Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random Forests

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ContentslistsavailableatSciVerseScienceDirect

International

Journal

of

Applied

Earth

Observation

and

Geoinformation

j ourna l h o me p a g e :w w w . e l s e v i e r . c o m / l o c a t e / j a g

Mapping

the

distribution

of

the

main

host

for

plague

in

a

complex

landscape

in

Kazakhstan:

An

object-based

approach

using

SPOT-5

XS,

Landsat

7 ETM+,

SRTM

and

multiple

Random

Forests

L.I.

Wilschut

a,b,∗

_,

_E.A.

_Addink

a

_,

_J.A.P.

_Heesterbeek

b

_,

_V.M.

_Dubyanskiy

c,d

_,

_S.A.

_Davis

e

_,

_A.

_Laudisoit

f,g

_,

M. Begon

f

_{, L.A.}

_Burdelov

d

_,

_B.B.

_Atshabar

d

_,

_S.M.

_de

_Jong

a

a_Utrecht_University,_Department_of_Physical_Geography,_{Heidelberglaan}_2,_PO_Box_80115,₃₅₀₈_TC_Utrecht,_The_Netherlands b_Utrecht_University,_Faculty_of_Veterinary_Medicine,_Yalelaan_7,₃₅₈₄_CL_Utrecht,_The_Netherlands

c_Stavropol_Plague_Control_Research_Institute,_Sovetskaya_13-15,_Stavropol_355035,_Russian_Federation

d_Anti-Plague_Institute,_M._{Aikimbayev’s}_Kazakh_Science_Centre_for_Quarantine_and_Zoonotic_Diseases,₁₄_Kapalskaya_Street,_Almaty_050074,_Kazakhstan e_RMIT_University,_School_of_Mathematical_and_Geospatial_Sciences,_Melbourne,_Victoria_3000,_Australia

f_University_of_Liverpool,_Institute_of_Integrative_Biology,_Crown_Street,_Liverpool,_UK

g_University_of_Antwerp,_Department_of_Biology,_{Groenenborgerlaan}_171,_B-2020_Antwerpen,_Belgium

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received17August2012 Accepted30November2012

Keywords:

Object-basedimageanalysis Stratiﬁcation Landscapeepidemiology Vector-bornedisease Zoonosis Yersiniapestis Greatgerbil Desertenvironment

a

b

s

t

r

a

c

t

PlagueisazoonoticinfectiousdiseasepresentingreatgerbilpopulationsinKazakhstan.Infectious dis-easedynamicsareinﬂuencedbythespatialdistributionofthecarriers(hosts)ofthedisease.Thegreat gerbil,themainhostinourstudyarea,livesinburrows,whichcanberecognizedonhighresolution satelliteimagery.Inthisstudy,usingearthobservationdataatvariousspatialscales,wemapthespatial distributionofburrowsinasemi-desertlandscape.

Thestudyareaconsistsofvariouslandscapetypes.Toevaluatewhetheridentiﬁcationofburrowsby classiﬁcationispossibleintheselandscapetypes,thestudyareawassubdividedintoeightlandscape units,onthebasisofLandsat7ETM+derivedTasselledCapGreennessandBrightness,andSRTMderived standarddeviationinelevation.

Inthefield,904burrowsweremapped.Usingtwosegmented2.5mresolutionSPOT-5XSsatellite scenes,referenceobjectsetswerecreated.RandomForestswerebuiltforbothSPOTscenesandusedto classifytheimages.Additionally,astratifiedclassificationwascarriedout,bybuildingseparateRandom Forestsperlandscapeunit.

Burrowsweresuccessfullyclassifiedinalllandscapeunits.Inthe‘steppeonfloodplain’areas, classi-ficationworkedbest:producer’sanduser’saccuracyinthoseareasreached88%and100%,respectively. Inthe‘floodplain’areaswithamoreheterogeneousvegetationcover,classificationworkedleastwell; there,accuracieswere86and58%respectively.Stratifiedclassificationimprovedtheresultsinall land-scapeunitswherecomparisonwaspossible(four),increasingkappacoefficientsby13,10,9and1%, respectively.

Inthisstudy,aninnovativestratiﬁcationmethodusinghigh-andmediumresolutionimagerywas appliedinordertomaphostdistributiononalargespatialscale.Theburrowmapswedevelopedwill helptodetectchangesinthedistributionofgreatgerbilpopulationsand,moreover,serveasaunique empiricaldatasetwhichcanbeusedasinputforepidemiologicalplaguemodels.Thisisanimportant stepinunderstandingthedynamicsofplague.

1. Introduction

Plague,adiseasemainlyspreadbyrodents,wasresponsiblefor thedeathsofnearlyone-thirdoftheEuropeanhumanpopulation

∗Correspondingauthorat:UtrechtUniversity,DepartmentofPhysicalGeography, Heidelberglaan2,POBox80115,3508TCUtrecht,TheNetherlands.

E-mailaddress:[email protected](L.I.Wilschut).

duringa pandemicin theMiddle Ages(Gage andKosoy, 2005; Haenschetal.,2010).PlagueiscausedbythebacteriumYersinia pestisandcaninfectover200mammalspecies(‘hosts’),suchas prairiedogsintheUnitedStates(Collingeetal.,2005),blackrats in Madagascar(Keelingand Gilligan,2000)andgreat gerbilsin Kazakhstan(Davisetal.,2004).Plagueisavector-bornedisease,i.e. thediseaseistransferredfromhosttohostbyavector,ﬂeasinthe caseofplague.Inthelastdecades,plague,alongwithseveralother vectorborne-diseases,hasbeenresurging(Gubler,1998).Itcauses

http://dx.doi.org/10.1016/j.jag.2012.11.007

Open access underCC BY license.

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about2000diagnosedhumancasesannually,ofwhichthe major-ityoccurinAfrica(WorldHealthOrganization,2005).Thedisease isstillconsidereddangerous;itisoneofthreediseasesforwhich notiﬁcationtotheWHOisobligatory(WorldHealthOrganization, 1983).

Muchofourknowledgeofdiseasedynamicsinnatural popula-tionsisgainedfrominvestigationsusingepidemiologicalmodels (Hudson et al., 2001). For example, populationsize or density oftenmustsurpassacertainthresholdin orderfora diseaseto invadeand/orpersist(Lloyd-Smithetal.,2005),andhigher pop-ulationdensitytypically increasesthetransmissionrate(Begon etal.,2002).Moreover,thespatialdistributionofhostsmay deter-mine local host connectivity, which when increased enhances thelikelihoodofsuccessfuldiseasespread(Keeling,1999).When hostdistributions arepatchy,increasing‘patchiness’mayeither increaseordecreasethechancethatanendemicinfectious dis-easewillpersist(Hagenaarsetal.,2004;KeelingandGilligan,2000; JesseandHeesterbeek,2011;Parketal.,2002).Thus,model stud-iessuggestthatthespatialdistributionofhostsinﬂuencesdisease dynamicsindifferentways,butempiricaldataonthespatial dis-tributionofinfectedandsusceptiblehostsonapopulationscaleis scarce.

Thedistribution ofanimals is increasingly mappedby earth observationandremotedevicesonseveralspatialscales.Onsmall scales,GPS-receiversarebeingusedtotracktheanimals.Onalarger scale,remotesensingisused:severalexamplesexistofhost-habitat mapping(Boghetal.,2007;Kallurietal.,2007;Estrada-Pe ˜na,2003). Suchremotesensingbasedmapsprovideusefulinformationon thecharacteristicsofafocus(i.e.anareawherethedisease caus-ingagentanditsassociatedvectorand/orhostarepresent).Asa consequenceofrapidtechnologicaladvancementsinrecentyears, highspatialresolutionimagerynowofferstheopportunitytomap actualanimaldistributions,providedthatafeatureassociatedwith theanimal,likeaburrow,canbeidentiﬁedonsatelliteimages.

Burrowsareconstructedandusedbycertainrodentsfor sleep-ing,nestingand foodstorage.Burrowingrodentsmaydiminish vegetationcoveronandaroundtheirburrowssuchthatitbecomes possibletoseethemonsatelliteimages,asisthecaseforwombats (Vombatusursinus)inAustralia(LöfﬂerandMargules,1980)and greatgerbils(Rhombomysopimus)in CentralAsia(Addinketal., 2010).

