ATBD_R
AlgorithmTheoreticalBaseDocument&Results
MapBiomasGeneral“Handbook”
CarlosSouzaJr.,Ph.D.
Version1.0–March22nd,2017
1
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
2
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
3
List of Tables
Table 1. Mapping projects at national level. .............................................................................. 8
Excluído:
Table 2. Classification scheme of MapBiomas Collection 2. .................................................. 10
Excluído:
Table 3. Integration rules applied to the annual classification results of the biomes and crosscutting themes. ......................................................................................................................... 12
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1. Introduction
1.1 Identification of Region of Interest (biome or themes)
TheRadamBrasilProjectwasthefirstnationalinitiativetomapvegetationtheentire
countryofBrazil.Thisprojectwasconductedfrom1975to1980basedonairborneradar
imagery,visualinterpretationandextensiveanddetailedfieldwork,involvingseveral
dozensoforganizations.TheRadamBrasilProjectproducemapsat1:250.000scale,andis
stillasolidreferenceforscientificandtechnicalstudiesaboutvegetation(Cardoso,2009).
In2004,theMinisterofEnvironmentlaunchedtheProbioinitiativeaimingatprovidingupto-datedinformationaboutlandcoverandlanduseinBrazil,consideringthatonlythe
AmazonandMataAtlanticbiomeswerebeingmonitoredafterRadamBrasilproject.The
Brazilianbiomeboundaries(IBGE,2004)wereusedasreferencefornationalmapping
initiative.TheProbioprojectwasbasedonLandsatimageryacquiredin2002,with
minimummappingunitvaryingfrom40to100hectares,andmappingscaleof1:250.000.
Accuracyassessmentwasbasedondigitalimageryproductsat1:100.000,withaminimal
overallaccuracyof85%.ThelandcoverclassesfollowedIBGEmanualforvegetation
mapping(IBGE,2004).TheProbioprojecthadupdatesforforestchangefortheyear2008
forallbiomes,andfortheyears2009,2010and2011dependingonthebiome.
TheMapBiomasprojectwaslaunchedinJuly2015,aimingatcontributingwiththe
understandingoflanduseandlandcoverchangedynamicsinBrazil,andothertropical
countries.StartedinBrazil,todayMapBiomasisalreadybeingimplementedintheChaco
region,includingthecountriesofBolivia,ParaguayArgentinaandBrazil.Thisprojectis
basedondigitalimageprocessingofLandsatimageryencompassingtheyearsfrom1985
throughthepresentdays.TheMabBiomasmappingeffortsweredividedinCollectionsfor
thefollowingperiods:
• Collection1:2008through2015(lauchedin2016)
• Collection2:2000through2016(lauchedin2017)
• Collection3:1985through2017(tobelauchedin2018)
Besidestheannualclassificationsofdigitalmaps,MapBiomasaimsatcontributingwiththe
developmentofafastandreliablemethodologytoprocesslargescaledatasets,togenerate
historicaltime-seriesoflandcoverandlandusemapsatlowcost.Inaddition,theprojectis
alsoproducingaweb-basedplatform(i.e.,MapBiomasPlatform)tofacilitationthe
implementationoftheimageprocessingmethodologyandmapproducerstoconducttheir
work.Finally,alldata,classificationmaps,software,statisticsandfurtheranalysesare
openlyaccessiblethroughtheMapBiomasPlatform.Alltheseispossiblethanksto:i)Google
EarthEnginePlatformwhichprovidesaccesstodata,imageprocessingstandardalgorithms,
andcloudcomputingcapabilities;ii)toorganizationsthatarepartofMapBiomasnetwork
thatsharedknowledgeandmappingtools;andiii)tovisionaryfundingagenciesthat
supporttheproject.
TheobjectiveofthisdocumentistopresenttheintegratedstructureoftheMapBiomas
whichiscomposedby:
• biomemaps(Amazonia,Caatinga,Cerrado,MataAtlântica,PampaandPantanal)
andcross-cuttingthematicthemes(Pasture,Agriculture,ForestPlantation,Costal
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Zone,UrbanInfrastructure;
• Landsatimagerycollection(Landsat5,Landsat7andLandsat8).
• Imageprocessing(GoogleEarthEngine,MapBiomasWorkspaceandalgorithms).
ThisdocumentfocusesonCollection2onlywhichisthemostadvancedstageofMapBiomas
project,andontheimageprocessingarchitecture,andonintegratedapproachforcombing
thebiomeandcross-cuttingthememaps.Forinformationaboutthealgorithmsand
proceduresappliedineachbiomeandthemeandinCollection1,please,seetheir
respectivedocuments.
