AtlanticForestBiomeATBD_RAlgorithmTheoreticalBasisDocument&Results

March,2017

EXECUTIVESUMMARYThisATBD_Risadynamicandevolvingdocument.Eachtimewelaunchanewcollection,thisdocumentwillberevisedaccordingly.Byreadingthedocumentoneshouldunderstandhowthecollectionofmapswereproduced,whatweretheutilizeddatasetsandmethodologicalapproachesandwhatweretherationaleandtheorybehindtheseapproaches.AtlanticForestTeamGeneralCoordinatorMarcosReisRosaBiomeCoordinatorFernandoF.PaternostTeamVivianeC.MazinJacquelineFreitasEduardoR.Rosa

TableofContents

EXECUTIVESUMMARY..............................................................................................................2

ListofTables..............................................................................................................................4

1. Introduction.......................................................................................................................5

1.1. IdentificationofRegionofInterest.............................................................................5

2. OverviewandBackgroundInformation.............................................................................6

2.1. ExistentMapsandMappingInitiatives.......................................................................6

3. AlgorithmDescriptions,Assumptions,andApproaches....................................................9

3.1. Algorithmdescription.................................................................................................9

3.2. Collection1...............................................................................................................11

3.2.1. Dataavailabilityandscreening..........................................................................11

3.2.2. Classificationschemeandparameters..............................................................14

3.2.3. Datavolumesandprocessingissues.................................................................17

3.2.4. PreliminaryResultAnalyze................................................................................17

3.2.5. Finalprocessing.................................................................................................20

3.3. Collection2...............................................................................................................21

3.3.1. Algorithm...........................................................................................................21

3.3.2. Dataavailabilityandscreening..........................................................................22

3.3.3. Classificationschemeandparameters..............................................................25

3.3.4. DecisionTree.....................................................................................................28

3.3.4. TemporalFilter..................................................................................................31

3.3.4. Datavolumesandprocessingissues.................................................................35

4. ValidationStrategies........................................................................................................36

5. ConcludingRemarksandPerspectives............................................................................37

6. References.......................................................................................................................39

7. Appendices.......................................................................................................................39

9.1. AlgorithmCode........................................................................................................39

9.2. SupplementalMaterial............................................................................................45

ListofFiguresFigure1.BiomesofBrazil(IBGE,2004)...........................................................................................................5Figure2.Mapfrom“AtlasdosRemanescentesFlorestaisdaMataAtlântica,2013-2014”...............................6Figure3.IBAMA´sAtlanticForestmonitoringwebsite....................................................................................7Figure4.Grids1:250.000inAtlanticForestBiome..........................................................................................9Figure5.ActivityflowcharttoproduceBiomeclassification..........................................................................9Figure6.ActivityflowcharttoproduceFinalclassification...........................................................................10Figure7.Qualityofeachmapmosaicincollection1.....................................................................................11Figure8.Endmemberscandidates(gray)andmedian(dashedline)toGV,NPV,SoilandShadeendmembers

(SOUZA;ROBERTS;COCHRANE,2005).................................................................................................12Figure9.NDFImosaicforeachyearincollection1.......................................................................................13Figure10.Decisiontreeincollection1(greenarrowequalYESandredarrowequalNO).............................14Figure11.RegionsusedtodivideAtlanticForestBiome...............................................................................14Figure12.Valuesforeachvariabletoeachregionfrom2008to2015...........................................................15Figure13.SpatialFilterappliedtoCollection1.............................................................................................15Figure14.Forestclassificationafterspatialfilterfrom2008to2015............................................................16Figure15.Generalrulestotemporalfilter....................................................................................................16Figure16.MapBiomasclassificationofSPcomparedwithreferencemaps...................................................17Figure17.ConsistencyAnalyze.....................................................................................................................18Figure18.StatisticsofforestforeachyearateachBrazilianfederationunit................................................19Figure19.FrequencyAnalysis.......................................................................................................................19Figure20.Forestfinalproductandstatistics.................................................................................................20Figure21.Collection1Finalproduct.............................................................................................................20Figure22.RegionsusedtodivideAtlanticForestBiome...............................................................................23Figure23.Mosaicqualityofeachgridineachyearincollection2................................................................24Figure24.DecisionTreetoAtlanticForestBiome.........................................................................................28Figure25.Distributionof667validationpoints............................................................................................36Figure26.Imagesofthesamepixelfrom2000to2016................................................................................37Figure27.Exampleofconfusionmatrixandomission,commissionandtotalaccuracy.................................37Figure28.LocationofsavannainAtlanticForestBiome...............................................................................38

ListofTablesTable1.TablewithSatellite/SensorandGEEDataSetforeachyearinCollection1......................................11Table2.TablewithSatellite/SensorandGEEDataSetforeachyearinCollection2......................................22Table3.Tableofinitialandfinalmonthforeachregion:..............................................................................23Table4.GeneralRules:Incaseof“Notobserved”repeatthelastvalidclass:...............................................32Table5.GeneralRules:Invalidtransitions....................................................................................................33Table6.GeneralRule:Invalidregenerationwithkernel5years....................................................................34Table7.RulestoFirstyear............................................................................................................................34Table8.RulestoLastyear............................................................................................................................35

1. Introduction

1.1. IdentificationofRegionofInterest

The Atlantic Forest Biome covered an area equivalent to 1,315,460 km2 and originallyextendedover17brazilianstates.Today, based on SOS Mata Atlântica1 map, there remain 8.5% of well preserved forestremnantsover100hectaresofwhatoriginallyexisted.Summedupall fragmentsofnativeforestabove3hectares,wecurrentlyhave12.5%.ThisforestisaglobalHotspot,oneoftherichestandmostendangeredareasontheplanet,andalsodeclaredaBiosphereReservebyUnescoandNationalPatrimony, in theBrazilianFederalConstitutionof1988.Almost72%of theBrazilianpopulationcurrently lives in theAtlanticRainforestBiome.In the project the we use as reference the “MAPA DE BIOMAS DO BRASIL – PrimeiraAproximação”,producedbyIBGE2,2004andpublishedinscale1:5.000.000.

Figure1.BiomesofBrazil(IBGE,2004)

1 SOSMataAtlânticaisBrazilianNGOresponsibletomapAtlanticForestBiome2 IBGE–GeographicandStatisticalBrasilianInstitute

2. OverviewandBackgroundInformation

2.1. ExistentMapsandMappingInitiatives

TheAtlanticforestbiomehasseveralmappingandmonitoringinitiatives.Themost importantmonitoring initiative is “Atlas dos Remanescentes Florestais daMataAtlântica”executedbySOSMataAtlanticaandINPE3.Itproducesdeforestationdataandmapsince 1985 until actual days. Since 2000 the initiative use visual interpretation of Landsatimagesat1:50,000scale.

Figure2.Mapfrom“AtlasdosRemanescentesFlorestaisdaMataAtlântica,2013-2014”

3 INPE(InstitutoNacionaldePesquisasEspaciais)-BrazilianNationalInstituteofSpaceResearch

The Brazilian Environment Ministry has also an important initiate called PROBIO4 thatproducedamapofAtlanticforestbiomelandcoverin2002at1:250,000scale.Thisproductwasdetailedandusedtoidentifydeforestationin2008and2009byIBAMA5.