Greatgerbils,socialrodentsthatarenumerousin(semi-)deserts in Central Asia, are important hosts of zoonotic infectious dis-eases,such as tularemia, cutaneous leishmaniasisand bubonic plague (Gage and Kosoy, 2005;Yaghoobi-Ershadi and Javadian, 1996;Atshabaretal.,2010).Plagueinthegreatgerbilhasbeen monitoredextensivelyin Kazakhstansince the1940s, although budgetcutssincethe1990shaveledtoaseveredecreaseinplague surveillance (Gage and Kosoy, 2005; Ouagrham-Gormley et al., 2008).Fromthismonitoring,weknowthatplagueisendemicin 20fociinKazakhstan,themajoritybeingsteppeanddesertfoci (Atshabaretal.,2010).Ineach ofthesefoci,plaguemonitoring, i.e.testingofrodentsandfleas forplague,hasbeencarriedout underthesupervision ofAnti-PlagueStations.Thefinestspatial scaleonwhichmonitoringiscarriedoutistheso-calledsector, anarea, defined by a latitude-longitudegrid, of approximately 9.3km×9.8km.

Thefrequentplagueepizootics(epidemicsinanimal popula-tions)donotresultinmassivedie-offsofgreatgerbils,because thegreatgerbilsare–ingeneral–quiteresistanttothedisease (GageandKosoy,2005;BigginsandKosoy,2001).Theoccurrence oftheseepizooticsisrelatedtotheabundanceofbothhostand vec-tor.AhostabundancethresholdwasﬁrstdiscoveredbyDavisetal. (2004)whereabundancewasmeasuredasthepercentageof bur-rowsoccupiedbyfamilygroups.Theplaguethresholdmodelwas laterimprovedbyincludingtheabundanceofﬂeas(Reijniersetal.,

2012).Inbothmodelsburrow(density)mapsformanimportant startingpointforthepredictionofplague.

InapilotstudybyAddinketal.(2010),greatgerbilburrowswere successfullyidentiﬁedinanareaof6km×10kmusingaQuickbird imagewithaspatialresolutionof2.4m.Althoughthisstudyoffered aconvincingproof-of-concept,burrowswereonlymappedinone landscapetype.Asthedistributionofgreatgerbilsisrelatedtothe landscapebyfoodavailability,localclimateandcompetitionwith otherspecies,thechallengeistomapthegreatgerbilabundance acrossdifferentlandscapes.Moreover,theareainvestigatedinthe pilotstudywassmallerthantheareaofsmallestscaleplague mon-itoringbytheAnti-Plaguestations.Therefore,therelationbetween plaguedynamicsontheonehandandhostabundanceandstructure ontheothercannotbestudiedusingthesedata.Mappingthegreat gerbilburrowsoverlargeareas,coveringseveralplaguemonitoring units,andacrossseverallandscapetypes,willoffertheopportunity tostudytherelationbetweenlandscape,greatgerbildistribution andplagueoccurrence.

Thispaperfocusesthereforeonclassifyingburrowsintwoareas, of60km×60kmand60km×85km,coveringlandscapetypesof ﬂuvialandaeolianorigin.Theobjectivesare:

(1) Toidentifygreatgerbilburrowsbysemi-automated classiﬁ-cation onhighresolutionSPOT-5XSimagesacrossdifferent landscapetypesandevaluatetheaccuracy.

(2)Toconstructamapoflandscapeunitsrepresentingthe variabil-ityandspatialstructureofthelandscapeinthisplaguefocus. (3)TostratifytheSPOT-5XSimagesonthebasisoftheselandscape

units,andsubsequentlyclassifyburrowsperlandscapeunit, usinglocaltrainingdata.

Themethodologyusedtomapthespatialdistributionof bur-rowswasasfollows:landscapeunitswerecreatedonthebasisof Landsat7ETM+andSRTMdata,usingobject-basedanalysis($4.1). Then,theSPOTimagesweresegmentedandtheburrowswere clas-sified,basedonunstratifiedandstratifiedRandomForests($4.2). Finally,theaccuracyforbothapproacheswascalculated($4.3). 2. Studyarea

ThestudyareaisaplaguefocuslocatedinEasternKazakhstan, southofLakeBalkhash(Fig.1),composedoffluvialand aeolian deposits.Theareameasuresapproximately250km×200kmand encompassesa delta systemdeveloped by the Ili River.Asthe courseoftheIliRiverhasshiftedovertime fromNNWtoNW, severalabandonedriver branchescanstillberecognizedinthe landscape(Fig.1).Largedunefieldshaveformedalongandontop oftheabandonedfloodplain.Theirrigatedagriculturalareanorth ofthetownBakanaswasmaskedout.

Soilsaresandy,withvariableclayandloworganicmatter con-tent:intheabandonedriverbedsgraveltosandymaterialisfound, whilefurtherfromtheriverbranches,soilsaremoreclay-richor havedunesformedontop.Insomeareassoilsarehighlysaline. Theclimateinthestudyareaisstronglycontinental.Mean tem-peraturesﬂuctuatedramatically,dailyandwithinseasons,ranging between−40◦Cinwinterto+40◦Cinsummer(Suslov,1961). Pre-cipitationislessthan200mmperyearandfallsprimarilyinwinter (assnow)andspring(Suslov,1961;Propastin,2008).Inspringsnow meltsquicklyandformssmalllakes(calledtakirs)intopographic depressions(Laity,2008).Oncedriedup,thesetakirsarerecognized easilybecauseoftheirhighalbedo.Vegetationcoverisvariable,but shows,aswellasarelationtosoilmoisturecontent,adeclining trendinthedirectionofLakeBalkhashandwithdistancefromthe IliRiver,withvegetationgraduallychangingfromlargershrubsand reedgrassthicketstolowershrubsandephemeralgrasses.Close

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Fig.1.ThestudyareainKazakhstansouthofLakeBalkhash(left),showingtheIliriverdelta,thelocationoftheSPOTsatellitescenesandtheresearchsectors(right).Source: Bingmaps,2011.

tolargeabandonedriverbranches,vegetationcoverishigher,most likelyduetosubsurfaceﬂow.Inseveralareas,thesoiliscoveredby alayeroflichenandmoss.Overall,plantdiversityandcoverislow duetotheyearlywaterdeﬁcitandextremetemperatures. 2.1. Greatgerbilsandtheirburrows

Inthissandy(semi-)desertintheBalkhashbasin,thegreat ger-bilistheprimaryreservoirhostforplague(Atshabaretal.,2010). Greatgerbilsarediurnal,mainlyfolivorousrodentsthatarewell adaptedtotheclimate. Theylivein familygroups usually con-sistingofonemale,oneormorefemalesandtheiroffspring,and occupyoneburrowperfamily(Randalletal.,2005).Therodents haveaterritorialnatureandspend90%oftheiractivitywithinthe boundariesoftheirownburrow(Rogovinetal.,2003),although visitstootherburrows dooccur(Rothshild,1978).Becausethe numbersofgerbilsﬂuctuategreatly,bothinter-andintra-annually, burrowsarenotalwaysoccupiedcontinuously:thepercentageof occupiedburrowsisreferredtoas‘occupancy’.Duringwinter, occu-pancyusuallydropsbecausegreatgerbilmortalityincreasesdue totheharshmeteorologicalconditions.Also,becausegreatgerbil movementsarerestricted,nonewburrowsarecreatedduringthis period.

Theburrowsofthegreatgerbil(Fig.2)areomnipresentinthe landscapeandcanbeconsideredasrelativelypermanent,static features (Naumov and Lobachev, 1975).Burrows havemultiple entrances that lead to a network of subterranean tunnels and chambers. Theyvary in size and shape in different landscapes

(NaumovandLobachev,1975).Alongdunes,burrowstendtobe moreelongated;onﬂatsandysubstrates,burrowsarecircular,and inclay-richsoiltheyaremorecompactanddeeper.Deeper bur-rowswillleadtoamorestabletemperatureinsidetheburrowand henceincreasesurvivalofthegerbils.Dependingonthestabilityof thesoil,aburrowcanextendto3mdeep(NaumovandLobachev,

Fig.2.Burrowinalluvialmaterialwithacrustedsoil.Multipleentrances,belonging tothesameburrow,arevisible.Blacksaxaultrees(Haloxylonammodendron),visible inthebackground,formanimportantfoodsourceforthegreatgerbil.

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Fig.3.Flowchartofthesequentialprocessingstepsoftheconstructionofthelandscapeunits(top)andtheburrowclassiﬁcation(bottom).

1975).Allburrowshavean‘ecologicalcentre’,i.e.thelocationof mostintenseactivitywhichcanberecognizedbyahighdensityof entrances.Onthesurfaceofaburrow,noorrelativelylittle veg-etationispresent.Thisleadstoanincreaseinalbedoandhence enablestherecognitionofburrowsonsatelliteimages.

3. Datapreparation 3.1. Fielddata

Dataonburrowandnon-burrowarealocationswerecollected duringﬁeldcampaignsinSeptember2010andApril2011,inseven sectors(Fig.1).Inthesesectors54squaresof200mwerelaidoutas referenceareas.Persector,twoorthreelocationswererandomly selected(asfarasaccessibilityallowed,becauseroadsarescarce), sothatvariability withinthesector wouldbe covered.Ateach location,twoorthreesquareswerelaidout,alwaysaminimum distanceof200mapartfromeachother.