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
MapBiomaswasoriginallydesignedtofillgapsongreenhousegasemissionestimatesofthe
landuseandlandcoverchangesector.However,otherscientificapplicationscanbederived
withannualtime-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
Thissectionaddresscomplementarycontextualandkeyinformationrelevantforthe
integrationofMapBiomasproductstogenerateCollection2(seeMapBiomasATBD
producedbyeachbiomeandcross-cuttingthemesforspecificinformationaboutbiomes
andthemes).Therelevantinformationincludesimageprocessinginthecloudand
crowdsourcingmappingusingEarthEngine,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,withalgorithmsfor
imageprocessingandspatialanalysisandprogramminglanguages(i.e.,Pythonand
Javascript)toimplementapplications.MapBiomasprojecthasdevelopedtwoapplications
basedonGoogleEarthEngine:
• Workspace-aweb-basedapplicationstoaccessimagerycollections,processthem,
andmanagestoretheresultsindatabasesandmapassets(i.e.,newcollections)
(Figure2).
• Javascriptcodes-thesecodesarewrittendirectlyintheEarthEngineCodeEditor
andareusedtoprototypenewimageprocessingalgorithmsandtestlargescale
imageprocessingtobeimplementedintheWorkspaceenvironment.
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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TheWorkspaceenvironmentallowstomanageimageanalystsindividuallyanddefineand
storedeimageclassificationparametersinapermapsheetbasis(Figure1).Thebiome
teamsofanalystscanworksimultaneouslytosettheimageclassificationparameters,preprocessandevaluatetheresultsandsubmittaskstolargescaleimageprocessingto
generatethefinalproducts,whichareLandsatimagemosaics,landcoverandlanduse
maps,annualandtransitionstatisticsoflandcoverandlandusemaps.Alltheseproducts
arepubliclyavailableinthewebplatformnamedMapBiomasDashboard.
2.2 Existent Maps and Mapping Initiative
Themappingeffortsatnationallevelareneitherfrequentandnorupdated(Table1).The
RadamBrasilwasconductedfrom1975through1980andbasedonairbornemicrowave
radarimagery.Probioisamorerecentinitiativeandproducedmapsfor2002.Forest
changeswerethenmonitoredfortheyears2008,2009and2011usingtheProBiobaseline
mapfrom2002.Therearealsoothermappinginitiativesatgloballevelthatcan
complementnationalmappingefforts.TheTreesProjectThesegloballandcoverlayersare
theproductofacollaborationbetweenUSGSandtheUniversityofMaryland.The
GlobCoverPortalisanotherinitiativefromtheEuropeanSpaceAgency(ESA)which
producedlandcovermapswithMERISsensorat300mspatialresolutionfortwoperiods:
December2004-June2006andJanuary-December2009.USGSalsoproducesaMODIS
landcovermapat500mpixelscale.GlobalForestWatch(GFW)andGoogleEarthEngine
providestheGlobalForestChangemapsfrom2000to2014derivedfromtheLandsat
imageryat30mresolutionproducedbyUniversityofMarylandGLCF.TheNational
GeomaticsCenterofChina(NGCC)producedGlobeLand30-ahigh-resolution(30m)full
coveragelandcovermapsforyears2000and2010.Finally,JapanAerospaceExploration
Agency(JAXA)alsoproducedaforest/non-forestmapfor2007-2010usinga25m-resolution
PALSARmosaic(Table1).Thereareotherglobalproductsthatwereproducedusinglower
spatialresolution(>500m)butarenotpresentedherebecausetheirresolutionslimits
applicationstoassessMapBiomasproducts,whichareproducedat30mLandsatpixel.
MapBiomasandtheavailableglobalandnationallandcoverproductscanbeused
complementarybuttherearepotentialadvantagesofMapBiomasmaps.First,the
MapBiomasmapswillreconstructtheentireLandsattime-series(>35years)atanannual
basis.Theclassificationschemeisalsomorerelevantfornationalapplicationsbecauseit
followstheBrazilianvegetationclassificationlegend(IBGE).Inadditio8n,MapBiomashas
thepotentialtomonitorprimaryforestchanges(i.e.,deforestationandforestdegradation),
secondaryforestregrowth,andlanduseclasses(pastureandagriculture,forest
plantations).EventhoughtheMapBiomasprojectfocusatnationalscale,thereisa
potentialtoreplicateittoothercountries.Currently,theChacoregionisalreadyadopting
theMapBiomasmethodologyandplatformandthePan-AmazoniacountriesandArgentina
arealreadytestingtheplatform.
Table 1. Mapping projects at national level.
