Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia

25 Pages·2015·13.92 MB·English

RESEARCHARTICLE
Combining a Spatial Model and Demand
Forecasts to Map Future Surface Coal Mining
in Appalachia
MichaelP.Strager1*,JacquelynM.Strager2,JeffreyS.Evans3,JudyK.Dunscomb4,Brad
J.Kreps4,AaronE.Maxwell5
1 DivisionofResourceManagement,WestVirginiaUniversity,Morgantown,WestVirginia,UnitedStatesof
America,2 NaturalResourceAnalysisCenter,WestVirginiaUniversity,Morgantown,WestVirginia,United
StatesofAmerica,3 TheNatureConservancy,FortCollins,Colorado,andDepartmentofZoologyand
Physiology,UniversityofWyoming,Laramie,Wyoming,UnitedStatesofAmerica,4 TheNature
Conservancy,Charlottesville,Virginia,UnitedStatesofAmerica,5 AldersonBroaddusUniversity,Philippi,
WestVirginia,UnitedStatesofAmerica
* [email protected]
Abstract
OPENACCESS PredictingthelocationsoffuturesurfacecoalmininginAppalachiaischallengingforanum-
berofreasons.Economicandregulatoryfactorsimpactthecoalminingindustryandfore-
Citation:StragerMP,StragerJM,EvansJS,
DunscombJK,KrepsBJ,MaxwellAE(2015) castsoffuturecoalproductiondonotspecificallypredictchangesinlocationoffuturecoal
CombiningaSpatialModelandDemandForecaststo production.Withthepotentialenvironmentalimpactsfromsurfacecoalmining,predictionof
MapFutureSurfaceCoalMininginAppalachia.PLoS
thelocationoffutureactivitywouldbevaluabletodecisionmakers.Thegoalofthisstudy
ONE10(6):e0128813.doi:10.1371/journal.
pone.0128813 wastoprovideamethodforpredictingfuturesurfacecoalminingextentsunderchanging
economicandregulatoryforecaststhroughtheyear2035.Thiswasaccomplishedbyinte-
AcademicEditor:JuanA.Añel,Universidadede
Vigo,SPAIN gratingaspatialmodelwithproductiondemandforecaststopredict(1km2)griddedcellsize
landcoverchange.Combiningthesetwoinputswaspossiblewitharatiowhichlinkedcoal
Received:September19,2014
extractionquantitiestoaunitareaextent.Theresultwasaspatialdistributionofprobabili-
Accepted:April30,2015
tiesallocatedoverforecasteddemandfortheAppalachianregionincludingnorthern,cen-
Published:June19,2015 tral,southern,andeasternIllinoiscoalregions.Theresultscanbeusedtobetterplanfor
Copyright:©2015Strageretal.Thisisanopen landusealterationsandpotentialcumulativeimpacts.
accessarticledistributedunderthetermsofthe
CreativeCommonsAttributionLicense,whichpermits
unrestricteduse,distribution,andreproductioninany
medium,providedtheoriginalauthorandsourceare
credited.
Introduction
DataAvailabilityStatement:Dataareavailable
throughFigshareat:http://dx.doi.org/10.6084/m9. TheAppalachianregionoftheeasternUnitedStatesisanimportantsourceoffossilfuelto
figshare.1411220.
meetenergyneeds.Withintheregion,surfaceproductionofcoalaccountsfortwothirdsof
Funding:MPS,JMSandAEMweresupportedwith totalproduction,whileundergroundminingcontributesaboutonethirdoftotalproduction
fundingfromTheNatureConservancycontract [1].Regionalcoalresourcesincludesteamcoalusedinelectricpowergeneration,and(toaless-
numberCVP_11132012.Thefundershadarolein
erextent)metallurgicalcoalusedinindustrialprocesses.
selectingthestudyarealocationandtechnical
TheoverallfutureofAppalachiancoalresourceextractionisincreasinglyuncertain.Thereis
specificsregardingtheminimalmappingunitusedin
acomplex,dynamicrelationshipbetweenthepriceofcoal,thepriceofcompetingresources(in
thestudy.Allotherdecisionsregardingthemodeling
methodweredecidedbyMPS,JMSandAEM. particularnaturalgas),andpotentialgreenhousegasemissionreductionpolicieswhichreduce
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CompetingInterests:Theauthorshavedeclared thedemandforcoal.Coalissubjecttoincreasedcompetitionfromnaturalgasasasourceofen-
thatnocompetinginterestsexist. ergyforelectricitygeneration,andmaybeequaledorsurpassedbynaturalgasinthenearfuture
dependingonoilandgasprices,greenhousegasrelatedpolicies,coalproductioncosts,and
otherfactors[2].Coalproductionisalsoshiftinggeographicallywithintheregion,asdemand
forcleaner-burning,lowersulfurcoalhasrisenduetoincreasedenvironmentalregulation.