Thesquareswerethensystematicallycheckedforburrowsof thegreatgerbil.Burrows,904intotal,werenumberedandmarked. WithaGPSnavigationdevice,theburrowsweremappedby recor-dingthecoordinatesattheecologicalcentre.Inaddition,burrow diametersweremeasured.

3.2. Satelliteimages

Three pan-sharpened 2.5m resolution orthorectiﬁed SPOT-5 images containing green, red and NIR spectral bands were acquiredforOctober2010(Astrium,2012).Theycovertwoareas of60km×85kmintheeast(SPOTEast)and60km×60kminthe west(SPOTWest)andoverlapthesevenresearchsectors(Fig.1). SPOTWestwasacquiredon14Octoberandthetwoimages com-posingSPOTEastwereacquiredonthe9October.

Landsat7ETM+images(28.5mresolution)from2000and2001 wereusedforthelandscapestratiﬁcation togetherwitha 90m resolutionDigitalElevationModel(DEM)acquiredbytheShuttle RadarTopographyMission(SRTM).BothLandsatandSPOTimages wereconvertedtotop-of-atmospherereﬂectance(Chanderetal., 2009).

4. Methods

AnoverviewoftheprocessingstepsisgiveninFig.3. 4.1. Deﬁninglandscapeunits

In order toconstructlandscape unitsin the Balkhashbasin, firstthemostimportantvariablesthatdeterminethevariability oflandscapesinthisareawerelisted.Thesevariablesaresoiltype, vegetationcoverandlocaltopography.Theselandscapevariables alsodeterminetoalargeextentthephysicalhabitatsuitabilityfor thegreatgerbil.Soiltypeinfluencesthepossibilitiesofquality bur-rowconstruction,asthecohesionofthesoildetermineswhetherit ispossibletoconstructadeepandstableburrow.Vegetationcover reflectsthepresenceoffoodresources.Localtopographyinfluences soildepth,asdepositionalanderosionalprocessesareinfluenced bytopography.Also,alongwithvegetationcover,ithasinfluence ontheabilitytohidefrompredators,likebirdsofprey.A combi-nationofthesethreevariableswillrepresentdifferentlocalgreat gerbilhabitatsandinadditionwilllikelyinfluencetheabilityto detecttheburrowsinsatelliteimages.

Thelandscapeunitsweredefinedinthreesteps(seealsoFig.3): (1)LandscaperepresentativelayerswerecalculatedusingLandsat 7 ETM+ scenesand theSRTM-DEM. Torepresentthe varia-tion in soil types, a soil Brightness layer wasderived from theLandsat imagesusing theTasselled Cap Brightness (TC-Brightness)(CristandCicone,1984).TC-Brightnessisameasure of overall reflectance and makes it possible todifferentiate lightsoilsfromdarkssoils.Thevariationinvegetationcover intheareawasextractedfromtheTasselledCap Greenness (TC-Greenness)(CristandCicone,1984).TC-Greennessdisplays thecontrastbetweenthenear-infraredreflectanceandthe vis-iblereflectanceandisameasureofthepresenceanddensityof greenvegetation.Thelocalvariationintopographywas com-putedusingtheSRTM-DEM,specificallythestandarddeviation oftheSRTM-DEM,calculatedusingawindowsizeof3by3 pixels.ThepixelsofthelocalSRTM-DEMstandarddeviation (DEM-SD)werethenmatchedto28.5mpixelsusingnearest neighbourresampling.

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(2)Multi-resolutionsegmentationwasappliedtothesethree lay-ers (giving each layer an equal weight), using eCognition®

Developer Software (Trimble, 2011). Segmentation is the processofgroupingneighbouringpixelsonthebasisofa homo-geneitycriterion(BlaschkeandStrobl,2001).Thishomogeneity criterion(i.e.thescaleparameter) waschosensuchthatthe objectswerelargeenoughtomeaningfullyrepresenta land-scapetype, butsmallenough toavoid groupingofdifferent landscapetypesintooneobject.Thisresultedinamedianobject sizeof6.9km2_.

(3)These objects were then classified into different landscape units.Classificationoftheobjectswasessential,becauseobjects withasimilarlandscapetypedonotnecessarilybordereach other,whichmeanstheywouldnothavebeengroupedtogether during segmentation.To classifyeach object inside thetwo SPOT areas,itsmeanvaluesofTC-Greenness,TC-Brightness, andDEM-SDwereobtained,toformthreeobjectattribute lay-ers.Themedianvalueofeachlayerwasthenusedtoseparateit intohighandlowclasses(aboveandbelowthemedian).These highandlowclassesforthethreevariableswerethencombined (2×2×2)toformeightlandscapeunitswitheitherahighora lowvalueforTC-Brightness,TC-Greenness,andDEM-SD,which wedenotebyacapitalandlowercaselettersforahighand lowvalues,respectively(B,b,G,gandD,d).Finally,landscape unitsweregivenageomorphologicalandlandscape-ecological interpretationanddescription,usingfieldobservationsand lit-eratureonthearea(Suslov,1961;Laity,2008).

4.2. Burrowclassiﬁcation

4.2.1. Creatingareferenceobjectdataset:combiningﬁeldand satellitedata

First, SPOT images were subjected to an edge-preserving smoothingfilter(NagaoandMatsuyama,1979)inordertoimprove objectstability(Addink,2012).Theywerethensegmentedusing tilingandstitching,tokeepthesegmentationprocessfastand effi-cient.Thethreebands weregiven equalweights andtheshape parameterwassetto0,whichmeansthatpixelsaregroupedbased ontheirreflectancevaluesonly(andnotontheshapeofthe result-ingobject)(BaatzandSchäpe,2000).Segmentationwascarriedout onsuchascalethatobjectsizecoincidedwiththesizeofburrows, resultinginameanobjectsizeof23.8m2_._In_total,_in_the

west-ernand eastern9,659,972 and12,868,017 objectswerecreated respectively.

Theobjectsresultingfromthesegmentation haveassociated attributesthatwerecalculatedusingeCognition®_._These_attributes

– also called independent variables – give information onthe spectral-,texture-,shape-,size-andneighbour-propertiesofthe objects.Forexample,thevariableMeanNIRgivesthemean reﬂec-tionintheNIRbandforanobjectandthevariableNumberofdarker objectsgivesthenumberofdarkerobjectssurroundingthespeciﬁc object.Intotal,67independentvariableswerecalculated,ofwhich 42%consistedofspectral-andtexture-,31%ofneighbour-and27% ofshape-andsizevariables.

Theobjectsandassociatedindependentvariableswereusedto createreferenceobjectsets (onefor eachSPOT scene), contain-ingburrowandnon-burrowareaobjects.Usingthemethodfrom Addinketal.(2010),objectsrepresentingburrowswereselectedin twosteps.First,allobjectswithintheestimatedpositionerrorofa burrowfieldlocation,asgivenbytheGPS,weremarkedas poten-tialburrowobjects.Second,fromthosepotentialburrowobjects, foreachfieldobservation,thebrightestobjectwasselected.This iscalculatedbytakingtheaverageofallpixelvaluesinanobject (so-calledBrightness-nottobeconfusedwiththeTC-Brightness mentionedin$4.1).Non-burrowareaobjectsweredefinedasthe

objectsthatwere notmarkedasa potentialburrowobjectand werelocatedcompletelywithinthe200mresearchsquares.The non-burrowareaobjectsincludethereforearangeoflocal land-scapefeatures,suchasapatchofshrubsorgrass,a(partofa)dune, atakiroraroad.

ThereferenceobjectdatasetforSPOTWestthenconsistedof 315burrowobjectsand787non-burrowareaobjects,andforSPOT Eastthesenumberswere589and2311,respectively.

4.2.2. BuildingRandomForests

RandomForestisarobuststatisticalclassiﬁerthatmakesa pre-dictionofatestsetbasedonobservationsfromatrainingset,using multipledecisiontrees(Breiman,2001).Usingthevalues ofthe independentvariables,eachtreeconsistsofnodeswherethe train-ingsetissplitintohomogenoussubsetsofburrowandnon-burrow areas untilthetree is fullygrown,i.e. until thewholetraining setissubdividedintothoseclasses.TheRandomForestsusedin thisstudywerecreatedwiththerandomForestpackage(Liawand Wiener,2002)implementedintheRstatisticalprogramming envi-ronment(RDevelopmentCoreTeam,2011).

