
ingdeepspoilpileshasalteredhowwaterandelementsmovethroughtheselandscapes[10].
Competinginterests:AuthorsChristianJ.Thomas, Instreamsdrainingvalleyfills,theflatlandscapesandincreasedstoragepotentialhavebeen
DavidA.Kroodsma,YolanditaFranklinandJohnF.
showntolowerstreamdischargeduringstormeventsandelevatebaseflow[11].Thewater
AmosareemployedbySkyTruth,MatthewF.
storedinthesevalleyfillsissteepedinareactivematrixofpyriteandcarbonaceousbedrock.
WassonisemployedbyAppalachianVoicesand
Pyrite,boundupincoalresidueandshales,producessulfuricacidwhenexposedtooxygen
NicholasE.ClintonisemployedbyGoogle,Inc.
Theseaffiliationsdonotalterouradherenceto andwater[12].InMTMVFspoils,thissulfuricacidisneutralizedbycarbonatematerials,
PLOSONEpoliciesonsharingdataandmaterials. whichareintentionallymixedwithspoilstopreventacid-minedrainage[13,14].Theready
Theauthorshavedeclaredthatnofurther
supplyofsulfuricacid,carbonatebedrock,andhighsurface-areaspoilmaterialscreatesideal
competinginterestsexist.
conditionsforsomeofthehighestweatheringratesintheworld.Thenetweatheringreactions
generatealkalineminedrainagewhichischaracterizedbyelevatedionconcentrationsofsul-
fate(SO 2-),calcium(Ca2+),magnesium(Mg2+),bicarbonate(HCO -),andasuiteofotherele-
4 3
mentsincludingmajoraquaticpollutantslikeselenium(Se)[15,16].Theincreasedion
concentrationsraisethemeanspecificconductanceofwaterinminedstreamsfromback-
groundvaluesbelow~200toaverageswellover1,000μS/cm[15].
ThroughoutCentralAppalachianstreams,thephysicochemicalimpactsfromminingoper-
ationshavebeenshowntodecreaseaquaticmacroinvertebratediversity[17,18,19],alter
microbialcommunities[20],negativelyimpactfish[15,16],lowersalamanderabundance[21],
anddecreasestreamleaf-litterbreakdownrates[22].Inadditiontothesenegativeaquatic
impacts,MTMVFhasbeenshowntosubstantiallyincreasethecarboncostofburningcoal
[23],fragmentforesthabitat[24,25],andelevatelocalsurfacetemperature[24].Finally,min-
ingoperationscanmobilizesignificantdustcloudswithparticulatesthatcancausedetrimental
humanhealthimpacts[26].
Mappingminingextent
DespitetheoverwhelmingandwidespreadnegativeconsequencesfromMTMVF,theexact
extentofminingoperationsintheAppalachianregionhasnotbeenupdatedsince2005[27].
Thatdataset(hereafterreferredtoasthe“MTM2009”datasetafteritsyearofpublication)
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
identifiessurfaceminesata30mresolution,detectableasearlyas1976andaslateas2005,
acrossmostofCentralAppalachia.TheMTM2009dataset’smajorlimitation,however,isits
temporalresolution;itonlymapsminingoperationsat~decadaltimescale.Whilethedataset
specifiesexactlywhereaminehasoccurred,itprovidesacoarseten-yearwindowofwhenthat
minemayhavebeeninactiveproduction.Toparsethehydrologic,biogeochemical,ecological,
andhuman-healthimpactsfrommining,researchersrequirefinertemporalresolutionmaps
ofminingextent.
Here,weaimtoimproveupontheten-yearMTM2009datasetbyofferingayearly,30m
datasetcoveringtheperiod1985through2015,andtomakethesedatafreelyandpublicly
accessibleforanyuse.Inparticular,weareinterestedinlocatingareasbeingactivelyminedin
anygivenyear.Webroadlydefineactiveminingasanylandwhereminingactivity(i.e.,earth
removalandreplacement)waslikelyoccurring,orwhereminingactivitieshadrecentlyceased
sothatthelandscapestillresembledamineinactivedevelopment.
