environments a before–after with control-impact statisticaldesign Remote monitoring sensing of land restoration interventions insemi-arid International Journal of Applied Earth Observation andGeoinformation

ContentslistsavailableatScienceDirect

International Journal of Applied Earth Observation and Geoinformation

j ou rn a l h o m epa 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

Remote sensing monitoring of land restoration interventions in

semi-arid environments with a before–after control-impact statistical design

Michele Meroni

, Anne Schucknecht

, Dominique Fasbender

, Felix Rembold

, Francesco Fava

, Margaux Mauclaire

, Deborah Goffner

, Luisa M. Di Lucchio

, Ugo Leonardi

aEuropeanCommission,JointResearchCentre,DirectorateD–SustainableResources,FoodSecurityUnit,ViaFermi2749,21027Ispra,VA,Italy

bInternationalLivestockResearchInstitute,P.O.Box30709,00100Nairobi,Kenya

cUniversityofBordeaux3,LabexDRIIHMandLesAfriquesdanslemonde(LAM),IEPdeBordeaux,alléeAusone11,Domaineuniversitaire,33607Pessac Cedex,France

dFrenchNationalCentreforScientificResearch,CNRS/UMIn^◦3189–Environment,HealthandSocieties,BdPierreDramard51,13344MarseilleCedex15, France

eUniversityofCopenhagen,DepartmentofGeosciencesandNaturalResourceManagement,Rolighedsvej23,1958Frederiksberg,Denmark

fFoodandAgricultureOrganizationoftheUnitedNations,SomaliaWaterandLandInformationManagementProject,P.O.Box30470-00100,Nairobi, Kenya

a r t i c l e i n f o

Articlehistory:

Received11January2017

Receivedinrevisedform17February2017 Accepted20February2017

Availableonline16March2017

Keywords:

Restorationinterventions Biophysicalimpact Landsat

MODIS

BACIsamplingdesign

a b s t r a c t

Restorationinterventionstocombatlanddegradationarecarriedoutinaridandsemi-aridareasto improvevegetationcoverandlandproductivity.Evaluatingthesuccessofaninterventionovertimeis challengingduetovariousconstraints(e.g.difficult-to-accessareas,lackoflong-termrecords)andthe lackofstandardisedandaffordablemethodologies.Weproposeasemi-automaticmethodologythatuses remotesensingdatatoprovidearapid,standardisedandobjectiveassessmentofthebiophysicalimpact, intermsofvegetationcover,ofrestorationinterventions.TheNormalisedDifferenceVegetationIndex (NDVI)isusedasaproxyforvegetationcover.Recognisingthatchangesinvegetationcoverarenatu- rallyduetoenvironmentalfactorssuchasseasonalityandinter-annualclimatevariability,conclusions aboutthesuccessoftheinterventioncannotbedrawnbyfocussingontheinterventionareaonly.We thereforeuseacomparativemethodthatanalysesthetemporalvariations(beforeandaftertheinter- vention)oftheNDVIoftheinterventionareawithrespecttomultiplecontrolsitesthatareautomatically andrandomlyselectedfromasetofcandidatesthataresimilartotheinterventionarea.Similarityis definedintermsofclasscompositionasderivedfromanISODATAclassificationoftheimagerybefore theintervention.Themethodprovidesanestimateofthemagnitudeandsignificanceofthedifferencein greennesschangebetweentheinterventionareaandcontrolareas.Asacasestudy,themethodologyis appliedto15restorationinterventionscarriedoutinSenegal.Theimpactoftheinterventionsisanalysed using250-mMODISand30-mLandsatdata.Resultsshowthatasignificantimprovementinvegetation coverwasdetectableonlyinonethirdoftheanalysedinterventions,whichisconsistentwithindepen- dentqualitativeassessmentsbasedonfieldobservationsandvisualanalysisofhighresolutionimagery.

Ruraldevelopmentagenciesmaypotentiallyusetheproposedmethodforafirstscreeningofrestoration interventions.

©2017TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).

∗ Correspondingauthor.Tel.:+390332786429;fax:+390332785162/9029.

E-mailaddress:michele.meroni@ec.europa.eu(M.Meroni).

1. Introduction

Desertification,definedaslanddegradationinarid,semi-arid anddrysub-humidareasresultingfromvariousfactors,including climate variation and human activities (UNCCD, 1994), repre- sentsamajorthreattopopulationsandecosystems(Low,2013;

Reynoldsetal.,2007).Besidesphysicallyaffectingecosystems,land http://dx.doi.org/10.1016/j.jag.2017.02.016

0303-2434/©2017TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/).

degradationcausesvarioussocio-economicproblems,suchasfood insecurityandconflicts(Mbowetal.,2015).Restorationinterven- tionsareamongthestrategiesthatcanbeputinplacetocombat landdegradation.Restorationactionsofteninvolvetheimprove- mentofvegetationcover(Zuccaetal.,2015),throughtheplanting ofappropriatespecies(e.g.Niangetal.,2014)orthroughimproved soil,waterandlandmanagement.

Thedefinition of “effectiveness”of a restoration action may coverdifferentaspectsoftheintervention,rangingfromthepurely biophysicaltotheecologicalandsocio-economicones(Shackelford etal.,2013).Withrespecttothebiophysicalimpact,guidelines for the ecological evaluation of restoration interventions focus onthecomparison betweentherestoration and referencesites for a number ofattributes measuredin thefield, rangingfrom speciescomposition,toecosystemfunctionandstability,andto landscapecontext(SocietyforEcologicalRestorationInternational Science&PolicyWorkingGroup,2004).Althoughcomprehensive, thisapproachisexpensiveandrequiresextensivefieldoperations.

Independentassessmentofthesuccessofrestorationprojects isoftenchallengingbecauseinterventionsmaybelocatedinareas thataredifficulttoaccessandhavepoorinfrastructure.Additional challengesrefertothelackofaffordableandstandardisedmethod- ologies/criteriaandthedifficultyofobtaininglong-termdatato monitortheeffectofaninterventionoutsidetheproject’stimes- pan. Verification performed by theimplementing agent is also frequentlynotavailable.Forexample,inarecentsurveyofrestora- tionprojectsintheMediterraneanBasinconductedbyNunesetal.