Figure3.IBAMA´sAtlanticForestmonitoringwebsite

4 ProjetodeConservaçãoeUtilizaçãoSustentáveldaDiversidadeBiológicaBrasileira-Brazilianconservationprogram5 IBAMA(InstitutoBrasileirodoMeioAmbienteedosRecursosNaturaisRenováveis)-BrazilianInstituteofEnvironmentandRenewableNaturalResources

FBDS6hasamappingprojecttosupportCAR7inAtlanticForestandCerrado.Theprojectaimstomeasure the environmental liabilities in APP8 area ofwater bodies, following the rulescontainedintheLawonProtectionofNativeVegetation(orNewForestCode,Law12.651/2012).Todothis,theFBDSusestheRapidEyeimages(1:20,000),performingthelandcovermapping,surveyingthedrainagenetworkandspatializingAPPareasthatmustberecovered.BytheendofJuly2016,dataanalyzedfor1800BrazilianmunicipalitiesinAtlanticForestandCerradobiomeshadalreadybeensharedwithFederalGovernment.Theprojectisfinancedbyfederationsandprivatesectorassociations9,basedonatechnicalcooperationagreementsignedwiththeMinistryofEnvironmentandtheEMBRAPA10.Thelandcovermapisbasedonimagesfrom2010,2011or2012.It’sverydetailedmapandoneofthebestreferencetochecktheconsistenceofMapBiomasproducttoAtlanticForest.

6 FBDS-FundaçãoBrasileiraparaoDesenvolvimentoSustentável-BrazilianNGO7 CAR-CadastroAmbientalRural-Nationalregisterofrurallandowners8 APP–ÁreadePreservaçãoPermanente-Permanentpreservationarea9 FEBRABAN,IBÁ,SRB,AGROICONE,ABAG,FENASEG,UNICAandInstitutoAçoBrasil10 EMBRAPA-EmpresaBrasileiradePesquisaAgropecuária-BrazilianAgriculturalResearchEnterprise

3. AlgorithmDescriptions,Assumptions,andApproaches

3.1. Algorithmdescription

ThemonitoringisbasedonthecomparisonofannuallandcovermappingproducedfromtheautomaticclassificationofLandsatimages.Herereferencedonlyas“landcover”.Thelandcovermapofeachyearisproducedbasedonanempiricalclassificationtreeappliedtothebestimagesmosaic.

Figure4.Grids1:250.000inAtlanticForestBiome

Figure5.ActivityflowcharttoproduceBiomeclassification

Thevaluesofeachnodeoftheclassificationtreemaybeparameterizedindependentlyforeach1:250,000gridandeachyeartoallowthenecessaryadjustmentstoavoidvariationsduetoseasonality,lighting,humidityorthesensoritself.Theclassificationtreenodescanbedefinedbasedonthevaluesorreflectanceindicesofeachpixel,onthefractionsofthecompositionofeachpixeloronadditionalinformationsuchaselevation,slopeoraspect.

Figure6.ActivityflowcharttoproduceFinalclassification

Afterthebiomeclassification,aspatialfilterisappliedtoremoveisolatedpixels.Atemporalfilterwitha3or5-yearkernelisalsoappliedtoavoidimpossiblechangesofclassesandtheproductisintegratedwithotherthemes(pasture,agriculture,etc.)toproducethefinallandcovermap.Withtheprojectdevelopmentthemappingwillincorporatemoreyears,aimingtomapsince1985(firstLandsatimageswithresolutionof30m)andagreaterdetailingofthelegend.

3.2. Collection1

3.2.1. DataavailabilityandscreeningCollection1mappedonly Forest andnon-Forest classes from2008 to2015using LandSatimages.Table1.TablewithSatellite/SensorandGEE11DataSetforeachyearinCollection1

2008 TM/Landsat5 LT5_L1T_SR2009 TM/Landsat5 LT5_L1T_SR2010 TM/Landsat5 LT5_L1T_SR2011 TM/Landsat5 LT5_L1T_SR2012 ETM/Landsat7 LE7_L1T_SR2013 OLI/Landsat8 LC8_L1T_TOA2014 OLI/Landsat8 LC8_L1T_TOA2015 OLI/Landsat8 LC8_L1T_TOA

ThemosaicsforeachyearwasproducedinitiallyconsideringthemedianofvalidpixelsfromAugust,SeptemberandOctober.Eachmosaicwasanalyzedandtheperiodwasadjustedconsideringamaximumof6-monthinterval.

Figure7.Qualityofeachmapmosaicincollection1

11GEE–GoogleEarthEngine

EachmosaicwasusedtocreatetheSMA12andfractionsofGV13,NPV14,SOILandSHADE.

Figure 8. Endmembers candidates (gray) and median (dashed line) to GV, NPV, Soil and Shadeendmembers(SOUZA;ROBERTS;COCHRANE,2005)

TheNDFI15wascreatedbasedonthefractionanditwasusedasthemainreferencetoclassifytheforest.

and

12 SMA-SpectralMixtureModel13 GV-GreenVegetation14 NPV-NonPhotosyntheticVegetation15 NDFI-NormalizedFractionDifferenceIndex

2008 2009 2010

2011 2012 2013

2014 2015

Figure9.NDFImosaicforeachyearincollection1

3.2.2. Classificationschemeandparameters

Thedecisiontreewasbuiltwith4variablesthatwereparametrizedforeachgridandeachyear:

Figure10.Decisiontreeincollection1(greenarrowequalYESandredarrowequalNO)

TheAtlanticForestBiomewasdividedin11regionsbasedonsimilarvegetation,landcoverandlatitude.

Figure11.RegionsusedtodivideAtlanticForestBiome

Theseregionswereusedtoidentifythebasicparametersthatwereusedasinitialvalueineachgrid:

Figure12.Valuesforeachvariabletoeachregionfrom2008to2015

AftertheclassificationofForestandNon-ForestaSpatialFilterwasappliedtofillgapsandremoveisolatedpixels.

Figure13.SpatialFilterappliedtoCollection1

Thespatialfilterproducedamapofforestforeachyear.

Figure14.Forestclassificationafterspatialfilterfrom2008to2015

AftertheSpatialFilteraTemporalFilterwasappliedtoremoveinvalidchangesintheperiod.Thebasicconceptistoavoidsomeerrorsliketheexample:2008:Forest–2009:Non-Forest–2010:Forest=2009correctedtoForest2008:Non-Forest–2009:Forest–2010:Non-Forest=2009correctedtoNon-ForestThegeneralrulesappliedtoTemporalFilterare:

Figure15.Generalrulestotemporalfilter

3.2.3. Datavolumesandprocessingissues

Thecollection1mapped8years(2008to2015)inthe122grids.Aftertheparameterizationofthebestperiodforeachyearandthebestparametersforeachvariable theprocessing timetogeneratecloud freemosaicandclassification tookabout3weeksinGoogleEarthEngine.The product was revised and the parameters were corrected when necessary. After thisrevision,anewversionwasproducedfromscratchandtookanother3weekstoprocess.

