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ATBD_RAlgorithmTheoreticalBaseDocument&Results

MapBiomasGeneral“Handbook”

CarlosSouzaJr.,Ph.D.

Version1.0–March22nd,2017

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Table of Contents

Table of Contents Table of Contents ....................................................................................................................... 2 List of Figures ............................................................................................................................ 3 List of Tables ............................................................................................................................. 4 1. Introduction ............................................................................................................................ 5

1.1 Identification of Region of Interest (biome or themes) ................................................... 5 1.2 Key Science and Applications ......................................................................................... 6

2. Overview and Background Information ................................................................................ 7 2.1 Context and Key Information .......................................................................................... 7 2.2 Existent Maps and Mapping Initiative ............................................................................. 8

3. Algorithm Descriptions, Assumptions, and Approaches ....................................................... 9 3.1 Algorithm description ...................................................................................................... 9

Earth Engine Built-In Algorithms ...................................................................................... 9 Spectral Feature Reduction Algorithms ........................................................................... 10 Classification and Post-Classification algorithms ........................................................... 10 Spatial Analysis Algorithms ............................................................................................ 12

4. Validation Strategies ............................................................................................................ 13 5. Map Collections and Analysis ............................................................................................. 14 6. Practical Considerations ....................................................................................................... 14 7. Concluding Remarks and Perspectives ................................................................................ 14 References ................................................................................................................................ 15

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List of Figures Figure 1. Brazilian Biomes mapped in the MapBiomas project. Cross-cutting themes described above are also mapped and integrated with the biome maps to generate the final Collection 2 product (source: MMA, 2006). ............................................................................. 6 Figure 2. MapBiomas Workspace interface. Brazil is divided in map sheets of 1 degree and 1.5 degree to establish classification parameters and store them in a database. ........................ 7 Figure 3. Steps to implement MapBiomas algorithms in Earth Engine. ................................. 10

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List of Tables Table 1. Mapping projects at national level. .............................................................................. 8 Table 2. Classification scheme of MapBiomas Collection 2. .................................................. 10 Table 3. Integration rules applied to the annual classification results of the biomes and cross-cutting themes. ......................................................................................................................... 12

Excluído: 9

Excluído: 11

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1. Introduction

1.1 Identification of Region of Interest (biome or themes) TheRadamBrasilProjectwasthefirstnationalinitiativetomapvegetationtheentirecountryofBrazil.Thisprojectwasconductedfrom1975to1980basedonairborneradarimagery,visualinterpretationandextensiveanddetailedfieldwork,involvingseveraldozensoforganizations.TheRadamBrasilProjectproducemapsat1:250.000scale,andisstillasolidreferenceforscientificandtechnicalstudiesaboutvegetation(Cardoso,2009).In2004,theMinisterofEnvironmentlaunchedtheProbioinitiativeaimingatprovidingup-to-datedinformationaboutlandcoverandlanduseinBrazil,consideringthatonlytheAmazonandMataAtlanticbiomeswerebeingmonitoredafterRadamBrasilproject.TheBrazilianbiomeboundaries(IBGE,2004)wereusedasreferencefornationalmappinginitiative.TheProbioprojectwasbasedonLandsatimageryacquiredin2002,withminimummappingunitvaryingfrom40to100hectares,andmappingscaleof1:250.000.Accuracyassessmentwasbasedondigitalimageryproductsat1:100.000,withaminimaloverallaccuracyof85%.ThelandcoverclassesfollowedIBGEmanualforvegetationmapping(IBGE,2004).TheProbioprojecthadupdatesforforestchangefortheyear2008forallbiomes,andfortheyears2009,2010and2011dependingonthebiome.TheMapBiomasprojectwaslaunchedinJuly2015,aimingatcontributingwiththeunderstandingoflanduseandlandcoverchangedynamicsinBrazil,andothertropicalcountries.StartedinBrazil,todayMapBiomasisalreadybeingimplementedintheChacoregion,includingthecountriesofBolivia,ParaguayArgentinaandBrazil.ThisprojectisbasedondigitalimageprocessingofLandsatimageryencompassingtheyearsfrom1985throughthepresentdays.TheMabBiomasmappingeffortsweredividedinCollectionsforthefollowingperiods:

• Collection1:2008through2015(lauchedin2016)• Collection2:2000through2016(lauchedin2017)• Collection3:1985through2017(tobelauchedin2018)

Besidestheannualclassificationsofdigitalmaps,MapBiomasaimsatcontributingwiththedevelopmentofafastandreliablemethodologytoprocesslargescaledatasets,togeneratehistoricaltime-seriesoflandcoverandlandusemapsatlowcost.Inaddition,theprojectisalsoproducingaweb-basedplatform(i.e.,MapBiomasPlatform)tofacilitationtheimplementationoftheimageprocessingmethodologyandmapproducerstoconducttheirwork.Finally,alldata,classificationmaps,software,statisticsandfurtheranalysesareopenlyaccessiblethroughtheMapBiomasPlatform.Alltheseispossiblethanksto:i)GoogleEarthEnginePlatformwhichprovidesaccesstodata,imageprocessingstandardalgorithms,andcloudcomputingcapabilities;ii)toorganizationsthatarepartofMapBiomasnetworkthatsharedknowledgeandmappingtools;andiii)tovisionaryfundingagenciesthatsupporttheproject.TheobjectiveofthisdocumentistopresenttheintegratedstructureoftheMapBiomaswhichiscomposedby:

• biomemaps(Amazonia,Caatinga,Cerrado,MataAtlântica,PampaandPantanal)andcross-cuttingthematicthemes(Pasture,Agriculture,ForestPlantation,Costal

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Zone,UrbanInfrastructure;• Landsatimagerycollection(Landsat5,Landsat7andLandsat8).• Imageprocessing(GoogleEarthEngine,MapBiomasWorkspaceandalgorithms).

ThisdocumentfocusesonCollection2onlywhichisthemostadvancedstageofMapBiomasproject,andontheimageprocessingarchitecture,andonintegratedapproachforcombingthebiomeandcross-cuttingthememaps.ForinformationaboutthealgorithmsandproceduresappliedineachbiomeandthemeandinCollection1,please,seetheirrespectivedocuments.

Figure 1. Brazilian Biomes mapped in the MapBiomas project. Cross-cutting themes described above are also mapped and integrated with the biome maps to generate the final Collection 2 product (source: MMA, 2006).

1.2 Key Science and Applications MapBiomaswasoriginallydesignedtofillgapsongreenhousegasemissionestimatesofthelanduseandlandcoverchangesector.However,otherscientificapplicationscanbederivedwithannualtime-serieshistoryoflanduseandlandcovermapsproduced,including.

● Mapping and quantifying land cover and land use transitions. ● Quantification of gross forest losses and gains. ● Monitoring of secondary growth forests. ● Monitoring of water resources and their interaction with land cover classes.

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● Expansion of infrastructure and urbanization. ● Regional planning. ● Management of Protected Areas.

Thispotentialapplicationswillbeaddressedinfuturephaseoftheproject.

2. Overview and Background Information

2.1 Context and Key Information ThissectionaddresscomplementarycontextualandkeyinformationrelevantfortheintegrationofMapBiomasproductstogenerateCollection2(seeMapBiomasATBDproducedbyeachbiomeandcross-cuttingthemesforspecificinformationaboutbiomesandthemes).TherelevantinformationincludesimageprocessinginthecloudandcrowdsourcingmappingusingEarthEngine,whichisdefinedbyGoogleas:

“a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users.”