Moreover,weaimtoautomatethemodelingprocesssothatfutureminingareascanbe
quicklyaddedtothedatasetasnewremotelysensedimagerybecomesavailable.The
MTM2009datasetreliedonatime-intensivesupervisedclassification,butforourupdated
approachwesoughttodevelopautomatedmethodsthatwouldenableannualestimatesofpast
miningandfacilitaterapidupdatingofthedatasetinthefuture.Suchautomationisfacilitated
inthiscasebecausethespectralcharacteristicsofasurfaceminevaryconsiderablyfromother
landcoverclassesinthisregion.
Tomodelminingextent,weusetheGoogleEarthEngineplatform.EarthEngineisafreely
accessible,cloud-basedGoogleproductdesignedtoenableremotesensingstudiesoverlong
timescalesandlargespatialextents.Inadditiontorunningdataprocessingoperations,Earth
Enginehostsfullcollectionsofpublicremotesensingdata.Inourcase,weuseEarthEngine’s
processingcapabilitiesanditscontinuallyupdatedarchiveofLandsatimagerytoproduceour
dataset.
WhileEarthEngineisitselfrelativelynew,manyresearchershaveusedmoderateresolution
LandsatimagerytoquantifylandcoverchangescausedbysurfacemininginAppalachiaand
elsewhere.Forinstance,researchersacquiredoneLandsatimageperyearforafour-county
areainVirginiatoidentifyminelandsatayearlytimescalefrom1984through2001[10].
OtherresearchersusedLandsatimageryforfouryearsatadecadalintervaltoidentifysurface
minesandreclamationactivity[5].AdifferentstudycomparedLandsatimageryandland
coverdatafrom1992and2001todeterminethatsurfacemininghadcausedanaccelerated
lossofecologicallyimportantinteriorforestinAppalachia[24].YetanotherusedLandsat
imagerytoshowthatspeciesplantedtoaidwithminereclamation,suchastheinvasive
autumnolive,couldbeidentifiedoverprevioussurfaceminesviaremotesensing[28].And
finally,theLandTrendrproduct,asetofalgorithmstoquantifypixel-levellandcoverchange
overtime,employsLandsatimageryforitsanalysis[29].
HereweuseEarthEnginetocreateanautomatedmodelthatidentifiessurfacecoalmining,
particularlyMTMVF,acrossCentralAppalachia(acontiguousareaincludingportionsofKen-
tucky,Tennessee,Virginia,andWestVirginia)atayearlytimescale.Wecallourapproach
“automated”becausethealgorithmsonlyrequiretheusertosupplytheraw,orthorectified
Landsatscenes;theuserdoesnotneedtomanipulateadditionalparameters.Wethenpresent
summarystatisticsfromthemodel,explorethemodel’saccuracy,andcomparethemodelto
findingsfromtheMTM2009dataset.Wedemonstrateoneexampleofusingthesedataincom-
binationwithotherdatasets,inthiscasetocorrelateminedareawithcoalproduction.We
explainsomedatasetlimitationsandconcludewithsuggestionsforfutureenhancementsto
ourmodel.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
Materialsandmethods
Studyarea
Wechosea74-county,83,000km2areainCentralAppalachia,composedofcountiesinKen-
tucky,Tennessee,Virginia,andWestVirginia,toconductourmodel(Fig1).Ourmodelulti-
matelyprocessedimagerycoveringtheentiretyofthisstudyarea.Surfacecoalmine
productionhasbeenreportedtotheUnitedStatesMineSafetyandHealthAdministrationin
allofthesecountiesatsomepointsince1983[30],andallofthesecountiesarewithintheCen-
tralAppalachianBasinasdefinedbyUnitedStatesGeologicalSurvey[31].
Analysismodel
WecarriedoutLandsatdataprocessingbywritingJavaScriptprocessingscriptsusingtheGoo-
gleEarthEngine’sapplicationprograminginterface(S1File);thesescriptscleanedeachinput
scene(i.e.,image)fordataabnormalitiesorcloudcover;determinedthenormalizeddifference
vegetationindex(NDVI)perscene;derivedagreenest-pixel(maximumNDVI)compositeper
year;andlabeledeachpixelwithineachcompositeaslikelyactivemineorlikelynon-mine
Fig1.Mapofstudyarea.Thestudyarearangesinlatitudefrom35.6444˚to39.0298˚andinlongitudefrom-79.6179˚to-85.8093˚.Geospatialdatadescribingthe
studyareaandtheultimatemodeloutputsareavailabletodownloadathttps://www.skytruth.org/mtr-data-files/.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
basedonanannual,county-scaleNDVIthresholdthatwascomputedasdescribedbelow.We
morenarrowlydefineactivemineareasasthoselocationswithamodel-indicatedlowNDVI
ascomparedtotheNDVIofnearby,forestedareas.Thissimpleclassificationreliesonthe
starkspectralcontrastinthisAppalachianregionbetweenminesandforests:ourmodelidenti-
fiesareaslargelydevoidofvegetationandcallsthemmines.Ouroutputdatasetisthusaseries
ofannual,30mpixelresolution,binaryimagesdepictinglocationswheremininglikely
occurredthroughoutthegivenyear.