(2016)among restoration professionalpractitioners, restoration successwasnotevaluatedin22%oftheprojectsandevaluatedonly inthefirstyearaftertheplantationin19%oftheprojects.When conducted,theevaluation wasbasedonplantcover and diver- sity(69%oftheprojects)andplantvitality(48%).Lackoffunds, togetherwithcapacity constraintsandlackofknowledge,were identifiedasobstaclestoprojectmonitoringbyrestorationpracti- tionersinSouthAfrica(Ntshotshoetal.,2015)andcanbeassumed torepresentcommonlimitationsinotherruralareasacrossthe continent.

The lack of evaluation and dissemination of the results of restorationstillrepresentaconstrainontheapplicationofthebest technologiesandapproachesavailable(Bautistaetal.,2010).Asa results,thereiswidespreadconsensusontheneedforinnovative approachesfor thesystematic evaluationoftheeffectivenessof restorationactions(Bautistaetal.,2010;Benayasetal.,2009;Birch etal.,2010;Papanastasisetal.,2015).

Remotesensing(RS)canhelpcopewiththewidespreadlack oftimely,long-term,reliable,andhomogeneousgroundinforma- tion,especiallyinAfricandrylands.FewexamplesoftheuseofRS datatoassessrestorationinterventionsareavailable.TheFoodand AgricultureOrganisation–Somalia WaterandLandInformation Managementproject(FAO-SWALIM)uses commercialvery high resolution(VHR)imagerytovisuallyappraisetheimplementation ofsurfacerun-offcontrolinfrastructuresinSomalia(e.g.rockdams, gabions,watercatchments)operatedbyvariouscontactors(FAO, 2015).Inthisway,however,itistheimplementationoftheinfras- tructurethatisscrutinised,notitsimpactorsuccesswithrespect tovegetationdynamics.Photointerpretationoftimeseriesofaerial photographywasusedbyRangoetal.(2002)toqualitativelyeval- uatethe long-termeffectiveness of restoration interventionsin NewMexicointermsofpersistencyintimeofrecognisablestruc- turessuchasterraces,grubbingpatterns,revegetatedareas,etc.

Recently,theOpenforisinitiativeoftheFAOprovidedafreeand open-sourcetool,namedCollectEarth,whichfacilitatesthevisual interpretationof VHR time series imageryof GoogleEarth and MicrosoftBingforpointsamplingandlandusechangedetection (Beyetal.,2016).Despitetheirusefulness,theresultsoftheanal- ysisare pronetointerpretationerrors asall ofthese examples

make useofphotointerpretation.A quantitativeevaluation ofa restorationinterventionusingAtriplexnummulariaplantationsin MoroccowasinsteadperformedbyZuccaetal.(2015) utilising SPOT5imageryandground-basedbiomassmeasurementstoderive thedrybiomassyieldoftheplantationsinMoroccoascompared toknownreferences.Landcoverclassificationandspatialpattern metricshavebeenanalysedbyFavaetal.(2015)tostudytheimpact ofrestorationactionsinMediterraneanrangelands.

VegetationindicessuchastheNormalisedDifferenceVegeta- tionIndex (NDVI;Rouseet al.,1974)canbeusedasproxies to monitorthefractionofvegetationcover,i.e.thefractionofground coveredbygreenvegetation(CarlsonandRipley,1997).However, evaluatingthe“greening”of arestoration intervention presents a challenge, becausethe direct comparison of the NDVI of the areabeforeandaftertheinterventionwouldnotbeinformative.

Infact,vegetationcoverwillchangeovertimeindependentlyof therestorationproject.Twomainsourcesdrivethetemporalvari- abilityofvegetationstatus:theannualseasonaldevelopmentcycle (oneormore)andtheinter-annualclimatevariability.Bothfluc- tuations hamperthepossibilityof makinga direct comparison.

In fact,even in theabsenceof disturbances(e.g.fires,pests), a differencein NDVIbetweentwo observations takenbeforeand aftertheinterventioncouldbeduetotheinterventionitself,the stageofdevelopmentofthevegetationatthoseparticulartimesof observation,andtheweatherconditionsexperiencedbythevege- tationintheweeks/monthsprecedingtheobservations.Assuming thatclimaticconditionsareratherhomogeneousintheneighbour- hoodoftherestorationproject,theproblemcanbeapproachedby comparingtheconditionsoftherestorationareabeforeandafter theinterventionwiththoseofsimilarareasnearby,asin Zucca etal.(2015).Therationaleisthattheanthropogenicintervention willcauseadifferentpatternofchangefrombeforetoafterthe interventioncomparedwithnaturalchangesinundisturbedand similarareas.Thisconceptformsthebasisofthebefore/aftercon- trol/impact(BACI)samplingdesign(Underwood,1992),originally developed inecology toassesstheimpactof astress (typically inducedbyindustrialactivities)ontheenvironment.BACIhasbeen successfullyappliedtostatisticallyevaluatepotentialenvironmen- talandecologicalimpacts(Smith,2002),buthasnotbeenusedby theRScommunitysofar.

InthisstudywemakeuseoftheBACIdesigntodevelopamethod toassesstheimpactofa restorationinterventiononvegetation fractionalcover solely basedonRS information(i.e.NDVI).The methodisintendedtoperformacost-effectiveverificationofthe effectivenessoftherestorationinterventionthatmaybeusedas afirstscreening,usabletoplanadditionalfieldverificationcam- paigns, andas amedium- tolong-termimpactmonitoringtool whenappliedrepeatedlyovertime.It isacknowledgedthatthe proposedmethodissuitedtorestorationinterventionsthatinvolve anincreaseinvegetationcover,whichisnotthecaseforanum- berofinterventiontypes(e.g.agreenlandscapeofinvasivespecies wheretherestorationwouldaimtochangetheplantcommunity composition;soilconservationmeasuressuchasrockdamstostop gullyerosion).

ToillustratetheapproachweapplyittoacasestudyinSene- gal,whereanumberofrestorationinterventionswereperformed inthecontextoftheGreatGreenWallfortheSaharaandtheSahel Initiative(GGWSSI), apan-Africaninitiativetocombatdesertifi- cation (African Union&Pan-AfricanAgency oftheGreat Green Wall,2012).ThebiophysicalimpactwasassessedusingRSdata attwodifferentspatialresolutions,namelytheModerateResolu- tionImagingSpectroradiometer(MODIS)at250mandLandsatat 30m,andcomparedwithqualitativeinformationfromfieldobser- vationsandphotointerpretationofVHRimagery.Theprosandcons ofusingMODISandLandsatdataarediscussed.