EvenwithcoalpredictedtoplayasmallerandsmallerroleinAmerica’senergymixinthe
future[3],theneedexiststomodelandspatiallypredictwheresurfacecoalminingisanticipat-
edinAppalachiaduetothepotentialenvironmentalimpactsofsurfacecoalmining.Many
studieshavedocumentedtheimpactsofcoalminingonbiodiversity[4,5],hydrology[6–8],
humanhealth[9],andwaterquality[10–12].Theadditiveeffectshavebeenexaminedrelated
tohowmultiplesurfaceminescanimpactstreams[13,14]andhowtheimportanceofspatial
locationandnetworkpositionwithotherpreexistingfactors(othersurfacemines,deepmines,
andresidentialdevelopment)cancontributetoecologicalstress[15–17].Bybetterpredicting
probableareasforsurfacecoalextraction,thepotentialenvironmentalimpactsonsensitive
ecosystemscanbeidentifiedandcontextdependentconservationprioritiescanbesetincom-
plexriversystems[18].
Thisstudyprovidesamethodforpredictingfuturesurfacecoalminingextentsbyintegrating
aspatialmodelwithproductiondemandforecaststobetterrepresentlandcoverchange.By
combiningthesecomponents,amoreholisticpredictioncanbemade.Thishasonlybeenrecent-
lypossibleduetoeffortsthatquantifiedthearealextentofsurfacecoalminingactivitiestocoal
production[19].Thisenabledustocombinevaryingestimatesofsurfacecoalmineproduction
[2]withspatiallyexplicitpredictivemodelingtomappotentialfuturesurfaceminingfootprints
onthelandscapethroughthefuture.Wedemonstratehowtheextentofsurfaceminingcanbe
predictedwiththisapproachandcomparetheresultstoactualrecentlysubmittedpermits.
Toourknowledge,themostcloselyrelatedeffortrelatedtoourworkwasbyWatson[20]
whomappedremainingcoalreserveswithhighmarketpotentialinthePittsburghcoalbed.
Ourapproachadvancestheeffortinthreedistinctways.Thefirstisthatthescaleofourstudy
iswiderinscopethanasinglecoalseam.WeincludedtheentireAppalachianregionwhich
coversnorthern,central,southern,andtheeasternIllinoissubbasins.Becauseofthiswewere
abletocreateaforecastoffuturecoalminingwithaspecificfocusonsurfaceminingthrough-
outtheAppalachianarea.Wealsofocusedonsurfaceminingratherthanvariousunderground
techniques(suchaslongwallunderground,roomandpillar).Forthepast10–15years,surface
mininghasbeenamorecommonpracticeacrossAppalachiaduetoexpansionofmountain
topremovaltechniques[21].Throughouttheregiontheremainingcoalseamsareoftentoo
thinordeeptoundergroundmineandtheunconsolidatedoverlayingrockmakestheroof
weakforundergroundminingtooccursafely[21].Second,ourapproachincorporatesfore-
casteddemandscenariosintothemodelpredictionwhichwerelinkedtoasurfaceareaextrac-
tionratiobyregion.Thesetwodevelopmentsenabledustospatiallyallocatethedemand
acrosstheregionunderdifferentscenarios.Andthird,whilewewouldhavebenefittedfrom
isopachsofcoalbedthicknessforallthecoalseamsthroughoutourregion,manyofthephysi-
calpropertydatasetswecreatedasmodelvariableswerelocallysampledvalueswhichweinter-
polatedwithgeostatisticalmodelingtocreateregionaldatasets.Thisenabledustouselocally
sampledinputdatatocreateinterpolateddatasetstoaddressthemainresearchquestionof
wherefuturesurfacecoalminingwaslikelytooccurthroughouttheregionalextent.
Methods
Anoverviewofthemethodologywhichincludesthespatialdatacollectionandinputdatasets
forthepredictivemodelofthisstudyareprovidedinFig1.
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Fig1.Projectmethodologyflowchart.
doi:10.1371/journal.pone.0128813.g001
Themethodologyforthisstudyincludeddefiningappropriatepredictorvariables,running
aRandomForests[22]spatialmodelandperformingpredictivemappingbyallocatingproduc-
tionforecastsforafuturesurfacedminefootprint.Afundamentalfirststeptothiseffortwasto
selectthoselandscapepredictorvariableswhichcanbeusedtoeffectivelymodelthelocations
offuturesurfacecoalmining.