NDVIimagesdepictareaswithhighlevelsofphotosyntheticallyactivevegetation.Calculat-
ingNDVIissimple,requiringonlyaredandnear-infraredreflectancevalueperpixel[32].A
vegetatedpixelwillhaveahighNDVIvalue(near1.0),whereasanon-vegetatedpixelwillhave
alowNDVI(near-1.0).WeexpecttoseehighNDVIvaluesacrosstheAppalachianlandscape,
especiallyinrelativelyundisturbedareas.Conversely,highlydisturbedareassuchassurface
mineswillhavearelativelylowNDVIcomparedagainstavegetatedbackground(Fig2).
WecleanedacollectionofrawLandsatscenesforundesirablepixelslikecloudsorsensor
errors;toexcludeareasofnon-vegetateddisturbanceunlikelytobemines,suchasurbanareas
orroads,wecompiledspatialinformationfrompubliclyaccessibledatasetsandmaskedout
thosepixels.Fromthosecleanedandmaskedimages,wedeterminedeachpixel’smaximum
NDVIperyeartoformaseriesofannual“greenest-pixel”images.Theseimagesshowthe
greatestphotosyntheticactivityatanygivenpixeloverthecourseofthatyear.Thenumberof
cleanedLandsatscenesusedtocreatethesegreenest-pixelcompositesvariedbypixelandby
year,rangingfromasfewasoneimageperpixelperyeartomorethan30,owingtofactorslike
lowimagefrequencyorfrequentcloudcover.BychoosingthemaximumNDVIvalueincreat-
ingthecomposites,minesthatwereestablishedoverthecourseofthatyearwouldlikelynot
appearinthegreenest-pixelimage;agreen,forestedpixelfromearlierthatyearwouldlikely
haveahigherNDVIvaluethantheminepixel,andconsequentlythatforestedpixelwould
appearinthecomposite.
Fig2.ExampleNDVIimageandassociatedtruecolorimage.TheseimagesfromMay2014showtheareanear
Spurlockville,WV,andinparticulartheHobet-21mountaintopcoalmine(38.08˚,-81.95˚).Darkercolorsinthe
NDVIimage(A)indicatelowerNDVIvalues.Truecolorimagery(B)demonstratesthat,inthevisualspectrum,
forestedareasappeargreenwhereasminedareasappeargray.BothimagesarederivedfromLandsat8top-of-
atmospherereflectanceimageryandwereprocessedinEarthEngineforvisualization.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
Foreachmaskedgreenest-pixelcompositeimage,weestablishedcounty-levelNDVI
thresholdsthatallowedustosortremainingpixelsintolikelyminesversusothertypesofsur-
facecovers.TheprocessingscriptsdeterminedthethresholdsbycollectingtheNDVIvaluesof
pixelspercountynotwithinaknownminepermitboundary(i.e.,likelyforestedpixels)and
settingthethresholdatthe0thto3rdpercentilemeanofthosepixels.Inotherwords,weidenti-
fiednearlythelowestNDVIofknownforestedareasandassumedanypixelswithNDVIval-
ueslessthanthatminimumvaluewerelikelymines.Weusedacountyscaletoreflectspatial
differencesinimagequality,aswellasnaturallandscapevariationoverspace.
Wethencleanedtheresultingbinaryimagestoremoveanynullvaluesortoremovevery
smallareaslabeledasmines.Inparticular,ifourmodelidentifiedapixelatyearnasamine
butidentifiedthatsamepixelatbothyears(n-1)and(n+1)asothersurfacecover,wereclassi-
fiedthepixelatyearnasnon-mine.Likewise,wereclassifiedanynon-minepixelatyearnasa
mineifthatpixelwasidentifiedasamineinyears(n-1)and(n+1).Finally,weremovedmine
patcheslessthan9,000m2.