2. Studyarea

The test case-study encompasses severalinterventions con- ducted in theLinguère departmentof the Lougaregion and in theRaneroudepartmentoftheMatamregionofnorthernSene- gal(Fig.1).TherelativelyflatstudyareabelongstotheSahelian acacia savannah ecoregion (Olson et al., 2001), and is charac- terisedbyahotariddesertclimate(BWh)accordingtotheupdated Köppen–Geiger climate classification (Peel et al., 2007). Mean annualtemperatureandprecipitationinthestudyarearangefrom 27to28^◦C(ECMWFERA-Interimovertheperiod1990–2014;Dee etal.,2011)andfrom270to390mm(CHIRPSrainfallestimates overthesameperiod;Funketal.,2015).Themajorityofprecipita- tionfallsduringtherainyseason,whichoccursbetweenJulyand September,andisrelatedtotheWestAfricanMonsoon(Nicholson, 2013).In thearea, severalrestoration projects, includingrefor- estationandimprovedforageproduction,havebeenimplemented between2007 and 2011 in the context of the GGWSSI by the GreatGreenWallagencyundertheresponsibilityoftheSenegalese MinistryofEnvironment.However,thetechnicalrationaleforthe selectionofprojectsandthecompletedescriptionoftheprojects (where,what,how,successrate,etc.)is,toourknowledge,not available.

3. Data

3.1. Remotesensing

Theanalysiswasperformedonfreelyavailablesatelliteimagery attwodifferentspatialscales:250-mMODISNDVIproductand30- msurfacereflectancesfromtheLandsatmissions.Forthemoderate resolution,weusedtheeMODISproductprovidedbytheUnited StatesGeologicalSurvey(USGS)andbasedonMODISdataacquired bytheTerra satellite. Theproductis a 10-day maximumvalue NDVIcomposite(Jenkersonetal.,2010)temporallysmoothedwith theSwetsalgorithm(Swetsetal.,1999).Compositesareproduced everyfivedays,resultinginsixtemporallyoverlappingcompos- itespermonth.Hereweonlyusedthecompositesfordays1–10, 11–20,and21-lastdayofeachmonth.Boththetimeseriesof10-day observationsandthemaximumannualNDVIvalue,representing vegetationpeakdevelopment,wereusedintheanalysis.

Inspectionof MODIS multi-annual temporal profiles for the interventionareaspermittedustodeterminetheperiodofvegeta- tiongrowth,whichroughlyrangesfromJunetoSeptember,with maximumdevelopmentreachedinlateAugust.Cloud-freeLand- satimagerywasselectedduringthisperiod.Landsat8Operational LandImages(OLI)dataareavailablesince2013;beforethenwe hadtorely onLandsat 5Thematic Mapper(TM)andLandsat 7 EnhancedThematicMapperPlus(ETM+)data.However,Landsat7 ETM+imagerycollectedafter31/05/2002hasdatagapsduetothe ScanLineCorrectorfailure(SLC-off;Andrefouetetal.,2003).The issuedoesnotpreventtheanalysisbuthastobeproperlytreated, asexplainedinthemethodssection.

Althoughnotstrictlyrequired,theBACIdesign benefitsfrom havingmultipletime observations beforeandafter thetime of intervention.WhereasgatheringmultipleMODISobservationsis straightforward,itisverychallengingforLandsat5and7inthese geographicalsettingswheretheavailabilityofcloud-freeimages duringthegrowingseasonisverylimited.Forinstance,alargedata gapexistsbetween2003and2007andbetween2007and2012, whennotasinglecloud-freeimageisavailableintheperiodof maximumvegetationdevelopment.ThelistoftheLandsatimages usedintheanalysisispresentedinTable1.

Landsat-based NDVI was computed using the red and near infraredbandsofsurfacereflectanceproducts(USGS,2016a,2016b)

Table1

AcquisitiondateandsensorofLandsatdataused(path204androw49).

Sensor Date

Landsat7ETM+ 19/07/2003

Landsat7ETM+ 16/09/2007

Landsat7ETM+ 13/09/2012

Landsat8OLI 24/09/2013

Landsat8OLI 11/09/2014

retrievedfromtheUnitedStatesGeologicalSurvey.Largelycloud- freeimagerywasselected,and theCFmaskbandofthesurface reflectanceproduct wasused tomask sparseclouds and cloud shadows.

Insummary,MODISandLandsat-basedanalysesdifferinthree aspects:i)thespatialresolution(250mvs.30m),ii)theRSvari- ableused(maximumseasonalNDVIvs.NDVIataspecific,anddata availability-driven,dateduringtheseason),andiii),thetemporal periodcoveredbeforeandaftertheintervention(uptofiveyears ofacquisitionsvs.asingleacquisition).

Finally,tochecktheconsistencyofBACIresults,VHRimagery fromGoogleEarth(GE)wasusedforthequalitativeandvisualeval- uationoftherestorationinterventions.ThevisualanalysisofVHR imagerybeforeandaftertheinterventiondateaimedtospotsigns ofinterventions,rangingfromsignsoftractorploughingtovisible patternsofregularplantationsandthegrowthofnewtrees.When imagerybeforetheinterventionwasnotavailableinGE(8cases outof15),theassessmentoftheinterventionwasperformedon theimageryonlyaftertheintervention,andwasbasedonacom- parisonofthevegetationcoverinsidetheinterventionareawith thatoftheareaoutside,withobviouslimitationsonthepossible interpretation.

3.2. Fieldmissionsandanalysedinterventions

Theoutlineoftheprojectpolygonsandthemainprojectinfor- mation(typeandyearofintervention)wereobtainedduringthree fieldvisits(2014–2015)performedbytheFrenchNationalCentre forScientificResearch(CNRS)andsupportedbytheSenegalState ServiceofWaterandForests.Asacentralisedandpublicrecord ofrestorationprojectsdoesnotexist,thelocationoftheinterven- tionprojectstobevisitedwasdefinedwiththestaffoftheSenegal StateServiceof WaterandForestsand theSenegaleseNational GreatGreenWallAgency.Thispreliminaryinformationwascom- plementedby visualinterpretationusing VHRsatelliteimagery fromGEbeforethefieldcampaignsandinterviewswithlocalcom- munitiesduringthecampaigns.Projectareaswerethendelineated inthefieldusingGPS.