Predictorvariables
Variablesincludedphysicalpropertiesofthecoalresource(coalgeologytype,sulfurcontent,
ashcontent,andBTUs),andinfrastructurerelatedpredictors(networkdistancetoexisting
coalfiredpowerplants,networkdistancetointermodaltransportationfacilities,networkdis-
tancetoinlandports,distancetorail,humanpopulationdensity).Allvariableswererepre-
sentedasrasterdatamodelswithacellsizeof1km2usingESRIArcGIS10.1software[23],
withanalysisextentlimitedtothecoalgeologyextentwithintheAppalachianLandscapeCon-
servationCooperative(LCC)[24].Fordistancerasters(distancetopowerplants,distanceto
railroadsetc.)distanceswerecalculatedtofeaturesoutsidetheAppalachianLCCpriortolimit-
ingrasterstothestudyareaboundary.AsummarylistofpredictorvariablesisshowninFig2.
Coalgeologytype. Generalizedcoalfieldboundarieswerederivedfromamapofcoal
fieldsoftheUnitedStatesata1:5,000,000scale[25].Generalizedcoalfieldsincludeareaswith
knowncoal-bearinggeology,andwereusedtolimittheextentofpredictedfutureminingprob-
abilitywithinthestudyarea(futureminingwaslimitedtoareaswithinmappedcoalfields).
Withinthiscoalfieldboundary,wealsoobtainedstatelevelgeologicmapsfromdatasets
compiledbyUSGSforU.S.states[26].Thegeneralizedstatelevelgeologicmapswereclassified
intogeologicunitscontainingcoal,andthosewithoutcoal.Finally,thegeologicunits
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Fig2.Summarylistofpredictorvariables.
doi:10.1371/journal.pone.0128813.g002
containingcoalwerefurthercross-referencedinto17differentgeologicalunitsregionwide
basedongeneralizedlithologyandformation.Thecrossreferencingprocesswasnecessarydue
toinconsistenciesandlabelingamongthedifferentstates.Thiswascompletedusingachronos-
tratigraphiccorrelationchart[27].Formationsweregroupedbasedongeologicagetoproduce
17finalmappedcategoriesofsimilarlithologythatarenotimpactedbystateboundaries.
Sulfurpercentageofcoal. Thesulfurcontentofcoalisoneaspectofcoalqualitywhich
wasimportanttocharacterizeasamodelvariable.Restrictionsonsulfurdioxideemissions
frompowerplantshavemadetherelativesulfurcontentofcoalanimportantconsiderationin
theeconomicviabilityofdifferentcoalresources(withlowsulfurcoalgenerallybeingmorede-
sirable).Thepercentageofsulfurcontentinthecoalwasinterpolatedusingboreholedatafrom
theUSGSCoalQualitydatabase[28].Priortointerpolation,boreholedatawerelimitedto
samplestakenatthesurface(undergroundordeepminesampleswereexcluded).Under-
groundandboreholesamples(excluded)wereidentifiedbysampledepthvaluesand/orde-
scriptivetextinthecommentsfieldinthesampledatabase.Surfacesampleswerealso
identifiedbyvaluesinthecommentsfieldindicatingsamplesweretakenatroadcuts,pits,and
stripmines[28].Whiledifferentcoalseamsmaybeencounteredwitheachoftheborehole
sites,anoverallsulfurpercentageisassumedforeachsite.
Theinterpolationprocessforsulfur,ash,andBritishThermalUnit(BTU)followedstan-
dardgeostatisticalkrigingsteps[29].Theyincludedfirstexploringthedatafornormality,ex-
aminingtrendsandthesemivariogram,andtestingmodeloutputrunsuntilasatisfactoryroot
meansquarederrorandmeanstandardizederrorfromthecrossvalidationpredictionerrors
werefound.
Forsulfur,anordinarykrigingmodelwasappliedandanisotropyexaminedtoaccount
fordirectionalinfluences.Thiswasusefulespeciallysincethecoalgeologyfollowsridgeandto-
pographicalfeatures.Atotaloftenlagswereappliedwithasizeof20,000tobestfitthedistri-
butionoftheinputpointlocations.Thesearchneighborhoodwasstandardsizedwitha
maximumof5neighbors.Theresultsforsulfurcrossvalidationindicatedanaccuratepre-
dictedsurfacewitharoot-mean-squarestandardizedpredictionerrorof1.009(avaluecloser
to1.0ispreferred[29]).