3.2.4. PreliminaryResultAnalyze

Afterinitialclassification,theproductwasanalyzedtochecktheconsistencyandqualityoftheclassification.Thepreliminarymapwascomparedwithreferencemapstoidentifyclassificationproblems.

Figure16.MapBiomasclassificationofSPcomparedwithreferencemaps

A visual inspection and statistical data also determined classification difference betweenadjacentgridsandsequentialyears.

Figure17.ConsistencyAnalyze

Figure18.StatisticsofforestforeachyearateachBrazilianfederationunit

Theanalysisofhowmany timesapixelwas classifiedas forest in all years alsohelped toidentifyareaswheretheclassificationisnotconsistent.

Figure19.FrequencyAnalysis

3.2.5. FinalprocessingAftertherefininginparameterizationtheclassificationwasreprocessedresultinginaforestclassificationtoAtlanticForestBiome.

Figure20.Forestfinalproductandstatistics

TheBiomeclassificationwasintegratedwithotherthemesgeneratingthefinalproduct,whichispubliclyavailableascollection1.

Figure21.Collection1Finalproduct

3.3. Collection2

3.3.1. Algorithm

Thealgorithmusedtoclassifythelandcoverincollection2wasthesameusedincollection1.Landsatimageswerefilteredtoidentifythebestimagesandbestpixelstoproducethemosaictoeachyear.Anempiricclassificationtreewasusedtoclassifyeachmosaicusingnodesfromfractions, fractions index, reflection indexorother information likewaterandcloudmask,elevationorslope.After the classification of each year a spatial and temporal filter was used to refine theclassificationandthemapswereintegratedwithotherthemeslikepasture,agricultureandcoastalvegetation.Collection2hasmapsfrom2000to2016andamoredetailedlegend.

3.3.2. DataavailabilityandscreeningCollection2usedLandsatL1Timages:Table2.TablewithSatellite/SensorandGEEDataSetforeachyearinCollection2

Year Sensor/Satellite GEECollection2000 ETM/Landsat7 LE7_L1T_SR2001 ETM/Landsat7 LE7_L1T_SR2002 ETM/Landsat7 LE7_L1T_SR2003 TM/Landsat5 LT5_L1T_SR2004 TM/Landsat5 LT5_L1T_SR2005 TM/Landsat5 LT5_L1T_SR2006 TM/Landsat5 LT5_L1T_SR2007 TM/Landsat5 LT5_L1T_SR2008 TM/Landsat5 LT5_L1T_SR2009 TM/Landsat5 LT5_L1T_SR2010 TM/Landsat5 LT5_L1T_SR2011 TM/Landsat5 LT5_L1T_SR2012 ETM/Landsat7 LE7_L1T_SR2013 OLI/Landsat8 LC8_L1T_TOA2014 OLI/Landsat8 LC8_L1T_TOA2015 OLI/Landsat8 LC8_L1T_TOA2016 OLI/Landsat8 LC8_L1T_TOA

Landsatsurfacereflectance imagesascomputedbythe .Reflectanceunit is rescaledto0-10,000.Thesameregionsfromcollection1wereusedincollection2toidentifyinitialandfinalmonthofeachgridtobuildthemosaic.

Figure22.RegionsusedtodivideAtlanticForestBiome

Table3.Tableofinitialandfinalmonthforeachregion:

Region Initial FinalG1 June SeptemberG2 June AugustG3 April AugustG4 May AugustG5 June SeptemberG6 June SeptemberG7 June SeptemberG8 May SeptemberG9 May SeptemberG10 June OctoberG11 June November

Themosaicperiodofeachyearisthenrevisedoneachgrid1:250,000toverifytheobservableareawithoutcloudcoverandthegeneralpatternoftheimageandthevegetation.Theinitialand final date of images of each grid are adjusted to correct problems when possible,extendingtheperiodtoupto6monthswhennecessaryoraddingimagesfromdrytowetseasonstoharmonizethepatternwithotheryears.Annex 1: Table with the parameters (start date, end date and cloud cover of each grid1:250,000scaleforeachyear.Thefiguresbelowshowtheaveragequalityofthemosaicsofeachgrid1:250,000foreachyear:

Figure23.Mosaicqualityofeachgridineachyearincollection2

3.3.3. Classificationschemeandparameters

Thecollection2inAtlanticForestwillhavethefollowingclasses:ForestThemapwasbasedontheFAO16definitionforForest:“Landwithtreecrowncover(orequivalentstockinglevel)ofmorethan10percentandareaofmore than0.5hectares (ha). The trees shouldbeable to reachaminimumheightof5meters(m)atmaturityinsitu.”NaturalForestintheBiomehasmoretreecrowncoverthenthedefinitionofFAO.Weusedadifferentvalueforeachnaturalforestformation:•DenseOmbrophilesForest-treecrowncoverofmorethan80%•MixedOmbrophilesForest-treecrowncoverofmorethan80%•OpenOmbrophilesForest-treecrowncoverofmorethan60%•SeasonalDeciduousForest-treecrowncoverofmorethan40%indryseason•SeasonalSemideciduousForest-treecrowncoverofmorethan40%indryseasonForestintheBiomehavehighNDFI,that’sandindexwherepixelswithgreaterfractionsofGVand/orSHADEhavehighervaluesandpixelswithmorefractionsofNPVand/orSOILhaslowervalues.NDFItoidentifyforestintheBiomehasameanvalueof185.ThisvalueislowerinDeciduousorSemideciduousForestastheynaturallyhavelesstreecrowncoverpercentage.AftertheidentificationofForestthesumofSOILandNPVfractionsisusedtosplititintwodifferentclasses:DenseForest

Forestwithnoorfewsignsofhumanalteration.ThesumofSOILandNPVfractionsislowthen8(meanvalue).InDeciduousandSemideciduousForestthisvalueishigherastheynaturallyhavelesstreecrowncoverandmoreNPVandSOIL.

