Overview of recent land cover changes, forest harvest areas, and soil erosion trends in Nordic countries

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Geography and Sustainability

journalhomepage:www.elsevier.com/locate/geosus

Overview of recent land cover changes, forest harvest areas, and soil erosion trends in Nordic countries ¹

Na Zhou

, Xiangping Hu

, Ingvild Byskov

, Jan Sandstad Næss

, Qiaosheng Wu

, Wenwu Zhao

, Francesco Cherubini

1School of Economics and Management, China University of Geosciences, Wuhan 430074, China

2Industrial Ecology Program, Department of Energy and Process Engineering, Norwegian University of Science and Technology, N-7491, Trondheim, Norway

3State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

h i g h l i gh t s g r a p h i c a l a b s t r a c t

•Weinvestigaterecent27-yearlandcover dynamicsinfourNordiccountries.

•Wetlandshowedthestrongestreduction, followedbyforestandsparsevegetation.

• Forest harvest areas have a non- negligibleoverlapwithforesttransition.

•SoilerosionratesinNordicarelowbut withexacerbatinglocationsinNorway.

a r t i c le i n f o

Article history:

Received 16 November 2020 Received in revised form 20 July 2021 Accepted 22 July 2021

Available online 28 July 2021 Keywords:

Land cover changes Spatiotemporal analysis Forest management Soil erosion

a b s t r a ct

Mappingspatiotemporallandcoverchangesoffersopportunitiestobetterunderstandtrendsanddriversofenvi- ronmentalchangeandhelpstoidentifymoresustainablelandmanagementstrategies.Thisstudyinvestigatesthe spatiotemporalpatternsofchangesinlandcovers,forestharvestareasandsoilerosionratesinNordiccountries, namelyNorway,Sweden,Finland,andDenmark.Thisregionishighlysensitivetoenvironmentalchanges,asit isexperiencinghighlevelsofhumanpressureandamongthehighestratesofglobalwarming.Ananalysisthat usesconsistentlandcoverdatasettoquantifyandcomparestherecentspatiotemporalchangesinlandcoverin theNordiccountriesismissing.TherecentproductsissuedbytheEuropeanSpaceAgencyandtheCopernicus ClimateChangeServiceframeworkprovidethepossibilitytoinvestigatethehistoricallandcoverchangesfrom 1992to2018at300mresolution.Thesemapsarethenintegratedwithtimeseriesofforestharvestareasbe- tween2004and2018tostudyifandhowforestmanagementisrepresentedinlandcoverproducts,andwith soilerosiondatatoexplorestatusandrecenttrendsinagriculturalland.Landcoverchangestypicallyinvolved from4%to9%ofthetotalareaineachcountry.Wetlandshowedthestrongestreduction(11,003km²,−11%

ofthewetlandareain1992),followedbyforest(8,607km²,−1%)andsparsevegetation(5,695km²,−7%), whileagriculture(15,884km²,16%)andsettlement(3,582km²,84%)showednetincreases.Wetlandshrinkage dominatedlandcoverchangesinNorway(5,870km²,−18%),followedbyforestandgrasslandwithanetgain of3,441km²(3%)and3,435km²(10%),respectively.InSweden,forestareasdecreased13,008km²(−4%), mainlyduetoagricultureexpansion(9,211km²,29%).InFinland,agriculturalareasincreasedby5,982km² (24%),andwetlanddecreasedby6,698km²(−22%).SettlementhadthelargestnetgrowthinDenmark(717

1 GivenhisroleasAssociateEditorofthisjournal,WenwuZhaohadnoinvolvementinthepeer-reviewofthisarticleandhasnoaccesstoinformationregarding itspeer-review.Fullresponsibilityforthepeer-reviewprocessforthisarticlewasdelegatedtoCarlaSofiaSantosFerreira.

∗Correspondingauthor:XiangpingHu

E-mailaddresses:[email protected](X.Hu),[email protected](Q.Wu).

#Theseauthorscontributedequally

∗∗Co-Correspondingauthor:QiaoshengWu

https://doi.org/10.1016/j.geosus.2021.07.001

2666-6839/© 2021TheAuthors.PublishedbyElsevierB.V.andBeijingNormalUniversityPress(Group)Co.,LTD.onbehalfofBeijingNormalUniversity.Thisis anopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/)

km²,70%),mainlyfromconversionofagricultureland.SoilerosionratesinNordiccountriesarelowerthanthe globalaverage,buttheyareexacerbatinginseverallocations(especiallywesternNorway).Theintegrationofthe landcoverdatasetswithmapsofforestharvestareasshowsthatthemajorityofthelossesinforestcoverdueto forestryoperationsarelargelyundetected,butanon-negligibleshareoftheforest-to-agriculture(upto19%)or forest-to-grassland(upto51%)transitionsoverlapwiththeharvestedsites.Forestryactivityinthestudyregion primarilyinvolvessmall-scaleharvesteventsthataredifficulttobedetectedatthe300mresolutionoftheland coverdataset.Anaccuraterepresentationofforestmanagementremainsachallengeforglobaldatasetsofland covertimeseries,andmoreinterdisciplinaryinternationaleffortsareneededtoaddressthisgap.Overall,this analysisprovidesadetailedoverviewofrecentchangesinlandcoverandforestmanagementinNordiccountries asrepresentedbystate-of-the-artglobaldatasets,andoffersinsightstofuturestudiesaimingtoimprovethese dataorapplytheminlandsurfacemodels,climatemodels,landscapeecology,orotherapplications.

1. Introduction

Influencedbyanthropogenicdriversandclimatewarming,changes inlandcoverareanimportantindicatorofglobalenvironmentalchange (Henriksen and Hilmo, 2015; Saco et al., 2018; Song et al., 2018; Huangetal.,2020;Huetal.,2021b),andplayakeyroleforclimate change mitigation, sustainablefoodsupply, andnature conservation (MousivandandArsanjani,2019;Roeetal.,2019;Smithetal.,2020).

Historically,land cover changes primarily occurred asdeforestation duetoagricultureexpansionandurbanization(Arsanjanietal.,2013; Nunesetal.,2016;Leirpolletal.,2021).Landcoversreflectthedistri- butioncharacteristicsofsurfacevegetationandecosystems(Szogsetal., 2017),andmonitoringtheirspatialdifferentiationandevolutiontrend isinstrumentalinmoresustainableplansofregionalecologicalenviron- ments,reducingcarbon emissions,andpreventingbiodiversitylosses andsoilerosion(Crooksetal.,2017;Cherubinietal.,2018b;Peietal., 2018;Taubertetal.,2018;Renetal.,2020;Huetal.,2021a).Ade- tailedunderstandingof landuse dynamicsisalsocrucialforsuccess- fulimplementationofvariousprogramswithinclimatechangemitiga- tion(Cherubini etal., 2018a;Huetal.,2019;Duveilleretal.,2020; Huangetal.,2020),foodsecurity(Gomesetal.,2019;Gavaetal.,2020), renewableenergysupply(Leirpolletal.,2021;Næssetal.,2021),and natureconservation(Leclèreetal.,2020;Strassburgetal.,2020).

Basedonsatellite remotesensingdata,many globalandregional landcoverdatasetshavebeenproduced,resultinginavarietyofglobal andregionallandcoverproducts(Grekousisetal.,2015),withdiffer- entland cover classifications,accuracy, resolution andtime periods (Mousivand andArsanjani,2019).One of thefirstgloballandcover productsistheInternationalGeosphere-BiosphereProgram’sDataand InformationSystem(Loveland andBelward,1997),developedbythe U.S.GeologicalSurveyandtheEuropeanCommission’sJointResearch Centre,containing17 classeswith1kmspatialresolution. TheEuro- peanCommissionissuedanothergloballandcoverproductGLC2000 with1kmresolutionand22classes(BartholomeandBelward,2005).