Ashcontentofcoal(ashyield). Ashcontentofcoalisalsorelatedtorelativecoalquality.
Ashcontentisrelatedtotheportionofcoalthatremainsaftercombustion.Ashyieldwasalso
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obtainedfromtheUSGSCoalQualitydatabase[28]andwasalsointerpolatedusingmethods
similartothoseusedforsulfurcontent.
Forash,againordinarykrigingwasappliedwithanisotropyexaminedforthedirectionalin-
fluenceswhichindicatedanimprovedfitwithanangleof44.6and45tolerance.Thelagsused
weredifferentforash–12totalwithalagsizeof12,000.Thesearchneighborhoodwasstan-
dardsizedwithamaximumof5neighborsaswithsulfur.Theresultsforsulfurcrossvalidation
indicatedanaccuratepredictedsurfacewitharoot-mean-squarestandardizedpredictionerror
of1.001.
BTUcontent. BTUcontentofcoalisrelatedtotheamountofenergyprovidedbyagiven
amountofcoal.BTUcontentofcoalperlb.wasderivedfromtheUSGSCoalQualitydatabase
[28]usingmethodssimilartoashandsulfurcontent.
FortheBTUinterpolation,asimplekrigingmodelwasappliedwithalogscoretransforma-
tiontomakethevariancesmoreconstantthroughoutthestudyareaandbringthedatacloser
tobeingnormallydistributed.Anisotropywasappliedtoaccountfordirectioninthesemivar-
iogramandcovariance.Thepreferredanglewas32witha21.4degreetolerance.Twelvelags
withasizeof15,000wasfoundtofitthemodelbestwiththeaverageddatapoints.Againhere,
thestandardneighborhoodsearchwasusedwithamaximumof5neighbors.ThefitforBTU
wasnotaswellasashandsulfurwitharoot-mean-squarestandardizederrorof0.887.
Distancetocoalfiredpowerplants. Existingcoalfiredpowerplantswereidentifiedusing
informationpublishedbytheU.S.EnergyInformationAdministration,basedonformEIA-
860AnnualElectricGeneratorReport[30].Thelocationsweredeterminedusinglatitude/lon-
gitudecoordinatesprovidedbySourceWatch[31]andshapefilesprovidedbyEnergyInforma-
tionAdministration[32].Weidentifiedatotalof318existingpowerplantsasof2011.We
thenremovedatotalof92oftheseplantsthatarescheduledforclosurebetween2013and
2020[31].Anadditional25newcoalfiredfacilities(includingpowerplants,cogenerationfacil-
ities,coaltoliquidsplants)wereaddedtothefinaldatasetthatareproposed,planned,inper-
mitting,orunderconstructionforthisareaasnotedbySourceWatch[31],theSierraClub
[33],andNationalEnergyTechnologyLaboratory[34].Forourfinalpredictorvariable,wecal-
culateddistancealongahighwaynetwork[35]to251coalfiredpowerplantfacilities(226ex-
isting,25new).Distancealongthehighwaynetworkwasinitiallycalculatedalong1km2cells
alongtheactualhighways,andwasthenextrapolatedouttocoverallcellswithintheAppala-
chianLCCusinganinversedistanceweightedinterpolator.
Distancetointermodaltransportationfacilities. Intermodaltransportationfacilitiesare
locationswherefreightmaybetransferredbetweendifferentmodesoftransportation(i.e.
trucktobarge,trucktorail,etc.).Intermodalfacilitypointlocationswereobtainedfromthe
NationalTransportationAtlasDatabase,andwerethenlimitedtoallfacilitiesexceptports,
whichweremappedseparately[36].Distancetointermodalfacilitieswasmappedalongthe
highwaynetwork,thenextrapolatedouttoallcellswithintheAppalachianLCC.
Distancetoinlandports. InlandriverportswerealsoobtainedfromtheNationalTrans-
portationAtlasDatabase[36]andwerelimitedtothoseportshandlingcoalandcoalrelated
commodities.Distancetoportswasmappedalongthehighwaynetwork,thenextrapolatedout
toallcellswithintheAppalachianLCC.