16 FAO-FoodandAgricultureOrganizationoftheUnitedNations

OpenForest ForestwithhighervaluesofthesumofSOILandNPVfractions.Planted forest is also classified as dense forest and they are identified as separated fromNaturalForestintheintegrationprocedure.Whenitscutorduringthegrowstage,theplantedforestisclassifiedasfarming.The greatest difficulty of the Atlantic Forest Biome are the non-forest areas, mainlyagriculture,whichareerroneously classifiedas forest.Theconfusionoccursmainlydue tovariation in planting seasons, seasonality, rainfall and lighting (angle of illumination andterrain).AftertheinitialseparationofwhatmaybeforestusingNDFI,asetofrulesisappliedtorefinetheclassification.Theserulesthatmayhaveeffectonlyonregions,years,orspecificcases,butallareconsideredessentialtoreduceclassificationerrors.Incaseswheretheseruleshavenoeffecttoreduceconfusion ithasgenericvaluesthatdonotdisrupttheclassificationofforest.NaturalNon-ForestWetlandsWetlandsisidentifiedbasedontheexistenceofvegetationandwater.Inthedecisiontree,itis identifiedas areaswithhighNDFI (the sameas forest) andhigh fractionof SHADE (thesurfaceofwaterisidentifiedasSHADEinSMA)inareaswithlowslope.Farming(biomes)This class identify non-forest areas that still have some vegetation. It includes areaswithpastureandagriculture.Thisgenericclasswillbe replaced in integrationwithpastureandagricultureclassification.Othernon-vegetatedareasThisclass identifyareaswithnovegetation. It includesurbanareas,dunes,sand,minesorexposedsoil(exposedsoilpreparedtoagriculturewasclassifiedasFarming).Partofthisgenericclasswillbereclassifiedintheintegrationwithotherthemes.WaterThisclass isproducesbyFMASKfilter (ZHU;WANG;WOODCOCK,2015;ZHU;WOODCOCK,2012).ItalsocomplementedbypixelswithhighvaluesofSHADEfractioninlowslope.Notobserved

Thisclass isproducesbyFMASKfilter (ZHU;WANG;WOODCOCK,2015;ZHU;WOODCOCK,2012).ItalsoincludesareaswithhighSHADEfractionandhighslopewheretheshadeoftheterraindoesnotpermittoclassifythearea.Incollection2thepixelsofforestwithhighvaluesofCLOUDfractionwasalsoclassifiedas“Notobserved”.Asmosaicusedinclassificationshouldhavenocloud,thesepixelsofforestwithhigh“CLOUD”wasconsideredcontaminatedanddiscarded.Classifiedas“notobserved”itmaybereplacedbythecorrectvalueinintegration.Collection2doesnotincludenaturalformationsofsavannaorgrassland.ThismaycausesomedifferencesintheborderwithCERRADO,CAATINGAandPAMPAbiomes.Naturalgrasslandappearsinhighelevationareasinthesouthregion,closetoPAMPAborder.SavannaappearinMGandBAclosetoCERRADOandCAATINGAborder.

3.3.4. DecisionTree

Figure24.DecisionTreetoAtlanticForestBiome

Annex2:Tablewiththeparametersforeachvariable.

Eachnodedescribedindetail:[1-1]CLOUD>=30Thisparameterclassifypixelwithhighfractionofcloudas“Notobserved”.IthaslittleeffectbecauseFMASKhasremovedmostofcloudpixelsbutstillnecessaryasFMASKdonotremoveallclouds.[2-1]WATER_MASK=255ThisparameterclassifiesareasmarkedasWaterbyFMASK.[3-2]SLOPE<10This parameter fix confusion with water in shades caused by terrain. High slope areasidentifiedaswaterbyFMASKisreclassifiedas“NotObserved”[3-1]SHADE>=92ThisparameterclassifiesasWaterareaswithhighshadefractionthatwasnotidentifiedbyFMASK.[4-2]SLOPE<10This parameter fix confusion with water in shades caused by terrain. High slope areasidentifiedaswaterbyhighshadeisreclassifiedas“NotObserved”[4-1]NDFI>=185HighNDFIisusedtoidentifyAtlanticForest.Thevalueisaround170to190anditidentifiesareaswithhighGVand/orSHADEandlowNPVandSOIL.[5-2]FCI>=95FCIisaForestCanopyIndexcalculatebasedontheGVnormalizedbySHADE.ThisparameterremovefromtheForestclasstheareaswithhomogenouscanopycoverandreclassifyitasFarming.[6-4]SLOPE<10Thisparameter reclassifiesFarmingasForestagain if it is inhigh slope. It isusedbecauseForestinhighslopewithhighilluminationmaybeclassifiedasFarmingbymistake.[6-3]NDFI_AMPLITUDE>=50ThisparameterusestheannualvariationofNDFItoremoveFarmingfromForest.TheAtlanticForestdon’thavehugevariationovertheyearexceptindeciduousorsemi-deciduousareas,wherethisvaluewasincreased.FarmingandplantationmayhavehighervariationsandtheyareremovedformForestclasswhenitsidentified.

ToavoidanyconfusionwithreamingcloudanypixelwithNDFIigualZEROwasremovedfromthemosaic.Someareaswithgreatvariationmaynotbeidentifiedbutit’ssafertomakesurenoforestwithcloudmaybelost.[6-3]SHADE>=85Thewater inwetlandsaffects thevalueof shade fractionand increases thevalueofNDFIincludingtheninForestbymistake.ThisparameterremovewetlandsfromForest.[8-10]SLOPE<10This parameter reclassifies thewetland as forest if it’s in high slope. The shade thatwasidentifiedisbasedonterrainandnotfromthewaterofwetlands.[8-9]EVI2<115EVI2isareflectanceindexcalculatedusingBnir(nearinfra-red)andRredLandSatbands:

ThisparameterutilizesEVI2toremovefarmingthatwasclassifiedasForestbyconfusion.Itusuallyoccursinareaswithshadescausedbyterrain.[9-17]CLOUD>=5ThefractioncloudisusedtoavoidpixelscontaminatedwithcloudtobeclassifiedasForest.MostofpixelsshouldhavecloudfractionveryclosetoZEROastheywerefilteredbyFMASK.When these contaminated pixels are classified as Forest they are reclassified as “NotObserved”.[10-33]NPVSOIL<8ThisparametersegregateDenseForestfromOpenForest.ThefractionsofSOILandNPVarebiggerinOpenForest.[5-1]NDFI>=65ThisparametersegregateareaswithanyGVorshadefromnon-vegetatedareas(soil,urbanareas,sand,etc.)

[6-1]NDFI_AMPLITUDE>=50Thisparameterincludesasagricultureareaswithsoilexposedorpreparedforplantingatthetimeoftheimagebutwhichhadvegetation(highNDFI)duringtheyear.Insteadofclassifyingthecurrentcoverage(soilexposed)theusewasclassified(agriculture).[7-1]SHADE>=85Thisparameterwiththeslopeinsequence,removesfromtheforestclasstheareaswithshadefromsolarilluminationresultedfromterrain(slopeswithoutvegetationandwithoutlighting)[8-2]SLOPE<10Thisparameterclassifiestheareaswithhighslopeandhighshadeas“NotObserved”.