Weassessedtheaccuracyofourdatasetbycomparingmanuallyclassifiedpointstotheclas-
sificationsdeterminedbyourmodel.Peryear,wegatheredtrue-andfalse-colorimageryfrom
LandsatorfromtheUnitedStatesDepartmentofAgriculture’sNationalAgricultureImagery
Program,whenavailable.Weestablished10,250km2,circularplotsrandomlythroughoutthe
studyarea,ensuringthateachstudyplotcontainedsomeactivemining.Wethenrandomly
distributedandvisuallyclassifiedaminimumof2,000pointsperyearandtookasubsetofthat
classificationsothateachplotinthatyearcontainedatleast150non-minepointsand50mine
points,oratotalofapproximately62,000classifiedlocationsover31years.Weusedthesedata
toassessthemodelaccuracyonanannualbasis.
IncorporatingMTM2009data
Sinceourmodelstartedat1985butweknowMTMVFhasoccurredpriortothatdate,we
incorporatedthe1976through1984subsetoftheMTM2009dataintoourdataset.Weselected
onlythosepixelsidentifiedasminesin1976or1984fromtheMTM2009dataandspatially
appendedthemtoour1985through2015cumulativeminingdataset.Wethusgenerateda
“first-mined”datasetthatrevealswhetheracertainareawasfirstconvertedintoasurfacemine
eitherby1976,between1977and1984,orinanyyearfrom1985through2015.Ofnote,many
mineslabeledbyMTM2009as“1976”likelystartedatsomeunknowndatepriorto1976,so
wecannotpreciselysaywhenthoseearliestminesbegan.Welikewisegenerateda“last-mined”
datasetthatsayswhenagivenareawasmostrecentlyanactivemine;however,thisdatasetcan-
notshowifagivenareawasonceminedbutbecamereclaimed(i.e.,ceasedmining)andlater
becameamineagain.Ourresultsbelowarebasedoffthe“first-mined”datasetortheannual
miningdatasetgeneratedinthisstudyalone.
Resultsanddiscussion
Totalminingextent
Between1985and2015,anaverageof87km2ofpreviouslyunminedlandwasconvertedtoa
surfacemineinanygivenyear,withthisannualrateofchangevaryingfromalowof31km2
yr-1in2015to116km2yr-1in1999(Fig3C).Overtime,thisaddsuptoatotalofapproxi-
mately2,900km2(orapproximately3.5percent)ofCentralAppalachiathathasbeenpartof
anactivesurfacecoalmineatsomepointbetween1985through2015(Fig3B).Ratesofboth
newmineareaexpansionandcoalproduction(Fig3C&3D)havedroppedoffprecipitously
since2010.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
Fig3.Activemining,cumulativemining,andcoalproductionovertime.“ActiveMining”(A)meansanylandarea
detectedbyourmodelaslikelymineforthegivenyear;“CumulativeMining”(B)isthenon-duplicativesummationof
activemineareaovertime;thissumincludesmineareasidentifiedfrompre-1976through1984fromtheMTM2009
data(seeabove).“Newly-MinedAreas”(C)isthelandareathatwasfirstconvertedintoamineinthegivenyear.
“SurfaceCoalProduction”(D)dataisfromtheMineSafetyandHealthAdministration[30]ratherthanourmodel;we
presentithereforcomparison.
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Thefullscopeofcumulativeminingfrom1976through2015,incorporatingthe1976and
1984subsetoftheMTM2009dataasdiscussedabove,yieldsatotalof5,900km2ofdetectable
miningoverthe40-yearperiod(Fig3B).Thistotalindicatesthat3,000km2wasfirstminedin
eitherpre-1976or1984.Forcomparison,thecumulativesurfaceminingareafrompre-1976
through2015comprises7.1percentofCentralAppalachia,andis18percentlargerthanthe
landareaofthestateofDelawareandonly3.3percentsmallerthantheareaofEverglades
NationalPark.
Wedemonstratethatcumulativeminingincreasesatanear-linearratefrom1985through
2015,increasingonaverage87km2yr-1(anewmine“conversionrate”frompreviously
unminedtominedland).However,between1984(i.e.,theendoftheMTM2009data)and
1985,thiscumulativetotaljumpsmorethan270km2(Fig3B).Inotherwords,ourresultssug-
gest270km2oflandpreviouslyunminedthrough1984suddenlybecameaminein1985,but
thenthatratedroppedtoanaverageof87km2yr-1after1985.