Distancetorail. AccordingtoU.S.EnergyInformationAdministrationdomesticcoaldis-
tributionstatistics,56%ofcoalproducedbythetencoal-producingstatesinthestudyareawas
distributedusingrailin2011[32].Inaddition,atotalof29%ofcoaldistributeddomestically
wasmovedbyriver(barges),withatotalof13%wastransportedbytruck.Thisimpliesthat
proximitytorail,river,andtruckingrelatedloadingfacilitiesmaybeanassetinlocationofpo-
tentialminingactivity.Miningrelatedfacilities(forloadingcoalontorailcars)arenotneces-
sarilylimitedtolocationsatendpointsofraillines.Mineloadingfacilitiescanalsobefoundat
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anypointalongraillines,notjustattheendpointsoratspurs.Mappingdistancetoexisting
railscapturesmorepotentiallocationsforaccesstoraillinesfromcoalminingpermitloca-
tions,ratherthanlimitingtherailfeaturedatasettoendpointsonlyofexistingrailroads.
LocationsofrailroadswereacquiredfromtheBureauofTransportationStatisticsU.S.Na-
tionalTransportationAtlasrailroadslayer,atthe1:100,000mapscale[37].Distancetonearest
raillinewasmappedasEuclideanstraightlinedistanceacrosstheAppalachianLCC(notlimit-
edtodistancealongnetwork).
Populationdensity. Populationdensitywascalculatedacrossthestudyareausing2010
Censusblockgroupdata,andwasthenconvertedtorasterformat,1km2cellsize[23].
Otherdataconsiderations. Economicfactorsareimportanttoconsiderforfuturemining
sincedevelopmentdecisionshavecostsultimatelybuiltintothedecisionprocess.Mostofthe
economicvariablesforminingarerelatedtothedepositgeometrystrippingratio,size,shape,
anddepthofstrikeofdeposit,rockconditions,productivitiesandmachinerycapacitiesaswell
assomeofthemorecommoneconomiccostsrelatedtocapitalrequirementsandoperating
costs,discountrate,investments,amortization,depreciation,recoveriesandrevenues,labor
forceavailability,andenvironmentalregulations[38].Otherfactorsconsideredtobeimpor-
tantfornewsurfaceminingactivityincludedpastandexistingmining,strippingratios(over-
burden,coalbedthickness),coalreservesremaining,surfaceownershippatterns,andcoal
qualityasrelatedtomarketdemand.Eachofthesefactorswerespecificallymentionedbyinter-
nalreviewersinvariousstagesofthisproject,andwerealsomentionedintheEnvironmental
ImpactStatementformountaintopremovalminingintheAppalachianregion[39].Ultimate-
ly,thesefactorswerenotincluded(directly)inthefinalmodelingprocess,afterinvestigationof
availabledatasetsanddataquality.Locationandextentofpastminingwerenotuniformly
availablefortheentirestudyarea,asminingdatasetsfromindividualstatesvariedgreatlyin
quality.Datarelatedtostrippingratios(overburden,seamthickness)wereavailableforsome
coalseams[40]andstatesinthestudyregion(Illinois[41];Indiana[42];WestVirginia[43];
Virginia[44]butnotothers.Remainingcoalreservesareavailableonacounty-by-countybasis
forsomestates(see[45]forexample)oronaregionallevelfromtheU.S.EnergyInformation
Administration,butreservedataarenotconsistentlypublishedatadetailedenoughspatial
scalefortheregioninordertobeincludedintheproject.Thefocusofourmodelingonsurface
miningactivityonly(ratherthansurfaceandundergroundcombined)alsoplacedmoreimpor-
tanceonoverburdencoalamountsaswellasaccessibilityfromthesurface.
Forsurfacelandownershippatterns,ithasbeensuggestedthatthedifferingnatureofland
ownershipamongstatesmayberelatedtosurfacemining–specificallythatsurfaceminesof
easternKentuckyarecharacterizedbysmallerlandowners,whilesurfaceminesinneighboring
southwesternWestVirginiaaremorelikelytobeownedbylargercorporatelandowners[39].
Basedonaquickcrossreferencewithexistingpermitdata,wedidnotfindthistoexistasthe
averagepermitsizeinKentuckywaslargerthantheaveragepermitsizeforWestVirginia.In
anycase,landownershipdataforsuchalargestudyregionisnearlyimpossibletoassemble,
particularlyinlightoftherelativelycoarsespatialscaleofthiswork(1km2cellsize).Wealso
didnothaveaccesstoadequatemineralrightsdatafortheentirestudyregion,anotherimpor-
tantconsideration.Whilethesedatalimitationsprecludeourabilitytomakethesamelocalde-
cisionsacoalcompanywouldmakeforasite,thegoalofthisprojectwastofocusonbroader
regionalpredictionsandforecasting.