Restoration interventions mainly involved tree plantations (Acacianilotica,Acaciasenegal,AcaciaseyalandBalanitesaegypti- aca),thefencingofplotstoenhancethenaturalregenerationof woodyspeciesandrestorerangeland grasses,andthecombina- tionofthetwo.TreeplantingusuallyoccurredinAugust,during therainyseason.Activitiesweredesignedtoimprovelandproduc- tivityoverthelongrununderthehypothesisthattheincreasein vegetationcoverduetotheinterventionwouldrestoresoilfertility andatthesametimeproviderelevantecosystemservicesforlocal communities(e.g.gumarabicproductionfromAcaciaSenegal,fruits fromBalanitesaegyptiaca,andgrassstrawtobeharvestedattheend oftheseasonandeitherusedorsold).Restorationinterventions wereimplementedbytheSenegaleseNationalGreatGreenWall Agencywithintheframeworkofacash-for-workprogramme.Itis notedthatoneoftheinterventionsconsideredinthetestcase(i.e.

projectno.81ofFig.1)doesnotbelongtotheGGWSSI,butrefers toanAcaciaSenegalplantationimplementedbyaprivatecompany fortheproductionofgumarabic.

Fig.1. Locationoftheinterventionsconsideredinthecasestudy(greenpolygonsandidentificationnumber,detailsinSection3.2).Areaswithinsufficientdocumentation aboutthetimingoftheintervention,interventionssmallerthan0.25km²,andareassubjectedtootherinterventiontypes(i.e.conservation)areingrey.Theredboxdelineates theboundariesoftheLandsatimageryused.Backgroundimageryisatruecolourcomposite(source:Esri).(Forinterpretationofthereferencestocolourinthisfigurelegend, thereaderisreferredtothewebversionofthisarticle.)

Totest theproposedmethodology,fromthelistofidentified interventionsitesweselectedthosewiththefollowingcharacter- istics:i) havingdocumentationoftheperiodofintervention,ii) coveredbyLandsatpath204androw49,iii)implementedafteryear 2002,andiv)withanareagreaterthan0.25km²(i.e.aminimum offourMODISpixels).Thisresultedinatotalof15interventions (greenpolygonsinFig.1).Interventionswithoutsuchcharacteris- ticsandanareasubjectedtonaturalconservation(greypolygons inFig.A)wereexcludedfromthecontrolsitesearch algorithm describedinthemethodssection.Abriefdescriptionofthevar- iousprojects,includingthetime and type ofintervention, field missionandVHRanalysisevaluation,ispresentedintheresults section(Table3).

Aqualitative evaluationof thesuccessof theintervention is availableforfivesitesthatwerevisitedinOctober2015andAugust 2016.Variouselementsweretakenintoaccountinthis evalua- tion:presenceandhealthstatusofnewlyplantedtrees,treeand herbaceouscoverdifferencewithrespecttosurroundings,informal interviewswithlocals.Thisinformation,togetherwiththevisual interpretationofVHRimagery,wasusedtocarryoutaconsistency checkwiththeresultsoftheproposedmethodology.

4. Methods

InBACIdesign,toaccountfornaturalchanges,theNDVIofthe restorationinterventionarea(i.e.the“impact”site)iscompared toanothersite,whichisreferredtoasthe“control”site(Smith, 2002).Theuseofmultiplecontrolsites(i.e.BACIwithmultiplesites) extendsthisideaandavoidsthecriticismthattheresultsofthe BACIexperimentaresolelyduetoapoorchoiceofthecontrolsite.

Thelocationofcontrolsisselectedrandomlyamongsitesthatare similartotheimpactsite(detailsinSection4.1).

4.1. Spatialsampling

Withrespecttotheimpactsite,acontrolareashouldhavethe followingcharacteristics:

i)similarlandcoverbeforetheintervention;

ii)relativelycloseinspaceinordertoexperiencethesameweather variability;

iii)not subjected to anthropogenic changes during the whole before–afterperiodbeinganalysed;

iv)randomlyselected.

Inaddition,evenifnotstrictlyrequiredbytheBACIdesign,we optedforselectingcontrolareaswithasizesimilartothatofthe impactareatoensureamorebalancedsamplingsize.Similarityin soilcharacteristics,knowntobeimportantdeterminantsofveg- etationinaridsystems,is expectedtobeimplicitlyensuredby conditioni.

Inordertofulfiltheserequirements,weproceedasfollowsfor eachoftheimpactsites.Whendifferentsettingsareusedforthe MODISandLandsatanalysis,thisisexplicitlymentionedinthetext andreportedinTable2.Someoftheintermediateproductsofthe analysisfortheLandsatdataandimpactsitenumber9areshown inFig.2asanexample.

First,werestricttheareafromwhichcontrolsareselectedto acircularareacentredonthecentroidoftheimpactsite.Pixels affectedbycloudcontaminationandSLC-offineitherthebefore

Fig.2. Exampleofintermediateresultsofthedescribedprocessingforprojectno.9(yellowpolygon,otherprojectsinred).Landsatimagesarefromthefollowingdates:

19/07/2003(before),13/09/2012(after).(A)nearinfraredfalsecolourcompositionLandsatimagebefore;maskedpixels(i.e.outsidesearcharea,SLC-off,detectedasclouds orcloudshadowsinthebeforeorafterimagery)areinblack, ¨stripes”areoriginatedbySLC-offaffectedpixels;(B)fiveclassesISODATAclassificationofthevalidpixels;

(C)classcompositionRMSEwithrespecttotheinterventionarea;RMSEofthewindowassignedtothecentralpixel;pixelswhosewindowwouldoverlapotherprojects aremaskedout(black);(D)greensquarepolygonsaretheselectedcontrols;NDVIdifference(valueafter–before)inthebackground.(Forinterpretationofthereferencesto colourinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)

Table2

ListofMODIS-andLandsat-specificparametersusedintheanalysis.

MODIS Landsat

Ratiorbetweensearchandimpactarea 600

NumbernofISODATAclasses 5

Similaritythresholds 0.9

Numberofcontrolsrandomly extracted(nc)

20

Targetvariable Maximumannual

valueofsmoothed NDVI

NDVIof within-season

imagery Additionalcloudscreening None Visualanalysis Temporalsampling(beforeandafter) 5+5samples 1+1sample

orafterimageryaremaskedout(Fig.2A).Therestrictiontosucha circularareahastheobjectiveoffulfillingconditionii,i.e.defining

“aneighbourhood”whereclimaticconditionsshouldnotsignifi- cantlychange.Theextentofthisareaisdefinedasamultipleof theimpactareasize(searcharea/impactarea=r).Wemadethe searchareaproportionaltothesizeoftheimpactareatoensure thatitcontainsaroughlyconstantnumberofpotentialcontrols, independentoftheimpactareasize.Theratiorwassetforthis studyto600.Iftheimpactareahadacircularshape,thiswouldcor- respondtoaratiobetweentheareasearchedandtheimpactradius of24.5.Inthecasestudy,thisresultedinanaveragesearchradius

of25km(range=9–61km),wheresimilarclimaticconditionscan bereasonablyexpected.