Thisseemingdiscrepancyislikelyaresultofcombiningourminingdetectionalgorithm
withtheMTM2009data,andnotasingleyeardramaticincreaseinminingrates.Onone
hand,ourstudyareaislargerthanthatoftheMTM2009study.Whilemanyofthelargest
mineswereidentifiedbybothstudies,oursomewhatlargerstudyareacouldhavegiventhe
appearanceofmuchnewmineareain1985byfindingminessimplynotlocatedwithin
MTM2009’sstudyarea.Additionally,weacknowledgethatourautomatedmodelislikely
morelenientindecidingifapixelrepresentsaminethanwasthesupervisedclassification
approachemployedbytheMTM2009study.Forexample,pixelsontheedgeofalargemine
areamayhavebeenlabeledasminesbyourstudybutnon-minesbyMTM2009,leadingtoa
furtherincreaseinourarealtotalby1985.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
Annualactiveminingextent
WhereastheMTM2009studyonlyshowswhereminingoccurredduringsometen-year
period,ourstudyrevealstheyearlyareasthatwereactivelybeingmined.Overtheperiod1985
through2015,wefindanaverageof940km2(greaterthan1.1percent)ofthestudyareaunder
activemininginanygivenyear(Fig3A).Activeminingrangesfrom610km2to1,300km2per
year.Thechangeinactiveminingareaperyearishighlyirregular,rangingfromanadditional
110km2ofminingbetweenoneyearandthenexttoadecreaseof150km2.
Toexplainthisirregularchange,however,wefindamoderatelystrong,positiverelationship
(r=0.63)betweenthechangeinactivemininginanygivenyearandthechangeincumulative
mininginthatyear(Fig4).Thisrelationshipsuggeststhatyearswithmuchactiveminingalso
hadmuchnewlyminedland(i.e.,alargeincreaseinthecumulativearea);andthatyearswith
littleactivemininghadlittlenewlyminedland(i.e.,asmallincreaseinthecumulativearea).In
thisregionofAppalachia,whenminecompaniesputforthheightenedminingeffort,thateffort
wentingeneraltowardminingnewlandsratherthanre-miningoldlands.Moreover,we
regressedthesedatatoshowthatevery1m2oflandunderactiveminingissignificantlyassoci-
atedwith0.22m2ofconversiontonewlyminedarea(p<0.001,r2=0.40).Inotherwords,
approximatelyone-fifthofactiveminelandinanygivenyearrepresentsnewlyconvertedarea.
Accuracyassessment
WefindthatourNDVI-basedmodelaccuratelyandefficientlyrevealsyearlyminingextent
(S1Table).OuraccuracyassessmentyieldedvaluesfortheCohen’sKappacoefficientranging
Fig4.Annualchangeincumulativeminingversuschangeinactivemining.“ActiveMinedArea”isanyareaper
yearwherethemaximumNDVIobservedinthatyearwaslessthantheNDVIthresholdsetpercountyperyear.
“CumulativeArea”isthesummationofuniqueactivemineareaovertime;ifsomelocationwasidentifiedasactive
mineinoneyear,itsareawouldnotbeaddedagaintothecumulativeminingtotalinfutureyears.Each1m2oflandin
activeminingisassociatedwith0.22m2ofnewlyminedarea.
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
from0.62to0.93peryear.Thesepositivevaluessuggestthat,inallcases,ourmodelperforms
62to93percentbetterthanrandomchance.Wealsofindthattheuser’saccuracyofmined
pointsforeachyearisatleast0.83orhigher,indicatingthatatleast83percentofpixelslabeled
asminesactuallyrepresentminesontheground.Ofthe31yearsanalyzed,21yearshavemine
useraccuracyvaluesgreaterthanorequalto0.90.