TheGlobeLand30datasetreleasedinChinacontains10landcovertypes with30mresolution(Chenetal.,2015).Globallandcovermapping fromModerate ResolutionImaging Spectroradiometer(MODIS)com- binesfivelandcoverclassificationschemesatanannualtimestepfrom 2001at1kmresolution(Friedletal.,2002).TheLand-useHarmoniza- tion2projectaimstosmoothlyconnectupdatedhistoricalreconstruc- tionsofland-usewithnewfutureprojections,andrecentlyproduced adatasetataresolutionof0.25 × 0.25degreeoveralongtimepe- riod(850–2100,withextensionsto2300)(Hurttetal.,2020).Allthese datasetsonlycoverspecificyearsorindividualyears,or,whentime- seriesareavailable,theyhaverelativelycoarseresolutions.Therecently releasedESAclimatechangeinitiativelandcoverproducts(ESA-CCI-LC) providetimeseriesofgloballandcovermapsfor37landcovercate- goriesfrom1992to2015atahighspatialresolutionof300matthe equator(ESA,2017).ThiswasfollowedbyconsistentmapsfromCoper- nicusClimateChangeServiceclimatedatastoreandlandcoverproducts (C3S-CDS-LC)from2016to2018(C3S,2019).Themethodusedand characteristicsofthe2016to2018landcovermapsareconsistentwith

theprocessusedtocreatetheESA-CCI-LCmaps.Thesedatasetscombine multipleremotesensingproductsandground-truthobservations,and theywerespecificallydevelopedtoadvanceamorerealisticrepresenta- tionoflandcoverdynamicsinclimatemodels(Plummeretal.,2017).

TheESA-CCI-LCdatasetwasalsousedtocharacterizetemporaldynam- ics andspatialpatterns ofchanges inland coveratalandscape and globallevel(Liuetal.,2018a;Liuetal.,2018b;MousivandandArsan- jani,2019).ExistingstudiesusedtheESA-CCI-LCdatasettoinvestigate aggregatedgloballandcoverchanges(Liuetal.,2018a;Mousivandand Arsanjani,2019;Huetal.,2021b).Forexample,MousivandandArsan- jani(2019)analyzedthegainsandlossesofdifferentlandcovertypes aroundtheworldfromtheyear1992to2015andpredictedthechanges ofdifferentlandcovertypesin2030and2050.They,however,didnot pointoutthespecificlocationsofthechanges.Huetal.(2021b)recently analyzedthelandcoverdynamicsusingESA-CCI-LCandC3S-CDS-LC products,buttheanalysisfocusedontheglobalscalewithnocountry specificinsights.

Thereisanunderrepresentationofborealandarcticregionsinthe availableEuropeanlanduseandlandcoverliterature(Plieningeretal., 2016).Tothebestofourknowledge,ananalysisthatusesconsistent datasettoquantifyandcomparetherecentspatiotemporalchangesin landcoverintheNordiccountriesismissing.Thisregionishighlysen- sitivetoenvironmentalchangesasitisexperiencinghighhumanpres- sure(agriculture,forestry,andurbanexpansion)andthehighestrates ofglobalwarming(IPCC,2019).Existingresearchmainlyfocusedon specificchangesandeffects,suchasforestcharacteristicsandkeycli- maticvariables(Lukesetal.,2016;Cherubinietal.,2017;Iordanetal., 2018a), biodiversity loss(Auffret et al., 2018; Iordan etal., 2018b; Fourcade et al., 2019), grasslandrestoration (Milberg etal., 2019), water risk (Räsänen et al., 2018), carbon reserves (Vauhkonen and Packalen,2018) andwoodouttake potentials(Huet al.,2018). The Nordicregionis highlydeveloped,butpopulationdensities,landuse characteristics,andthecorrespondinghumanfootprintvaryacrossthe region(Venteretal.,2016).Forexample,croplandisthemostextensive landuseinDenmark(Karvonenetal.,2018;Osei-Owusuetal.,2019; Vogdrup-Schmidtetal.,2019)butitismuchlesspresentinotherNordic countries(Strand,2013;(FAO,2020)).ForestscoverlargepartsofNor- way,SwedenandFinland(FAO,2020),withthemajoritybeingman- agedforestsperiodicallyexposedtoharvest(Ceccherinietal.,2020).It islargelyunclearhowforestharvestareasaredetectedbytheESA-CCI- LCandC3S-CDS-LCproducts,andtherearerisksthattheyarereported astransitionsof foresttoothertypesoflandcover (e.g.,agriculture orgrassland).TheintensivemanagementofforestsinNordiccountries makethemtheidealcaseforstudyingthisissue.Therearealsodynam- icsotherthanmanagementthatareinfluencingchangesinforestar- easinNordiccountries.Anthropogenicactivitiessuchasdomesticgraz- inghavehistoricallykepttheforestextentdowninoutfieldareas,but recentlyreducedgrazingintensityhasspurrednaturalforestregrowth (Wehnetal.,2012;Miennaetal.,2020).Owingtothepolaramplifica- tioneffect,theNordicregionisexperiencingthehighestratesofglobal warmingintheworld,affectinglanduse dynamicssuchastreelines (BrynandPotthoff,2018),greeningofbareareaandscarcevegetated areas (Myers-Smithetal.,2020),andhigherrisks ofwetland drying

(Lohilaetal.,2010;Werneretal.,2013).Forestclearancehasalsooc- curredduetorecenturbanexpansion(vanVliet,2019).Agriculturalin- tensificationhasledtoincreasedhomogenizationofNordiclandscapes, withbothcroplandexpansionintopreviouslymosaiclandscapes,and forestexpansionintonewlyabandonedmarginalareas(Auffretetal., 2018;Auneetal.,2018).AgriculturallandscapesinNordiccountries alsofacechallengesofsoilerosion,andmoreresearchisneededtobet- terunderstandtheinterplaybetweenhumandrivingforcesandthecli- maticdrivers(windandprecipitation),inordertotakemeasuresagainst erosion(Ulénetal.,2012).

Thepurpose of this studyis toinvestigate therecentland cover changesandlandtransitionsfrom1992to2018inNorway,Sweden, Finland,andDenmark,asrepresentedbytheESA-CCI-LCandC3S-CDS- LCdatasets.HereafterthenameCCI-LCdatasetisusedwhenthesetwo landcoverdatasetsarecombined.Thisanalysisshowswherelandcovers aredistributedandwherethemajortransitionsoccurred,aswellasthe changesinsoilerosionassociatedwithagricultureactivities.Wealso identifyareasofforestharvestfromanotherindependentdatasetand investigatetheoverlapbetweenclear-cutareasandforesttransitionsto otherlandcovers(agriculture,grassland,sparsevegetation,andshrub- land)inordertoinvestigatehowforestmanagementisrepresentedby theCCI-LCdataset.

2. Methodology 2.1. Landcoverdatasets

Theoriginallandcoverdataisfromthelandcovermapsissuedby ESAandtheEuropeanCentreforMedium-RangeWeatherForecasts.The mapshaveaspatialresolutionof0.002,778degree(approximately300 mattheequator)onanannualbasisfrom1992to2018.Thedataun- til2015comefromtheESA-CCI-LCproduct(V2.0.7b),andfrom2016 to2018fromtheC3S-CDS-LCproduct(V2.1.1).Thetwoproductsare highlyconsistentanduse theclassificationsystemdeveloped bythe UnitedNationsFoodandAgricultureOrganization,whichdividesthe globallandcoverinto37classes.Tomoreaccuratelymapandquan- tifychangesinlandcoverattheregionalandnationallevels,themore genericIPCClandclassificationisusedtoregrouptheoriginal37land coverclassesinto9classesusingacross-walkingtable(Table1).Thanks tothis,wecanavoidfalsedetectionbetween semanticallycloseland coverclasses(ESA,2017).