Everytreewasconstructedusinga2/3bootstrap(defaultvalue) of thetraining set.The 1/3bootstrap not usedtocalibrate the tree,theso-called“Out-Of-Bag”(OOB)data,are,byrunningthese samplesthroughtheRandomForest,usedtoderiveanerror esti-mate(“OOB-error”).TheaveragevalueoftheOOB-errorsforallthe treesgivesagoodindicationofhowwellclassescanbeseparated (Rodriguez-Galianoetal.,2012b).

ARandomForestchoosesforeachnodeonatreethebest pre-dictivevariablefromarandomsubset(withthesizeofthesquare rootofthetotalnumberofindependentvariables)ofthe predic-torvariables. Thismeans thatif thereare many variables with littlepredictivepower,theRandomForestmightbesub-optimal. Therefore, it isusefulto selectthemostpredictivevariables in advanceofbuildingtheﬁnalRandomForest(Rodriguez-Galiano etal.,2012a).TheRandomForestclassiﬁeritselfcontainsanovel methodtoestimatevariableimportance(Verikasetal.,2011;Cutler etal.,2007),whichcanbeusedtoexcludetheleastpredictive vari-ables inadvance.ByrunningtheRandomForestiterativelyand excludingtheleastpredictivevariablesateveryrun,themost parsi-monioussolutioncanbefoundthathastheleastvariablesamongst thosewithinthesamplingerrorfromthesolutionwiththeabsolute smallestOOB-error(Diaz-Uriarte,2007).

TheRandomForestsbuiltinthisstudyused10,000trees,which isasufficientamountoftreestoensuretheOOB-errorisstable; forotherRandomForestsettingsthedefaultvalueswereused.All RandomForestswereoptimizedbyremovingtheleastpredictive variablesusingtheVarSelRFRpackage(Diaz-Uriarte,2007). 4.2.3. Unstratifiedclassification

ClassiﬁcationusingRandomForestisdonebyrunningall obser-vations,inthiscaseobjectswithassociatedvariables,alongeach tree,sothateachobjectgetsmultiple,votes(inthiscase10,000). Themajorityofallthevotesdecidewhetherburrowornon-burrow areaisassignedtoaspeciﬁcobject(LiawandWiener,2002).This isthenrepeatedforalltheobjects.

OneRandomForest wasconstructedperSPOTsceneto clas-sifytheburrows.ConstructingoneRandomForestforbothSPOT WestandEastwasnotdesirable,becausetheSPOTimageswere acquiredondifferentdateswithslightlydifferentatmospheric con-ditions.TheRandomForestsweretrainedwithtrainingsetsthat containedequalnumbersofburrowandnon-burrowareaobjects, sinceimbalancedtrainingsetsconsiderablyinﬂuencetheoutcome oftheRandomForestclassiﬁcation(StumpfandKerle,2011).80% oftheburrowswereusedfortraining,20%wasusedforvalidation. Forexample,intheeasternImage470burrows(=80%of587)were selectedbydrawingarandomsample.Then,inthesamemanner,

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Table1

LandscapeunitsintheBalkhashbasinandtheassociatedﬁelddata.Thenamesoftheunitsarebuiltupoutofthreeletters,representingahigh(capitalletter)orlowvalue (lowercaseletter)ofTC-Brightness(B/b),TC-Greenness(G/g)orDEM-SD(D/d).RFdenotesRandomForest.

Landscapeunit Locationﬁelddata Numberofsquares availableforRFanalysis

Numberofburrows availableforRFanalysis

StratiﬁedRF

bgd East 6 124 RFbgd-East

Bgd EastandWest 24 452 RFBgd-WestandRFBgdEast

bGd East 16 172 RFbGdEast

BGd – 0* ₀* _(Only_{unstratiﬁed}_RF)

BgD EastandWest 6 141 RFBgDWestandRFBgDEast

bgD – 0* ₀* _(Only_{unstratiﬁed}_RF)

bGD East 2 15* _(Only_{unstratiﬁed}_RF)

BGD – 0* ₀* _(Only_{unstratiﬁed}_RF)

Total EastandWest 54 904

* _No_research_squares,_or_too_few_burrows_are_located_in_this_landscape_unit,_so_the_RF_built_to_classify_this_area_is_{unstratiﬁed,}_i.e._based_on_all_the_squares_in_the_respective

SPOTscene.

470non-burrowareaobjectswereselected.Togethertheyformed theunstratiﬁedtrainingsetfortheeasternSPOTimage.

4.2.4. Stratifiedclassification:classificationperlandscapeunit Stratificationis theprocessofusing ancillary informationto dividetheinvestigated areaspatiallyinto units(strata),sothat theycanbeanalyzedseparately (Vintrouetal., 2012;Hornstra etal.,2000).Stratificationpriortoclassificationallowsanoptimal classificationprocedureforspecificconditionsindifferentareas. Inourresearch,stratificationwasusedbecauseofthehypothesis thattheabilitytoidentifyburrowswouldbedifferentiallyaffected byrelationshipbetweenaburrowanditssurroundingsindifferent landscapes.Forexample,inthesparselyvegetated,inactive flood-plainsinthestudyarea,burrowsarebetterrecognizablebecause the“background”,i.e.non-burrowarea,isrelativelyhomogenous. Intheareascloserto(abandoned)riverbeds,wherewater avail-abilityleadstomorevariationinvegetationcoverandtype,the burrowsarelessdistinctagainsttheirbackground.

TostratifytheSPOTscenes,theobtainedlandscapeunitsdefine thebordersofthestrata,i.e.insteadofbuildingoneRandom For-estperSPOTscene,RandomForestsarebuiltperlandscapeunit. StratifiedRandomForestscanonlybebuiltwhensufficientfield observationsareavailable,which is thecasefor fourlandscape units(Table1),namelybgd(East),Bgd(WestandEast),bGd(East) andBgD(WestandEast).Foreachofthesesixunitsstratified Ran-domForestswerebuilt(Table1).ThestratifiedRandomForests were–liketheunstratifiedRandomForests–trainedwithbalanced trainingsets,usingburrowsandnon-burrowareaslocatedinthe correspondinglandscapeunit.

4.2.5. ReﬁningtheRandomForestclassiﬁcation

Duetotheterritorialnatureofgreatgerbilfamilies,burrows arealwaysatacertainminimumdistancefromeachother,i.e.they haveaninhibitedpattern.Thisinformationwasusedtoimprove theclassificationfollowingtheprocedureofAddinketal.(2010):if aburrowwasadjacenttootherclassifiedburrowobjectsitwould remainclassifiedasaburrowwhentheBrightnessofadjacent bur-rowobjectswaslower,otherwiseitwouldbereclassifiedintoa non-burrowarea.Thisensuredthatlargeburrowsconsistingoftwo objectswouldonlybecountedasoneburrow.

4.3. Validation

RandomForestswerecreatedusing80%oftheburrowﬁelddata andtheremaining20%wasusedforvalidation.Forvalidationitis importanttohavearepresentativevalidationdataset(Congalton, 1991).The20%validationsetwasconstructedinsuchawaythat theratioofburrowstonon-burrowareasequalledtheratiofound intheﬁeld.

Validationwasdonebycalculatingtheproducer’s,user’sand overallaccuracy(Lillesandetal.,2004)basedoncountsforevery

landscape unit, using the burrowlocations bufferedwith their diameter.AlthoughlandscapeunitsBgdandBgD arepresentin bothSPOTscenes,validationoflandscapeunitsBgdandBgDwas donein thesame manner.The kappacoefficient,a measure of overallaccuracybasedontheclassificationerrormatrix,wasalso calculatedforeachunit(Congalton,1991).Tocomparethe clas-sification accuracies fordifferent landscapeunits, thevalues of theproducer’s,user’sandoverallaccuracy,aswellasthekappa coefficientwerecompared.Toevaluatethedifferencebetweenthe unstratifiedandstratifiedmethod,additionally,significance was testedatthe95%confidencelevelusingtheKappaAnalysisTestof Significance(Congaltonetal.,1983;Congalton,1991).

Moreover,based onthe field data, thedensities of burrows intheresearchsquareswerecalculated,bydividingthenumber ofburrowsinsidetherespectivesquaresbytheareacoveredby thesquares.Then,theratiobetweenthedensitypredictedbythe classificationandthedensityobservedinthefieldwascalculated, togetanestimateoftheaccuracyofthedensityofthecreated burrowmaps.

5. Results

5.1. Landscapeunits

Eight landscape units were distinguished in the Balkhash basinresultingfromthelandscapestratificationusingthelayers TC-Brightness,TC-GreennessandDEM-SD(Table2andFig.4).Four unitsdescribethefloodplaindeposits:steppeoninactive flood-plain(bgd),steppeoninactivefloodplainwithsaltflats(Bgd),active floodplainwithshrubdominatedvegetation(bGd)andfloodplain withdevelopingvegetation(BGd).Theotherfourunitsdescribethe dunes:dynamicdunes(BgD),stabilizedduneswithinactive vege-tation(bgD),stabilizeddunes,withactivevegetationonmoistsoil (bGD)andstabilizedduneswithactivevegetation(BGD).