Activesurfaceminepermitlocations
Thepreviouslylistedindependentvariableswereanalyzedwiththedependentvariableofloca-
tionofactivesurfaceminepermits.Thecentroidsofeachpermitwerecalculatedforthemodel
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runs.Surfaceminingpermitlocationswereobtainedfromindividualstateagenciesfortheten
coal-producingstateswithinthestudyarea.Miningpermitswerefurtherlimitedtoactivesur-
faceminingpermitsonlybyexcludingundergroundminesandpermitsassociatedwithinac-
tiveorhistoricalmines.Incertainstates,ifpermitstatus(active/inactive)wasnotindicated,
permitswerelimitedtothosewithdatesfromtheyear2000tothepresentonly,inanattempt
tolimitanalysistocurrent,activemines.
Exclusionareas
Inadditiontotheabovementionedpredictorvariablesandsurfaceminepermitlocationswe
alsointegratedspatialdatasetsas“exclusions”orareaswheresurfacecoalminingcouldnot
occur.Areasexcludedfromourpredictivemodelingoffuturesurfaceminingincludeperma-
nentconservationlandsandareaswithexistinglandusesthatarenotconducivetominingac-
tivities(urbananddevelopedlands,water)basedonthe2006NationalLandCoverDataset
[46].Forpurposesofthiswork,weconsideredpermanentconservationlandstobe(inmost
cases)landscompiledintheConservationBiologyInstitute’sProtectedAreasDatabase[47]
withGapAnalysisstatus1or2.ConservationlandswithGapAnalysisstatus1and2[48]gen-
erallyindicateareaswithpermanentprotectionfromlanduseconversionand/ormanagement
plansdesignedtolimitdisturbanceandmayincludenationalparks,nationalwildliferefuges,
stateparksandpreserves,andU.S.ForestServicewildernessareas(amongothers),although
furtherassessmentofoutstandingmineralleasesonthesetractsmayresultintheirre-inclusion
intheareawhereminingmayoccur.Inall,57,185km2throughoutthestudyarea(9.6%)was
excludedduetolanduserestrictions,while14,366km2ofthestudyareawasexcludeddueto
presenceofconservationlands(2.4%).
Areaswerealsoidentifiedthatcontainedanextensiverecenthistoryofsurfacemining,as
weareassumingtheseareastobe“minedout”,meaningtheywillnotbesurfaceminedagain
inthefuture.Minedoutareaswereidentifiedascellswithincurrentactivesurfacemineper-
mitsthatwereclassifiedasBarrenlandcoverinthe2006NationalLandCoverDataset[46].
Thismethodensuredwewerecapturinglargecontiguousareasofprevioussurfacemining,
andnotnewlyopenedmines(sincewewereusing2006landcover).Byusingthismethod,we
excludedminingonatotalof567km2,or12%oftheareacontainedwithinactivesurfacemine
permits.
Predictivecoalmodel
Weusedthenon-parametricmodel,RandomForests[22],toestimatesurfacecoaldevelop-
mentprobability,foreachofthe1km2cells,withhigherprobabilitiesindicatingagreaterlike-
lihoodoffuturemining.TheRandomForestsalgorithmoffersmanyadvantagesinthatitdoes
notadheretoparametricassumptions,canutilizemixeddatatypewithdifferentscale,handles
highdimensionaldata,isrobusttooutliersandnoise,isnotsensitivetoautocorrelation,quan-
tifiesimportanceofthepredictorvariablesandrequiresminimalparametrization[49–52].
TheRandomForestsmodelisaweak-learnerensembleapproach,whereaseriesofuncon-
strainedClassificationandRegressionTrees(CART)arecreatedusingabootstrapsamplewith
replacement.TheCART’sareconstructedusinganentropynodesplittingstatisticthatrecur-
sivelypartitionsthedataintomorehomogeneoussubsetsandresultsinahierarchicalclassifi-
cationthataccountsfor1stand2ndorderstatisticalvariation.Theout-of-bag(OOB)data
withheldineachbootstrapsampleisusedtoassessfit,ateachmodeliteration,andresultsin
convergentfitstatisticswithoutrequiringdatabewithheldforvalidation.Theindependent
variablesarerandomlypermutatedthroughthenodesandmeandecreasesinaccuracythus
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accumulateprovidinganimportancemeasureofeachvariable.Thepluralityofvotesacrossthe
ensembleconvergesontheoptimalfittothedataandprovidesarobustestimate[49].