Comparisontomineproductiondata
Asawaytoassessourmodelresultsandtoexplorerelationshipsbetweenextractionanddis-
turbance,weinvestigatedthedegreetowhichourmineareatotalsarecorrelatedwithknown
coalproductionfromsurfaceminesinCentralAppalachia.Theoretically,thelandimpactsof
anyincreaseordecreaseinproductionfromsurfaceminesshouldshowupinourmineextent
data,butwithalagbetweenproductionandtheassociatedactiveminingarea.Alaglikely
existsbecauseittakesyearsforaclearedareatorevegetate—particularlyanareathathas
undergonethehighintensityofdisturbancecausedbyasurfacecoalmine.Moreover,ina
largeoperationlikeamountaintopminewheremultiplecoalseamsmaybeminedsucces-
sively,anareamaycontinuetobeminedforseveralyearsasminecompaniesblastanddig
throughhundredsoffeetofelevationtoaccessallofthecoalseams.
WeacquiredcoalproductiondataprovidedquarterlytotheMineSafetyandHealth
AdministrationbyallminingcompaniesoperatingintheU.S.[30].Thesedatareporthow
muchcoalwasproducedbydifferentminingtechniquessuchasundergroundminingand
stripmining.Forthisstudy,weonlyusedproductiondatareportedfromCentralAppalachian
"strip"mines,astheothertechniqueshaveverylittlesurfaceimpactpertonofcoalproduced.
Weregressedyearlymineproductionquantitieswithareainactivemining,initiallyfinding
virtuallynomodelfitwhenassumingnotimelag(r2=0.093;p<0.1).Bylaggingactivemine
areaby5years,however,themodelfitdramaticallyimproved(r2=0.68;p<0.0001)(Fig5);
Fig5.Yearlymineproductionversusactiveminearea,withandwithouta5-yeartimelag.Ratesofcoalproduction
fromsurfacecoalminesintheregionarecomparedtothescaleofactiveminingestimatedinthesameyear(red
circles)andfiveyearspreviously(blackcircles).Thereisnorelationshipwhendataareanalyzedfromthesameyear,
buttheamountofcoalproducedfiveyearsearlierexplains68%ofthevarianceinactiveminingarea(regressionfit
showninbluewith95%confidenceintervalingray).
https://doi.org/10.1371/journal.pone.0197758.g005
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MappingtheyearlyextentofsurfacecoalmininginCentralAppalachia
thecorrelationsfor4and6yearsweresimilarinfit,so5yearsrepresentstheaveragetimefor
theimprintofcoalextractiontoremainonthelandscape,asdefinedbyourNDVIthresholds.
Inotherwords,theamountofcoalproducedtodaycanpredicttheamountofareathatwillbe
categorizedas“activemining”5yearsintothefuture.
Usingthis5-yearregressionmodel,wedeterminedthatacrossAppalachiafortheperiod
from1985through2015,foreverymetrictonofcoalproduced,approximately12m2oflandis
activelymined.Thisstatisticdoesnotindicatewhetherthe12m2landdisturbanceisareapre-
viouslyminedornot,butnonethelessindicatestheactivemineareanecessarytoproducea
certainamountofcoal.Forcomparison,apreviousstudyofAppalachiansurfaceminingused
adecadalmineextentdatasettodeterminethata1metrictonproductionofcoalwasassoci-
atedwith0.96m2oflanddisturbance[3].Thatourmodelpointstogreaterthanamagnitude
moreofareaminedpermetrictonislikelyexplainedbyadifferenceinmethodsandstudy
area.Thepriorstudy[3]regressedtotalmineproductionpercountyovera20-yearperiod
againstdisturbedminearea,whereasweregressedyearlymineproductionacrosstheentire
studyareaagainstyearlydisturbedarea.Moreover,thepriorstudy[3]onlyinvestigatedcoun-
tiesinKentuckyandWestVirginia,thetwostateswiththegreatestcoalproductionandmin-
ingarea,whereaswealsoincludedcountiesinVirginiaandTennessee.Inshort,wehavea
finertemporalresolutionbutawiderspatialextent,indicatingtypicallyacrossAppalachiathat
12m2ofactivemineareaisnecessarypermetrictonofcoal.Forthecoal-richstatesofKen-
tuckyandWestVirginia,however,perhapslesslandareaisrequiredforthesamequantityof
coal.
Wealsoinvestigatedthetrendofthisratioofactivemineareapermetrictonofcoalbyyear
(Fig6).Wefindthisratioremainsrelativelylowfrom1985throughapproximately1997,
Fig6.Ratioofactiveminelandpermetrictonofcoalproducedovertime.
https://doi.org/10.1371/journal.pone.0197758.g006
PLOSONE|https://doi.org/10.1371/journal.pone.0197758 July25,2018 10/15