The steppe on inactive floodplain (bgd) is a unit where a homogenouslow vegetationcoverformedonaveryfine sandy-siltysoil(Fig.5a).Thesteppeoninactivefloodplainwithsaltflats (Bgd)alsohasalowvegetationcoveronmostlysilttoclay-rich soils.Abundant takirs have formedin small depressions inthe landscape(Fig.5b).UnitbGd,theactivefloodplainwithshrub dom-inatedvegetation,coversmostofthecurrentIlideltaandpartof theabandonedIliriverbranch.Inthisunitvegetationcoverismore abundantandconsistsmostlyof2–3mhighshrubs(Fig.5c).The floodplainwithdevelopingvegetation(BGd)isnot prominently presentinthestudyarea.Itconsistsofchlorophyllrichandactive vegetationsuchasreedgrassesformedinsmalldepressionscloseto theIliRiver.Thedynamicdunes(BgD)arelowdunes(<10m)with alowcoverofgrasses(Fig.5d).Thestabilizedduneswithinactive vegetation(bgD)coversonlyasmallareaofthestudyareaandis typifiedbyalowandinactivevegetationcover.Thestabilizeddunes

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Table2

Landscapeunitsintheresearcharea.

Landscapeunit[TC-Brightness(B/b) TC-Greenness(G/g)DEM-SD(D/d)]

Description Areacover(%)on

theSPOTscenes

Fielddata Picture

bgd Steppeoninactiveﬂoodplain 3.4 Yes Fig.5a

Bgd Steppeoninactivefloodplainwithsaltflats 47.5 Yes Fig.5b bGd Activefloodplainwithshrubdominatedvegetation 22.5 Yes Fig.5c BGd Floodplainwithdevelopingvegetation 1.5 No –

BgD Dynamicdunes 11.8 Yes Fig.5d

bgD Stabilizeddunes,withinactivevegetation 0.0 No – bGD Stabilizeddunes,withactivevegetationonmoistsoil 12.7 Yes Fig.5e BGD Stabilizeddunes,withactivevegetation 0.6 No –

withactivevegetationonmoistsoil(bGD)ispresentinthesouth andclosetotheIliRiver.Thesedunesarehighercomparedtothe dynamicdunesandhavevegetationthatconsistsofbothgrasses andlowshrubs(Fig.5e).Thestabilizedduneswithactive vegeta-tion(BGD)iscomparabletothepreviousunit(bGD)butoccursin thenorthwherelesswaterisavailable,whichresultsinahigher valueinTC-Brightness.

Ingeneral,TC-Brightness,ameasureofoverallreﬂectanceinthe Landsatbands,showsanincreasefromsouthtonorth,andislowest inareaswithahighwatercontent,suchasintheactiveﬂoodplain withshrub-dominatedvegetation(bGd).TC-Greennessshowsan oppositepatternanddecreasestowardsLakeBalkhash,whileitis highestintheIliRiverdelta.

AlltheeightlandscapeunitsarepresentintheSPOTsatellite scenes(Table2),althoughthestabilizedduneswithinactive veg-etationunit(bgD)onlycoversanareaofabout4km2_._The_steppe

unitoninactivefloodplainwithsaltflats(Bgd)ismostabundant andrepresents47.5%ofthesurfaceareainSPOTWestandEast. Interestingly,thefloodplainwithdevelopingvegetationunit(BGd) alsoappearsnorthofSPOTEastwhilethereisnotanactiveriver present;thisis presumablybecauseof subsurfaceflow towards LakeBalkhashthroughtheabandonedriverbranch,whichenables moregrowthofvegetation.

Infiveoutofthesevenunitslocatedintheareacoveredbythe SPOTscenes,fieldworkdataareavailable(Table2).Diametersof eachburrowweremeasuredinthefield(Fig.6).Burrowdiameters

Fig.4.LandscapeunitsidentiﬁedintheBalkhashbasinbasedonhighandlowvaluesofTC-Brightness(B/b),TC-Greenness(G/b)andlocalvariationintopography(D/d).The descriptionoftheunitsisgiveninTable2.

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Fig.5.Photographsofthedifferentlandscapeunits.(a)Unitbgd,steppeoninactivefloodplain.Inthefrontaburrowismeasured.Theareaisflat,formedbyfluvialdeposits andhaslittlevegetation.(b)UnitBgd,steppeoninactivefloodplainwithsaltflats.Inthebackgroundalakeformedbythesnowmeltisdisplayed.(c)UnitbGd,active floodplainwithshrubdominatedvegetation.(d)UnitBgD,dynamicdunes(BgD),withanoccupiedburrowinthefront.(e)UnitbGD,stabilizedduneswithactivevegetation onmoistsoil.Herevegetationconsistsofgrassesratherthanshrubs.

Fig.6.Burrowdiametersperlandscapeunit(colourscorrespondwithcoloursin

Fig.4).Thewidthsoftheboxesareproportionaltothesamplesizeineachlandscape unit.Thecoloursoftheasterisks(*)indicatewithwhichlandscapeunitthereisa signiﬁcantdifferenceinthemean(a=0.05).Thecodesofthelandscapeunitscanbe foundinTable2.

rangefromameanof21m(minimum4m)intheclay-richsoilsin theactiveﬂoodplain(bGd)toameanof37m(maximum62m)in thestabilizeddunesinthesoutheast(bGD)(Fig.6).Burrow diam-eterssmallerthan10moftencorrespondtooldand abandoned burrows.Thelargestdiametersarefoundinthedunes,wherethe burrowsareelongatedandsituatedinlinewiththedunes.

ThevarianceofburrowdiametersislargestinunitbGdand dif-ferssignificantlyfromthevariancesintheotherunits(F-testofthe equalityofvariancesusingthenaturallogarithmofthediameters, at_˛=0.05).Thehighdiameter-burrowsinthisunit(seeoutliers inunitbGd,Fig.6)arealmostallfoundintwosquares,whichare locatedontheborderwithunitbGD.Thisexplainstherelatively highvarianceinthisunit.Whenthesesquaresareremovedfrom theanalysis,thevarianceofunitbGdisonlysignificantly differ-entfromthevarianceinunitBgD,butequaltotheotherunits.The varianceinburrowdiametersislowestinthesteppeoninactive floodplainunit(bgd),whichreflectsthelandscapehomogeneityin thisunit.

Themeandiametersoftheburrowdiametersperlandscapeunit differsigniﬁcantlyfromeachother,exceptforbgd-bGd,Bgd-bGd andBgD-bGD(Fig.6),whichwastestedpair-wiseusingt-testswith Bonferroniadjustment,at˛=0.05,onthenaturallogarithmofthe diameters.

Burrowslocatedinﬂuvialdepositsareonaveragesmallerthan burrows in aeolian deposits.Thisresult is not surprising,as in

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Fig.7.Mapsshowingexamplesofclassiﬁcationresultsindifferentlandscapeunits.Theresultsarelocatedin(a)unitbgd;(b)unitBgd;(c)unitbGd;(d)unitBgDand(e) bGD.ThecodescanbefoundinTable2;thelocationofthelandscapeunitsintheareacanbefoundinFig.4.Ofthesquaresdisplayed,bgd,Bgd,bGdandbGDarelocatedin SPOTEast;theBgDsquaredisplayedislocatedinSPOTWest.

clay-richﬂuvialsoilsburrowstendtobemorecompactanddeep, whereasinsandyaeoliansoilsburrowsarelarger,butlessdeep (NaumovandLobachev,1975).

5.2. Unstratiﬁedclassiﬁcation

Inthissectionwedescribetheresultsoftheunstratified clas-sification ofburrows,which we evaluateper landscapeunit. In Fig.7examplesaregivenofclassificationresultsinthefive land-scapeunitswhereburrowreferencedataareavailable.Thesemaps showthatburrowclassificationissuccessfulacrossdifferent land-scapeunits,inbothSPOTimages,althoughwithvaryingaccuracies. For example, in unit bgd (Fig. 7a), 22 of 23 burrows are cor-rectlyidentified.In unit bGd(Fig.7c)it canbeseen thatsmall takirsareoftenmisclassifiedasburrow.Inthedisplayedsquares of the dunes (Fig.7d and e), 22 out of 27 burrows are recog-nized. Burrows locatedin theshadowside ofa dune areoften notrecognizedandespeciallyinbGDthereisanoverestimation ofburrows.