2.2. Trendsoflandcoverchanges

ForeachNordiccountry,wecalculatedtheinterannualchangesin landcoverclassrelativeto1992usingEquation(1).

AreaChange_{𝑖,𝑗,𝑡}= Area_{𝑖,𝑗,𝑡}

Area_{𝑖,𝑗,1992} (1)

wherei,j,tistheindexforlandcoverclasses,countries,andyear,re- spectively.Area_i,j,tdenotestheareaoflandcovertypeiincountryjin yeart.Duetothecurvatureoftheearth,areasofgridswith300mres- olutionattheequatorarevaryingwithlatitudeandcanbeobtainedby Equation(2).

𝐴_𝑞=

π𝑟²×||lon₂−lon₁||×|||sin( lat₂)

−sin( lat₁)|

||

180 (2)

where,A_qisthecalibratedareaofgridq,lon₁,lon₂,lat₁andlat₂are thelongitudeandlatitudeofthegridq,ristheearthradius.Foragiven yeart,thetotalareaoflandcovertypeiincountryjiscalculatedby Equation(3),

Area_{𝑖,𝑗,𝑡}=

∑𝑁 𝑞=1

𝐴𝑞 (3)

whereNdenotesthetotalnumberofgridsoflandcovertypeiincountry jat300mresolution.Theanalysisoflandcoverchangesisbasedon

theoriginalspatialresolution(300m)oftheCCI-LCdataset,andlower resolutionsareonlyusedinthefiguresoftheresultsforvisualization purposes.

2.3. Landcovertransitionanalysis

Toexplorethetypicalcharacteristicsofchangesinlandcoverinthe fourNordiccountries,wequantifiedthelandcovertransitionsforeach pairoflandcoverclassesbetween1992and2018byatransitionmatrix.

Inthetransitionmatrix,eachelementistheareathatchangedfromone categorytoanother.Forinstance,theelementinthep-throwandq-th columnofthetransitionmatrixistheareaoflandcovertransitionfrom landcoverclasspin1992tolandcoverclassqin2018,andtheareaof transitionwasobtainedbysummingallthepixelsareasobtainedfrom Equation(2)forallthegridsthatareclassifiedaslandcoverclasspin 1992butaslandcoverclassqin2018whenp≠q.Whenp=q,the elementinthetransitionmatrixissetto0.

Wealsoinvestigate thespatialpatternsofchangesin landcover.

Wefirstidentifythelandcovergains(codedas+1)andlosses(coded as−1)ineachpixelwiththeoriginal300mresolutionforeachland coverclass.Ifthereisnochangeinthepixel,itiscodedas0.Wethen aggregate theresultsto9kmresolutionandcalculatethearearatio ofthechangesintheaggregatedgrid.Thisaggregationhelpstobetter visualizethespatialdistributionofthechangesineachlandcover.

2.4. ForestmanagementinNordiccountries

Inthe fourstudiedNordiccountries,forestshavebeen underin- tensivemanagement(Schelhaasetal.,2018;Ceccherinietal.,2020), andpracticesofsustainableforestmanagementhavebeenpursuingto achieveecosystemservicessuchascarbonsequestration,woodproduc- tion,natureconservationandclimatechangemitigation.Commonhar- vest methodsfocusonlimitedareaextents,withforestpatcheskept underrotation,andinFinlandandSwedenthinningismorecommon thaninNorway.About50%oftheforestclear-cutareasinFinlandin- volvepatcheslargerthan7ha,andabout20%oftheharvestedsites arebetween3.5and7ha(therestoccursinareassmallerthan3.5ha) (Ceccherinietal.,2020).InSweden,theaveragesizeofclear-cutareas largerthan7haisabout75%,whereasinDenmarkitismuchsmaller (20%).

Itislargelyunclearifandhowforestharvestareasaredetectedby theCCI-LCproduct.Giventheaveragesmallerextentofharvestedareas thantheresolutionofthelandcovermaps,theymightbeundetectedor reportedasforesttransition,mostlikelyagriculture,grassland,sparse vegetationorshrubland.Weinvestigatethepotentialoverlapbetween foresttransitionsandforestharvestareausingtheglobalforestchange (GFC)mapsfromHansenetal.(2013).TheseGFCmapsprovidesyearly estimatesoftreecoverchange(gainsandlosses)relativeto2000witha spatialresolutionofapproximately30m(attheequator),andtheyare availableinGoogleEarthEngine(GEE)(Gorelicketal.,2017).GEEisa platformcontainingahugeamountofEarthSciencedataandprovides parallelcloud-computingandgeospatialoperationwithlotsofready- to-usefunctions.Ceccherinietal.(2020)usedtheGEEplatformtoin- vestigatetheharvested(clear-cutonly)forestareasover26European countriesbydetectingtheforestcoverchangeswhichwereattributed toeitherforestmanagement,i.e.,forestharvest,ordisturbancedueto fireandwindstorms.Theyassumedthatwindstormscauselargerarea lossesthanforestmanagement,andtheythusfilteredoutthelossesthat arethreetimesthemedianabsolutedeviationawayfromthemedian.

TheEuropeanforestfireinformationsystemdatasetisusedtomaskout burnedforest(EFFIS,2019).Theremaininglossesinforestcovercan thusbetreatedasaproxyforforestharvestarea.

WeaggregatetheGFCforestlossmapsto300m,i.e.,thesamespatial resolutionofourlandcoverdatasets,andcomputetheannualpercent- ageofforestlossesbetween2004and2018usingtheGEEplatform.We thenpostprocesstheresultstoobtaintheforestharvestmapsfollowing

Table1

ConversionoftheoriginallandcoverclassesinCCI-LCdatasetintothestandardIPCClandcoverclasses(basedon ESA(2017)cross-walkingtable).AGR:agriculture,FOR:forest,GRA:grassland,WET:wetland,SET:settlement,SHR:

shrubland,SPA:sparsevegetation,BAR:barearea,WAT:water,SNO:permanentsnowandice.

IPCC classes Code Legend used in the CCI-LC product 1 AGR 10, 11, 12 Rainfed cropland

20 Irrigated cropland

30 Mosaic cropland ( > 50%) / natural vegetation (tree, shrub, herbaceous cover) ( < 50%) 40 Mosaic natural vegetation (tree, shrub, herbaceous cover) ( > 50%) / cropland ( < 50%) 2 FOR 50 Tree cover, broadleaved, evergreen, closed to open ( > 15%)

60, 61, 62 Tree cover, broadleaved, deciduous, closed to open ( > 15%) 70, 71, 72 Tree cover, needleleaved, evergreen, closed to open ( > 15%) 80, 81, 82 Tree cover, needleleaved, deciduous, closed to open ( > 15%) 90 Tree cover, mixed leaf type (broadleaved and needleleaved) 100 Mosaic tree and shrub ( > 50%) / herbaceous cover ( < 50%) 160 Tree cover, flooded, fresh or brackish water

170 Tree cover, flooded, saline water

3 GRA 110 Mosaic herbaceous cover ( > 50%) / tree and shrub ( < 50%)

130 Grassland

4 WET 180 Shrub or herbaceous cover, flooded, fresh-saline or brackish water

5 SET 190 Urban

6 SHR 120, 121, 122 Shrubland

7 SPA 140 Lichens and mosses

150, 151, 152, 153 Sparse vegetation (tree, shrub, herbaceous cover) 8 BAR 200, 201, 202 Bare areas