Second,weusetheimagesacquiredintheperiodbeforethe intervention(Fig.2A)toperformaniterativeself-organisedunsu- pervisedclusteringalgorithm(ISODATA)withnclassesspatially restrictedtothesearcharea(Fig.2B).Withatrialanderrorprocess basedonthequalitativecomparisonoftheISODATAclassification mapandVHRimagery,wesetn=5inthisstudy.Alargernumber ofclassescanbeselectedifthelandscapeismoreheterogeneous.

TofullycomparetheresultsgatheredwiththeLandsatandthe MODISanalysis,weperformtheclassificationusingeitherLandsat orMODISdataforthetwotypesofanalysis,implyingthatdifferent controlsitesareselected.FortheLandsatanalysis,allthebandsin thereflecteddomainofasingleimageareusedfortheclassifica- tion,whereasforMODISwefollowtheapproachproposedbydeBie etal.(2011),usingthemulti-temporalNDVItrajectoryinsteadof themultispectralinformation.Theclassificationisthusperformed onafive-yearmulti-temporaldatasetof10-daycompositesend- ingtheyearbeforetheimplementationoftherestorationaction.

Afterthisclassificationstage,thefractionalclasscompositionofthe impactareaiscomputed.

Third,wedefineagenericcontrolasasquarespatialwindow withthesameareaoftheimpactsite.Thepopulationofpotential controlsisthusformedbyallthepossibleandoverlappingwin-

dowscentredoneachofthepixelsbelongingtothesearchareas.

Potentialcontrolsthatoverlapotherimpactsitesorexcludedareas (theareasubjecttoenvironmentalconservationinourcasestudy) areexcluded.Potentialcontrolshavingmorethan50%ofinvalid pixels(astheyarecoveredbythecloudandshadowmask)arealso excluded.Then,thefractionalclasscompositioniscomputedfor eachpotentialcontrol.

Fourth,thelandcoversimilaritybetweeneachpotentialcon- trolandtheimpactisdefinedasthecomplementoftherootmean squareerrorbetweenthefractionalcompositionsandone,i.e.sim- ilaritys=1–RMSE(Fig.2C).Valuesclosetoonethusindicatenearly identicaloverallclasscompositionofapotentialcontrolandthe impact.NotethatthesimilarityofNDVIvaluesbeforetheinter- ventionisnotconsideredhereastheBACIdesigndoesnotrequire similarlevelsofthevariableofinterest.

Fifth,wesubsamplethepopulationofpotentialcontrolsbydis- cardingthosewithasimilaritysmallerthans(0.9).Atthispoint wehaveasampleofpotentialcontrolsthatfulfilconditionsiand ii.Fromthissamplewerandomlyextractn_ccontrolsites(n_c=20in thisstudy,Fig.2D).Randomextractionisexecutedusingprobability proportionaltosizesampling(Lohr,2010),inwhichtheselection probabilityforeachelementisproportionaltoitssimilaritytothe impactsite.Inthisway,themostsimilarcontrolshaveahigher probabilityofbeingselected.Oncea controlisextracted,allits overlappingpotentialcontrolsareexcludedforfurtherselection andtherandomextractionisrepeateduntilalltherequiredcon- trolsareselected.Itisnotedthatthisproceduredoesnotguarantee thatalldesirednccontrolsareactuallyavailable.Ifthenumberof selectedcontrolsisconsideredtobeinsufficient,onemayincrease thesearchareaorreducetherequiredsimilaritystoincreasethe populationofcandidatecontrolsandthusthenumberofselected controls.

Oncethelocation ofthecontrolsis established,theNDVI is extractedforallvalidpixelsbelongingtotheimpactandcontrol areasfortheperiodbeforeandaftertheintervention.Theselection processdescribedsofarwasimplementedinIDL(HarrisGeospatial Solution,Inc.)andfullyautomatised.

Finally,conditioniiiwastestedbyvisuallyinspectingtheavail- abletimeseriesofLandsatimageryoftheselectedcontrolsites.It isnotedthatonlyclearlandusechanges,forinstancefromnat- uralvegetationtocroplandor tosettlements,are detectablein suchaway.Thepossibleoccurrenceoflessvisiblechanges,such asunreportedrangelandmanagementpractices,canthereforenot beexcluded.Theimpactofthepotentialselectionofsuchunsuit- ablecontrolsisexpectedtobemitigatedbygatheringarelatively largenumberofcontrolsites.

4.2. TreatmentofLandsat7SLCgaps

Anestimated22%oftheLandsat7scenesislostbecauseofthe SLC failure (http://landsat.usgs.gov/productsslcoffbackground.

php).TheSLC-offeffectsaremostpronouncedalongtheedgesof thescene andgraduallydiminishtowarditscentre.Theprecise location of the missing scan lines varies from scene to scene.

Therefore,itisdifficulttoanticipatethefractionofmissingdata forindividual impactareas.Withourtest cases,thefractionof missingdatavariedbetween0%and40%andoperatedasarandom subsampling with no expected consequences on the following BACItest.InthepresenceofthisSLCproblem,theaffectedpixels wereconsideredasbelongingtoanadditionallandcover class, thuscontributing tothesimilaritymeasure described above.In thiswaywefavouredtheselectionofcontrolsshowingasimilar fractionofSLC-affectedpixels.

4.3. Temporalsampling

Multipletemporalsamplingbeforeandaftertheputativeimpact ispreferableasitensuresthatcoincidentaltemporalfluctuations in either locationdo not confound thedetection ofthe impact (Underwood, 1992). Due to the limited frequency of temporal acquisition,wecouldnotretrievemultipleobservationimagery beforeand after theintervention from Landsat, and we conse- quently applied BACI based ona single couple of before–after observations and multiple control sites. The closest cloud-free imagesbeforeandafterthetimeofinterventionwerethusselected foreachrestorationsite.ThemorerobustBACIdesign,withobser- vationsfrommultipledatesandsites,wasinsteadusedwiththe hightemporalfrequencyMODISdata.Thatis,uptofiveannualval- uesofmaximumannualNDVIwereextractedfromtheMODIStime series.