Foreachlandscapeunittheproducer’s,user’sandoverall accu-racieswerecalculated,aswellaskappacoefficientsofagreement andtheaccuracyofthepredictedburrowdensity(Table3). Accu-raciesandkappacoefficientsareingeneralhighbutnonetheless varyconsiderablybetweenlandscapeunits.Classificationof bur-rowsworksextremely wellin thesteppeoninactivefloodplain (bgd),whereproducer’sanduser’saccuraciesreach88%and92% respectively.Classificationworksleastwellintheactivefloodplain withshrubdominatedvegetation(bGd),wheretheuser’saccuracy is only48%. In mostsquaresin this unit burrows are overesti-mated,butthereisaconsiderablevariationinaccuracybetweenthe

squares.Classificationinthedynamicdunes(BgD)works remark-ablywell:producer’sanduser’saccuraciesamount84%and77%. Inthestabilizedduneswithactivevegetationonmoistsoil(bGD) classificationaccuraciesarealsoquitehigh(producer’sanduser’s accuracyof100and60%respectively)andindicateclassification ofburrowsisalsopossibleinthismorecomplexlandscapeunit.It shouldhoweverbenotedthatonlyfewvalidationdatawere avail-ableinthisunit(Table1)andthereforetheproducer’saccuracyfor thisunitislikelytobeoverestimated.

Theratioofpredictedburrowdensitytoobservedburrow den-sityshowshowwellburrowdensitycanbepredicted:valuesrange from0.86to1.75.InareaswithhighTC-Greenness(G)burrowsare overestimated.In unitbGdtheoverestimationislargest.Thisis partlyduetotheoccurrenceofalargenumberoftakirsincertain ﬁeldsquares,astakirsareeasilymisclassiﬁedasburrows.When threesquareswithalargenumberoftakirsareremovedfromthe densitycalculationforthisunit, theratiopredictedtoobserved decreasesto1.5. Forthetotalarea, i.e.includingthedataof all landscapeunits,theratiobetweenpredictedandobservedburrow densityis1.16.

TheunstratifiedRandomForestscreatedtoclassifytheSPOT scenesuseaselectionofthe67independentspectral,shapeand neighbourvariablesincludedintheinitialreferenceobjectsets. BoththeRandomForestalgorithmsusedfortheunstratified classi-fication,i.e.oneforthewesternandonefortheeasternscene,used mostlyneighbourvalues(78%Westand53%East)suchasMean differencetoneighboursandRelativebordertobrighterneighbours. Spectralandtexturalvariablesamounted22%intheWestern Ran-domForestand35%intheEastern.Leastselectedwerevariables indicatingtheshapeofobjects.

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Table3

Classificationaccuraciesoftheunstratified(US)andstratified(S)classificationperlandscapeunit.LandscapeunitsBGd,bgDandBGDdonothavesquaresforvalidationand arethereforeexcludedfromthistable.

Landscapeunit Producer’saccuracy(%) User’saccuracy(%) Overallaccuracy(%) Kappacoefﬁcient (*_signiﬁcant_difference betweenUSandSat ˛=0.05)

Ratiopredictedburrow density:observedburrow density US S US S US S US S US S bgd 88 88 92 100 91 95 0.81 0.89* _0.95_(U) _0.99_(U) Bgd 77 75 81 81 85 87 0.69 0.70* _1.06_(O) _0.99_(U) bGd 86 86 48 58 87 91 0.54 0.64* _1.75_(O) _2.04_(O) BgD 84 93 77 87 86 91 0.72 0.80* _0.94_(U) _0.86_U) bGD 100 – 60 – 86 – 0.66 – 1.58(O) –

Totalarea 82 – 73 – 86 – 0.67 – 1.16(O) –

5.3. Stratiﬁedclassiﬁcation

Forfourlandscapeunits,i.e.unitsbgd,Bgd,bGdandBgD, strati-fiedRandomForestswerecreated,toevaluatewhetherthiswould improveclassificationaccuracies(bgDwasleftoutbecausetoofew fielddatawereavailable).Thekappacoefficientsand their vari-ances(Congaltonetal.,1983;Foody,2004)fortheunstratifiedand stratifiedclassificationwereusedtocomparetheperformanceof theunstratifiedandstratifiedapproach(two-sidedtestofkappa coefficients,at˛=0.05).

Stratified classification results in significantly higher kappa coefficientsinallfourlandscapesunits(Table3).Theimprovement duetostratificationisclearestinunitbgd,wherefouroutoffive metricsimproveandoneremainsthesamewhenstratified classifi-cationisused.ForunitBgDtheimprovementisvisibleinfouroutof fivemetrics;onlythedensityisslightlylesswellpredicted.InbGd however,onlythreemetricsimprovewhenusingstratified clas-sification;theproducer’saccuracyremainssimilar,whiletheratio predictedtoobserveddensityshowsthereisanincreased overesti-mationofburrows.Thisoverestimationoccurredinsquareswitha largenumberoftakirs.Whenthreesquareswithalargenumber oftakirs areremoved fromthecalculation,thepredicted accu-racyimprovesto1.35,i.e.thenstratificationdoesimprovealsothe predictedaccuracy.InBgdstratificationimprovedthekappa coeffi-cientslightly.Thepredicteddensityinthisunitusingthestratified RandomForestishoweveralmostcompletelyaccurate(0.99).

ThestratiﬁedRandomForestseachusedasubsetofvariables thatresultedinthelowestOOB-error. Between3 and27ofthe independent67variableswereselectedforthesixstratiﬁed Ran-domForests(Table5).Ofthe67variables,32variableswerenever selected.Thesevariablesconsistedofeighteen(56%)spectral vari-ables,thirteen(40%)shapeandone(3%)neighbourvariable.

ThemajorityofthevariablesselectedintheRandomForests, likeintheunstratifiedRandomForests,areneighbourvariables; variablesleastselectedareshapeandsizevariables.Mostselected variablesare MeandifferencetoneighbourRed and Relative bor-dertobrighterobjectsNIR(Trimble,2011);theywereselectedby fiveoutofsixstratifiedRandomForests.Intotal25variableswere selectedbytwoormoreRandomForests;in otherwords,there wasaconsiderableoverlapinselectedvariables.Noremarkable dif-ferencesinselectedvariables,orindistributionbetweenspectral, neighbourandshapevariables,werefoundthatcouldberelated directlytocharacteristicsofthespecificlandscapeunit.

Thevariables selectedbythestratifiedRandomForestswere comparedwiththeselectedvariablesintheunstratifiedRandom Forests.ForfiveoutofsixstratifiedRandomForests,fewer vari-ableswereselectedcomparedtotheunstratifiedRandomForests, only Bgd(West) selected two variables more compared tothe unstratifiedWesternRandomForest.Thevariablesselectedbythe unstratifiedandstratifiedRandomForestresembledconsiderably,

forexample,26outof27variablesselectedbythestratified Ran-domForestbgd(East)werealsoselectedbytheunstratifiedEastern RandomForestandall14variablesselectedbystratifiedRandom ForestbGd(East)werealsoselectedbytheunstratifiedEastern RandomForest.InBgD(East)therewasadifference;here,the rel-ativecontributionofshape variablesincreasedcomparedtothe unstratifiedEasternRandomForest.

5.4. Burrowdensitiesinthelandscapeunits

UsingtheresultsfromunstratifiedclassificationforunitsBGd, bgD,bGDandBGDandthestratifiedclassificationforunitsbgd,Bgd, bGdandBgDthefinalburrowmaps(WestandEast)werecreated. Meanburrowdensitieswerecalculatedperlandscapeunit(Fig.8), basedontheentireunitsforthesmallerunits(bgd,BGd,bgDand BGD)andonaminimumareaof96km2_for_the_larger_units_(Bgd,

bGd,BgDandbGD).Themeanburrowdensitiesperlandscapeunit rangefrom2.9burrows/haintheactivefloodplainwithshrub dom-inatedvegetation(bGd)to3.9burrows/hainthesteppeoninactive floodplainunit(bgd).Theseburrowdensityresultscorrespondto therangeofburrowdensityestimatescollectedbytheAnti-Plague stationswhichvaryfrom0.89to4.8burrows/ha(Davisetal.,2008). Onaverage,burrowdensitiesarelowerinthefouraeolianunits thaninthefloodplainunits.Onthefloodplain,areaswithhigh TC-Greenness(G)havelowerburrowdensitiescomparedtoareaswith lowTC-Greenness,whereasinthedunes,theoppositeistrue.

Fig.8.Burrowdensityperlandscapeunit(coloursmatchwiththeunitsonthemap inFig.4),calculatedbasedontheburrowmaps.

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Fig.9. (a)Thelocationofsector10521.(b)Sector10521withitsburrowdensitiescalculatedfor25cells.(c)Zoomofsector10521.Individualmappedburrowsareshown.