9 WAT 210 Water

theapproachdescribedbyCeccherinietal.(2020).Theseforestharvest mapsareintegratedwiththelandcovermapstoinvestigatetheextent towhichforestharvestareasoverlapwiththeforesttransitionreported bytheCCI-LCproductsusingthefollowingEquation(4):

𝑟_𝑗=area_{𝑗,forestharvest}∩ area_{𝑗,for→agr}

area_{𝑗,for→agr} (4)

wherer_jistheoverlapratio,area_{forestharvest}is the area of harvested for- est,area_for→agris thearea offorest toagriculture transition,jisthe index ofcountry,and∩isthelogicalconjunctionoperator.Thesameproce- dureisusedfortheothertypesofpossibletransitionsthatcanrepresent forestmanagement:foresttograssland,foresttosparsevegetationand foresttoshrubland.Wefurtherinvestigatethetrendsoftheforesthar- vestin thefourNordiccountries.Theareasof annualforestharvest from2004to2018inSweden,DenmarkandFinlandcanbedirectly obtainedfromCeccherinietal.(2020)whiletheannualforestharvest areasinNorwayareestimatedfollowingthesameapproachgivenby Ceccherinietal.(2020).

2.5. Changesofsoilerosion

We explore the changes in soil erosion rates (SERs) due to agriculture activity in Nordic countries using the SER maps from Borrellietal.(2017),whichcontainstheinformationonSERfor2001 and2012.TheSERmapsareobtainedusingamodelapproachbased on the revised universal soil loss equation (RUSLE) (De Vente and Poesen,2005).TheRUSLE-basedmodelling approachis widely used forpredictingsoil erosion(Borrellietal.,2017;Borrellietal., 2020) and provides estimates of potential soil displacement rates by wa- tererosion(DeVenteandPoesen,2005).TheSERmapsprovidedby Borrellietal.(2017)furtherconsideredthelandcovertypes,theirdy- namicsandfarmingsystem.FollowingHuetal.(2021b),weassume thatSERchangedlinearlyduringthetwoyears,

𝑘𝑞,𝑗=SER_{𝑞,𝑗,2012}−SER_{𝑞,𝑗,2001}

Δ𝑡 (5)

forgridqincountryj,and∆t=11.InEquation(5), onlythegridq thatisclassifiedasagriculturebothin2001and2012wasconsidered tosingleoutsoilerosionduetoagricultureactivity.WithEquation(5), thetotalincreasedsoilerosionincountryj(denotedas∆SE_j) inthe

period2001-2012wasobtainedasfollows

ΔSE_𝑗=∑

𝑞 11∫

0 𝑘_𝑞,𝑗𝑡𝑑𝑡×𝐴_𝑞=11 2

∑

𝑞

(SER_{𝑞,𝑗,2012}−SER_{𝑞,𝑗,2001})

×𝐴_𝑞 (6)

where𝑘_𝑞,𝑗 is givenin Equation (5), andA_q is theareaofgrid q. In Equation(6),thesumisonlyovergridqthatisclassified asagricul- turebothin2001andin2012.

3. Results

3.1. Variationoflandcovers

Table2showstheareaofeachlandcoverinthefourNordiccoun- triesin1992and2018accordingtothedatasetprocessedinthisanaly- sis.ForestisbyfarthelargesttypeofsurfacecoverinNorway,Sweden andFinland,accountingformorethan40%ofthetotalareaineach country.AgriculturallandisthemostcommonlandcovertypeinDen- mark.Despitehavingthesmallestpopulation,Norwayhasthelargest extentinurbansettlementsinNordiccountries.Asimilarextentofwet- landareas ispresentinalltheNordiccountries,whilegrasslandand sparsevegetationaremainlyabundantinNorwayandSweden.

Thetrendsinlandcoverchangesarecomparedintermsofnormal- izedinterannualchangesbetween1992and2018inFig.1.Agriculture remainedrelativelyconstant(orslightlyreduced)inDenmark,butital- mostlinearlyincreasedintheotherthreecountries(atahigherpacein SwedenandFinlandthanNorway).Relativechangesinforestareaswere smallerthanthoseinotherlandcovers,largelyduetotheabundantfor- estareasinthecountriesthatminimizethenormalizedchangesinthis landusetype.Forestareasincreasedinallcountriesatasimilarrate, exceptforSweden,whereforestareassteadilydeclined.Bothgrassland andbareareainFinlandshowhighnormalizedgains,whilevariations arelimitedintheothercountries.Thismightbeduetothelimitedarea extentofthesetwolandcovertypesinFinland,whichmakenormal- izedresultsverysensitivetoevensmallchangesinareas.Wetlandis firmlydeclininginNorwayandFinland,whilesettlementexpandsin everycountry(especiallyinNorway).Shrublandandsparsevegetation showcontrastingtrends,especiallyinFinland.Theselandcoverclasses areverysimilarintermsofvegetationcover,andtheirdistinctioninthe landcoverdatasetischallenging.Theirunivocalidentificationandin- terchangesshouldbeinterpretatedwithcare.Moreexplanationsabout thedriversofthesechangesareprovidedinthefollowingsections.

Table2

AreaofeachlandcoverinthefourNordiccountriesin1992and2018(Unit:1,000 km²).AGR:agriculture,FOR:forest,GRA:grassland,WET:wetland,SET:settlement, SHR:shrubland,SPA:sparsevegetation,BAR:barearea,WAT:water.

Categories

Norway Sweden Finland Denmark

1992 2018 1992 2018 1992 2018 1992 2018

AGR 13.48 15.13 32.02 41.23 24.48 30.47 32.29 31.33 FOR 137.60 141.04 309.79 296.79 238.58 239.42 5.26 5.38 GRA 35.77 39.20 17.41 19.01 1.16 1.94 2.84 2.99 WET 31.88 26.01 32.40 33.96 31.13 24.43 0.66 0.66 SET 1.69 3.54 1.13 1.98 0.44 0.60 1.03 1.75 SHR 3.19 3.19 0.38 0.63 0.23 0.39 0.00 0.00 SPA 61.97 58.93 14.43 13.82 6.66 4.62 0.01 0.01 BAR 17.61 17.44 2.68 2.74 0.18 0.38 0.06 0.06 WAT 29.58 28.28 43.22 43.30 36.26 36.89 3.51 3.48

Fig.1.Areachangesofeachlandcoverclass relativetoitsextentin1992foreachNordic country.Lines of different colors with solid dotsrepresentthefourNordiccountries:blue forNorway(NOR),orangeforSweden(SWE), greyforFinland(FIN)andyellowforDenmark (DNK).Thehorizontalaxisisthestudyperiod, andtheverticalaxisistheareachangeofeach yearrelative to1992. Valueshigherthan 1 showanincreaserelativeto1992,whileval- uessmallerthan 1indicateadecrease.AGR:

agriculture,FOR:forest,GRA:grassland,WET:

wetland,SET:settlement,SHR:shrubland,SPA:

sparsevegetation,BAR:barearea.

Ingeneral, theinterpretation ofthesetrendsshouldconsiderthat theyrefer tothechanges relativetothe areaextension within each country in 1992, and they arehighly sensitive tothe initial extent oftheparticulartypeoflandcoverinagivencountry.Forexample, largenormalizedchangesinonelandcoverinonecountrycancorre- spond tomuch smallerhectaresof landthanacountrythat showed minimal increases in normalizedvalues, if thelatter countryhad a much largerpresenceof thelandcoverin questionin 1992.Results inFig.1arethustobeinterpretedtogetherwiththeabsolutevaluesin Table2.