3.3.4. TemporalFilter

“There are two basic approaches for change detection; (1) comparative analysis ofindependentlyproducedclassifications fordifferentdatesand (2) simultaneousanalysisofmultitemporaldata.“(SINGH,1989).MapBiomasusesthefirstapproachandapplyaTemporalFiltertomaintainconsistencebetweeneachyear.TemporalFiltermayanalysestheclassificationof1yearbeforeand1yearafter(kernel3)tochangetheclassofcurrentyearifnecessary.Itmayalsoanalyze2yearsbeforeand2yearsafter(kernel5)or,inspecialcaseslikefirstyear(2000incollection2),analyzeonly2yearsafter(2001and2002)orlikethelastyear(2016incollection2),analyzeonly2yearsbefore(2014and2016).Analyzingotheryearshelpstocorrectinvalidtransitionscausedbymisclassification.Apixelcouldneverbeagricultureinoneyearthenforestinnextandagricultureagain2yearslater.Thealgorithmisappliedinthefirstyearandthenusethecorrecteddataasreferencetoallsequentialyears.Thereareasetofrulestocorrectimpossibletransitionappliedtocollection2.Inthetablebelow,thecodeforeachclassare:3=DenseForest4=OpenForest11=NaturalNon-ForestWetlands21=Farming25=Non-VegetatedArea26=Water27=Notobserved

Table4.GeneralRules:Incaseof“Notobserved”repeatthelastvalidclass:

Rule Type Kernel1yearBefore

CurrentYear

1yearAfter Result

Comment

R01 RG 3 3 27 4 3 KeeplastclassinnotobservedR02 RG 3 3 27 21 3 KeeplastclassinnotobservedR03 RG 3 3 27 25 3 KeeplastclassinnotobservedR04 RG 3 3 27 26 3 KeeplastclassinnotobservedR05 RG 3 3 27 11 3 KeeplastclassinnotobservedR06 RG 3 3 27 27 3 KeeplastclassinnotobservedR07 RG 3 4 27 3 4 KeeplastclassinnotobservedR08 RG 3 4 27 21 4 KeeplastclassinnotobservedR09 RG 3 4 27 25 4 KeeplastclassinnotobservedR10 RG 3 4 27 26 4 KeeplastclassinnotobservedR11 RG 3 4 27 11 4 KeeplastclassinnotobservedR12 RG 3 4 27 27 4 KeeplastclassinnotobservedR13 RG 3 21 27 3 21 KeeplastclassinnotobservedR14 RG 3 21 27 4 21 KeeplastclassinnotobservedR15 RG 3 21 27 11 21 KeeplastclassinnotobservedR16 RG 3 21 27 26 21 KeeplastclassinnotobservedR17 RG 3 25 27 3 25 KeeplastclassinnotobservedR18 RG 3 25 27 4 25 KeeplastclassinnotobservedR19 RG 3 25 27 11 25 KeeplastclassinnotobservedR20 RG 3 25 27 26 25 KeeplastclassinnotobservedR21 RG 3 26 27 3 26 KeeplastclassinnotobservedR22 RG 3 26 27 21 26 KeeplastclassinnotobservedR23 RG 3 26 27 25 26 KeeplastclassinnotobservedR24 RG 3 26 27 4 26 KeeplastclassinnotobservedR25 RG 3 26 27 11 26 KeeplastclassinnotobservedR26 RG 3 26 27 27 26 KeeplastclassinnotobservedR27 RG 3 11 27 3 11 KeeplastclassinnotobservedR28 RG 3 11 27 21 11 KeeplastclassinnotobservedR29 RG 3 11 27 25 11 KeeplastclassinnotobservedR30 RG 3 11 27 4 11 KeeplastclassinnotobservedR31 RG 3 11 27 26 11 KeeplastclassinnotobservedR32 RG 3 11 27 27 11 KeeplastclassinnotobservedR80 RG 3 21 27 27 21 KeeplastclassinnotobservedR81 RG 3 25 27 27 25 Keeplastclassinnotobserved

Table5.GeneralRules:Invalidtransitions

Rule Type Kernel1yearBefore

CurrentYear

1yearAfter Result

Comment

R33 RG 3 3 21 3 3 1dif.valuebetween2equalsR34 RG 3 3 25 3 3 1dif.valuebetween2equalsR35 RG 3 3 27 3 3 1dif.valuebetween2equalsR36 RG 3 3 26 3 3 1dif.valuebetween2equalsR37 RG 3 3 11 3 3 1dif.valuebetween2equalsR38 RG 3 3 4 3 3 1dif.valuebetween2equalsR39 RG 3 4 27 4 4 1dif.valuebetween2equalsR40 RG 3 4 21 4 4 1dif.valuebetween2equalsR41 RG 3 4 25 4 4 1dif.valuebetween2equalsR42 RG 3 4 3 4 4 1dif.valuebetween2equalsR43 RG 3 4 11 4 4 1dif.valuebetween2equalsR44 RG 3 4 26 4 4 1dif.valuebetween2equalsR45 RG 3 21 3 21 21 1dif.valuebetween2equalsR46 RG 3 21 4 21 21 1dif.valuebetween2equalsR47 RG 3 21 25 21 21 1dif.valuebetween2equalsR48 RG 3 21 27 21 21 1dif.valuebetween2equalsR49 RG 3 21 11 21 21 1dif.valuebetween2equalsR50 RG 3 21 3 25 21 1dif.valuebetween2equalsR51 RG 3 21 4 25 21 1dif.valuebetween2equalsR52 RG 3 21 27 25 21 1dif.valuebetween2equalsR53 RG 3 21 11 25 21 1dif.valuebetween2equalsR54 RG 3 21 26 21 21 1dif.valuebetween2equalsR55 RG 3 21 26 25 21 1dif.valuebetween2equalsR56 RG 3 25 3 25 21 1dif.valuebetween2equalsR57 RG 3 25 4 25 21 1dif.valuebetween2equalsR58 RG 3 25 21 25 21 1dif.valuebetween2equalsR59 RG 3 25 27 25 25 1dif.valuebetween2equalsR60 RG 3 25 11 25 25 1dif.valuebetween2equalsR61 RG 3 25 3 21 21 1dif.valuebetween2equalsR62 RG 3 25 4 21 21 1dif.valuebetween2equalsR63 RG 3 25 27 21 21 1dif.valuebetween2equalsR64 RG 3 25 11 21 21 1dif.valuebetween2equalsR65 RG 3 25 26 25 25 1dif.valuebetween2equalsR66 RG 3 25 26 21 21 1dif.valuebetween2equalsR67 RG 3 26 21 26 26 1dif.valuebetween2equalsR68 RG 3 26 25 26 26 1dif.valuebetween2equalsR69 RG 3 26 27 26 26 1dif.valuebetween2equalsR70 RG 3 26 3 26 26 1dif.valuebetween2equalsR71 RG 3 26 11 26 26 1dif.valuebetween2equalsR72 RG 3 26 4 26 26 1dif.valuebetween2equals

Rule Type Kernel1yearBefore

CurrentYear

1yearAfter Result

Comment

R73 RG 3 11 21 11 11 1dif.valuebetween2equalsR74 RG 3 11 25 11 11 1dif.valuebetween2equalsR75 RG 3 11 27 11 11 1dif.valuebetween2equalsR76 RG 3 11 26 11 11 1dif.valuebetween2equalsR77 RG 3 11 3 11 11 1dif.valuebetween2equalsR78 RG 3 11 4 11 11 1dif.valuebetween2equalsTable6.GeneralRule:Invalidregenerationwithkernel5years