4.4. Statisticalanalysis

Alinearmixed-effectsmodelonNDVIsite-levelaverageswas usedtotesttheimpactoftherestorationinterventionasinSchwarz (2015). In this context, the period (before/after), the site class (impact/control)and theinteractionofsite classandperiodare fixed effects while the site and the samplingtime, beingnon- exhaustivesamplesofthepotentialsitesandsamplingtimes,are consideredtoberandomeffects.Linearmixed-effectsmodelsuse maximumlikelihoodtoestimatetheparametersofthelinearfunc- tioncontainingboth fixedandrandomeffects. Outputisin the formofapproximatez-ratiosornormaldeviates,whichallowssta- tistical testsonanylinearcombination ofthefixedparameters (PinheiroandBates,2000).Toevaluatetheimpactoftheinter- ventionwewereinterestedintheinteractionoftheperiodand thesiteclass(the so-calledBACIeffect)representingthediffer- entialchangebetweenimpactandcontrolsitescomparedbefore andaftertheintervention.The(null)hypothesisofnochangewas rejectedattheconventional5%significancelevel.

TheBACIanalysisprovidestwoimportantstatistics(amongoth- ers):thesignificancelevel(i.e.P-value)oftheBACIeffecttest(i.e.

nochangenullhypothesis)andtheBACIcontrast.TheBACIcon- trastiscalculatedasthedifference(controlsvs.impact)between themeandifferences(aftervs.before):

BACIcontrast=(␮CA−␮CB)−(␮IA−␮IB) (1)

Where␮isthesite-specificspatialmeanofthevariableselected torepresenttheimpact(hereNDVI);CA,IAstandforControland ImpactAfter,respectively;CBandIBforControlandImpactBefore, respectively.Byconvention,anegativecontrastindicatesthatthe variablehasincreasedmore(ordecreasedless)intheimpacted sitewithrespecttocontrolsinthetimeperiodrangingfrombefore toaftertheimplementationoftherestorationproject. TheBACI contrastisexpressedinthesameunitsofthevariableofinterest, hereNDVI.Inordertohighlightthemagnitudeofthecontrastwith respecttotheinitialconditions,wenormaliseitbythemeanofthe NDVIoftheimpactareabeforetheinterventiontookplace(␮IB) andexpressitasapercentage.Thisderivedvariableisreferredto as“relativecontrast”inthefollowing.

Itisnotedthat,despitethefactthatNDVIcomputedfromLand- sat7and8(ETM+andOLIsensors)maybeslightlydifferentbecause ofthedifferentspectralresponsesofthebands(Royetal.,2015)and differentatmosphericcorrectionalgorithm,thisimpactsboththe projectsiteandthecontrolsandhencedoesnothaveaneffecton theBACIanalysis,whichworksonthedifferencebetweenthetwo typesofarea.

Table3

Maininformationofanalysedinterventions,fieldmissionevaluation,visualinterpretationofGoogleEarthVHRimageryandBACIresultsonMODISandLandsatdata.n.a.

standsfornotavailable.ThemeanoftheRSvariableiscomputedastheoverallmeanextractedbeforetheintervention(allsites,allsamplingdates).Green(likelysuccess), lightgreen(moderateorambiguoussuccess)andgreybackground(likelyfailure)isusedtoranktheintervention’ssuccessbasedonthefieldmissionandVHRqualitative evaluation.GreenbackgroundisusedintheBACIsectiontohighlightnegativeBACIcontrasts(inbold)thataresignificantatthe0.05P-value.Greybackgroundindicatesa non-significantBACIeffect.

Theopensourcestatistical softwareR(RDevelopment Core Team,2016)wasusedtodevelopascripttoautomatisethesta- tisticaltestfollowingSchwarz(2015).

5. Resultsanddiscussion

ResultsoftheBACIanalysis,alongwithprojectinformation,VHR photointerpretationandfieldmissionqualitativeevaluation,are reportedinTable3.Thenumberofcontrolsitesexcludedfromthe analysisaftervisualinspectionrangedfromzerotoamaximum ofsix.Theanthropogenicchangesdetectedintheperiodafterthe interventionmainlyrefertoappearanceofagriculturalfieldsand settlements.

5.1. BACIanalysis

AsignificantlynegativeBACIcontrast(i.e.improvementinNDVI withrespecttocontrolsaftertheintervention)wasdetectedinfive andfouroutof15sitesusingMODISandLandsatdata,respectively.

Forthemajorityofsites,the(null)hypothesisofnochangecould notberejected.Forthreesites,thecontrastwasindeedpositive, i.e.therewasarelativedecreaseinNDVIintherestorationarea.

FocussingonthesitesforwhichasignificantBACIeffectwas detected, the average relative contrast is −20% and −27% for MODISandLandsatdata,respectively.ConsideringNDVIasarough approximationofthefractionalvegetationcover,thesenumbers translateintoasignificantimprovementinthevegetationcover withrespecttothecontrols.

AsanexampleofthedatausedfortheBACIanalysis,impact andcontrolaveragesareshowninFig.3forfourrepresentative interventions:no.9,whereasignificantlynegativeBACIeffectis foundusingbothLandsatandMODIS;no.81,wherethenegative contrastissignificantatthe0.05levelforMODISonly(P<0.1for Landsat);no.17withapositivebutnon-significantcontrast;and

finallyno.4withapositiveandnon-significantcontrast(P<0.1for Landsat).

InordertogaininsightsintothedifferencebetweentheMODIS andLandsatanalyseswefocusontheagreementbetweentworel- evantBACIstatistics(i.e.contrastandP-value).First,Table3shows aperfectmatchintheBACIcontrastsign.Thatis,bothtypesofanal- ysisagreeintheevaluationofthesignoftheintervention,either re-greening(negativecontrast)ordegradation(positivecontrast) oftheimpactsitecomparedtothecontrols.Themagnitudeofthe contrastandthemeanoftheRSvariablecanbedifferentbetween thetwotypesofanalysisbecausetheRSvariableisdifferent:the maximumseasonalNDVIforMODIS,andtheNDVIvalueduring thegrowingseasonataspecificsamplingdatedictatedbyimage availabilityforLandsat.

Second,largeagreementinthedetectionof a significantre- greening of the intervention (i.e. negative BACI contrast with P-value<0.05)existsbetweenthetwotypesofanalysis.Onlyone caseofminordisagreementisfoundforsiteno.81(Table3and Fig.3),forwhichthevisualanalysisofGoogleEarthVHRimagery indicatesthat theplantationwas actuallyimplemented. In this casebothtypesofanalysiscomputeanearlyidenticalandnega- tiveBACIcontrast(−0.12)whereastheydifferinthesignificance levelattributed.However,theP-valueofLandsat(0.058)isnotfar fromthethreshold(0.05)usedtorejectthe(null)hypothesisofno change.Asaresult,thechangedetectedusingLandsatdatahasa lowerconfidencelevel(P<0.10).