3.2. LandcovertransitionsinNordiccountries

Thenettrendsoflandcovertransitionsinthefourcountriesfrom 1992to2018areshowninFig.2.InNorway,thedominantnetchange in landcoveris anetlossof 5,870km² in wetland,whichis about 18%ofthewetlandareain1992.Wetlandmostlytransitionedtofor- est,becauseofprogressivewetlanddryingduetoclimatechange,water drainageandotheranthropogenicfactorssuchasexcessivewaterwith- drawalsor eutrophication(Lohila etal.,2010; Werneret al.,2013).

Agriculture,forest,andgrasslandshowanetgainof1,650km²(12%), 3,441km²(3%)and3,435km²(10%),respectively(Fig.2a).Thegross areagainofsettlementis1,857km²,ofwhich59%and31%arederived fromforestandagriculture,respectively.InNorway,46%ofthereduc- tionsinsparsevegetationisduetoconversiontograssland,and42%

isconvertedtoforest.ThelatterisalsoobservedinFinland,andcan beassociatedtoreducedhumanpressure,e.g.,decreasinggrazingrates (Miennaetal.,2020),and/oracceleratedvegetationgrowthcausedby climatechange(Kellomäkietal.,2018).InSweden,thereisanetloss inforestareaof13,008km²(−4%),mainlycausedbyanexpansionof agriculture,makingitthelargestnetgain(9,211km²,29%)(Fig.2b).

Theprimarysourcesofsettlementexpansionareagricultureandfor- est,which accountedfor45%and43%ofSweden’ssettlementgross gain,respectively. ForestshowsthelargestgrossincreaseinFinland, andwetlandwasitslargestsource,makingwetlandthelargestnetloss (6,698km²,−22%).Incontrast,agriculturewasthecountry’slargestnet gain(5,982km²)whichincreasedby24%comparedto1992,mainly fromforest(Fig.2c).Theabsolutenetareachangesofsettlement,shrub- land,andbareareainFinlandwererelativelysmallcomparedwithother landcovers,whichwere160km²,160km²,and198km²,respectively.

DuetothelowproportionoftheabovethreelandcoversinFinland, however,therelativechanges ofthese landcoverclasseswere37%, 69%,and108%respectively.Theareatakenfromforestwasthelead- ingsourceofFinland’ssettlementexpansion,accountingfor67%ofthe grossgain(180km²),andthecontributionofagriculturewas21%(34 km²).During thestudyperiod,settlementhasthelargestnetgrowth inDenmark(717km²,70%),mainlytransitionedfromagricultureland (Fig.2d).Besides,theareaofforestandgrasslandalsoshowedanet increase,whileagricultureshowedthelargestnetreduction(956km²,

−3%).

Fig.2.Landcovertransitionsbetween1992 and2018in(a)Norway (NOR),(b)Sweden (SWE),(c)Finland(FIN),(d)Denmark(DNK), and (e) comparison of land cover changes acrosscountries.For(a)-(d),thedifferentland cover types (IPCC classification system) are showninthehorizontalaxis,andthevertical axisistheareachangedof landcover.Posi- tivevaluesindicateagrossincreaseandneg- ativevaluesagrossdecreaseintheextentof thecorrespondinglandcovers(i.e.,“converted to” ifnegative,and“convertedfrom” ifposi- tive).Legendwithdifferentcolorsshowsthe typeoflandcovertransitions.Reddotsshow thenetgainsorlossesofeachchange.In(e), theverticalaxisisthepercentagechangeof eachlandcoverrelativetoeachcountry’sto- talarea,andthehorizontalaxisisforthefour Nordiccountries.AGR:agriculture,FOR:for- est,GRA:grassland,WET:wetland,SET:settle- ment,SHR:shrubland,SPA:sparsevegetation, BAR:barearea.

Table3

ComparisonofchangesinlandcoverinNorway(NOR),Sweden(SWE),Finland(FIN),Den- mark(DNK)andintheentireregion(Nordic)from1992to2018(Unit:1,000km²).AGR:

agriculture,FOR:forest,GRA:grassland,WET:wetland,SET:settlement,SHR:shrubland, SPA:sparsevegetation,BAR:barearea.

Region AGR FOR GRA WET SET SHR SPA BAR

NOR +1.65 +3.44 +3.44 − 5.87 +1.86 +0.01 − 3.05 − 0.17 SWE +9.21 − 13.01 +1.60 +1.56 +0.85 +0.25 − 0.60 +0.07 FIN +5.98 +0.84 +0.77 − 6.70 +0.16 +0.16 − 2.04 +0.20 DNK − 0.96 +0.12 +0.15 +0.004 +0.72 0.00 − 0.001 − 0.03 Nordic +15.88 − 8.61 +5.96 − 11.00 +3.58 +0.41 − 5.70 +0.10 Note:Thevaluesinthetableshowtheabsolutechangeofeachlandcovertypefrom1992 to2018ineachcountry.Positive:netgains,negative:netloss,and0.00:nochange.

Anoverallcomparisonofthechangesinlandcoverinthefourcoun- triesisshowninFig.2eandTable3.Landtransitionstypicallyinvolved from4%to9%ofeachcountry’’s totalarea.Thehighestpercentage ofchange in landcoverisin Norway,followedbyFinland, Sweden, andDenmark.ThereductionofwetlandinNorwayandFinlandmade itthestrongestnetlossofasingleclassinthestudyregion(11,003 km², −11%),followedbyforest(8,607km², −1%)andsparsevege- tation(5,695km²,−7%).Theareaofforestin Swedendecreased, in contrasttotheotherthreecountries,whichledtoadeclineinforest throughouttheregion.Otherlandcoverssuchasagriculture,grassland,

settlement, shrubland, andbarearea, showednetincreases. Agricul- tureareadecreasesinDenmarkbutincreasesinotherthreecountries.

Therefore,theagrcultureareainNordicregionincreases,asshownin Table3.

3.3. Spatialdistributionoflandcoverchanges

Heatmapsthatidentifymajorhotspotsofchangesinlandcoverare showninFig.3.AgriculturedecreasedinDenmarkandincoastalar- easofNorway,andmainlyincreasedinthesouthernpartof Sweden

Fig.3. SpatialdistributionoflandcoverchangesinNordiccountries.Thevalue inthecolorbarshowsthefractionoflandcoverchangeineach9km×9kmgrid cellfrom1992to2018.Apositivevalueindicatesanincreaseandanegative valueadecreaseofthelandcover.AGR:agriculture,FOR:forest,GRA:grass- land,WET:wetland,SET:settlement,SHR:shrubland,SPA:sparsevegetation, BAR:barearea.Notethatthescalesofcolorbarsaredifferentineachsubfig- ure.Theaggregationfromtheoriginalresolutionof300misforvisualization purposesonly.

andFinland.ThesedynamicscanbeassociatedwithlocalandEUpoli- ciesfavoringlocalagriculturecommodities,whilecroplandabandon- mentisoftenaresultofsocio-economicgrowthor,tosmallerextent inEU,due toseveredeclinesinsoil fertility(Alcantaraetal., 2013; Yinetal.,2020).ForestareasexpandedespeciallyincentralNorway andthenorthernpartofNordiccountrieswhiletheydeclinedinSwe- den,southernNorwayandFinland.Majorwetlanddeclinesoccurredin NorwayandnorthernFinland,typicallyonareaswhereforestexpanded.