Rule Type Kernel2yearsBefore

1yearBefore

CurrentYear

1yearAfter

2yearAfter Result

R79 RG 5 21 21 3 3 21 21Comment:After2yearsoffarmingit’simpossibletoregeneratedirectlytoDenseForestfor2yearsandbecamefarmingagaininthenextyear.Table7.RulestoFirstyear

Rule Type Kernel 2000 2001 2002Result2000

Comment

RP01 RP 3 21 3 3 3 InvalidregenerationRP02 RP 3 25 3 3 3 InvalidregenerationRP03 RP 3 25 21 21 21 InvalidTransitionRP04 RP 3 25 21 25 21 InvalidTransitionRP05 RP 3 25 25 21 21 InvalidTransition

RP06 RP 3 27 21 21 21Replacednon-observedby

repeatedvalue

RP07 RP 3 27 25 25 25Replacednon-observedby

repeatedvalue

RP08 RP 3 27 26 26 26Replacednon-observedby

repeatedvalue

RP09 RP 3 27 11 11 11Replacednon-observedby

repeatedvalue

RP10 RP 3 27 3 3 3Replacednon-observedby

repeatedvalue

RP11 RP 3 27 4 4 4Replacednon-observedby

repeatedvalue

RP12 RP 3 27 3 4 3Replacednon-observedby

repeatedvalue

RP13 RP 3 27 21 25 21Replacednon-observedby

repeatedvalue

RP14 RP 3 27 25 21 21Replacednon-observedby

repeatedvalue

Table8.RulestoLastyear

Rule Type Kernel 2014 2015 2016Result2016

Comment

RU01 RU 3 21 21 3 21 InvalidregenerationRU02 RU 3 21 21 4 21 InvalidregenerationRU03 RU 3 25 25 3 25 InvalidregenerationRU04 RU 3 25 25 4 25 InvalidregenerationRU05 RU 3 21 25 3 21 InvalidregenerationRU06 RU 3 21 25 4 21 InvalidregenerationRU07 RU 3 25 21 3 21 InvalidregenerationRU08 RU 3 25 21 4 21 InvalidregenerationRU09 RU 3 3 3 27 3 RepeatedlastvalidclassRU10 RU 3 4 4 27 4 RepeatedlastvalidclassRU11 RU 3 21 21 27 21 RepeatedlastvalidclassRU12 RU 3 25 25 27 25 RepeatedlastvalidclassRU13 RU 3 26 26 27 26 RepeatedlastvalidclassRU14 RU 3 11 11 27 11 Repeatedlastvalidclass

3.3.4. Datavolumesandprocessingissues

The collection 2 mapped 17 years (2000 to 2016) in the 122 grids. That’s 2.074 grids toparametrizeandprocess.TheuseofGEEassetshasincreasedtheprocessperformance.Oncetheparametersofmosaicaredefinedtheresultisstoredasassetwithallfractionsandindex(24bandsintotal).Theclassificationprocessmaybedoneseveraltimestoadjustthecorrectparametersanditsmuchfasterprocessingthemosaicassetforeachgrid.

4. ValidationStrategiesThevalidationisbeingdonebasedin667randompointsselectedovertthegridofBrazilianforestinventoryperformedbySFB17-MMA18.Webcollectisatoolimplementedtoevaluateeachpointbasedonvisualinterpretationofthesame Landsat mosaic used in the classification. Each point is evaluated by 3 differentinterpreterswithlongexperienceinLandsatimageinterpretationandAtlanticforestmapping.Theevaluationconsiderstheexactpixelthatisviewedintheimageforeachyear.The interpreter is instructed to consider the rules of temporal filter applied in theclassification.Ifthepixelisnotavailableinonespecificyear,theinterpretershouldrepeatthelastvisibleclassuntilanewimageisavailable.

Figure25.Distributionof667validationpoints

17 SFB-ServiçoFlorestalBrasileiro-BrazilianForestService18 MMA-MinistériodoMeioAmbiente-MinistryoftheEnvironment

Figure26.Imagesofthesamepixelfrom2000to2016

Thefinalclassofeachpointistheclassequallyclassifiedbyatleast2interpreters.Thisreferenceclassofeachyeariscomparedwithresultmapfromtemporalfiltertobuildtheconfusionmatrixandevaluateomissionandcommissionerrorsforeachyear.

Figure27.Exampleofconfusionmatrixandomission,commissionandtotalaccuracy

5. ConcludingRemarksandPerspectives

Collection2isagreatimprovefromcollection1data.Collection3willproducemapsfrom1985to2017usingLandsatimagesandmoredetailedlegend.In collection 2 the high-altitude savannawas not included in the classification because ofconfusion with pasture and some farm. To include savanna formation in the biome itsnecessarytodefineregionwhereitmayoccur:

Figure28.LocationofsavannainAtlanticForestBiome

It’s important to start some tests with Sentinel images with 10m spatial resolution on aperspectivetoimprovenewmapsproducedafterCollection3.

6. ReferencesSINGH,A.ReviewArticleDigitalchangedetectiontechniquesusingremotely-senseddata.InternationalJournalofRemoteSensing,v.10,n.6,p.989–1003,1989.SOUZA,C.M.;ROBERTS,D.A.;COCHRANE,M.A.Combiningspectralandspatialinformationtomapcanopydamagefromselectiveloggingandforestfires.RemoteSensingofEnvironment,v.98,n.2–3,p.329–343,2005.ZHU,Z.;WANG,S.;WOODCOCK,C.E.ImprovementandexpansionoftheFmaskalgorithm:cloud,cloudshadow,andsnowdetectionforLandsats4–7,8,andSentinel2images.RemoteSensingofEnvironment,v.159,p.269–277,mar.2015.ZHU,Z.;WOODCOCK,C.E.Object-basedcloudandcloudshadowdetectioninLandsatimagery.RemoteSensingofEnvironment,v.118,p.83–94,mar.2012.

7. Appendices

9.1. AlgorithmCodevaranos=['2015','2016'];//TabelacomParâmetrosvarparamsFusionTable="ft:1XbDRVJ0FUX8Bl6ghFPWo25DggtriIETxRfjI7Dri";varparamsFeatureColection=ee.FeatureCollection(paramsFusionTable);varcartaslist=['SF-22-Z-C'//,'SG-22-V-B','SG-22-X-A',//'SG-22-V-C','SG-22-V-D','SG-22-X-C'];//Gridcomcartas1:250.000vargridFusionTable='ft:1wCmguQD-xQs2gMH3B-hdOdrwy_hZAq4XFw1rU8PN';vargridFeatureCollection=ee.FeatureCollection(gridFusionTable);//FusionTablecomolimitedoBIOMA//Fonte:IBGE.5.000.000,2007varbiomaFusionTable="ft:1H32agP9xr-BIDKl4irmhFCLxI5_TOs9nvbEz-C6u";varbiomaFeatureCollection=ee.FeatureCollection(biomaFusionTable);//FusionTablecomolimitedosEstadosBrasileiros//Fonte:IBGE500.000,2010varufFusionTable="ft:1n-9np_o8hbDaCuYFwhkgEulN8770mW0n302YyHGo";varufFeatureCollection=ee.FeatureCollection(ufFusionTable);/*=========================================================================================FUNCTIONNAME:decisionTreeOmbrofilaPURPOUSE:

ARGUMENTS:[arg1]imagemsma[arg2]imagemndfi=========================================================================================*/vardecisionTree_Ombrofila=function(info,imagem){varano=info.carta1.year//Renamefractionbandsvargv=imagem.select('gv');varfci=imagem.select('fci');varnpv=imagem.select('npv');varsoil=imagem.select('soil');varcloud=imagem.select('cloud');vargvs=imagem.select('gvs');varwater_mask=imagem.select('water_mask');varndfi=imagem.select('ndfi');varndvi=imagem.select('ndvi');varevi2=imagem.select('evi2');varwvi=imagem.select('wvi');varmosaico_ano_dif=imagem.select('ndfi_amplitude');varslope=imagem.select('slope');vargvnpvs=imagem.select('gvnpvs');varshade=imagem.select('shade');varndwi=imagem.select('ndwi');varnpvsoil=imagem.select('npvsoil');//Variablesvarv1=info.carta1.dtv.v1;varv2=info.carta1.dtv.v2;varv3=info.carta1.dtv.v3;varv4=info.carta1.dtv.v4;varv5=info.carta1.dtv.v5;varv6=info.carta1.dtv.v6;varv7=info.carta1.dtv.v7;varv8=info.carta1.dtv.v8;varv9=info.carta1.dtv.v9;varv10=info.carta1.dtv.v10;//DefineclassificationimagevardataClass=ee.Image(0).toByte();//MuitoAltoNDFI//varfloresta_densa1=(ndfi.gt(300));varfloresta_densa2=(ndfi.gte(v1)).and(fci.gte(v2)).and(slope.gte(v7));dataClass=dataClass.where(floresta_densa2.eq(1),3);//floresta_densa2var floresta_densa1 =(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.lt(v9)).and(npvsoil.lt(v4)).and(evi2.gte(v3)).and(cloud.lt(v10));dataClass=dataClass.where(floresta_densa1.eq(1),3);//Forestdensa

var nao_flo_evi2 =(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.lt(v9)).and(npvsoil.lt(v4)).and(evi2.lt(v3)).and(cloud.lt(v10));dataClass=dataClass.where(nao_flo_evi2.eq(1),14);//nao_flo_evi2varnao_obs1=(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.lt(v9)).and(npvsoil.lt(v4)).and(cloud.gte(v10));dataClass=dataClass.where(nao_obs1.eq(1),32);//nãoobservarfloresta_aberta1=(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.lt(v9)).and(npvsoil.gte(v4)).and(cloud.lt(v10));dataClass=dataClass.where(floresta_aberta1.eq(1),4);//floresta_aberta1varnao_obs2=(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.lt(v9)).and(npvsoil.gte(v4)).and(cloud.gte(v10));dataClass=dataClass.where(nao_obs2.eq(1),32);//nãoobservarfloresta_densa3=(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.gte(v9)).and(slope.gte(v7));//.and(gv.lte(v2));dataClass=dataClass.where(floresta_densa3.eq(1),3);//floresta_densa3vararea_umida=(ndfi.gte(v1)).and(fci.lt(v2)).and(shade.gte(v9)).and(slope.lt(v7));//.and(gv.lte(v2));dataClass=dataClass.where(area_umida.eq(1),11);//area_umidavaragricultura_amplitude=(ndfi.gte(v1)).and(fci.lt(v2)).and(mosaico_ano_dif.gte(v8));//.and(gv.lte(v2));dataClass=dataClass.where(agricultura_amplitude.eq(1),14);//Forestdensavaragricultura_alta=(ndfi.gte(v1)).and(fci.gte(v2)).and(slope.lt(v7));dataClass=dataClass.where(agricultura_alta.eq(1),14);//agricultura//MédioNDFIvaragr_pasto1=ndfi.lt(v1).and(ndfi.gte(v5))//.and(mosaico_ano_dif.gte(v8));dataClass=dataClass.where(agr_pasto1.eq(1),14);//nat_nao_flo//BaixoNDFIvaragricultura3=ndfi.lt(v5).and(mosaico_ano_dif.gte(v8));dataClass=dataClass.where(agricultura3.eq(1),14);//agricultura3varnao_veg=ndfi.lt(v5).and(mosaico_ano_dif.lt(v8)).and(shade.gte(v9)).and(slope.lt(v7));dataClass=dataClass.where(nao_veg.eq(1),22);//nao_vegvarnao_obs=ndfi.lt(v5).and(mosaico_ano_dif.lt(v8)).and(shade.gte(v9)).and(slope.gte(v7));dataClass=dataClass.where(nao_obs.eq(1),32);//nao_obsvarOutros=ndfi.lt(v5).and(mosaico_ano_dif.lt(v8)).and(shade.lt(v9));dataClass=dataClass.where(Outros.eq(1),22);//OutrosvarAgua=water_mask.eq(255).and(slope.lt(v7));dataClass=dataClass.where(Agua.eq(1),31);//AguavarAgua2=shade.gte(91).and(slope.lt(v7));dataClass=dataClass.where(Agua2.eq(1),31);//Outrosvarsombra2=shade.gte(91).and(slope.gte(v7));dataClass=dataClass.where(sombra2.eq(1),32);//Outrosvarsombra=water_mask.eq(255).and(slope.gte(v7));dataClass=dataClass.where(sombra.eq(1),32);//sombrareturndataClass.mask(dataClass.neq(0));};/*=========================================================================================FUNCTIONNAME:mapAddLayersPURPOUSE:AdicionaascamadasaomapaARGUMENTS:[arg1]

