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To meet the global challenges of climate change and human activity pressure on biodiversity conservation, it has become vital to map such pressure hotspots. Large areas, such as nationwide regions, are difficult to map from the point of view of the resources needed for such mapping (human resources, hard and soft resources). European biodiversity policies have focused on restoring degraded ecosystems by at least 10% by 2020, and new policies aim to restore up to 30% of degraded ecosystems by 2030. In this study, methods developed and applied for the assessment of the degradation state of the ecosystems in a semi-automatic manner for the entire Romanian territory (238,391 km 2) are presented. The following ecosystems were analyzed: forestry, grassland, rivers , lakes, caves and coastal areas. The information and data covering all the ecoregions of the Ro-mania (~110,000 km 2) were analyzed and processed, based on GIS and remote sensing techniques. The largest degraded areas were identified within the coastal area (49.80%), grassland ecosystems (38.59%) and the cave ecosystems (2.66%), while 27.64% of rivers ecosystems were degraded, followed by 8.52% of forest ecosystems, and 14.05% of lakes ecosystems. This analysis can contribute to better definition of the locations of the most affected areas, which will yield a useful spatial representation for future ecological reconstruction strategy.
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Int.J.Environ.Res.PublicHealth2021,18,11416.https://doi.org/10.3390/ijerph182111416www.mdpi.com/journal/ijerph
Article
ApplyingaComplexIntegratedMethodforMapping
andAssessmentoftheDegradedEcosystemHotspots
fromRomania
SorinAvram1,2,IrinaOntel3,CarmenGheorghe1,StelianaRodino4,5andSandaRoșca6*
1
NationalInstituteforEconomicResearch“CostinC.Kiriţescu”(INCE),RomanianAcademy,13September
StreetNo.13,050711Bucharest,Romania;avram.sorin@ucv.ro(S.A.);carmen.adriana@ince.ro(C.G.)
2
DepartmentofGeography,UniversityofCraiova,Al.I.CuzaStreetNo.13,200585Craiova,Romania
3
RemoteSensingandSatelliteMeteorologyLaboratory,NationalMeteorologicalAdministration,
013686Bucharest,Romania;irina.ontel@meteoromania.ro
4
NationalInstituteofResearchandDevelopmentforBiologicalSciences,Spl.IndependenteiNo.296,
060031Bucharest,Romania;steliana.rodino@yahoo.com
5
InstituteofResearchforAgricultureEconomyandRuralDevelopment,Bd.MarastiNo.61,
011464Bucharest,Romania
6
FacultyofGeography,BabesBolyaiUniversity,400006ClujNapoca,Romania
*Correspondence:sanda.rosca@ubbcluj.ro
Abstract:Tomeettheglobalchallengesofclimatechangeandhumanactivitypressureonbiodiver
sityconservation,ithasbecomevitaltomapsuchpressurehotspots.Largeareas,suchasnation
wideregions,aredifficulttomapfromthepointofviewoftheresourcesneededforsuchmapping
(humanresources,hardandsoftresources).Europeanbiodiversitypolicieshavefocusedonrestor
ingdegradedecosystemsbyatleast10%by2020,andnewpoliciesaimtorestoreupto30%of
degradedecosystemsby2030.Inthisstudy,methodsdevelopedandappliedfortheassessmentof
thedegradationstateoftheecosystemsinasemiautomaticmannerfortheentireRomanianterri
tory(238,391km
2
)arepresented.Thefollowingecosystemswereanalyzed:forestry,grassland,riv
ers,lakes,cavesandcoastalareas.TheinformationanddatacoveringalltheecoregionsoftheRo
mania(~110,000km
2
)wereanalyzedandprocessed,basedonGISandremotesensingtechniques.
Thelargestdegradedareaswereidentifiedwithinthecoastalarea(49.80%),grasslandecosystems
(38.59%)andthecaveecosystems(2.66%),while27.64%ofriversecosystemsweredegraded,fol
lowedby8.52%offorestecosystems,and14.05%oflakesecosystems.Thisanalysiscancontribute
tobetterdefinitionofthelocationsofthemostaffectedareas,whichwillyieldausefulspatialrep
resentationforfutureecologicalreconstructionstrategy.
Keywords:degradedecosystems;terrestrialecosystems;freshwaterecosystems;marine
ecosystems;Romania
1. Introduction
Theevaluationofthestateofecosystems,asthefundamentalstructuralandfunc
tionalunitoflivingmatter,isaconstantconcernofglobalandEuropeanpolicies,inorder
toestablishguidelinesforpreventingthelossoftheirfunctions.Theassessmentofthe
conditionofecosystemsnecessitatesextensiveanalysisoftheirphysical,chemicalandbi
ologicalqualityataparticularmomentandmeasurementoftheimpactsofmajorpres
suresthatarearising.Naturalecosystemsareconstantlyexposedtopressuresfromover
exploitationofresources,extensivehunting,climatechangeandpollution[1,2].Someau
thorsconsiderthatthehighestdirectimpactonanecosystem’sstateisrepresentedby
anthropogenicpressures(overharvestingandlandusechange)leadingtobiodiversity
loss[3].
Citation:Avram,S.;Ontel,I.;
Gheorghe,C.;Rodino,S.;Roșca,S.
ApplyingaComplexIntegrated
MethodforMappingand
AssessmentoftheDegraded
EcosystemHotspotsfromRomania.
Int.J.Environ.Res.PublicHealth2021,
18,11416.https://doi.org/10.3390/
ijerph182111416
Received:17September2021
Accepted:25October2021
Published:29October2021
Publisher’sNote:MDPIstaysneu
tralwithregardtojurisdictional
claimsinpublishedmapsandinstitu
tionalaffiliations.
Copyright:©2021bytheauthors.Li
censeeMDPI,Basel,Switzerland.
Thisarticleisanopenaccessarticle
distributedunderthetermsandcon
ditionsoftheCreativeCommonsAt
tribution(CCBY)license(https://cre
ativecommons.org/licenses/by/4.0/).
Int.J.Environ.Res.PublicHealth2021,18,114162of22
Toaccuratelyevaluatetheecosystemservicesprovidedbyaparticulararea,first,the
stateoftheecosystemmustbestudied.Thestateoftheecosystemisthefirstlevelinthe
flowofservicesfromnaturetosociety[4],anditdefinestheabilityoftheecosystemto
provideservices.Pressuresfromhumanactivity,suchaspollutionoroveruse,canhave
animpactonthestateoftheecosystem,thusreducingitsabilitytoprovideservicesto
society[5,6].Thegoodconditionofecosystemsisnotconsideredaserviceitself;however,
itisindispensableasitisanessentialconditionforhumanactivity.
Theecosystemisdegradedwhentheviabilityofnaturalprocessesandrelationships
withinitareremovedordisturbedbyanthropogenicactivityortheactionofnaturalfac
tors[7].Itisalsoaprocesswithmultipleeffectsonclimatechange,biodiversitychanges
andecosystemservices[8].
Europeisfacingacontinuinglossofbiodiversity,andnaturalecosystemsaredimin
ishing,especiallywetlands,byabout50%[9].AsamemberoftheEuropeanUnion(EU),
Romaniapromotesandsupportstheprotectionofecosystems,beingpartoftheUnited
NationsConventiononBiologicalDiversity(“ConventiononBiologicalDiversity,”1992).
However,likemostEuropeancountries,Romaniaisexperiencinganincreaseinitsshare
ofdegradedecosystems[10,11].
Therestorationofthegeologicalenvironmentandtheaffectedterrestrialecosystems
involvesbringingthemascloseaspossibletotheirnaturalstate,byapplyingcomplemen
taryandcompensatorycleaning,remediationand/orreconstructionmeasuresandby
eliminatinganysignificantriskaccordingtothecategoryoflanduse.Todothisitisnec
essarytoproperlyevaluatetheirdegradationstateandthedriversofthisdegradation.