Atasmallerscale,burrowdensityismorevariable,as demon-stratedbythevariablenumbersoffieldobservationsofburrows detectedintheresearchsquares:thelowestdensityofburrows wasfoundinthevegetatedfloodplainanddelta(bGd);the mini-mumwas0burrows/ha.Amaximumdensityof6.25burrows/ha wasfoundinaresearchsquarelocatedinthesparselyvegetated floodplain(Bgd).

5.5. Burrowmapsforepidemiologicalmodelling

Anexampleof adensity mapof asectoris shown inFig.9. Thedensitiesareshowninsquaresof∼1.8km×2km(Fig.9b).The meandensityinthissectoris3.7burrows/haandvariesbetween 3.1and4.4burrows/ha.TheAnti-Plaguestationestimatedthe den-sityinthissectorat3.0burrows/ha.Individualmappedburrowsare showninFig.9c.

Theproducer’saccuracywasevaluatedforoccupiedandempty burrowsseparately.Producer’saccuracywasfoundtobe85%for occupiedburrowsand 74% for emptyburrows.In otherwords, occupiedburrowsaremoreeasilyrecognizedthanemptyones. 6. Discussion

6.1. Accuracyoftheburrowmaps

Remotesensingisincreasinglyusedinepidemiologicalresearch oninfectiousandvector-bornediseases(Hay,1997;Atkinsonand Graham,2006;Becketal.,2000;Neerinckxetal.,2008).Commonly, remotelysensedderivedvariablesareusedtomaphabitatsuitable forvectors(Tranetal.,2008;Kallurietal.,2007;HayandLennon, 1999).Inthisstudy,remotesensingwasusednotonlytomap habi-tatsuitableforhosts,buttomaptheexactdistributionofthehost’s residencesovervariouslandscapetypes.

Thegreat gerbilburrowmapswerecreatedbyusing a Ran-domForest classiﬁcationalgorithm onburrowandnon-burrow areaobjectswithassociatedvariablescreatedfromhigh-resolution satelliteimagery.Thisresultedinburrowmapswithmedian pro-ducer’sanduser’saccuraciesof86%(77–100%)and81%(58–100%). Theclassiﬁcationaccuraciesfoundinthisstudyare,comparedto thepilotstudy carried out byAddink et al. (2010), higher and

morebalanced. Producer’sanduser’s accuraciesforthepresent study,calculatedforthestudyareaofAddinketal.,are90%and 95%respectively.Producer’sanduser’saccuraciesfoundbyAddink etal.are 85%and62%,whiletheirtrainingsethad more train-ingdataavailable.Thehigherclassificationaccuracyfoundinthis studyislikelycausedbytwo factors.Firstly,theRandomForest classifierisamoreadvancedandrobustclassificationalgorithm (Rodriguez-Galianoetal.,2012b;Duroetal.,2012)thanthe deci-siontreeclassifierwhichwasusedbyAddinketal.(2010).Secondly, inthisstudy,thenon-burrowareaselectionwasunbiased,which was possiblebecause of the burrowfield samplingin squares, whereasAddinketal.selectednon-burrowareasinthefieldwhich ismorepronetobias.Theclassificationaccuraciesfoundinthe cur-rentstudyarecomparabletootherobject-basedhigh-resolution studiesthatmaprelativelysmalllandscapefeaturesinnatural envi-ronments,suchasthemappingofdownedlogs(Blanchardetal., 2011).

6.2. Validityoftheburrowmaps

The burrow maps are based onSPOT-5 images acquired in October 2010. The temporal validity of the maps is currently unknown, because detailed information on the lifetime and renewalrateofburrowsislacking.Thelifetimeofaburrowdepends onvariousfactors(NaumovandLobachev,1975).Itislikely influ-encedbytheoccupancytimeandonthetimeittakestobecome re-occupiedincasetheburrowisabandoned.AccordingtoNaumov andLobachev(1975),thelifetimeofburrowscanreachhundredsof years,butthismaywelldependonthetypeofsoilinwhichtheyare constructed.Asmentionedpreviously,burrowsinmoreclay-rich materialtendtobemoresolidanddeep,whereastheburrowsin sandysoilsuchasinthedunesaremoreshallowandelongated.As observedinthefield,thetimeofdisappearanceofburrows,i.e.the timethatelapsesfromthemomentaburrowisabandoneduntil theburrowcanbeneitherrecognizednorre-colonized,seemsto beaffectedbylandscapetype,althoughnodetailedfielddataare yetavailable.Asaresultofthelowercohesionofthesoil,thetime ofdisappearanceislikelytobeshorter inthesandysoilsinthe dunes comparedtotheclay-richsoilsinthefloodplain.For the burrowsmapscreated,itmeansthattheperiodofvalidityfora

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mapwillbelongerfortheﬂoodplainareas(landscapeunitbgd, BgdandbGd)thanforareaswithmostlysandymaterial(BgD,bGD andBGD).Thequestionofthelifetimeofburrowscouldbe investi-gatedbymappingtheburrowsusingthemethodpresentedinthis paperinsubsequentyears,andapplychange-detectiontoanalyse thedifferencesanddynamics.

6.3. Classificationperlandscapeunitversusunstratified classification

Inadvanceofclassiﬁcationperlandscapeunit,landscapeunits werecreatedbasedonTC-BrightnessandTC-GreennessofLandsat scenesincombination withthe SRTM-DEMderived local topo-graphicvariability(DEM-SD).Thisresultedinamapwithaclearand realisticspatialpattern,correspondingwithﬁeldobservations.The useofLandsatimagerywithitsspatialresolutionof28.5moffered theopportunitytomapthelandscapeoverlargeareas.Although thespatialresolutionoftheSRTM-DEMislowerthanthe resolu-tionofLandsat,theSRTM-DEMstilladdsusefulinformationforthe landscapemapping.Usinghighresolutionspatial imagery,such asSPOT-5XS,forlandscapemappingwouldnotbeassuccessful becausethesmallpixelsizewouldresultinatoohighvariance tobeabletomaplandscapeunitsaccurately.Thecombinationof highandmediumresolutionimageryforburrowmappingacross landscapesthusoffersaneffectivemethod.

Theclassificationperlandscapeunitresultedinconsiderably higher classification accuracies in three landscape unitsand in slightlyhigheraccuraciesinonelandscapeunit.Generally, strat-ificationin combination witha Random Forest classifier might improveclassificationresultsbecause(1)thevariableselectionis fittedtothespecificarea,(2)theobjecttrainingsetusedtotrain theRandomForestisselectedfromthespecificareaandhencethe thresholdvaluesusedinthetreetoseparateburrowsfrom non-burrowareasaremorelikelytofittheobjectsinthespecificarea.

InthecaseoftheBalkhasharea,burrowdiametersshowedthat signiﬁcantdifferencesperlandscapeunitexistinburrowsize. Non-burrowareasalsoobviouslydifferperlandscapeunit,whichwas shownbythedifferencesinvaluesinTC-Brightness,TC-Greenness andSRTMDEM-SD.

In the three landscape units where stratification improved resultsconsiderably,fewervariables wereselectedcomparedto theunstratifiedclassification.Also,therangeforanoftenselected variable such as Mean difference to neighbours NIR is smaller forstratifiedtrainingsets thanfortheunstratifiedtraining sets (Fig.10), whichmightbeanadditionalexplanationforthe suc-cessofthestratification.InunitBgdstratificationdidnotimprove resultsmuch,butitdidnotdecreaseclassificationaccuracieseither. Apossibleexplanationforthisisthatthevariablesassociatedwith theburrowandnon-burrowareasintheBgdtrainingsetcovera (nearly)similarlylargerangeastheunstratifiedtrainingset(seefor exampleFig.10),whichmeansthatstratificationhaslessinfluence. Theunderlyingreasonforclassificationresultsonlyimproving slightlyinBgdisthusthattheBgdunititselfismore heteroge-neousthantheotherunits.Thiscouldbeimprovedbysplitting thisareaintotwosub-units,forexamplebasedonTC-Brightness. However,stratifiedclassificationmightperformlesswellifthere isalackoffielddataperlandscapeunit.Ontheotherhand,when stratificationcreatesonlyfewlandscapeunits,theunitsmaybetoo heterogeneous,andconsequentlythetrainingsetwillbeless rep-resentative.Generally,forstratificationitisthereforeimportantto findabalancebetweenthehomogeneityofalandscapeunitand theamountoffielddataforclassificationperunit.Toachievethis, landscapestratificationcanbecarriedoutinadvanceofthe cre-ationofasamplingscheme.Afterthis,athoroughlookattheranges ofvariablesmightgiveanindicationofwhetherstratificationwill improveresults.

Fig.10.BoxplotsofMeandifferencetoneighboursNIRineachlandscapeunitfor burrowobjects.Therangediffersperlandscapeunit.