Overall,EarthEnginecombinesimagerydatasets,namedCollections,withalgorithmsforimageprocessingandspatialanalysisandprogramminglanguages(i.e.,PythonandJavascript)toimplementapplications.MapBiomasprojecthasdevelopedtwoapplicationsbasedonGoogleEarthEngine:

• Workspace-aweb-basedapplicationstoaccessimagerycollections,processthem,andmanagestoretheresultsindatabasesandmapassets(i.e.,newcollections)(Figure2).• Javascriptcodes-thesecodesarewrittendirectlyintheEarthEngineCodeEditorandareusedtoprototypenewimageprocessingalgorithmsandtestlargescaleimageprocessingtobeimplementedintheWorkspaceenvironment.

Figure 2. MapBiomas Workspace interface. Brazil is divided in map sheets of 1 degree and 1.5 degree to establish classification parameters and store them in a database.

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TheWorkspaceenvironmentallowstomanageimageanalystsindividuallyanddefineandstoredeimageclassificationparametersinapermapsheetbasis(Figure1).Thebiometeamsofanalystscanworksimultaneouslytosettheimageclassificationparameters,pre-processandevaluatetheresultsandsubmittaskstolargescaleimageprocessingtogeneratethefinalproducts,whichareLandsatimagemosaics,landcoverandlandusemaps,annualandtransitionstatisticsoflandcoverandlandusemaps.AlltheseproductsarepubliclyavailableinthewebplatformnamedMapBiomasDashboard.

2.2 Existent Maps and Mapping Initiative Themappingeffortsatnationallevelareneitherfrequentandnorupdated(Table1).TheRadamBrasilwasconductedfrom1975through1980andbasedonairbornemicrowaveradarimagery.Probioisamorerecentinitiativeandproducedmapsfor2002.Forestchangeswerethenmonitoredfortheyears2008,2009and2011usingtheProBiobaselinemapfrom2002.Therearealsoothermappinginitiativesatgloballevelthatcancomplementnationalmappingefforts.TheTreesProjectThesegloballandcoverlayersaretheproductofacollaborationbetweenUSGSandtheUniversityofMaryland.TheGlobCoverPortalisanotherinitiativefromtheEuropeanSpaceAgency(ESA)whichproducedlandcovermapswithMERISsensorat300mspatialresolutionfortwoperiods:December2004-June2006andJanuary-December2009.USGSalsoproducesaMODISlandcovermapat500mpixelscale.GlobalForestWatch(GFW)andGoogleEarthEngineprovidestheGlobalForestChangemapsfrom2000to2014derivedfromtheLandsatimageryat30mresolutionproducedbyUniversityofMarylandGLCF.TheNationalGeomaticsCenterofChina(NGCC)producedGlobeLand30-ahigh-resolution(30m)fullcoveragelandcovermapsforyears2000and2010.Finally,JapanAerospaceExplorationAgency(JAXA)alsoproducedaforest/non-forestmapfor2007-2010usinga25m-resolutionPALSARmosaic(Table1).Thereareotherglobalproductsthatwereproducedusinglowerspatialresolution(>500m)butarenotpresentedherebecausetheirresolutionslimitsapplicationstoassessMapBiomasproducts,whichareproducedat30mLandsatpixel.MapBiomasandtheavailableglobalandnationallandcoverproductscanbeusedcomplementarybuttherearepotentialadvantagesofMapBiomasmaps.First,theMapBiomasmapswillreconstructtheentireLandsattime-series(>35years)atanannualbasis.TheclassificationschemeisalsomorerelevantfornationalapplicationsbecauseitfollowstheBrazilianvegetationclassificationlegend(IBGE).Inadditio8n,MapBiomashasthepotentialtomonitorprimaryforestchanges(i.e.,deforestationandforestdegradation),secondaryforestregrowth,andlanduseclasses(pastureandagriculture,forestplantations).EventhoughtheMapBiomasprojectfocusatnationalscale,thereisapotentialtoreplicateittoothercountries.Currently,theChacoregionisalreadyadoptingtheMapBiomasmethodologyandplatformandthePan-AmazoniacountriesandArgentinaarealreadytestingtheplatform.Table 1. Mapping projects at national level.