Changesinecosystemsarefrequentlyidentifiedonthebasisofsatelliteimagery
[12,13].Mostmethodsfocusonbiomassevaluation[14–16],leafareaindex(LAI)[17]or
productivity[18–20].TheGlobalClimateObservingSystem(GCOS),promotesLAIasan
essentialclimatevariable(ECV),beingakeyparameterusedinwoodyecosystems[21].
Inmanystudies,thehealthoftheecosystemisanalyzedbasedonGPP(grossprimary
production)orNPP(netprimaryproduction)indicesandavegetationindex,suchasthe
normalizeddifferencevegetationindex(NDVI).Thehighvalueoftheseindicesdoesnot
necessarilymeanagoodstateofhealth[22],asitmaybeduetoinvasivespecies.Inthe
sameway,primaryproductivityisrelatednotonlytovariationsinCO2intheatmosphere
butalsotoclimatechange.Globally,productivityhasincreasedinthelast20years[23].
MachinelearningalgorithmssuchasRandomForest(RF)andSupportVectorMa
chine(SVM)areusedtoidentifyandmonitorvegetationtypeswithinforest[24]and
grasslandecosystems[25].Moreover,theSVMimageclassificationmethodisusefulin
identifyinginvasiveplantspecies[26,27],oneoftheimportantcriteriaintheevaluation
ofgrasslandecosystems[27].Someauthorsusedsatelliteimagery(Sentinel2,Landsat8)
andmachinelearningprocessestolocateforesttreatmentsoverlargespatialextents[28].
Anotherrecentanalyticalframeworkforthemappingandassessmentofecosystem
condition[29]proposedindicatorsrelatedtoenvironmentalpressures(physicaland
chemicalquality)andecosystemattributes(biologicalquality)basedonacombinationof
individualmetrics.
InOctober2010,JapanandtheEUmemberstatesattheNagoyaBiodiversitySummit
signedtheConventiononBiologicalDiversity.InordertoachieveitsEUbiodiversitypol
icygoalsandtoalignwithinternationalcommitmentsintheConventiononBiological
Diversity,inMay2011,theEUadoptedtheBiodiversityStrategyto2020.ToachieveTar
get2:”Maintainandrestoreecosystemsandtheirservices”,i.e.,therestorationofatleast
15%ofthedegradedecosystemsby2020,thedocumentproposesthatby2014eachmem
berstateshoulddevelopastrategicframeworkforestablishingtheprioritiesfortheres
torationofecosystemsatanationallevel.Inordertorespondtothedevelopmentneeds
andtocontributetotheEU2020Strategy,in2014,theLargeInfrastructureOperational
Program(LIOP)strategywaselaboratedinRomania.WithinLIOP,priorityaxis4was
establishedforthepromotionofecologicalreconstructionprojects[30].Therefore,anas
sessmentandmappingofRomaniandegradedecosystemswasnecessary.Giventheneed
Int.J.Environ.Res.PublicHealth2021,18,114163of22
forcompositeindicatorsonecosystemconditionthatcanreflecttheoverallqualityofan
ecosystemassetintermsofitscharacteristics,themappingandevaluatingofdegraded
ecosystemsinRomaniaweredone.
TheresultsofthispaperarebasedonresearchstartedinApril2016,comprisingde
tailedassessmentforeachtypeofecosystem.Thedatabasesusedwereprovidedbyvari
ousRomanianandEuropeaninstitutions.Theseweremainlyspatialdata,statisticaldata
andsatelliteimages.Inthefirstphase,thedegradationcriteriaandindicatorsforeach
typeofecosystem,thedegradationclassesandthesustainabilitythresholdswereestab
lished,aswellasthelimitsandthemethodologyforecosystemmapping.Inthesecond
phase,themappingandevaluationofthenaturalecosystemswereperformed,aswellas
thevalidationoftheresults.Theintegrationofalldatawasachievedandcompletedin
May2021.
EUBiodiversityStrategyto2020setobjectivestowardmappingandassessingthe
stateofecosystemsfromeachmemberstate.Thetargetofthisstrategywastorestore15%
ofdegradedecosystems[30]andthecurrentEUwideBiodiversityStrategyto2030aims
toprotectatleast30%oflandand30%ofseainEurope[31].
Themainobjectivesofourstudywereasfollows:todefineandidentifythetypesof
naturalandseminaturalecosystemsexistinginRomania;todevelopanationallyappli
cablemethodologyfortheevaluationofeachtypeofnaturalandseminaturalecosystem;
and,inthispaper,toassessthecompleteecosystem’sconditionacrossRomanianterritory
inordertooutlinedirectionsfortheirconservationstatus.
2.MaterialsandMethods
2.1.StudyArea
Romaniacovers238,391km2andislocatedinsoutheasternEurope,borderingonthe
BlackSeaandtheDanube.Themajorlandformsareconcentricallydistributed[32],the
Transylvaniandepressioninthecenter,surroundedbytheCarpathianMountainsand
hills.Twolargeplainssurroundthehigherarea,namelytheRomanianPlainandthe
WesternPlain,towhichisaddedinSEtheDanubeDeltaandDobrogeaPlateau(Figure
1).AccordingtotheKöppen–Geigerclimateclassification,Romaniahasatemperatecon
tinentalclimate[32].Fromabiogeographicalpointofview,inRomania,therearefive
biogeographicalregions:Pannonian,Alpine,Continental,SteppicandBlackSea[33].
Inapreviousstudy,deliverableoftheprojectNatureinpublicdecisions—N4D,the
followingtypesofecosystemswereidentifiedwithintheRomanianterritory:terrestrial
(woodlandandforest,grassland,cave),freshwater(riversandlakes),andmarine
(coastal).Forestecosystemsoccupyatotalareaofabout71,890.84km2,andabout32,357.14
km2ofRomanianareaisgrassland.Therearealso339caves.Riversare84,068.17kmin
length,lakesrepresent2248.28km2,and1574km2arecoastalecosystems[10,34].
Int.J.Environ.Res.PublicHealth2021,18,114164of22
Figure1.LocationofRomaniaandmajorlandforms.
2.2.DataUsed
Duetothecomplexityofeachecosystem,alargenumberofdatawereusedfrom
differentnationalandinternationalsourcesavailablefortheentireterritoryofRomania.
SatelliteimagesandsatelliteimageryproductssuchasMODIS,LandsatandSentinel2
wereused.Furthermore,vectordatasuchaslanduselimits(CORINELandCoverorLand
parcelidentificationsystem),limitsofterritorialadministrativeunits(TAUs),limitsof
protectednaturalareas,thehydrographicnetwork,theroadnetwork,pollutionsources,
soiltypes,etc.andstatisticaldatasuchaslivestockandthenumberofinhabitantswere
used.ThedatasetsusedforeachecosystemaredescribedinTable1.
Table1.Usedgeodatabase.