[arg2]=========================================================================================*/varmapAddLayers=function(imagem,imagemclass,dTree,carta,year,gridName,sensor){varndfi_color='FFFFFF,FFFCFF,FFF9FF,FFF7FF,FFF4FF,FFF2FF,FFEFFF,FFECFF,FFEAFF,FFE7FF,'+'FFE5FF,FFE2FF,FFE0FF,FFDDFF,FFDAFF,FFD8FF,FFD5FF,FFD3FF,FFD0FF,FFCEFF,'+'FFCBFF,FFC8FF,FFC6FF,FFC3FF,FFC1FF,FFBEFF,FFBCFF,FFB9FF,FFB6FF,FFB4FF,'+'FFB1FF,FFAFFF,FFACFF,FFAAFF,FFA7FF,FFA4FF,FFA2FF,FF9FFF,FF9DFF,FF9AFF,'+'FF97FF,FF95FF,FF92FF,FF90FF,FF8DFF,FF8BFF,FF88FF,FF85FF,FF83FF,FF80FF,'+'FF7EFF,FF7BFF,FF79FF,FF76FF,FF73FF,FF71FF,FF6EFF,FF6CFF,FF69FF,FF67FF,'+'FF64FF,FF61FF,FF5FFF,FF5CFF,FF5AFF,FF57FF,FF55FF,FF52FF,FF4FFF,FF4DFF,'+'FF4AFF,FF48FF,FF45FF,FF42FF,FF40FF,FF3DFF,FF3BFF,FF38FF,FF36FF,FF33FF,'+'FF30FF,FF2EFF,FF2BFF,FF29FF,FF26FF,FF24FF,FF21FF,FF1EFF,FF1CFF,FF19FF,'+'FF17FF,FF14FF,FF12FF,FF0FFF,FF0CFF,FF0AFF,FF07FF,FF05FF,FF02FF,FF00FF,'+'FF00FF,FF0AF4,FF15E9,FF1FDF,FF2AD4,FF35C9,FF3FBF,FF4AB4,FF55AA,FF5F9F,'+'FF6A94,FF748A,FF7F7F,FF8A74,FF946A,FF9F5F,FFAA55,FFB44A,FFBF3F,FFC935,'+'FFD42A,FFDF1F,FFE915,FFF40A,FFFF00,FFFF00,FFFB00,FFF700,FFF300,FFF000,'+'FFEC00,FFE800,FFE400,FFE100,FFDD00,FFD900,FFD500,FFD200,FFCE00,FFCA00,'+'FFC600,FFC300,FFBF00,FFBB00,FFB700,FFB400,FFB000,FFAC00,FFA800,FFA500,'+'FFA500,F7A400,F0A300,E8A200,E1A200,D9A100,D2A000,CA9F00,C39F00,BB9E00,'+'B49D00,AC9C00,A59C00,9D9B00,969A00,8E9900,879900,7F9800,789700,709700,'+'699600,619500,5A9400,529400,4B9300,439200,349100,2D9000,258F00,1E8E00,'+'168E00,0F8D00,078C00,008C00,008C00,008700,008300,007F00,007A00,007600,'+'007200,006E00,006900,006500,006100,005C00,005800,005400,005000,004C00';//defineosparâmetrosdevisualizaçãodasimagensLandsat8varvisNDFI={'min':0,'max':200,'palette':ndfi_color};var visDT ={'min':1,'max':12,'palette':'#006400,#00FF00,#0000FF,#C2DB6E,#45C2A5,#65C24B,#FFFF8F,#FF0000,#000000,#10290F,#FFAD2A,#FFD882','format':'png'};varvisSMA={'bands':['soil'+ano,'gv'+ano,'npv'+ano],'gain':[6.0,4.0,6.0]};Map.addLayer(imagem, {'bands': ['swir1', 'nir', 'red'],'gain' : [0.08, 0.06, 0.2],'gamma': 0.5}, ano + "Reflect "+gridName,false);Map.addLayer(dTree,{"min":0,"max":33,"palette":"ffffff,129912,1f4423,006400,00ff00,65c24b,"+"687537,29eee4,77a605,935132,ffe599,45c2a5,"+"f1c232,b8af4f,ffffb2,ffd966,ffe599,f6b26b,"+"e974ed,d5a6bd,c27ba0,a64d79,ea9999,cc4125,"+"dd7e6b,e6b8af,980000,999999,b7b7b7,434343,"+"d9d9d9,0000ff,d5d5e5,FF0000","format":"png"},year+"DTree"+gridName,false);};

/*=========================================================================================FUNCTIONNAME:getGridNameInfoPURPOUSE:LêinformaçõesdecadacartaARGUMENTS:[arg1][arg2]=========================================================================================*/vargetGridNameInfo=function(features,gridname){varprop=-1;for(vari=0;i<features.length;i++){varfeature=features[i];if(feature.properties.GRID_NAME==gridname){prop={year:feature.properties.YEAR,t0:feature.properties.T0,t1:feature.properties.T1,cc:feature.properties.CLOUD_COVER,gridname:feature.properties.GRID_NAME,dtv:{v1:feature.properties.DTV1,v2:feature.properties.DTV2,v3:feature.properties.DTV3,v4:feature.properties.DTV4,v5:feature.properties.DTV5,v6:feature.properties.DTV6,v7:feature.properties.DTV7,v8:feature.properties.DTV8,v9:feature.properties.DTV9,v10:feature.properties.DTV10},chave:feature.properties.CHAVE,sensor:feature.properties.SENSOR,tag:{on:feature.properties.TAG_ON,ndfi:feature.properties.NDFI,sma:feature.properties.SMA,ref:feature.properties.REF,dt:feature.properties.DT},save:feature.properties.SAVE,bioma:feature.properties.BIOMA,region:feature.properties.REGION,};}}

returnprop;};/*=========================================================================================FUNCTIONNAME:mapBiomasDoItPURPOUSE:ExecutaasfunçõesdametodologiaparaumdeterminadoanoARGUMENTS:[arg1]info.yYYYY(YYYY=ano)[arg2]data=========================================================================================*/varmapBiomasDoIt=function(info,carta_atual,imagem){//criaacoleçãodeimagenseaplicaofiltroporperíodoif(info.carta1.tag.on){//geraclassificacaoswitch(info.carta1.chave){case1:vardTree=decisionTree_Ombrofila(info,imagem);break;}varresult=dTree;}else{varresult=-1;}returnresult;};//*********************************************************************//IníciodaRotina//*********************************************************************//console.log('------------------Statistics------------------');varfeaturesTable=[];varfeatureCol=null;for(vari_ano=0;i_ano<anos.length;i_ano++){varano=anos[i_ano];vardir='projects/mapbiomas-workspace/MOSAICOS/workspace/MATAATLANTICA_'vardirclass='projects/mapbiomas-workspace/COLECAO2_1/classificacao/MATAATLANTICA_'varfcSelected=paramsFeatureColection.filterMetadata('YEAR','equals',ano);varfeatures=fcSelected.getInfo().features;for(vari=0;i<cartaslist.length;i++){varcarta=cartaslist[i];

varinfo={carta1:getGridNameInfo(features,carta)};varcarta_atual=gridFeatureCollection.filterMetadata('name',"equals",carta)varimagem=ee.Image(dir+carta+'_'+ano+'_0');varimagemclass=ee.Image(dirclass+carta+'_'+ano+'_0');vardTree=mapBiomasDoIt(info,carta_atual,imagem);varmapcarta1=mapAddLayers(imagem,imagemclass,dTree,carta_atual,ano,carta,info.carta1.sensor);}}//--------------------------------------------------------------------------------------------//AdiconaoGrid//--------------------------------------------------------------------------------------------varblank=ee.Image(0).mask(0);varoutline=blank.paint(gridFeatureCollection,'AA0000',1);varvisPar={'palette':'AA0000','opacity':0.6};Map.addLayer(outline,visPar,'GRID250mil',false);//--------------------------------------------------------------------------------------------//AddBioma//--------------------------------------------------------------------------------------------varblank=ee.Image(0).mask(0);varoutline=blank.paint(biomaFeatureCollection,'000000',2);varvisPar={'palette':'000000','opacity':0.7};Map.addLayer(outline,visPar,'Region',false);

9.2. SupplementalMaterial

Annex1:Tablewiththeparametersofeachgrid1:250.000foreachyear.Annex2:Tablewiththeparametersforeachvariable.