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Mapping Initiative/Scale (National, Global)

Period Source

RadamBrasil Project/National

1975 - 1980 IBGE: http://mapas.ibge.gov.br/bases-e-referenciais/bases-cartograficas/cartas

Probio/National 2002 MMA: http://mapas.mma.gov.br/mapas/aplic/probio/datadownload.htm?/

Global Land Cover Map/Global

Tree Cover: 2010 Water Persistance: 2000-2010

https://landcover.usgs.gov/glc/

GlobCover/Global December 2004 - June 2006 and January - December 2009

http://due.esrin.esa.int/page_globcover.php

Global Forest Change - UMD/Global

2016 UMD: https://earthenginepartners.appspot.com/science-2013-global-forest

GlobLand30/Global 2000, 2010 NGCC:http://www.globallandcover.com/GLC30Download/index.aspx

3. Algorithm Descriptions, Assumptions, and Approaches

3.1 Algorithm description ThealgorithmusedtoproducetheMapBiomasproductscanbeorganizedin4categories:

• EarthEnginebuilt-inalgorithms.• Spectralfeaturereductionalgorithms.• ClassificationandPost-Classificationalgorithms.• SpatialAnalysisalgorithms.

ThesealgorithmswereimplementedusingGoogleEarthEngineandCloudComputinginfrastructurestogenerateallMapBiomasproducts(Figure3).Here,wepresentageneraldescriptionofthesealgorithms.Please,refertothespecificATBDsfordetailsaboutthem.

Earth Engine Built-In Algorithms Thesealgorithmsincludefunctionsandprocedurestoaccess,filterandprocesstheLandsatdatacollectionsandderivedassets(i.e.,classificationmaps)usingtheGooglecomputingcloudinfrastructure.AllbiomesgenerateannualLandsatimagemosaicsbasedonspecificperiodsoftimethatoptimizethespectralcontrasttodiscriminatethelandcoverandlandusesclasses.AmedianreductionalgorithmwasappliedtogeneratedthebestgroundobservedpixelofthetemporalperiodandproducetheimagemosaicforeachyearTheCoastalZonecross-cuttingthemeusedalsoannualtemporalmosaicsandmapsheetsas

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mappingunit,andtheotherthemesprocessedtheLandsatimageryinaperscenebasis.

Spectral Feature Reduction Algorithms ThisclassofalgorithmwasappliedtotheLandsatscenestoreducedatadimensionalityand/ortoaddnewspectralfeatures.Twotypesoffeaturereductionalgorithmswereused.First,whole-pixelspectralindices,suchasNDVI,EVI,NDWI,amongothers,wereobtainedfromtheLandsat.Then,fractionalinformationatsub-pixelwerederivedusingSpectralMixtureAnalysis(SMA)togenerateGreenVegetation,Soil,Non-PhotosyntheticVegetation(NPV),ShadeandCloudfractions.NDFI(NormalizedDifferenceFractionIndex)wascalculatedusingSMAfractionresultsThespectralindicesandSMAfractionswerecombinedwithmedianspectralbandstogeneratethefinaltemporalmosaicassetforeachyear.

Figure 3. Steps to implement MapBiomas algorithms in Earth Engine.