Major
Ecosystem
Category
EcosystemType
forMappingand
Assessment
NameofDatasetsResolution/Minimum
MappingUnit
Time
ReferenceSource
Terrestrial
Forest
Landparcelidentification
system(LPIS)NA2013Landparcelidentificationsystem
fromRomania[35]
VegetationContinuous
Fields(MOD44B)250m2000–2013LAADSDAAC[36]
HansenGlobalForest
Change30m2000–2013GlobalForestChange[37]
Grassland
Landparcelidentification
system(LPIS)NA2017Landparcelidentificationsystem
fromRomania[38]
Limitsofterritorial
administrativeunits
(TAUs)
NA2016NationalAgencyforCadastreand
LandRegistrationofRomania[39]
Digitalsurfacemodel(EU
DEM)25m2011CopernicusLandMonitoring
Service[40]
Livestocknumbersand
typesfromTAUsNA2010NationalStatisticsInstituteof
Romania[41]
Int.J.Environ.Res.PublicHealth2021,18,114165of22
EuropeanSettlementMap10m2010–2013CopernicusLandMonitoring
Service[42]
Sentinel2images10m2015–2017CopernicusOpenAccessHub
[43]
Cave
Natura2000(N2K)NA2017MinistryofEnvironment,Waters
andForests[44]
Orthophotos0.5m2016NationalAgencyforCadastreand
LandRegistrationofRomania[39]
CORINELandCover
(CLC2012)100m2011–2012CopernicusLandMonitoring
Service[45]
EuropeanSettlementMap10m2010–2013CopernicusLandMonitoring
Service[42]
Europeancatchmentsand
Riversnetworksystem
(ECRINS—damson
rivers)
1:250,0002012EuropeanEnvironmentAgency
[46]
RoadsandrailwaysNA2016OpenStreetMap[47]
Freshwater
Rivers
EUHydro—River
Network1ha2006–2012CopernicusLandMonitoring
Service[48]
Europeancatchmentsand
Riversnetworksystem
(ECRINS—damson
rivers)
1:250,0002012EuropeanEnvironmentAgency
[46]
Digitalsurfacemodel(EU
DEM)25m2011CopernicusLandMonitoring
Service[49]
CORINELandCover
(CLC2012)100m2011–2012CopernicusLandMonitoring
Service[45]
EuropeanSettlementMap10m2010–2013CopernicusLandMonitoring
Service[42]
RiparianZones2012—
LandUseLandCover0.5ha2011–2013CopernicusLandMonitoring
Service[49]
UrbanWastewater
Treatment(Waterbased
UWWTD)
NA2015EuropeanEnvironmentAgency
[50]
Natura2000(N2K)NA2017MinistryofEnvironment,Waters
andForests[44]
RoadsandrailwaysNA2016OpenStreetMap[47]
RomaniasoilsmapNA2017
NationalInstituteofResearchand
DevelopmentforPedology,Agro
chemistryandEnvironmental
Protection[51]
Numberofinhabitants
fromTAUsNA2017NationalStatisticsInstituteof
Romania[52]
Limitsofterritorial
administrativeunits
(TAUs)
NA2016NationalAgencyforCadastreand
LandRegistrationofRomania[39]
Lakes
Permanentwaterbodies20m2012CopernicusLandMonitoring
Service
CORINELandCover
(CLC2012)100m2011–2012CopernicusLandMonitoring
Service[45]
Digitalsurfacemodel
(EUDEM)25m2011CopernicusLandMonitoring
Service[40]
RoadsandrailwaysNA2016OpenStreetMap[47]
Exploitationareasof
naturalresourcesNA2017NationalAgencyforMineral
Resources
Natura2000(N2K)NA2017MinistryofEnvironment,Waters
andForests[44]
Int.J.Environ.Res.PublicHealth2021,18,114166of22
RomaniasoilsmapNA2017
NationalInstituteofResearchand
DevelopmentforPedology,Agro
chemistryandEnvironmental
Protection[51]
UrbanWastewater
Treatment(Waterbased
UWWTD)
NA2015EuropeanEnvironmentAgency
[50]
Limitsofterritorial
administrativeunits
(TAUs)
NA2016NationalAgencyforCadastreand
LandRegistrationofRomania[39]
Landsat830m2016[53]
MarineCoastal
CORINELandCover
(CLC2012)100m2011–2012CopernicusLandMonitoring
Service[45]
Digitalsurfacemodel
(EUDEM)25m2011CopernicusLandMonitoring
Service[40]
RoadsandrailwaysNA2016OpenStreetMap[47]
UrbanWastewater
Treatment(Waterbased
UWWTD)
NA2015EuropeanEnvironmentAgency
[50]
EuropeanSettlementMap10m2010–2013CopernicusLandMonitoring
Service[42]
Landsat830m2016[53]
Orthophotos0,5m2016NationalAgencyforCadastreand
LandRegistrationofRomania[39]
2.3.Methods
Theworkflowfortheevaluationoftheecosystem’scondition(Figure2)consistedin
threephases.Thefirstphaseincludedstateoftheartmethodsforidentifyingthe
potentialpressuresoneachecosystem;searchingforecosystemsustainabilitythreshold
definitions;identificationofdatasetsavailableforthecalculationofdegradation
indicators;dataprocessingandanalysis;validationoftheappliedmethod.Foreach
ecosystem,aspecificassessmentmethodologywasinvolved.Thesecondphaseincluded
correctionandadjustmentofeachindicatorusedaccordingtothefieldresults;finaldata
processing,fieldverificationandvalidation.Inthefinalstage,atopologicalattribute
verificationwasperformed.
Int.J.Environ.Res.PublicHealth2021,18,114167of22
Figure2.Methodologicalflowinordertoassessthestateofecosystemdegradation.
Themethodologiesforestablishingthelevelofdegradationoftheanalyzed
ecosystemsincludedelementsthathaveanimpactontheirhealthandsustainability.The
determinationofthelevelofdegradationandtheclassesofdegradationalsotookinto
accountthecapacityofecosystemstosupportandprovideecosystemservicesin
accordancewiththeirbasicfunctions(SupplementaryMaterial).
Themethoddescribingtheforestecosystem’sstatuswasbasedontheidentification
ofdeforestedareasusingthechangedetectionalgorithmbetweenthelandusecategory
accordingtoLPISdata[38,54]andthelanduseinthereferenceyearof2000,accordingto
thetreecanopycoverfromLandsat[37].Theconservationstatusofforestecosystemswas
establishedbasedontheVCFMODIS[37]productfrom2000to2013andthecalculation
ofthelineartrendforeachpixel[10].Forestdegradationwasanalyzedbypermanent
changesintermsoflandcoverandlanduse.Thesechangesreduceecologicalintegrity
andhealth(SER,2004)affectingthebiodiversityandproductivityofforests.
Theevaluationofthegrasslandecosystemswasmadebasedonsixcriteria,each
criterionhavingaspecificweightinthefinalresult,Equation(1).Thesixcriteriareferto
theanthropozoogenicimpact(proximitytolocalities,proximitytosheepfolds,thetotal
livestockdensity)[55],stationaryconditions(slope)andstructuralcharacteristics
(invasivespeciesandbaresoil/erosions).Eachcriterionwasdividedintothreeclassesof
values,andeachclassreceivedascorecorrespondingtotheecosystemcondition,as
follows:0—natural,1—semidegraded,2—degraded[10].Theidentificationofinvasive
speciesandsoilorbaresoilerosionwasbasedonmachinelearningalgorithmssuchas
RandomForest(RF)andSupportVectorMachine(SVM).Thedegradationstatewas
assignedaccordingtothevalueofthedegradationindex(DI).Thus,DIvaluesbetween0
and30indicatednaturalgrasslandecosystems,between35and60theyrepresentedsemi
degradedandbetween65and180degradedgrasslandecosystems.
DI=(5×C1)+(20×C2)+(5×C3)+(10×C4)+(50×C5)+(100×C6)[10](1)
where
Int.J.Environ.Res.PublicHealth2021,18,114168of22
C1=proximitytolocalities(>4km=natural,2–4km=semidegraded,<2km=
degraded);
C2=proximitytosheepfolds(>2km=natural,0.5–2km=semidegraded,<0.5km=
degraded);
C3=slope(<15°=natural,15°–30°=semidegraded,>30°=degraded);
C4=totallivestockdensity(<±10%=natural,±10–50%=semidegraded,>±30%=
degraded);
C5=invasivespecies(<5%=natural,5–20%=semidegraded,>20%=degraded);
C6=baresoil/erosions(<5%=natural,5–20%=semidegraded,>20%=degraded).
CaveecosystemshavebeenassessedonthebasisoftheCaveConservationIndex
(CCI),whichdeterminestheimpactofthecaveenvironmentandthethreatsandthe
vulnerabilityoftheintrinsiccharacteristicsofthecaves[56].CCIiscalculatedusingthe
scoreobtainedbycompletingtheformsforestablishingtheimpactontheenvironmentof
acave,RapidAssessmentProtocol(RAPcei)andthescoreobtainedbycompletingthe
formtoestablishthevulnerabilityofacave,inordertoprioritizeconservationand/or
restorationactions[57].Thus,forvaluesbetween0and34,thecaveecosystemwas
classifiedasnatural,between35and84wassemidegraded,andover85,itwasclassified
asdegraded[10].