Tospecifyabinominalresponsevariable(y)where;presence(surfacemininghasoccurred
duringthepermitperiod)andabsence(nosurfacemininghasoccurredduringthepermitperi-
od),weutilizedsurfaceminepermitcentroids,onlyusingobservationspermittedaftertheyear
2000.Toensurethatstatisticalandspatialvariabilitywasrepresentedwithoutintroducinga
zero-inflationissue[49],wecreatedfivesetsofpseudo-absencedatabycreatingrandompoints
andthenremovingobservationsoccurringwithinacurrent-permitor0.5milesofasurface
minecentroid.Foreachtrainingsubset,weusedanequalnumberofpresence(n=5,165)and
absence(n=5,165)observations,withthesamepresencedatausedineachsubset.Theinde-
pendentvariableswereappendedtothepoints,fromthecorrespondingrastercell(s),usingthe
softwaretoolGeospatialModelingEnvironment[51].
UsingthecompliedtrainingdatawespecifiedfiveRandomForestsmodels,representing
eachrandomsubset,usingtheRandomForests[53]packageinR[54].Wetestedmodelsbyre-
movinglow-performingparametersandobservedadecreaseinmodelperformanceascom-
paredtothefullmodel.Modelerrorconvergedinfewerthan1,000bootstrapreplicates
however,sincevariableinteractionsstabilizeataslowerratethanerror,wefixedthenumberof
bootstrapreplicatedatn=1,000.BecauseRandomForestsisanensembleapproach,aslongas
theparameterspaceremainsfixed,independentmodelscanbecombinedintoasingleensem-
ble-model[52].Usingonlyconsistentlyselectedparametersinthemodelselection,wefitfinal
modelsforeachrandom-subsetandcombinedthemintoafinalensemble-model.Modelsig-
nificancewasevaluatedusingapermutated(n=999)randomizationprocedureandanitera-
tive10%withholdcross-validationusingtherfUtiltiesRpackage[55].Theprobabilityofthe
presenceclass{1}waspredicted,usingthescaledposteriordistributionofthevoteplurality
[50],withtheRrasterpackage[54].Theestimatedextentwaslimitedtotheknownextentof
coalintheregion.
PredictiveMapping:FutureSurfaceMiningFootprint
Inordertomapfuturepotentialsurfaceminingactivitiesonalandscapescale,weusedresults
fromtheprobabilisticRandomForestsmodelingofsurfaceminepotentialalongwithregional-
levelestimatesoffuturecoalminingproductionfortheyears2012through2035.
RegionalcoalproductionestimatesforthefourEIAcoalsupplyregions(northern,central
andsouthernAppalachians,easterninterior/Illinois)(Fig3)wereobtainedusingvariouscoal
productionscenariosfromtheEIA’sAnnualEnergyOutlook[2].Valueswereobtainedfortwo
differentEIAeconomic/coalproductionscenariosforcomparison:alowcoalproductionsce-
narioandahighcoalproductionscenario.Thelowcoalproductionscenario(“GHG25+low
gas”)predictsthelowestfuturecoalproductionofanyofEIA’s28totalscenarios,duetovery
restrictivegreenhousegasemissionspoliciesandlowpricesforcompetingresourcesofoiland
gas.Thehighcoalproductionscenario(“lowcoalcost”)predictsthehighestcoalproduction
duetolowercostsforcoalminingwages,transportation,andmineequipment(leadingtoin-
creasedcoalproduction).
EIAcoalproductionestimatesprovidetotalproductionestimatesonly(surfaceandunder-
groundcombined).Welimitedfutureproductionprojectionstosurfaceprojectionsonlyby
multiplyingeachproductiontotalbythepercentagesurfaceaccordingtothefollowingregional
figuresbasedon2010–2011productiondataintheAnnualEnergyOutlook:northernAppala-
chians:20.08%surface,centralAppalachians:48.68%surface,southernAppalachians:40.06%
surface,easterninterior/Illinois:30.72%surface.Surfaceminingproductionestimatesfromthe
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PredictingFutureSurfaceCoalMining
Fig3.U.S.EnergyInformationAdministration(EIA)coalsupplyregionswithintheAppalachianLandscapeConservationCooperativeboundary
usedinthisproject.CountiescontainedwithinEIAcoalsupplyregions(Northern,CentralandSouthernAppalachian,EasternInterior/Illinois)were
PLOSONE|DOI:10.1371/journal.pone.0128813 June19,2015 9/25
PredictingFutureSurfaceCoalMining
determinedfromtheEIAAnnualEnergyOutlook[2].Theareamodeledwasfurtherlimitedtotheintersectionofthecoalsupplyregionswithgeneralizedcoal
fieldboundariesfortheUnitedStates,obtainedfromtheU.S.GeologicalSurvey[25].Thisfigureshowstheintersectionofthecoalsupplyregionswithactual
coalfieldboundaries.TheboundaryoftheAppalachianLandscapeConservationCooperativeisshownasathinblueline,obtainedfromtheU.S.Fishand
WildlifeService.
doi:10.1371/journal.pone.0128813.g003
year2012throughtheyear2035werethensummedtoproduceatotalcumulativesurfacecoal
productionvalueforeachregion.