Warmingofclimateinhighlatitude,availabilityofnutrientforplant growthandextensivewaterwithdrawalsarethemaindriversofwet- landdrying(Werneretal.,2013;Greenetal.,2017;Huetal.,2021b).

Wetlandisanimportantecosystemthatregulateslocalclimateandbio- diversity,butitishighlysensitivetoclimatechangeandanthropogenic pressure (Ghajarniaetal., 2020).Intheinvestigated CCI-LCdataset, wetlandinNorwayshowsincreasingdeclinesmostlytoforestandsparse vegetation.Onthecontrary,wefindasmallincreaseinwetlandinSwe- den(5%).Thismightreflecttheoutcomeofmajorpolicyprogramsand effortsinSwedentopreservewetlands,whichevenledtolarge-scale constructionsofnewwetlandswithassociatedagro-environmentalben- efitstonutrientretentionandbiodiversity(StrandandWeisner,2013).

Sparsevegetationdeclinedinthenorthernpartoftheanalysisdomain, mostlyconcomitantwithexpansionofgrasslandorforest,andthisisre- latedtothegreeningoftheearth(Zhuetal.,2016).Settlementmainly increasedinflatareasinthewestandsouthernpartofNordiccoun- triesandaroundthemainexistingurbanareas,anditmightbelinked tothegrowingpopulationin theNordiccountries.Secondhomesin theNordicregionmightalsobeapotentialdriverofsettlementarea increases(Müller,2007;LarssonandMüller,2019).

3.4. Forestharvestareasandtheirrelationshipwithlandcovertransitions

Theareaofclear-cutforestharvestinNordiccountriesandthepo- tentialoverlapwiththeforesttransitionstootherlandcoverclasses areshowninTable4andFig.4.Thecumulativeareaofforestharvest between 2004and2018isabout6,556km²inNorway,36,945km² in Sweden,29,158km² in Finland,and661 km² inDenmark.These numbersreflectthemuchlargerforestmanagementactivitiesinSwe- denandFinlandthanNorway,andtheverylimitedoperationsinDen- mark.Therehasbeenincreasingtrendsinannualforestharvestareas inSweden,FinlandandNorway,especiallyinthelatestperiod2016–

2018,whenthethreecountriesachievedtheirmaximumclear-cutar- eas of 3,282 km² yr⁻¹, 3,331 km² yr⁻¹ and647 km² yr⁻¹, respec- tively(Fig.4a).Thistrendmightfurtherincreaseaccordingtofuture climatechangemitigationscenariosthatsustainableintensificationof forestrytomeetrisingdemandsforrenewableproductsandmaterials (Laurietal.,2017;Huetal.,2018;Verkerketal.,2019).Denmarkdoes notfollowthesamepatternandshowsanegativetrendinforesthar- vestarea,largelyduetolimitedforestresourcesinthecountry.Fig.4b showsthespatialpatternsofthecumulativeforestharvestedareasbe- tween2004and2018.Themostmanagedareaslargelycorrespondto thosewiththelargestpresenceofforestresources,andharvestintensity showsaclearpattern.Managementactivitiesaremoreintenseinsouth EasternNorway,andmostpartsofsouthandcentralSwedenandFin- land,wheretheycanbeupto20%ofanaggregatedgridcell.Intherest ofNorwayandinNorthernSwedenandFinlandforestharvestedareas aremuchlessabundant(lessthan3%).

The locations of theforest toagriculture(FOR-to-AGR) or grass- land(FOR-to-GRA)transitionsbetween2004and2018fromtheCCI- LCdatasetareshownin Fig.4cand4d,respectively.Thetransitions toshrublandandsparsevegetationarealsoconsidered,butnotshown becauseoftheirsmallerextent.Acomparisonbetweentheareaswhere thesetransitionsaredetectedandtheareasofharvestedforestsfrom GFCdatasetcaninformabouttheextenttowhichforestcoverdistur- bancescausedbyforestmanagementareinterpretedasforesttransitions toothertypesoflandcoverbytheCCI-LCdataset.Notethatthecom- parisonis doneatthesameresolutionoftheoriginalCCI-LCdataset (300m,towhichtheGFCdataareaggregated),andthelowerresolu- tionof9kmisonlyusedinthefigurestoimprovevisualization.There is anon-negligibleoverlapbetween theCCI-LC-basedtransitionsand theforestharvestareas.AsshowninTable4,between10%(Norway) and19%(Finland)oftheareasidentifiedasFOR-to-AGRtransitionsoc- curredinthesamelocationsidentifiedasclear-cutareasforthesame time-periodbytheGFCdatabaseInabsoluteterms,FOR-to-AGRisthe transitionwiththelargestoverlap,whichis mainlyfoundin Sweden (1,031km²),followedbyFinland(617km²)andNorway(213km²).

TheoverlapislargelydistributedincentralSwedenandsouthernFin- land,whereforestmanagementactivitiesaremoreintense(Fig.4e).In

Table4

Areasofforestharvest(1,000km²),annualaverageharvestedarea(1,000km²),areaofforest toagriculturetransition(1,000km²),andoverlapbetweenforestharvestareasandforesttransi- tionsfromtheCCI-LCdatasetinabsoluteterms(1,000km²)andrelativeterms(%).FOR-to-AGR:

foresttoagriculturetransition.FOR-to-GRA:foresttograsslandtransition.FOR-to-SHR:forestto shrublandtransition.FOR-to-SPA:foresttosparsevegetationtransition.NaN:notanumber.

Norway Sweden Finland Denmark Area of forest harvest (2004–2018) 6.6 36.9 29.2 0.7 Annual average area of forest harvest (2004–2018) 0.4 2.5 1.9 0.04

Area of FOR-to-AGR (2004–2018) 2.1 7.0 3.2 0.2

Overlap between harvested area and FOR-to-AGR 0.2 1.0 0.6 0.02

FOR-to-AGR in harvested area (%) ^a 10 15 19 11

Area of FOR-to-GRA (2004–2018) 0.7 1.1 0.2 0.05

Overlap between harvested area and FOR-to-GRA 0.01 0.5 0.1 4.7 ^∗10 ⁻³

FOR-to-GRA in harvested area (%) ^a 2 51 49 11

Area of FOR-to-SHR (2004–2018) 0.08 0.2 0.2 0

Overlap between harvested area and FOR-to-SHR 0.01 0.1 0.07 0

FOR-to-SHR in harvested area (%) ^a 14 43 35 NaN

Area of FOR-to-SPA (2004–2018) 0.3 0.6 0.2 0

Overlap between harvested area and FOR-to-SPA 0.01 0.2 0.06 0

FOR-to-SPA in harvested area (%) ^a 5 40 30 NaN

Area of all CCI transitions 3.1 8.8 3.8 0.2

Overlap between all CCI transitions and harvested area 0.3 1.9 3.8 0.02 All CCI transitions in harvested area (%) ^a 8 21 22 11 Forest harvested area in all CCI transitions (%) ^b 4 5 3 3 aThepercentagerepresentstheoverlapareadividedbytheforesttransitionarea.

bThepercentagerepresentstheoverlapareadividedbytheforestharvestarea.

Table5

Changesofsoilerosionrate(unit:kgha⁻¹yr⁻¹)onagriculturelandintheNordiccountries.SER changesareweightedmeanbetween2012and2001usingpixelareaastheweight,andvaluesin thebracketisthestandarderroroftheweightedmean.“Total” showsthetotalincreased(positive values)ordecreased(negativevalues)soilerosionintheperiod2001–2012.SER:soilerosionrate.