Riverecosystemswereassessedbasedon13criteria,groupedinto4majorclasses:A.
Indicatorsofthehumanpressureonriparianareas(anthropization,vegetationcover,
humansettlements,sewagetreatmentplants,majorpollutionsources,transportnetwork,
naturalprotectedareas),B.Indicatorsofsubstrateofthelandadjacenttothewatercourse
(slope,soilpermeability),C.Indicatorsassociatedwithrivers(humaninterventions,
ecologicalstatusofwaterbodies)andD.Indicatorsofthemorphologicalcomplexityof
watercourses(sinuosity)[58].Eachindicatorusedinthemulticriteriaanalysiswas
assignedaweightinthefinalanalysis.Thehighestweightsusedinthemulticriteria
analysisforhumanpressureonriparianareaswereasfollows:theanthropizationofthe
adjacentterritoryofawatercourse,vegetationcoverinriparianareas,thepresenceof
majorpollutionsources,thelengthofthetransportnetwork,humaninterventionsinthe
riverbanksandtheecologicalstatusofwaterbodies[58].
Theassessmentoflakeecosystemswasperformedbycombiningthepotential
pollutantload(PPL)developedby[59],wastewater(W)–recreational(R)–agricultural
(A)–size(S)–transportation(T)–industrial(I)–cover(C)–pollutantload(WRASTIC)[59]
andlakevulnerability(LV)[60],resultinginanewindex:WRASTICHIindex[61,62].This
methodologicalstageincludedtheanalysisofatotalof3189lakesandtheirclassification
bydegradationclasses.
Theassessmentofdegradationstatusofthecoastalecosystemwasmadebasedon
eightindicators,whichcanbegroupedintobiologicalindicators(relatedtotheabsence
orpresenceofinvasivespecies),hydromorphologicalindicators(relatedtothepresence
ofwastewatertreatmentplants,thepresenceofdemographicaggregationpoles,shoreline
artificialization,shorelineerosionrate)andphysical–chemicalindicators,datarelatedto
transportinfrastructure(roadinfrastructure,navigationchannels)andintensityof
maritimetraffic.
Eachindicatorwasgivenascore,andthefinalresultwasobtainedbysummingall
thescores[10].Thus,intheterrestrialcoastalarea,scorevalues≤4meanthecoastal
ecosystemisnatural,between5and12itissemidegradedandover13itisdegraded,and
inthemarinecoastalarea,scorevalues≤5meanthecoastalecosystemisnatural,between
5and15itissemidegradedandover13itisdegraded.
Inordertoidentifythelocationofthemostdegradedecosystems,thedensityofeach
degradedecosystemandthehotspotanalysisbasedontheGetis–OrdGi*[63,64]was
computed.ThehotspotanalysiswasperformedinArcGISusingtheMappingClusters
tool[65],basedonEquation(2)[66].
Int.J.Environ.Res.PublicHealth2021,18,114169of22
𝐺 𝑤,𝑥 𝑋
𝑤,
𝑆𝑛𝑤,
 𝑤,
𝑛 1
(2)
whereG_j^*statisticsisazscore,xjistheattributevalueforfeaturej,wi,jisthespatial
weightbetweenfeatureiandj,nisequaltothetotalnumberoffeaturesand:
𝑋
(3)
and
𝑆 𝑥
𝑛 𝑋
(4)
Thedensityvaluesofeachecosystemwereclassifiedintofivedensityclasses,and
eachclasswasgivenascorefrom1to5.Value1representsverylowdensity,2—low
density,3—medium,4—high,and5—veryhighdensity.Thesumofallthelayersledto
anewlayerwiththedensityofdegradedecosystemsinRomaniaandthehotspotanalysis
basedontheGetis–OrdGi*wasperformed.Thepurposeofobtainingacumulativemap
ofalldegradedecosystemsistohighlighttheirspatialdistribution,especiallytheareasof
maximumconcentrationofdegradation,sothatstructuralandnonstructuralmeasures
toreducedegradationcanbetakenintoaccount.
3.ResultsandDiscussions
TheintegrationofeachRomanianecosystemtypeassessmentindicatedthatthe
coastalecosystemisthemostdegradedecosystem,with86.55%degradedarea(1362.32
km2).Thegrasslandecosystem’sevaluationresultedinclassificationof38.59%areaof
grasslandasbeingdegraded(12,486.37km2),while27.64%ofriverecosystemswere
degraded(23,800.22kmlength),
Ashareof92.92%ofcaveecosystemsweresemidegraded,followedby67.67%for
lakesand52.94%forrivers.
Forestecosystemsoccupythelargestareaofallecosystems,andashareof88.54%of
thisecosystemwasnatural,nondegraded.Thus,fortheidentificationofdegraded
forests,theVCFMODISsensorwasused,whichallowedthemappingofforestswitha
consistencybetween30%and80%,whichshowedaconsistencyreductionofover10%.
Accordingtotheanalysis,only8.52%offorestecosystemsweredegraded(Table2).
Table2.Summaryofecosystemassessmentresults.
EcosystemNatural%SemiDegraded%Degraded%Total
Forest(km2)63,651.3488.542115.272.946124.238.5271,890.84
Grassland(km2)7080.0521.8812,790.7239.5312,486.3738.5932,357.14
Cave(no.)154.4231592.9292.66339
Lake(km2)410.9518.281521.4167.67315.9114.052248.28
River(km)16,320.5219.4144,508.8252.942238.8227.6484,068.17
Coastal(km2)42.52.71362.3286.55169.1749.81574
ThelargestareaofforestecosystemswaslocatedintheCarpathianMountains,about
31%intheEasternCarpathians,16%intheWesternCarpathiansand14%intheSouthern
Carpathians(Figure3a).Thelargestdegradedforestecosystemareaswerelocatedinthe
EasternCarpathianMountains,areawheredeforestationhotspotshavebeenidentifiedin
severalsimilarstudies[67,68].Themaincauseisdeforestationresultinginecosystemloss
andfragmentation.Approximately1124km2weredeforestedintheEasternCarpathians,
fromwhich790km2weretransformedintounproductiveland,652.5km2intopastures
Int.J.Environ.Res.PublicHealth2021,18,1141610of22
and177km2intobuiltupareas.Atthesametime,significantforestareasweredeforested
intheWesternCarpathians(approximately320km2)andtheSouthernCarpathians(256
km2).Intheplateauandplainareas,themaincauseofdegradationwasconversionto
agriculturalland.

(a)(b)

(c)(d)
(e)(f)
Figure3.DistributionofecosystemsinRomania:(a)forestecosystems;(b)grasslandecosystems;(c)caveecosystems;(d)
riverecosystems;(e)lakeecosystems;(f)coastalecosystems.
ThegrasslandscoverthesecondlargestecosystemareawithinRomanianterritory
(Figure3b).Thelargestdegradedareaofthegrasslandecosystemwasinthe
TransylvanianDepression(approximately2340km2),followedbytheEastern
Carpathianswithapproximately2220km2andtheSubCarpathianswith1433km2.
SimilarstudiesongrasslanddegradationintheSubCarpathianareahavedrawn
attentiontothedegradationratesofgrasslandinthisareaandtheinfluenceofthisprocess
Int.J.Environ.Res.PublicHealth2021,18,1141611of22
inthemanifestationoflandslides[69].Thesethreelandformsareconcentratedon
approximately50%oftheareaofdegradedgrasslandecosystemsinRomania.Thecauses
aremultiple,fromthepresenceofinvasivespeciessuchasshrubstoagropastoral
activitiessuchasexcessivegrazing.
MostofthecaveecosystemsinRomaniaarelocatedintheWesternCarpathians(179
caves),followedbytheSouthernCarpathians(101caves)andtheEasternCarpathians(21
caves),(Figure3c).Mostofthecaves(50.4%)arelocatedintheContinentalbiogeographic
region,47.8%inthetemperatecontinentalclimaticand1.8%inthecoldsemiaridclimate
(DobrogeaPlateau).