Becauseburrowsvary inappearanceand heterogeneityover differentlandscapes,theuseofmultiplescaleparametersmight furtherimproveclassificationaccuracy,becauseacrosslandscape units,slightly differentheterogeneitythresholdsmightbe opti-mal(Andersetal.,2011).ThiscouldbeofuseforunitbGd,where bothunstratifiedandstratifiedclassificationresultedinrelatively low classification accuracy. In this unit, objects are often large comparedtotheburrowsize,whichmeansthattrainingofthe Ran-domForest,butalsotheclassificationitselfperformslesswell.To improvetheaccuraciesinthisunit,theunitcouldbesplitintofour subunitsbasedonTC-BrightnessandTC-Greenness,thenseparate scaleparameterscouldbeusedincombinationwithmoreintensive groundtruthcollection.

6.4. Burrowdensityinthelandscapeunits

The ratio of predicted and observed densities for the land-scapeunitshasshownthatdensitycanbepredictedmoderately toveryaccurately.In unitswitha highTC-Greenness (bGdand bGD)burrowsaresystematicallyoverestimated,whichcanlikely beattributedtotworeasons.Firstly,thevegetationcoverinunits withahighTC-Greennessisoftenrelativelyheterogeneous,which meansthatareaswithalowervegetationcoverareoftenincorrectly classiﬁedasburrow,becausetheirdifferencetotheirneighbours resemblesthedifferencebetweenburrowsandtheirneighbours. Secondly,intheareaswithhigherTC-Greennessvegetationoften consistsofshrubs, which arenot alwaysremovedby thegreat gerbilsfromthesurfaceofburrows.Theseburrowsthereforedo nothavesuchadistinctsignal.InareaswithlowTC-Greenness (bgd, Bgd and BgD) vegetation cover is much more homoge-neous(compareforexampleFig.7bwithc).Additionally,insome areas,especiallyinunitsbgdandBgd,vegetationalsoconsistsof lichen,butisremovedfromthesurfaceofburrowsandtherefore enhancesthecontrastbetweenburrowsandtheirsurroundings. ThedifferenceinaccuracybetweenunitsbgdandBgdisrelated tothenumberoftakirs,whichleadstoaloweraccuracy.Inunit BgD,thevegetationonthedunes isnotyetwelldevelopedand easilyremovedbythegerbils,whichleadstoverywellrecognizable burrows.

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Meanburrowdensitiesvarybetweenlandscapeunits.The dif-ferencesareprobablyevenhigherinreality,becausethereisvery likely anoverestimation ofburrows in theunitswith high TC-Greennessvalues.Highestburrowdensityisfoundinthesteppe oninactivefloodplainunit(bgd).Apossibleexplanationforthis couldbethesuitablestructureofthesoil.Thelowerburrow den-sityintheBgdfloodplainunitislikelyduetothehigheramount of saltflats, where burrows canhardly beconstructedbecause ofthecrustedsoil.LowerburrowdensitiesinunitsbGdandBGd mightbeexplainedbycompetitionwithotherrodentsifthehigher TC-Greennessimpliesa greatervarietyoffoodtypesandhence opportunitiesforotherspecies.Interestingly,inthedunes,burrow densitiesincreasewithhigherTC-Greenness.Itmightbethatinthe dunes,thelimitingfactorisfoodavailability.

The varying burrow densities may imply that also plague dynamicsaredifferentacrossdifferentlandscapetypes,although itisnotclearyethowburrowdensityexactlyinﬂuencesplague dynamics. This is not clear yet, because the question remains whetherandhowgreatgerbilmovementsarerelatedtoburrow density,i.e.whethergreatgerbilsmove“set”distancesorwillmove totheclosestneighbouringburrow, which isof importancefor thespreadandpersistenceofplague.Also,burrowdensityisnot necessarilylinearlyrelatedwithgreatgerbilabundance,sincealso greatgerbilabundancevariesperburrowandmayalsodifferper landscapeunit.

Atthescaleofthe200mresearchsquares,burrowdensityvaries morethanbetweenthelandscapeunits.Thisraisesthequestionat whatscaleburrowdensitywillvarymostandwhetherthiscanbe relatedtospeciﬁclandscape-ecologicalfactors.Thistopicispartof ongoingresearch.

6.5. Usingthecreatedburrowmapsformonitoringandthestudy ofplague

Thenewclassificationmethodproposedhereisasuitableand helpfultool for the monitoringof themain plague host inthe Balkhash area, for example by detecting whether burrows are presentinacertainareaandwheretherehasbeenanexpansion orreductionofthegreatgerbils’territory.Also,theburrow den-sitymapscouldbecombinedwithfielddataonburrowoccupancy andfleaburden,which havebeenshowntobekey elementsin predictingoutbreaks(Reijniersetal.,2012).Thiswouldallow bet-terpredictionofwhereandwhenoutbreaksofplaguewilloccur ingreatgerbilsthancurrentlyispossible,certainlyifmoreinsight hasbeenobtainedinspatialvariabilityofoccupancy.

Determiningtheburrowoccupancyfromspaceisanambitious nextobjective. Thepresent studyfoundthat occupiedburrows areeasierrecognizedinimagesthanemptyones.Thismatches intuitiveexpectation,sinceemptyburrowswillusuallyhavemore vegetationgrowingonthesurfaceoftheburrows.Futurestudies usingthecurrentburrowmapstomapoccupancy,shouldaccount forthiseffect.

Theburrowmapscanalsobeusedtoindicatetheriskof trans-missionofplaguetohumans,sincethisriskincreaseswhenthe animals’territoryencroachestownsandvillages.Ourmapsshow forexample,thattheburrowsofthegreatgerbilarelocatedwithin 2km ofthemain town(Bakanas).In 2010,plaguewasisolated fromgreatgerbilsinanareaconnectedtoandlessthan5kmaway fromtheBakanasarea,whichconﬁrmsthenecessityformonitoring plagueinthisarea.

7. Conclusions

Thisstudypresentsa newmethodusingobject-basedimage analysis and Random Forests to map burrows across varying

landscapetypes.Classificationaccuraciesarehighwithproducer’s anduser’saccuraciesrangingfrom88%and100%inthesteppeon inactivefloodplainto86%and 58%intheactivefloodplainwith shrub dominated vegetation. The predictedburrow density for thetwoimagesalsoshowsourmethodperformswell:theratio betweenpredictedandobservedburrowdensityforthewholearea is1.16.Ourresultsareunique:asfarasweknow,amapof popula-tionsofhostshasneverbeenmadeonsuchadetailedandextensive spatialscale.Theburrowmapsarevaluableforthemonitoringof plague,becausetheyprovideacompleteoverviewofthespatial dis-tributionofburrowsandallowforthedetectionofburrowscloseto humansettlements.Moreover,themapscancontributeto epidemi-ologicalmodelsandlandscape-ecologicalanalysis.Epidemiological modellingcouldprovidemoreinformationonwhetherhigh den-sityareasprovideahigherrisk,forexampleforplaguepersistence ortransmissiontohumans;theburrowmapsarethenveryusefulto identifythesehighdensityareas.Ourclassificationmethodisalso suitableformappingburrowsofotherrodents,suchasmarmots andprairiedogs,whicharebothhostsofplagueinotherfoci.

Theobject-based stratification methodusingboth high-and medium resolution imagery resulted in improvedclassification accuracies,especiallyinthemorehomogeneousareas.Sucha strat-ifiedmethodcouldwellbeextrapolatedtootherstudiesincomplex landscapes.

Acknowledgements

ThisprojectwaspartoftheWellcomeTrustproject(WT046498) ‘Advancesin thepredictionof plagueoutbreaksinCentral Asia’ andoftheVICIproject918.56.620‘Populationdynamicsand con-trolofinfectiousdiseases:modellinginteractionatdifferentscales’ funded by the Netherlands Research Organisation. The SPOT-5 XSimageswereobtainedthrough theISISprogramme ofCNES and SPOTIMAGE in ISIS projects 395 and 476 ‘Plague Surveil-lance’,CopyrightCNES.Wewouldliketothankallpeopleatthe Anti-PlagueﬁeldstationinBakanas,aswellasthepeoplethat con-tributeddirectlytotheﬁeldworkinSeptember2010andApril 2011:V.S.Ageyev,V.G.Meka-MechenkoandM.Jesse.Wewould liketothankN.WandersforhishelpusingR.Finally,wethankthe reviewersfortheirvaluablefeedback.

AppendixA. Supplementarydata

Supplementarydataassociatedwiththisarticlecanbefound,in theonlineversion,athttp://dx.doi.org/10.1016/j.jag.2012.11.007. ThesedataincludeaGooglemapofthestudyareaandresearch squaresdescribedinthisarticle.

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