Norway Sweden Finland Denmark

Average (standard error) SER in 2001 2,253 (20.7) 239 (0.97) 48.1 (0.19) 321 (0.56) Average (standard error) SER in 2012 2,267 (20.7) 234 (0.96) 51.3 (0.18) 335 (0.59) Average (standard error) change in SER 14.2 (0.23) − 4.95 (0.15) 3.17 (0.08) 14.2 (0.20)

Total (kt) 101 − 91 46 245

relativeterms,amuchhigheroverlapisfoundbetweentheFOR-to-GRA transitionsandtheclear-cutareas,whereabout50%oftheidentified transitionsinSwedenandFinlandareactuallylocatedinforestharvest areas.However,thisisinconsistentacrossNordiccountries(theoverlap isonly2%inNorway),anditseemstobehighlyfrequent(largerthan 50%)inspecificregions,suchascentralSwedenandscatteredplacesin Finland.Asimilarresultisobtainedforthetwoothertransitions(FOR- to-SHRandFOR-to-SPA),wheretheirrelevantoverlapwithclear-cut areasishigherinSwedenandFinlandthanNorway,andmostlylocated incentralSwedenandsomespotsinFinland.Arelevantshareoffor- esttransitionsdetectedbytheCCI-LCdataarethusoverlappingwith forestharvestareas.Whensummedalltogetherpercountry,between 8%and22%ofthedetectedtransitionsoverlapwithmanagedforest areas.Asanalternativeindicatortomeasuretheintegrationofthetwo datasets,thefractionofthetotalforestharvestareathatoverlapwith theforesttransitionsrangesfrom3%(Finland)to5%(Sweden).These numbersarelowerbecausethepercentageiscalculatedbydividingthe overlapareabythetotalforestharvestarea(whichislargerthanthe foresttransitionareausedfortheratiosabove).

Ingeneral,akeyroleisplayedbythespecificmanagementpractices ofthestudyregion,whichprimarilyinvolvesmall-scaleharvestevents thataredifficulttobedetectedattheresolutionoftheCCI-LCdata(300 m).Forexample,Swedenisthecountrywiththelargestoverlapbetween foresttransitionstoothertypesoflandcoverandforestharvest.Thiscan beexplainedbytheaveragelargersizeofclear-cutareasinSwedenthan otherNordiccountries,whichmakethesedisturbanceseasiertodetect bygloballandcoverproducts. Ontheother hand,Denmarkhasthe smallestaveragesizeofclear-cutandthelowestoverlapbetweenthe twodatasets.However,thelargestshareoftemporarylossesinforest

coverduetoforestmanagementinNordiccountriesarenotdetectedby theCCI-LCdataset.Anon-negligiblefractionofthemisinsteadclassified astransitionstodifferenttypesoflandcoverclasses,mainlyagriculture, butalsograssland,shrublandorsparsevegetation.

3.5. Statusandtrendsinsoilerosion

BasedontheintegrationoftheESA-CCI-LC(ESA,2017)withasoil erosiondataset(Borrellietal.,2017),soilerosiontrendsduetoagricul- tureactivities(e.g.,areaswhichwereclassifiedasagriculturelandboth in2001andin2012)intheNordiccountriesareshowninTable5and Fig.5.Areasexperiencingsoilerosionlargerthan10Mgha⁻¹yr⁻¹are typicallydefinedasaffectedbysevereerosionrates,becausetheystart toconsiderablylosetheirproductivity(PanagosandKatsoyiannis,2019; Prăvălieetal.,2021).However,soilconservationprogramscanalsocon- siderlowerthresholdvaluesofapproximately5–12Mgha⁻¹yr⁻¹,and otherstudiesrecommendasa‘precautionaryprinciple’toaddresssoil erosionratesabove1or2Mgha⁻¹annuallyastheyarealreadyunsus- tainableinthelongterm(Prăvălieetal.,2021).InNordiccountriesin 2012,theareaofagriculturallandwithsoilerosionrateshigherthan 5Mgha⁻¹yr⁻¹are1,008km²inNorway,235km²inSweden,and0 km²inbothFinlandandDenmark.Ifalowerthresholdofsoilerosion rateisconsidered(i.e.,1Mgha⁻¹yr⁻¹),theagriculturallandrequiring interventionsare2,541km²inNorway,1,471km²inSweden,0.2km² inFinland,and2,674km²inDenmark.

Comparedtoglobaltrends(Borrellietal.,2017;Huetal.,2021b), changesinSERsbetween2001and2012inNordiccountriesarerela- tivelysmall(Fig.5a).Thehighestincreasesinratesofsoilerosionin agriculturallandfrom2001to2012occurredinNorwayandDenmark

Fig.4. Clear-cutareasinNordiccountriesandpotentialoverlapswithforest transitionsidentifiedbytheCCI-LCdataset.Forestharvesttrends(a),spatial distributionsofcumulativeharvestedforest(2004–2018)(b),foresttoagricul- ture(FOR-to-AGR)transition(c),foresttograssland(FOR-to-GRA)transitions (d),overlapbetweenharvestforestareasandFOR-to-AGR(e),overlapbetween harvestforestandFOR-to-GRAtransitions(f),overlapbetweenharvestforest areaandforesttoshrubland(FOR-to-SHR)transitions(g)andoverlapbetween harvestforestareasandforesttosparsevegetation(FOR-to-SPA)transitions(h).

In(a),thesolidlinesaretheareasofannualforestharvestnormalizedtotheir maximumvalues(Sweden:3,282,Denmark:86,Finland:3,331,Norway:647, unit:km²),andthedottedlinesarethelinearfitofthetemporaltrend.In(b)–

(h),mapsareaggregatedto9kmresolutiontoimprovevisualization.Notethat thescalesofcolorbarsaredifferentineachsubfigure.

(about14.2±0.2kgha⁻¹yr⁻¹),followedbyFinland(3.2±0.1kgha⁻¹ yr⁻¹),andtheratesaremuchlowerthantheglobalaveragechanges in SERs in agricultural land reported by Borrelli et al. (2017) and Huetal.(2021b).ThechangeofSERinSwedenisnegative,meaning thatthesoilerosionismitigatedbetween2001and2012.Theaverage lowsoilerosionratesinNordiccountriesareprobablyduetomoresus- tainableagriculturepracticesthanotherplacesintheworld,although theSERsincreasesinsomelocations,suchassouthernSwedenandFin- land(Fig.5b).NorwayandDenmarkhaveareaswhichareclassifiedas potentiallyunsustainable(SERhigherthan1Mgha⁻¹yr⁻¹)inthelong term(Fig.5c),butonlyNorwayhasregionsthataresufferinghighSER

(higherthan5Mgha⁻¹yr⁻¹),especiallyonthecoastalarea(Fig.5d), whichwouldrequireurgentmitigationmeasures.

4. Discussions

Ouranalysisisbasedontworecentlyreleasedhighspatialresolu- tionlandcoverproducts,theESA-CCI-LC,andC3S-CDS-LCproducts.De- tailedquantitativeanalyseswereperformedtodepictthemajortrends inlandcoverdynamicsandspatialdistributionpatternsinfourNordic countries.There havebeenmultiple validationeffortsofthis dataset (Huaetal.,2018;Lietal.,2018;Liuetal.,2018a).Anaverageglobal accuracyof71%hasbeenestimatedwithvariationsamonglandcover typesandregions(ESA,2017).InFinland,theoverallaccuracyofthe ESA-CCI-LCdatasetis64%,butaccuracytypicallyincreasewhenthe37 subclassesareaggregatedtobroaderclassesasdonehere.Forexample, whenitisusedasaforestproducttheaccuracyinFinlandis88%,with misclassificationsmainlyoccurringinthefarnorth.Anotherstudyes- timatedanoverallaccuracyoftheESA-CCI-LCproductof64%inthe ArcticCircle(Liangetal.,2019).