Classification and Post-Classification algorithms Then,anempiricaldecisiontreeclassificationistrainedforeachmapsheetandyear.Overall,aparsimonyprincipleisusedtobuildtheempiricalclassificationtreessuchthattheparametersusedtodefinethestructureandthesplittingandclassificationrulescanbereplicateoverspaceandtime.Inotherwords,oncearobustsetofparametersandempiricaltreesarewell-calibrated,theycanbereplicatedautomatically.Furtherevaluationoftheclassificationresultsthroughvisualinspectionisconductedtoassessqualitativelytheempiricaltreeresults(quantitativeaccuracyassessmentisperformedafterwards).TheCoastalZoneandUrbanInfrastructurecross-cuttingthemealsousedanempiricaldecisiontreeapproachwhereastheotherthemesusedrandomforestalgorithmavailableinEarthEngineintheirclassificationapproach.Finally,apost-classificationtemporalfilterwasappliedtoeachbiomeandcross-cuttingthemestodetectandreclassifydisallowableclasstransitions.TheclassificationschemeofCollection2(Table2)isobtainedafterintegrationofbiomesandcross-cuttingclassificationproducts.Formoredetailsaboutthesedecisiontreealgorithms,specificclassificationschemesandapplicationoftemporalfilterrules,please,refertospecificATBDs.Table 2. Classification scheme of MapBiomas Collection 2.

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Valor Cor Código da cor Classe 0 #FFFFFF Sem informação

1 #129912 Formações florestais

2 #1F4423 Formações florestais naturais

3 #006400 Floresta densa

4 #00FF00 Floresta aberta

5 #687537 Mangue

6 #76A5AF Floresta alagada

7 #29EEE4 Floresta degradada

8 #77A605 Floresta secundária

9 #935132 Silvicultura

10 #FF9966 Formações naturais não florestais

11 #45C2A5 Áreas úmidas naturais não florestais

12 #B8AF4F Vegetação campestre (Campos)

13 #F1C232 Outras formações não florestais

14 #FFFFB2 Agricultura ou Pastagem

15 #FFD966 Pastagem

16 #F6B26B Pastagem em campos naturais

17 #A0D0DE Outras pastagens

18 #E974ED Agricultura

19 #D5A6BD Culturas anuais

20 #C27BA0 Culturas semi-perene (Cana de Açúcar)

21 #A64D79 Agricultura ou Pastagem

22 #EA9999 Áreas não vegetadas

23 #DD7E6B Praias e dunas

24 #BD91E4 Infraestrutura urbana

25 #FF99FF Outras áreas não vegetadas

26 #0000FF Corpos d'água

27 #D5D5E5 Não observado

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Spatial Analysis Algorithms Spatialoverlappingrulesbasedonclassprevalencewereappliedtoclassificationresultsfrombiomesandcross-cuttingthemes,ineachyear,togeneratedtheannualconsolidatedlandcoverandlandusemapsproducts.TheintegrationsrulesareapresentedinTable3.Thisalgorithmisasimpleoverlappingspatialoperationthatfollowsaprevalenceorder(forexemple,ifUrbanInfrastructureoverlapswithanyclass,thetheoverlappingareaisclassifiedasUrbanInfrastructure).Oncetheintegratedconsolidatedannualmapsaregenerated,classstatistsarecalculatedandstoredinthedatabaseasmapsandtablesforbiomes,statesandmunicipalities.Wealsocalculateandstoremapsandstatisticsofallpossibleclasstransitionsamongpairsofconsecutiveyears.Table 3. Integration rules applied to the annual classification results of the biomes and cross-cutting themes.

Prevalence LegendCode ClassName

1 4.2 InfraestruturaUrbana

2 4.1 Praiasedunas

3 1.1.3 Mangue

4 5 CorposDágua

5 1.2 Silvicultura

6 1 FormaçõesFlorestais

6 1.1 FormaçõesFlorestaisNaturais

6 1.1.1 FlorestaDensa

6 1.1.2 FlorestaAberta(SavanaArborizada)

6 1.1.4 FlorestaAlagada

6 1.1.5 FlorestaDegradada

6 1.1.6 FlorestaSecundária

7 2.1 ÁreasÚmidasNaturaisnãoFlorestais

7 2.2 VegetaçãoCampestre(Campos)

7 2.3 OutrasformaçõesnãoFlorestais

8 3.2 Agricultura

8 3.2.1 CulturasAnuais

8 3.2.2 CulturasSemi-Perene(CanadeAçucar)