Otherminordifferences(i.e.notleadingtodifferenttestout- come)betweentheresultsofthetwotypesofanalysisreferto differentmagnitudesoftheP-value.TheP-valueofMODISisgen- erallylowerthanthatofLandsat.Fig.4showstheP-valueofthe twotypesofanalysisvs.theabsolutevalueoftherelativeBACI contrast.

Bothtypesof analysisshowareduction in theP-valuewith increasingabsolutevalueoftherelativecontrast,asthetestessen- tiallybuilds(also)onthemagnitudeoftheBACIcontrast.However,

Fig.3.TemporalprofilesofmeanNDVIvaluesforselectedimpact(bluelines)andcorrespondingcontrolsites(redlines)forLandsat(left)andMODIS(right)data.Sampling datesbeforeandaftertheinterventionareseparatedbytheverticalblackline.TheP-value(P)andthepercentrelativecontrast(RC)arereported.(Forinterpretationofthe referencestocolourinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)

theMODISP-valuesaremostlylowerthanthoseofLandsatforsim- ilarrelativecontrasts.Therefore,themultipletemporalsampling thatcanbeachievedusingMODISdataappearstobeinstrumental inincreasingthesignificancelevelofthetestwithrespecttothe singletimeanalysisofLandsat.Thisislikelyduetothreereasons:

i)increasedsamplesizeforMODISanalysis,ii)betterrepresen- tationof theoverall vegetationcover offeredby themaximum NDVIwithrespecttothesingledateNDVI,andiii)reduceddepen- dencyofMODISonaspecificyearandtime.Concerningthelatter, withtheMODISset-upweanalysethecontrol/impactdifferential behaviourinamulti-yeartime span,makingit lesssensitiveto possibleyear-specificpeculiaritiesthatmayaffectthesingle-year high-resolutionLandsatset-up(seeFig.3).Itisnotedthatthesame multiple-period design canbeapplied tohigh-resolutionfreely availabledataingeographical settingswithahigheravailability

of cloud-free Landsat imagery, or when analysing more recent projectsthatcanexploitthemorefrequentavailabilityofLandsat8 imageryandotherrecentlyavailableinstruments(e.g.Sentinel2).

Theuncertaintiesconnectedtotheuseofasingleimagebeforeand aftertheinterventionarewellexemplifiedbythetemporalevolu- tionofNDVIforprojectno.81inFig.3.Anon-significantBACIeffect is detectedusing thesingle-imageset-up ofLandsat (P=0.058) despiteaquitelargenegativerelativecontrast(−27.95%).Forthe samesite,theMODISmulti-yearprofileshowsalargeinter-annual variability.TheLandsatsingle-imageset-uppickedup2007asthe year“before”,whenthecontrolhadthethird-highestMODISNDVI.

Ifadifferentyearwereavailable,forexample2006whenthecon- trolhadthesecond-lowestMODISNDVI,thismayhaveresultedin adifferent(lower)P-value.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0 10 20 30 40

P-v al u e

|% relave contrast|

Landsat MODIS P=0.05 P=0.1

Fig.4.ScatterplotoftheabsolutevalueoftherelativeBACIcontrast(equalto100*

|contrast|/meanofRSvariablebeforetheintervention)vs.theP-valueoftheBACI test.NullhypothesisrejectionoftypicalP-valuethresholdsof0.05and0.1areshown asdashedlines.

Projectno.4 (showingdegradationwithrespecttocontrols) showsanoppositebehaviour:lowerP-value(i.e.higherconfidence) fortheLandsatanalysis.Here,thesmallsizeoftherestorationarea playsarole,resultinginapoorMODISspatialsampling,ontheone handbyreducingthesamplesizeandthepowerofthetest,andon theotherbymakingthefewMODISsampleslessreliable.Infact, theactualareasensedbytheinstrumentisgreaterthanthenomi- nalspatialresolution,andhasanellipticalshapecontrolledbythe sensorcharacteristicsandobservationgeometry(Duveilleretal., 2011;DuveillerandDefourny,2010).Thus,afractionofthesignal inpixelslocatedattheborderoftheprojectareamayoriginate fromanareaoutside.Thiseffectmaybenon-negligiblewhenthe projectareaiscomposedofonlyafewMODISpixels,asforproject no.4.

Besidesthestatisticaltestresult(i.e.rejectionofthenullhypoth- esisofnochange),therelativeBACIcontrastcanprovideadditional insights into the extent of the successof a given intervention project.Forinstance,withMODISanalysis,thisrangesfrom+6.3%

(degradationforprojectno.4)to−27.7%(improvementforproject no.14),indicatingadifferentmagnitudeoftheeffectofthedifferent restorationinterventions.

5.2. BACIresultsvs.qualitativeinformation

A general agreement between the qualitative information extracted from Google Earth VHR imagery and BACI results is observed.Inallsiteswherenosignsofinterventionsornodiffer- encewiththesurroundingareaswasobservedinVHRimagery,the BACIeffectisnotsignificant.Inallsiteswhereapatternofregularly plantedandestablishedtreeswereobserved,theBACIcontrastis negativeandtheBACIeffectissignificant,withtheexceptionof siteno.81whichisnotsignificantwhenLandsatisused.Thetest oftheBACIeffectalsoagreeswiththefieldqualitativeevaluation availableforfivesites.Amongthefivesites,twowereevaluatedas beingrelativelysuccessfulandarematchedbyasignificantBACI effect(sitesno.14and15),andthreewerenegativelyevaluated andarematchedbyanon-significantBACIeffect(sitesno.9,16 and44).Siteno.5,wherethepresenceofreforestationintervention wasnotvisible,wasinsteadfoundtohaveasignificantandnegative BACI.However,thefieldevaluationdidnotprovideanyinforma- tionaboutthegrasslandcoverthatmayhaveimprovedafterthe

fencingintervention,thustriggeringthestatisticaldetectionofa greeningeffect.