Theassessmentofthedegreeofcavedegradationinvolvedtheassessmentofthe
environmentalandundergroundimpactonthecaveecosystem’senvironmentalimpact
andundergroundimpact(slopecollapsesthatledtocloggingofentrancesoropeningof
newentrances,watercatchmentsinthekarstimpluvium,constructions,communication
routesintheperimeterofthecave,storageofhouseholdwasteorothermaterial,excessive
and/ordisorganizedtourism),evaluationofthepaleontologicaldeposit—thanatocoenosis
(fossildepositaffectedbyillegalexcavations/vandalism,presenceofvandalized
bioglyphs),archeologicevaluationofthedeposit(incisions/drawingswithvandalized
coal,stone/bone/metaltoolsdestroyedorremovedfromthe
archaeological/sedimentologicalcontext),assessmentofthebiodiversityofthe
undergroundenvironment—invertebratefauna,vertebratefauna(dependingon
diversityspecifictothefaunaofvertebratesandinvertebratesincaves).
Theanalysisshowedthatapproximately90%ofthecaveecosystemsweresemi
degradedandonly2.66%weredegraded.
MostriversinRomaniaspringintheCarpathianMountains,flowingintohillyareas
(smallrivers)andlowlandareas(largerivers).Theirconditionisinfluencedbythe
physical–geographicalandsocioeconomiccharacteristicsoftheareastheypassthrough.
Themountainousareascover46.2%ofthelengthoftheriversinRomania,thehilland
plateauareaconstitutes35.8%andplainandDanubeDeltaareas18%.
Apreponderanceofdegradedandsemidegradedriverecosystemswereobserved
inareaswithlowattitudes,intheplains(Figure3d).Therewereapproximately4700km
ofdegradedriversintheRomanianPlainandanother5000kminasemidegradedstate,
and685kmwereinanaturalstate.
IntheTransylvanianDepression,approximately4150kmwereclassifiedtoastateof
degradation,towhichwereadded5450kminastateofsemidegradation,leavingonly
390kminanaturalstate.
MostlakeecosystemsarelocatedinthesoutheasternandeasternpartofRomania,
respectively,intheDanubeDelta(43.1%),theRomanianPlain(22.17%),theDobrogea
Plateau(6.62%)andtheMoldavianPlateau(5.39%),(Figure3e).Thelargestdegradedarea
oflakeecosystemswaslocatedintheRomanianPlainwithanareaof177.06km2,
representing35.52%ofthelakeecosystemsinthisarea,followedbytheMoldavian
Plateauwith45.9km2andtheDobrogeaPlateauwith32.16km2.Moreover,inthe
DobrogeaPlateauandintheMoldavianPlateauwerethelargestareasofsemidegraded
lakeecosystems,116.48and116.09km2,respectively.
ThelargestdegradedareasofcoastalecosystemswereinthePeriboina–CapSingol
area(71.05km2),theMangaliaPlateau(20.34km2)andtheChitucGrind(18.49km2)
(Figure3f).ThelargestsemidegradedareaswereintheareasSulina–Periboina(963.83
km2),Periboina–CapSingol(258.11km2)andEforie–VamaVeche(88.81km2).
ThehighestdensityofdegradedforestecosystemswasidentifiedintheNortheastern
Carpathians,thenortherngroupoftheWesternCarpathiansandintheSouthern
CarpathiansandalsointheSubCarpathians(Figure4).Moreover,intheseareas,the
confidencelevelofthehotspotwasover99%.Theextensiveforestareasthatwere
deforestedinnorthernRomania,intheMaramureș Mountainarealedtolandscape
degradationanddecreasedairqualityandcontributedtotheaggravationofthenegative
effectsoftorrentialfloodsduetothelimitedcapacitytoretainwaterinthecanopy.
Int.J.Environ.Res.PublicHealth2021,18,1141612of22
Figure4.Forestdegradedecosystemdensityandhotspotanalysis.
Thehighestdensityofdegradedgrasslandecosystemswasidentifiedinthe
NortheasternCarpathians,intheTransylvanianDepression,thenorth,centraleastern
andsouthernparts,andtheconfidencelevelofthehotspotwasover99%(Figure5).A
highdensitywasalsoobservedintheNordicgroupoftheWesternCarpathians.
Figure5.Grasslanddegradedecosystemdensityandhotspotanalysis.
Int.J.Environ.Res.PublicHealth2021,18,1141613of22
ThedensityofdegradedcaveecosystemsinRomaniaisverylow;however,some
hotspotscanbeobservedinthesouthoftheWesternCarpathiansandintheSouthern
Carpathians(Figure6).Coldspotsidentifiedformountainareaswithahighdensityof
cavesinthecaseoftheApuseniMountainswereduetotheirlowdegradation,manyof
thempresentingspeciesfromtheRedListofRomaniancavefauna[70].Thehotspots
identifiedforthedegradedcaveswereconcentratedinthenorthernApuseniMountains,
theBanatMountains,thesouthernRetezatandParângmountains,aswellasinFagaraș
(Figure6).Intheseareas,therearenumerouscaveswithahighnumberoftourists,which
increasesintemperaturebyupto2degreesandincreasesthepathogenicmicroorganisms,
asdeterminedlocallyandinstudiesconductedforMuierilorCaveandPolovragiCave
(fromParângMountains)andUrșilorCaveandMeziadCave(fromApuseniMountain)[71].
Figure6.Cavedegradedecosystemdensityandhotspotanalysis.
Thedensityofdegradedriverecosystemswasveryhighinthenorthernandcentral
partoftheTransylvanianDepression,inthenorthernhalfoftheWesternPlainbutalsoin
thesouthoftheMoldavianPlateau(Figure7).Statisticallysignificanthotspotswerealso
registeredinthecentralnorthernpartoftheRomanianPlain,inthenortheastofthe
MoldavianPlateauandinthesouthoftheEasternCarpathians.
Theconcentrationofriversinthehighdegradationclassintheplainareas
(MoldavianPlainlocatedinnortheasternRomania,WesternPlainandcenterofthe
RomanianPlain)iscausedbyagriculturalpracticesthatleadtowaterpollutionduetothe
useofclimaticfertilizersandincreaseinsalinizationagainstthebackgroundofincreasing
averagetemperatures.InthecaseoftheTransylvanianDepression,ahighconcentration
ofdegradedareaswasalsoidentified.Thisareaisknowntobedegradedduetothe
expansionofurbanagglomerationsbutalsobecauseofthenumerousruralsettlements
wheretheseweragesystemsdonotcomplywiththeenvironmentalregulationsinforce
sothatnitrogenpollutionishigh[72]sothattheriverdegradationclassishigh.
Thehighestdensityoflakeecosystemswasidentifiedinthecentralpartofthe
RomanianPlain,inthesouthoftheTransylvanianDepressionandinthenorthofthe
MoldavianPlateau(Figure8).Statisticallysignificanthotspotscouldalsobeobservedin
theWesternPlain.
Int.J.Environ.Res.PublicHealth2021,18,1141614of22
Theanalysisofthestateofdegradationofthecoastalenvironmenthighlightedtheareas
ofexpansionofinvasivespeciessuchasAilanthusaltissima,Amorphafruticosa,Elaeagnus
angustifoliaand,fromthecategoryofmarinespecies,RapanavenosaandMnemiopsisleidyi[73].
Figure7.Riverdegradedecosystemdensityandhotspotanalysis.
Figure8.Lakedegradedecosystemdensityandhotspotanalysis.
Theinfluenceofwastewaterdischargesandtheinfluenceoftouristactivitieswas
visiblefortheterrestrialenvironmentofthecoastalarea,notinginparticularthecoastal
Int.J.Environ.Res.PublicHealth2021,18,1141615of22
areasouthoftheDobrogeaPlateau,aswellasthesouthoftheDanubeDelta,territories
wherethedensityoftouristresortsishigh,thusinducinganegativeeffectonthestudied
ecosystem(Figure9).
Figure9.Coastaldegradedecosystemdensityandhotspotanalysis.