LimitationsoftheESA-CCI-LCandC3S-CDS-LCproductsaremainly frompossiblemisclassifications.Wetland,shrublandandsparsevegeta- tionhaveahigheruncertaintythanclassessuchascroplandandforest, whicharetypicallyregisteredwithhigheraccuracy(ESA,2017).Itfol- lowsthattransitionsinvolvinglandcoverclassesthathavelargerun- certaintyarelessrobustthantransitionsbetweentwolandcoverclasses withhighercertainty,suchasforesttoagriculture(andviceversa)tran- sitions.Besides,theaccuraciesoftheindividuallandcovervaryinthese twolandcoverdatasets.Forinstance,theaccuracyofurbanclassinthe C3S-CDS-LCdatasethasbeenimprovedrelativetothatintheESA-CCI- LCdataset.Theseaspectsshouldbeconsideredwheninterpretingthe resultsofourstudy.

ThelandcoverchangeisdetectedbytheESA-CCI-LCproductwhen itisconfirmedovertwoconsecutiveyears(ESA,2017),andthisaspect influencesthechangesbetweentwoadjacentyearsandforthefirstthree years(1992–1994)ofthetimeseries.Thespatialresolutionoftheland cover datasetis300 m,meaningthat onlychanges inland coverof certainsizecan bedetected.Thismayalsoinfluencetheaccuracyat whichCCI-LCdatacancapturechangeshappeningalongthecoastlines.

Ourstudyshowsthat,maybeduetothesmallscaleatwhichforest managementactivitiesareundertakeninNordiccountries,themajority offorestcoverclearanceisnotdetectedbytheCCI-LCdataset.Acer- tainfractionthatvariesbycountryandlandcovertypesisidentified asforesttransitionstoagricultureorgrassland(andtoasmallerex- tenttoshrublandandsparsevegetation).Theseaspectsshouldbecon- sideredwhenfurtherimprovingtheaccuracyofthelandcoverprod- ucts.Themapsused toidentifyforestharvestareas arealsosubject touncertainties, whichhave beendiscussedin detailin theoriginal study (Ceccherini et al., 2020) andfollowing commentaries (Palahí etal.,2021;Wernicketal.,2021)andrebuttal(Ceccherinietal.,2021).

ThecapacityoftheGFCmapstodetectforestlosshasbeenvalidated (Ceccherinietal., 2020),anduncertaintiesarefoundtobe lowerin someyearscomparedtoothersandforlargepatches(forestpatchsize greater than0.27ha)thanfragmentedareas.Theclassificationaccu- racyisparticularlyhigh(morethan82%correctdetection)forpatches largerthan4.5ha.Argumentsonthelimitationsoftheseforestharvest mapsincludeaspectssuchasreportedchangesmayreflect analytical artefactswithinconsistenciesintheforestchangetimeseries,uncer- tainfactorsinthealgorithmusedtoidentifyforestharvestarea,mis- attributionofnaturaldisturbancesasharvests,andalackofcausality withthesuggestedbioeconomypolicyframeworks(Palahí etal.,2021; Wernicketal.,2021).Especially,theabruptchangestypicallyobserved intheperiod2016-2018areessentiallyanartefactstemmingfromin- correctuseoftheGFCdatatimeseries,assimilartrendsareobservedin otherregionsoftheglobe(Palahí etal.,2021).Further,theGFCdataset hasahighresolutionofabout30mandisabletorecordclear-cutsofa givensize,butsmall-scaleremovalsandthinningoperationscannotbe

Fig.5. Spatialdistributionofsoilerosionrate andincreasedtotalsoilerosioninagricultural land.Soilerosionratein2012(a),soilerosion ratechangebetween2012and2001(b),areas withsoilerosionratehigherthan1Mgha⁻¹ yr⁻¹ (c), areaswithsoilerosionratehigher than5Mgha⁻¹yr⁻¹(d).Mapsin(c)and(d) areaggregatedto24kmspatialresolutionto improvevisualization,andthevaluesshowthe percentagesofareaabovethegiventhresholds intheaggregatedgrids.Agriculturelandrefers tothegridcellsclassifiedasagricultureland bothin2001and2012.

seenbythesatellitewhenthechangeincrowncoverisnotlargeenough tobedetected.

Forestmanagement representsachallenge whencompilinglarge- scale global datasets of land cover time series. Given the long- termclimatic and environmentalimplications of forest management (Naudtsetal.,2016;Huangetal.,2020),itishighlybeneficialtoestab- lishaclearerrepresentationingloballandcoverdatasetsof forested areas andtransitional, age-dependent, forest dynamics, so to facili- tatethe modellingof their effectsbyclimate andecosystemmodels (Cherubinietal.,2018b;Lindeskogetal.,2021).Ouranalysisshowsthe potentialtointegratetheCCI-LCproductsandtheGFC-basedforesthar- vestmapstoimprovetherepresentationofgloballandcoverdynamics, buttechnicalchallengesforasuccessfulintegrationstillremain.Future studiesshouldexploretheextenttowhichthesedatasetsoverlap,and thetypeof foresttransitionsinvolved,especiallyinareaswithforest managementactivitiesthataredistinctlydifferentthanthoseusedin Nordiccounties.Ideally,therepresentationofforestmanagementareas ingloballandcoverdatasetswouldrequirethedefinitionofanewland coverclassforauniqueidentificationofthistypeoftemporaltransition (fromatechnicalpointofview,harvestedareasaretobeclassifiedas forestremainingforest).Morecollectiveinternationaleffortsareneeded toobtainhighqualitydataatdifferenttemporalandspatiallevelsrep- resentativeofforestmanagementanddynamics(Palahí etal.,2021).

Theanalysisof SERalso haslimitations.Two majorpotential is- suesaretheresolutionof theSERmapsandlandcoverdatasetused by Borrelliet al. (2017). Compared tothe resolution of theCCI-LC dataset,thespatialresolutionof SERmapsislow,whichmeansthat

somechangesinSERcannotbedetectedwithacoarserresolution.Fur- ther,theSERmapsusedadifferentlandcoverdatasetthaninourstudy, andthemismatchofthelandcoverclassescanbe asourceofuncer- tainty.Futurehigh-resolutionmapsofSERwithconsistentlandcover datasetcanthushelptomoreaccuratelyquantifySERchangesrelated tolanduseandlandcoverchanges.

5. Conclusions

Understandingthespatio-temporaldynamicsoflandusechangeis criticaltoaddressglobalenvironmentalchallengessuchasfoodsecurity, climatechange,andbiodiversityloss.Wecombinedandreclassifiedthe ESA-CCI-LCandC3S-CDS-LCproductstomeasurechangesinlandcover from1992to2018inNorway,Denmark,Sweden,andFinland,andan- alyzethetrendsandspatialdistributionsofthetransitions.Wefound extensivelandcoverdynamicsinthesecountries,withdifferentspatial patterns,thataretheresultsofdirecthumaninterferencesand/orcli- matechange.Forexample,settlementsexpandedthemostinrelative terms,andwetlandshrinkageisamajortransitionintheNorthernpart ofthestudyarea.Thetrendisconcerninggiventhekeyrolethatwet- landsplayinregulatingthelocalclimateandsupportingavarietyof ecosystemservices,includingbiodiversity,anddeservescloser(ground- level)monitoringandtheimplementationofmitigationmeasures.Over- all,ourresultsofferinsightsonthemaincharacteristicsofthesedatasets onchangesinlandcoverandareinstrumentaltofuturestudiesapplying thesedatainlandsurfacemodels,climatemodels,landscapeecology, orsimilarapplications.