5.3. Applicabilityofthemethodtodifferentinterventiontypes

Albeitrestorationinterventionsthatdonotinvolvea“green- ing”cannotbescrutinisedusingNDVI,therangeofapplicability maybeexpandedusingthesamestatisticalframeworkwithother RS-based quantitative indicators, when considered relevant for assessingthesuccessofaspecifictypeofinterventionatagiven scale of analysis. For instance, soil erosion processes couldbe assessed by detecting erosionfeatures and eroded areas or by estimatingerosion-controllingfactors,suchassoilmoistureand surfaceroughness(AndersonandCroft,2009;Vrieling,2006).Spa- tialpatternmetricscouldsupporttheassessmentofrestoration interventionsthatimpacthabitatcomposition,fragmentation,and connectivityatlandscapelevel,alsoinrelationtolanddegrada- tionprocesses(Favaetal.,2015;Kéfietal.,2007).Asanadditional example,fine-scalequantitativemappingofspecificplantspecies (e.g.invasive)couldbecriticaltomonitortheeffectivenessofplant removalorcontrolefforts(PysekandRichardson,2010).

5.4. Applicabilityofthemethodtodifferentlandscapesettings

Topographicvariationsarenotexplicitlyaccountedforinthe describedmethod.Althoughnotanissueintheflatcase-studyland- scape,twoeffectsoftopographycanbeenvisagedinregionswith significantrelief.First,differentvegetationtypesgrowinlocations withdifferentelevation,slopeand aspect.Thus,controlsshould beselectedwithsimilartopographiccharacteristicswithrespect totherestorationsite.Asweexpectdifferentvegetationtypesto bespottedbytheclassificationofRSimagery,thisfirsteffectdoes nothampertheproposedmethod.Inaddition,inthecaseswhere suchtopographiccharacteristicsareexpectedtobeimportant,they couldbeaddedtotheinputlayersoftheclassification.Thesecond effectoftopographyisonthegeometryofthesun-target-sensor system,andthusonthereflectance.Moderatereliefvariationsare expectedtohaveaminorimpactonthemethodastheuseofaband ratiosuchastheNDVIwillreducethetopographiceffect(Leeand Kaufman,1986).Inaddition,differentilluminationconditions(at leastthoserelatedtothedirectlightcomponent)canbenormalised using,forinstance,slope-aspectcorrections(e.g.Teilletetal.,1982).

Thus,topographycanbetreatedanddoesnotlimittheapplicability ofthemethod.

TheBACIanalysisisacomparativemethodinwhichthetem- poral variability due to natural environmental conditions (i.e.

weather)isaccountedforusingcontrols.Therandomselectionof multiplecontrolsitesandthevisualinspectionoftheirstability overtheanalysisperiodminimisestheimpactoftheselectionof unsuitablecontrols(i.e.affectedbynonweather-drivenchangesin greennessafterthetimeofintervention).However,ifthelandscape aroundtherestoration areais subjectedto widespreadanthro- pogenicchanges(e.g.agriculturalintensification,urbanisation),the possibilityofselectingmultiplesuitablecontrolswillbeseverely limited,affectingthediscriminationpowerofthetest.Onthecon- trary,possiblenaturaldisturbancessuchasfiresorpestscanbe accountedforbythetest.Infact,adecreaseingreennesswouldbe detectedifthedisturbanceaffectedonlytherestorationsitewhile arelativeincreasewouldbemorelikelytobedetectedifthedis- turbanceaffectedseveralcontrols.Thechangeingreennessmaybe theninterpretedasdecreased(orincreased)vulnerabilitytosuch disturbancesduetotheintervention.

6. Conclusions

Forthefirsttime,abefore/aftercontrol/impact(BACI)design wasappliedtoRSdatatoevaluatethebiophysicalimpactofrestora- tionprojects. Large agreementwasfoundin thestatistical test outcomesusingeitherMODISorLandsatdata.Theavailabilityof frequentMODISobservationsmakesthedataofthisinstrument wellsuitedtothemostrobustBACIdesign,exploitingmultiplecon- trolsandmultipleobservationsbeforeandaftertheintervention.

TheuseofLandsatdatainourtestcasestudywaslimitedbythe pooravailabilityofcloud-freeimagery,compellingtheapplication ofasingle-timeBACIdesignandresultingingenerallylowercon- fidence(i.e.highersignificancelevel,P-value)ofthetestresults.

Theanalysisofmorerecentinterventionprojectswillbenefitof theavailabilityofmorefrequentsatelliteobservationsfromLand- sat8andSentinel2satellites.Thecombinationofhighspatialand temporalresolutionofferedbysensorssuchastheSentinels2may considerablyincrease thepotential ofthe proposedmethod.In addition,forearlierproject,theuseofcommercialsatellite(e.g.

SPOT4and5,RapidEye)maybeconsideredtocomplementthe freeimageryandincreasedataavailability.

Resultsofthestatisticalanalysiswereinagreementwiththe qualitativeinformationprovidedbyfieldobservationsandvisual interpretation of the VHR imagery in Google Earth. The pro- posedapproachcanbeconsideredafirstscreeningofrestoration interventionsthatmaydrivefurtherandcomplementaryinsitu analyses,thusincreasingthecost-efficiencyandfeasibilityofthe evaluationofrestorationinterventions.Inaddition,themethod- ology can be usedfor thelong-term monitoring of restoration interventions,thusallowingthebenefitsoftheinitialinvestment anditssustainabilitytobeevaluated.

WhenNDVIisused,theapplicabilityoftheproposedmethod islimitedtotheverificationofabiophysicalimpactintermsof variationinvegetationcover.Thisisnotlimitedtoreforestation andrangelandimprovementbuttoarangeofinterventions(e.g.

soilconservation,surfacewaterrun-offcontrol,infrastructuresfor irrigation,improvedlandgovernanceandmanagement,etc.)that alsocausere-greening.Theuseofotherremote-sensing-derived variables(e.g.soilmoisture,surfaceroughness,fragmentation,VHR plantspeciesmapping)mayfurtherextendtheapplicabilityofthe statisticalframeworktootheraspectsofrestorationinterventions.

Insitu analysesremainof fundamentalimportance,notonlyto provideamoredetailedsetofbiophysicalindicatorstargetedat thespecificrestoration,butalsotoconsiderotherkeyaspectsof restorationrelatedtosocialperceptionandeconomicimpacts.

Acknowledgements

WewouldliketothankMoustaphaBassimbéSagnaforthesup- portduringthefieldmissionsandtheCNRSHumanEnvironment Observatory(Tessekeré,Senegal)forthefinancialsupportforfield missions.Theworkwasfunded bytheAdministrativeArrange- mentbetweentheEuropeanCommissionDGDEVCOandtheJRC for ¨Technicalandscientific supporttoagricultureand foodand nutritionsecuritysectors”(TS4FNS2,ref.33272).

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