Finally,bycombiningthedensitiesofalltheanalyzedecosystems,themapofthe
densityofdegradedecosystemsinRomaniawasobtained(Figure10).
Figure10.Densityofdegradedecosystemsandhotspotanalysis.
Int.J.Environ.Res.PublicHealth2021,18,1141616of22
Thishighlightsastatisticallysignificanthighdensityinthecentralpartofthecountry
duetothenumerousnaturalmeadowsthatareinamediumandhighdegradationstage
duetotheanthropogenicpressureonthem,thehighnumberofriversegmentsthatarein
anadvancedstageofdegradationduetonumeroussourcesofpollutionmainlycaused
bythelackofseptictanksandtheinefficientuseofchemicalfertilizersinagricultureand
thesouthoftheCarpathianMountains,where,alongwiththedecliningforest,thereare
ahighnumberofdegradedmeadowsbutalsodegradedriversegments.
StatisticallysignificanthotspotswerealsoobservedintheMoldavianPlateauandin
thenorthoftheWesternPlainwheretherewasahighnumberofdegradedlakesand
riversashighlightedbytheexpertsinvolvedintheprojectonsite(Table3).
Table3.ClassificationofreliefunitsinRomaniabydegradationclasses.
Degradation.Low
(1–11)
Medium
(11–15)
High
(15–22)
Reliefunitskmp%kmp%kmp%
EasternCarpathians2778.98.123,597.668.77949.823.2
SouthernCarpathians2759.619.57120.750.34265.830.2
BanatMountains1747.825.14448.463.8780.311.2
SubCarpathians215.51.36884.741.59473.057.2
ApuseniMountains275.62.65976.256.14399.741.3
TransylvanianDepression0.00.02079.08.223
,
183.691.8
TheWesternHills951.67.48034.862.73825.729.9
MehedintiPlateau18.12.3519.565.1259.732.6
TheGeticPlateau1879.413.67247.752.64654.933.8
ThePlateauofMoldova1215.95.312,506.054.89091.939.9
WesternCamp4059.425.310,012.962.32001.912.5
TheRomanianPlain24
,
664.950.622
,
218.345.51903.23.9
DobrogeaPlateau6587.665.13424.233.8105.51.0
TheDanubeDelta4403.199.043.11.00.00.0
Low,mediumandhighclasseswereobtainedusinganaturalbreakclassificationofthedensityofdegradedraster
ecosystemspresentedinFigure10.
Relativeoperatingcharacteristics(ROC)analysiswasusedtodeterminetheaccuracy
ofdeterminingthedegradationstageforthesixtypesofecosystemsanalyzed.The
methodprovidesacurvegivenbyaconfusionmatrixofbinaryclassificationaccordingto
fourpossibleoutcomes:truepositive,truenegative,falsepositiveandfalsenegative.The
resultsarederivedbycomparingresultsofthemodelwiththegroundtruthsurvey(GTS),
whichareestablishedbythroughfieldcampaignscarriedoutinspring,summerand
autumnforall6typesofecosystemswiththehelpof35environmentalexpertsfromthe
project,whoaimedtoidentifythestateofecosystemswithanemphasisontheir
degradation.StatisticalanalyseswereconductedusingtheSPSSsoftwareprogram.
Theoutcomesarederivedbycomparingresultsofthemodelwiththegroundtruth
survey(GTS),approximately100pointschosenrandomlyforeachtypeofecosystemso
astocoverallthecountiesofRomania.TheROCcurveisamethodthatcomparestrue
positiveratesagainstfalsepositiverates.Foreachrandompoint,abufferareaof300m
wasanalyzedtoverifythepresenceorabsenceofdegradation.
FollowingtheanalysisofROCcurvesforthesixtypesofdegradedecosystems,itcan
beseenthatthemodelsthathaveahighdegreeofrepresentativenessfortheanalyzed
problemwerethosethatfocusedonidentifyingdegradedecosystems(characterizedbya
valueoftheareaofundertheROCcureof0.916)andthemodelfordeterminingthe
degradedlakes(characterizedbyavalueoftheareaundertheROCcureof0.918)(Figure
11).
Int.J.Environ.Res.PublicHealth2021,18,1141617of22

(a)(b)
(c)(d)
(e)(f)
Figure11.Relativeoperatingcharacteristics(ROC)curveandAUROCvaluefordegraded
ecosystems.(a)Forestecosystems;(b)grasslandecosystems;(c)caveecosystems;(d)river
ecosystems;(e)lakeecosystems;(f)coastalecosystems.
Inthecaseofthemodelsthattargetedtheecosystemsofforests,cavesandrivers,
lowervalueswerecalculatedbutlocatedabovethelimitof0.800,consideredathreshold
valueinordertoframetheresultsinthecategoryofstrongandmoderatemodels[74].
However,thesevaluesarejustified,takingintoaccountthediversityofthecausesof
degradationaswellastheirunevendistributionatthenationallevel[75].Themodelwith
alowvalidationratewasthecoastalecosystemsmodelforwhichimprovementscanbe
madeinfuturestudies,aimedatloweringthedistinctionbetweenthedistributionof
disturbingfactorsonlandandwaterandtheirdispersionwithdistancefromshore[76].
Int.J.Environ.Res.PublicHealth2021,18,1141618of22
However,weconsideredthat,forthepresentstudy,weshouldkeepallsixmodelssothat
thefinalmapofcumulativedegradationofallecosystemscanbemadeatthenational
levelanddrawattentiontohotspotsthatrequiredetailedstudiesorcasestudiestoanalyze
intimethecurrentsituationofdegradation.
4.Conclusions
Identifyingdegradedecosystemsisakeyelementoftheecologicalreconstruction
strategy.Inthissense,theiranalysiscontributestoabetterunderstandingofthe
mechanismsthathaveledtochangesinthestructureandfunctioningofecosystems,with
adirectimpactonecosystemservices.Theconceptualapproachbasedonthemapping
andassessmentofecosystemservicescontributessignificantlytothedevelopmentofan
integratedvisionofecologicalreconstruction.
Representedbythevarietyofecosystems,speciesandgenes,thebiodiversityin
Romaniaisthenationalnaturalcapital,beinganintegralpartofsustainabledevelopment,
byprovidinggoodsandservicessuchasfood,carbonsequestrationandredistributionof
marineandterrestrialwater,whichunderlieprosperity,economicdevelopment,social
welfareandqualityoflife.
Humanactivitiesareassessedintermsofdirectorindirectimpactonthecomponents
ofbiologicaldiversityinordertoapplyappropriatemeasurestominimizeadverseeffects,
reconstruction,rehabilitationandremediationofaffectedecosystems.
Consideringthefactthatforall6typesofecosystemsagroupof35environmental
professionalsperformedfieldstudiesandidentifiedthestateoftheecosystemswithan
emphasisontheirdegradation,weconsiderthatthedatabaseusedreachesthedegreeof
detailnecessarytodrawgeneralconclusionsintermsoftheconcentrationofdegraded
areasinRomania.Followingthehotspotanalysis,itwasidentifiedthatthelargest
degradedsurfacesarethecoastalones(49.80%),followedbythegrasslandecosystems
(38.59%)andthecaveecosystems(2.66%),whilethedegradedriversecosystemsare
degradedbyaproportionof27.64%,degradedforestecosystemsby8.52%,anddegraded
lakesecosystemsby14.05%.Relativeoperatingcharacteristics(ROC)analysishighlights
thatthemodelsthathaveahighdegreeofrepresentativenessarethegrassland
ecosystems(characterizedbyavalueoftheareaofundertheROCcurveof0.916)and
lakeecosystems(withROCcurevalueof0.918).Ecosystemscharacterizedbyagreat
diversityofthecausesofdegradationaswellastheirunevendistributionatthenational
levelsuchastheecosystemsofforests,cavesandriverswithaROCvalueabovethelimit
of0.800.
Thedegradationofaparticularecosystemmustbeassessedbythecharacteristicsof
theecosystemtoberestored.Themethodologyforassessingthedegreeofdegradationof
ecosystemsisbasedonaseriesofactivities,criteria,methodsandproceduresfor
estimatingthevaluesoftheparametersthatindicatethestateoftheseecosystems.