Inordertoestimatesurfaceareaimpactedbycoalminingactivities,anumericrelationship
wasrequiredbetweensurfacemineproductionamountsandacorrespondingareadisturbed.It
wasinitiallyproposedtousecurrentactivesurfaceminepermitdataalongwithrecentproduc-
tionstatisticsinordertoderiveaproductiontoarearatio.However,singleminesmayproduce
coalforextendedperiodsoftime,andthismethodwouldnotadequatelycapturetheentirelife
cycleofamine.Inaddition,mappedminepermitpolygonsmayincludeareasthatarenotac-
tuallydisturbedduringsurfacemining,sotheactualdisturbedareamaybemuchsmallerthan
mappedpermitarea.Arecentstudyconcludedthatmappedminepermitsdonotofferanac-
curatewaytoestimateareadisturbedbysurfacemining,basedoncurrentpermitdatabaseand
mappingmethodsusedinWVandKY[56].Instead,Lutzetal.[19]developedaregression
modeltoestimatetonsofcoalproducedperunitarealdisturbancefor47countiesinsouthern
WVandeasternKY.Themodelwasbasedontotalareaofsurfaceminingdisturbancefrom
1985–2005(at5yeartimeintervals),comparedwithsurfacecoalproductionstatisticsforcor-
respondingtimeperiods.Lutzetal.[19]estimatedthat1tonofcoalequatesto0.87m2ofsur-
facedisturbance.Forthecurrentstudy,thisfigurewasconvertedto1.15milliontonsofcoal
producedpersquarekilometerofsurfacelanddisturbance.
Futuresurfaceminingscenariosanalyzedincludedlowcoalproductionandhighcoalpro-
ductionmodels[2]fortheyears2012–2035.Foreachscenario,wecreatedanewmaplayer
showingpotentiallocationsforfuturesurfaceminingactivitiesonacell-by-cellbasisusinga
1km2gridforthestudyarea.Usingthefigureof1,150,000shorttonsperkm2,weallocatedfu-
tureminingproductiononacell-by-cellbasiswithineachEIAregionfirsttothosecellswith
thehighestfutureminingprobability,thencontinuingtocellswithlowerfutureminingproba-
bility,untilthetotalamountoffutureproductionforaparticularscenarioandregionwasallo-
cated.Priortoallocation,adjacentcellswithidenticalminingprobabilityvaluesweregrouped
togethertoensurethatcontiguousareasofhighminingprobabilitywerepreservedinthere-
sults(ratherthanassigning“new”miningtosinglecells).Cellscontainingurbanorbuiltup
land,water,conservationlands,andcentroidsofexistingminingpermitswereexcluded
(maskedout)priortobuild-outanalysisasdescribedearlier.
Results
RandomForestsModel(ProbabilityofFutureSurfaceCoalMining)
ThefinalRandomForestsmodelscenarioincludedtheoriginal9predictorvariablesofFig2.
Weexperimentedbyremovinglow-performingvariablesfromthemodelbasedonvariable
contributiontotheoverallresult.However,alternativemodelswithfewervariablesdidnotper-
formaswellasthefullmodel,producinghigherclassificationerrorrates.Modelsignificance
wastestedvs.randomlygeneratedmodelsandwasfoundtobesignificantp=0.01.
ThefinaloutputoftheRandomForestsmodelisapixelbasedprobabilityoffuturesurface
miningpresence(Fig4).Asestimatedbytheout-of-bagmeandecreaseinaccuracy,thecoalge-
ologytypeandthesulfurcontentwerefoundtobethemostimportantpredictorvariablesin
themodel,thoughallvariablescontributed(Fig5).Foreachtrainingdataset,theout-of-bag
errorestimatewasaround15%andthemisclassificationofpresenceandabsencepointswere
PLOSONE|DOI:10.1371/journal.pone.0128813 June19,2015 10/25