9 3.1 Pastagem

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Prevalence LegendCode ClassName

9 3.1.1 PastagememCamposNaturais(Combinação)

9 3.1.2 OutrasPastagens

10 2 FormaçõesNaturaisnãoFlorestais

10 3.3 AgriculturaouPastagem

10 3 UsoAgropecuário

11 4 Áreasnãovegetadas

12 4.3 Outrasáreasnãovegetadas

13 6 Nãoobservado

4. Validation Strategies The methodology for analyzing the accuracy of the annual land cover and use maps of the MapBiomas project was designed following this protocol:

a) Define the sampling plan for calculating the number and location of sampling points necessary for estimating the accuracy of mapping of Brazilian biomes and cross-cutting themes. b) Conduct an analysis of the accuracy of Collections 1 and 2 of the annual mapping (in other words, of all of the years of mapping those collections) of land cover in Brazil performed by the project:

The samples from each Collection were interpreted using Landsat color composits through two softwares (AccTools and WebCollect) by three independent evaluators. In each pixel, interpretation is made of the fractions occupied by each class of land cover in the pixel. The AccTools runs on top of Earth Engine Code Editor and was developed by Imazon programmers. WebCollect, is the official package for MapBiomas project and is advanced development stage. These softwares implement the same methodology and can import/export table input data and results. The interpretation is conducted at the pixel level to estimate the fraction (i.e., proportion from 0 to 100%) occupied by MapBiomas mapping classes. Classes not identified by the interpreter are included in the category “other” class. For the accuracy analysis, the dominant class visually interpreted with the Landsat images will be taken as the reference class of the pixel for assess the mapping classification results. The dominant class is the class with the largest proportion in the pixel, automatically calculated by the system. Besides the proportions of the pixel occupied by each class of cover, the following information is extracted: biome, dominant class, page, pixel number in the block, sample number, geographic coordinate of the sample and observations. This protocol was applied by each biome. The cross-cutting themes used a similar protocol for accuracy assessment. For details about how these protocols were applied, refer to specific ATBDs.

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5. Map Collections and Analysis The annual time-series classification dataset produced for MapBiomas project can be used in several types of spatial Analyses. For that purpose, only Collection 2 will be used, since it represents a more robust classification dataset as compared to the previous one. Examples of potential applications of MapBiomas classification results includes:

• Quantification of gross annual deforestation. • Quantification of annual forest degradation. • Annual quantification of secondary growth forests. • Quantification of annual forest fragmentation. • Quantification of annual net forest balance. • Land cover and land use transitions. • Spatial analyses of agreement and disagreement among existing forest and land cover

maps. • Quantification of the natural dynamics of water surfaces.

6. Practical Considerations This is a live document and we expect to update it as soon as Collection 2 becomes available. New information about the results will be included. Right now, MapBiomas project is running Collection 2 and the results are planned to be published in the end of April. Cloud computing processing is under way and we are experiences some coding issues related that are generating slow processing of some datasets. The programming codes for running the MapBiomas algorithms are publicly available and accessible through mapbiomas.org. Detailed descriptions of these algorithms are presented in specific ATBDs.

7. Concluding Remarks and Perspectives The proposal algorithms for pre-processing and classifying Landsat imagery hold promise for revolutionizing the production of land cover and land use maps at a large scale. Thanks to Google Earth Engine and open source technology it is possible to access and process large scale datasets of satellite imagery such as the one generated by MapBiomas project. The replication of this type of project is viable for other areas of the planet. The next step of this project is to expand mapping and monitoring of the Pan-Amazon and other tropical forest regions. In addition, the project team will engage in producing Collection 3, which will include a large time-series (1985 to the present). Future developments include using the entire spectral-temporal information of Landsat data in a per pixel basis and integration with other sensors such as Sentinel-2 and AWiFs-Resourcesat.

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