Therefore,itisimportanttodiscoverthenaturalprocessesthattakeplaceinthesystem
andtoanalyzethechangesproducedbytheimpactofanthropogenicactivities.
Conservationstatusassessmentandmonitoringconsistsofidentifyingdirectorindirect
risksandassessingthedegreeofhabitatthreat.
Thestudycarriedoutonthechangesthatoccurredinthenaturalenvironmentonthe
Romanianterritoryshowshowthedeteriorationandpollutionoftheareasisdirectly
relatedbothtotheindustrialactivitiesintheareaandtotheinevitableclimaticchanges
andothernaturalphenomena.
Inconclusion,thehighestdensityofdegradedecosystemsinRomaniaislocatedin
thecentralpart,intheTransylvanianDepressionandsouthoftheCarpathianMountains,
intheSubCarpathians.Themainfactorsthatledtothedegradationofecosystemsin
Romaniawereanthropogenicbutalsonatural.
Thiscomprehensivestudyisanimportantstepinthefieldofecological
reconstructioninRomania,asthestartingpointforfuturestudiesandsupplementary
rehabilitationactions.
Int.J.Environ.Res.PublicHealth2021,18,1141619of22
SupplementaryMaterials:Thefollowingareavailableonlineat
www.mdpi.com/article/10.3390/ijerph182111416/s1,Ecosystemservicesandtypeandsourcesof
degradationusedinthisstudy.
AuthorContributions:Conceptualization,S.A.andC.G.;methodology,S.A.andI.O.;software,I.O.
andS.R.(StelianaRodino);validationS.R.(SandaRoșca);formalanalysis,S.A.andC.G.;
investigation,C.G.,S.A.andI.O.;resources,C.G.,S.A.andI.O.;datacurationI.O.andS.R.(Steliana
Rodino);writing—originaldraftpreparation,S.A.andI.O,;writing—reviewandediting,S.R.
(SandaRoșca);visualization,I.O.;supervision,S.A.andC.G.Allauthorshavereadandagreedto
thepublishedversionofthemanuscript.
Funding:Thisresearchreceivednoexternalfunding.
InstitutionalReviewBoardStatement:Notapplicable.
InformedConsentStatement:Notapplicable.
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
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... Achieving accurate and up-to-date mapping requires integrating remote sensing, machine learning (ML), and field-based observations [19]. However, Romania's diverse forest landscapes necessitate careful calibration of remote sensing data with regional measurements [17,20]. Variability in data collection methods, sensor limitations, and environmental factors introduce uncertainties, making robust validation frameworks crucial for ensuring the reliability of these forest assessments [20]. ...
... However, Romania's diverse forest landscapes necessitate careful calibration of remote sensing data with regional measurements [17,20]. Variability in data collection methods, sensor limitations, and environmental factors introduce uncertainties, making robust validation frameworks crucial for ensuring the reliability of these forest assessments [20]. ...
... Hyperparameters in Google Earth Engine were manually tuned due to the platform's lack of automated options. For GBTA, we tested learning rates (0.01, 0.05, 0.1, 0.2), max depths (3,5,7), and min split losses (0, 0.01, 0.1); for RF, we varied variables per split (auto, sqrt, log2) and min leaf populations (1,5,10,20); for CART, we optimized max depths (5, 10, 15) and min samples split (2,5,10). A grid search combined with cross-validation ensured robust model evaluation, balancing computational efficiency and performance [71]. ...
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Forest attributes, such as standing stock, diameter at breast height (DBH), tree height, and basal area, are critical for effective forest management; yet, traditional estimation methods remain labor-intensive and often lack the spatial detail required for contemporary decision-making. This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. During these campaigns, detailed measurements were recorded for every tree within selected plots. For each tree, DBH was measured directly, and tree heights were obtained either by direct measurement—using hypsometers or clinometers—or, when direct measurements were not feasible, by applying well-established DBH—height allometric relationships that have been calibrated for the local forest types. This comprehensive approach to ground data collection, supplemented by an independent dataset from Brasov County collected using the same protocols, allowed for robust training and validation of the machine learning models. This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Classification and Regression Trees (CART), and the Gradient Boosting Tree Algorithm (GBTA)—in predicting the forest attributes from Sentinel-2 satellite imagery. While Random Forest consistently delivered high R² values and low root mean square errors (RMSE) across all attributes, GBTA showed particular strength in predicting standing stock, and CART excelled in basal area estimation but was less reliable for other attributes. A sensitivity analysis across multiple spatial resolutions revealed that the performance of all algorithms varied significantly with changes in resolution, emphasizing the importance of selecting an appropriate scale for accurate forest mapping. By focusing on both the methodological advancements in machine learning applications and the rigorous, detailed empirical forest data collection, this study provides a clear solution to the problem of obtaining reliable, spatially detailed forest attribute maps.
... Achieving accurate and up-to-date mapping requires integrating remote sensing, machine learning (ML), and field-based observations [19]. However, Romania's diverse forest landscapes necessitate careful calibration of remote sensing data with regional measurements [17,20]. Variability in data collection methods, sensor limitations, and environmental factors introduce uncertainties, making robust validation frameworks crucial for ensuring the reliability of these forest assessments [20]. ...
... However, Romania's diverse forest landscapes necessitate careful calibration of remote sensing data with regional measurements [17,20]. Variability in data collection methods, sensor limitations, and environmental factors introduce uncertainties, making robust validation frameworks crucial for ensuring the reliability of these forest assessments [20]. ...
... Hyperparameters in Google Earth Engine were manually tuned due to the platform's lack of automated options. For GBTA, we tested learning rates (0.01, 0.05, 0.1, 0.2), max depths (3,5,7), and min split losses (0, 0.01, 0.1); for RF, we varied variables per split (auto, sqrt, log2) and min leaf populations (1,5,10,20); for CART, we optimized max depths (5, 10, 15) and min samples split (2,5,10). A grid search combined with cross-validation ensured robust model evaluation, balancing computational efficiency and performance [71]. ...
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Full-text available
Forest attributes such as the standing stock, diameter at the breast height, tree height, and basal area, are essential in forest management. Conventional estimation methods, which are still largely used in many parts of the world, are typically resource intensive. Machine learning algorithms working with remotely sensed data trained by ground measurements may provide a promising, more efficient alternative. This study evaluates the performance of three machine learning algo-rithms, namely Random Forest, Classification and Regression Trees, and Gradient Boosting Tree Algorithm in estimating these forest attributes. Ground truth data was sourced by measurements carried out in relevant forests from Romania and by an independent dataset from Brasov County. The predictive ability of the tested algorithms was examined by considering several spatial resolu-tions. The results showed varying degrees of performance. Random Forest was the best performer, with RMSE and R2 values over 0.8 for all attributes. GBTA excelled in predicting the standing stock, achieving R2 values over 0.9. The validation based on the independent dataset has confirmed higher performance for both RF and GBTA. In contrast, CART excelled in predicting the basal area, but struggled with breast height diameter, standing stock, and tree height. A sensitivity analysis that concerned the spatial resolution revealed high degrees of discrepancy. Random Forest and Gradient Boosting Tree Algorithm were more consistent when estimating the standing stock, but they have shown inconsistency for breast height diameter and tree height, while CART showed important variations. These results provide useful insights into the strengths and weaknesses of these algo-rithms, and provide the information required to select the best option when aiming to use similar solutions for estimation.
... As shown in Figure 20, the agricultural areas' change in bioclimate is projected in general for both investigated scenarios in RO. Concerning the Ref, a slight increase in the humid class's frequency is demonstrated for p1 RCP7 and in the humid to extremely humid classes for p1 RCP8.5 (e.g., 25% vs. 21 ...
... The de Martonne classes' relative frequency over the natural areas of Romania.As shown inFigure 20, the agricultural areas' change in bioclimate is projected in general for both investigated scenarios in RO. Concerning the Ref, a slight increase in the humid class's frequency is demonstrated for p1 RCP7 and in the humid to extremely humid classes for p1 RCP8.5 (e.g., 25% vs.21.9% in p1 RCP7 and 22.8% in the Ref, for the very humid category). The highest agricultural areas characterized by a semi-dry bioclimate is exhibited for the latest period under the extreme RCP8.5 (27.2% vs. 17.3 in p3 RCP7 and 1% in the Ref) at the expense of all remaining categories. ...
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The changing climate is closely related to changes in the bioclimate. This research deals with the present bioclimate and its projected evolution over the entirety of the natural and agricultural lands of south-eastern Europe and individual countries (Bulgaria, Greece, Kosovo, N. Macedonia, Romania, and Serbia). For this purpose, an ultrahigh spatial resolution of the de Martonne bioclimatic index pattern was elaborated and analysed for the first time. The survey is performed over the reference period (1981–2010) and future time frames (2011–2040; 2041–2070; 2071–2100) under SSP370 and SSP585 emission scenarios. On a territorial level, both natural and agricultural areas appear as highly impacted by the future changes of bioclimate; the highest xerothermic trend is expected to influence the latter areas, mostly in 2071–2100 and under the higher emission scenario. The natural areas will face an expansion in the semidry class from 0.9% (of the total area) during the reference period to 5.6% during 2071–2100 under the RCP8.5 scenario as the dominant extremely humid class falls from 53.5% to 32.9% for the same periods and scenario. On the other hand, agricultural areas will face a more intense xerothermic alteration going from 4.9% to 17.7% for the semidry class and from 41.1% to 23.5% for the dominant very humid class for the same periods and scenario. This study presents the spatial statistics per country for the selected scenarios and periods to provide information for stakeholders. This study’s results highlight the necessity for intensifying adaptation plans and actions aiming at the feasibility of agricultural practices and the conservation of natural areas.
... Landsat mission with 30 m spatial resolution, launched for the first time in the early 1970s, offers the longest temporal series with adequate spectral resolutions (11 bands in Landsat-8) [63]. However, the use of Landsat images to detect and model IAP on coastal areas could decrease in the future and be replaced by the latest satellite images with finer spatial resolution and similar revisit period (e.g., Sentinel-2, PlanetScope, etc.) [58][59][60]72]. The most recent RS platforms as UAVs with ultra-high spatial resolution (below 1 m) certainly offer adequate images to detect and model IAP and to analyze the entire complexity of coastal environments [50]. ...
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Coastal environments are highly threatened by invasive alien plants (IAP), and Remote Sensing (RS) may offer a sound support for IAP detection and mapping. There is still a need for an overview of the progress and extent of RS applications on invaded coasts that can help the development of better RS procedures to support IAP management. We conducted a systematic literature review of 68 research papers implementing, recommending, or discussing RS tools for IAP mapping in coastal environments, published from 2000 to 2021. According to this review, most research was done in China and USA, with Sporobolus (17.3%) being the better studied genus. The number of studies increased at an accelerated rate from 2015 onwards, coinciding with the transition from RS for IAP detection to RS for invasion modeling. The most used platforms in the 2000s were aircraft, with satellites that increased from 2005 and unmanned aerial vehicles after 2014. Frequentist inference was the most adopted classification approach in the 2000s, as machine learning increased after 2009. RS applications vary with coastal ecosystem types and across countries. RS has a huge potential to further improve IAP monitoring. The extension of RS to all coasts of the world requires advanced applications that bring together current and future Earth observation data.
... This study involves a deterministic approach, which aims to establish some geomorphological indicators and analyse the behaviour of each one from the point of view of the vulnerability it induces. The result of such a model is a landslide susceptibility map, imperative to predict their occurrence, manage the hazard, reduce damage to infrastructure and loss of life (Shuin et al., 2014;Meten et al., 2015;Dahal, 2017) and to analyze the impact on the environment (Avram et al., 2021). ...
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Having a strong impact on human activities, landslides represent one of the most frequent hazards encountered throughout the world, but also in Romania. As a result, various exhaustive scientific approaches try to identify the areas affected by this phenomenon or at risk, among the proposed methods being those offered by G.I.S. techniques of spatial analysis in tandem with statistical methods. In the present study, G.I.S. methods of spatial analysis were used, with a focus on methodologies capable of determining the probability of occurrence of landslides, possible and viable within any territory. The analysis was carried out in the area of the commune of Bicazu Ardelean, Neamț County, Romania, where multiple areas with a medium-high and high probability of vulnerability were identified, by means of a deterministic “white-box” type model, followed by an evaluation from the point of view of the risk induced on the territorial infrastructures. Both the model and the evaluation generated suitable results, validated in G.I.S. and in the field. The obtained results attested the viability of the working method, as well as the potential of its application in any other areas with similar morphometric characteristics.
... The increase in precipitation intensity influences the surface runoff and implicitly the soil erosion [58][59][60]. An increase in heavy rainfall in areas where agricultural land management is deficient also causes degradation of agricultural plots and meadows [61], thus influencing environmental factors, inducing the need for the analysis performed in the present study. The identification of the areas vulnerable to soil erosion in the Guruslău Depression and the establishment of measures to combat and prevent this process may have the effect of increasing agricultural productivity. ...
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Changes in land use, increasing of agricultural areas to the detriment of wooded ones, and poor management of agricultural land, along with the impact of current changes in the climate (reflected in the increase of the climate aggression index) makes soil erosion one of the main risks associated with improper land use, with a direct impact on its productivity and an indirect impact on human beings. The aim of this study is to assess the risk induced by surface soil erosion on land use, using as our main method of investigation the development of two models of integrated spatial analysis of the territory: a derived model of the universal soil loss equation (USLE) and a qualitative model that integrates the result of soil erosion assessment with the database representing the land use. This was carried out in order to highlight the impact on the territory. The spatial analysis models were developed on a structure of vector spatial databases, through which the soil type, soil texture, climate aggression coefficient, and land use were mapped, and alphanumeric databases, representing the market cost of land, in EUROs, that highlight the quality of cultivated land (in terms of productive economic potential). The induced risk estimation is based on a qualitative rating of soil erosion vulnerability on a scale from 1 to 5 (1-low vulnerability; 5-high vulnerability) and of the reduction of the economic value of the land (according to the vulnerability rating). The implemented methodology highlights the quantitative risk, with a maximum value of about 46.000 EUROs, spatially identified on large surfaces on the outskirts of the Jibou municipality. It is mainly caused by the impact of soil erosion on large areas of orchards, which provide necessary products for human consumption. The present methodology can be implemented on similar areas and can be used as a model of good practices in risk assessment based on financial losses by local public authorities.
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As the population has increased and the economy has developed in the Qaidam Basin, the demand for food and energy in the basin has increased, and the contradiction between economic development and ecological protection is gradually becoming prominent. In this study, the eco-environmental quality of the Qaidam Basin from 1986 to 2019 was evaluated and analyzed based on the Modified Remote Sensing Ecological Index (MRSEI) retrieved by the Google Earth Engine (GEE) and meteorological and socioeconomic auxiliary data. The results show that (1) the Qaidam Basin had a lower overall level of eco-environmental quality, with higher eco-environmental quality in the southeastern part of the basin and lower eco-environmental quality in the central and northwestern parts of the basin. (2) During the period of 1986 to 2019, the eco-environmental quality of the Qaidam Basin started to reverse in 2003; it decreased first and then increased, and the overall performance showed an upward trend over the past 34 years. The most obvious changes were in the northwestern, northeastern, southwestern and central parts of the basin. The eco-environmental quality continued to decline in the northwestern and rise in the northeastern and southwestern regions, and in the central part, it decreased first and then plateaued. (3) The eco-environmental quality of the Qaidam Basin was affected by both natural and human factors. From 1986 to 2019, the “warm and wet” climate in the basin promoted the growth of vegetation. Furthermore, the optimization of industrial structures alleviated the pressure of agriculture and livestock and jointly improved the ecological environment in the Qaidam Basin.
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