8
Mapping Initiative/Scale
(National, Global)
RadamBrasil
Project/National
Period
Source
1975 - 1980
Probio/National
2002
Global Land Cover
Map/Global
Tree Cover: 2010
Water Persistance:
2000-2010
IBGE:
http://mapas.ibge.gov.br/bases-ereferenciais/basescartograficas/cartas
MMA:
http://mapas.mma.gov.br/mapas/a
plic/probio/datadownload.htm?/
https://landcover.usgs.gov/glc/
GlobCover/Global
December 2004 June 2006 and
January - December
2009
2016
Global Forest Change UMD/Global
GlobLand30/Global
2000, 2010
http://due.esrin.esa.int/page_globc
over.php
UMD:
https://earthenginepartners.appspo
t.com/science-2013-global-forest
NGCC:
http://www.globallandcover.com/GL
C30Download/index.aspx
3. Algorithm Descriptions, Assumptions, and Approaches
3.1 Algorithm description
ThealgorithmusedtoproducetheMapBiomasproductscanbeorganizedin4categories:
• EarthEnginebuilt-inalgorithms.
• Spectralfeaturereductionalgorithms.
• ClassificationandPost-Classificationalgorithms.
• SpatialAnalysisalgorithms.
ThesealgorithmswereimplementedusingGoogleEarthEngineandCloudComputing
infrastructurestogenerateallMapBiomasproducts(Figure3).Here,wepresentageneral
descriptionofthesealgorithms.Please,refertothespecificATBDsfordetailsaboutthem.
Earth Engine Built-In Algorithms
Thesealgorithmsincludefunctionsandprocedurestoaccess,filterandprocesstheLandsat
datacollectionsandderivedassets(i.e.,classificationmaps)usingtheGooglecomputing
cloudinfrastructure.AllbiomesgenerateannualLandsatimagemosaicsbasedonspecific
periodsoftimethatoptimizethespectralcontrasttodiscriminatethelandcoverandland
usesclasses.Amedianreductionalgorithmwasappliedtogeneratedthebestground
observedpixelofthetemporalperiodandproducetheimagemosaicforeachyearThe
CoastalZonecross-cuttingthemeusedalsoannualtemporalmosaicsandmapsheetsas
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mappingunit,andtheotherthemesprocessedtheLandsatimageryinaperscenebasis.
Spectral Feature Reduction Algorithms
ThisclassofalgorithmwasappliedtotheLandsatscenestoreducedatadimensionality
and/ortoaddnewspectralfeatures.Twotypesoffeaturereductionalgorithmswereused.
First,whole-pixelspectralindices,suchasNDVI,EVI,NDWI,amongothers,wereobtained
fromtheLandsat.Then,fractionalinformationatsub-pixelwerederivedusingSpectral
MixtureAnalysis(SMA)togenerateGreenVegetation,Soil,Non-PhotosyntheticVegetation
(NPV),ShadeandCloudfractions.NDFI(NormalizedDifferenceFractionIndex)was
calculatedusingSMAfractionresultsThespectralindicesandSMAfractionswerecombined
withmedianspectralbandstogeneratethefinaltemporalmosaicassetforeachyear.
Figure 3. Steps to implement MapBiomas algorithms in Earth Engine.
Classification and Post-Classification algorithms
Then,anempiricaldecisiontreeclassificationistrainedforeachmapsheetandyear.
Overall,aparsimonyprincipleisusedtobuildtheempiricalclassificationtreessuchthatthe
parametersusedtodefinethestructureandthesplittingandclassificationrulescanbe
replicateoverspaceandtime.Inotherwords,oncearobustsetofparametersand
empiricaltreesarewell-calibrated,theycanbereplicatedautomatically.Furtherevaluation
oftheclassificationresultsthroughvisualinspectionisconductedtoassessqualitativelythe
empiricaltreeresults(quantitativeaccuracyassessmentisperformedafterwards).The
CoastalZoneandUrbanInfrastructurecross-cuttingthemealsousedanempiricaldecision
treeapproachwhereastheotherthemesusedrandomforestalgorithmavailableinEarth
Engineintheirclassificationapproach.Finally,apost-classificationtemporalfilterwas
appliedtoeachbiomeandcross-cuttingthemestodetectandreclassifydisallowableclass
transitions.TheclassificationschemeofCollection2(Table2)isobtainedafterintegration
ofbiomesandcross-cuttingclassificationproducts.Formoredetailsaboutthesedecision
treealgorithms,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
Spatialoverlappingrulesbasedonclassprevalencewereappliedtoclassificationresults
frombiomesandcross-cuttingthemes,ineachyear,togeneratedtheannualconsolidated
landcoverandlandusemapsproducts.TheintegrationsrulesareapresentedinTable3.
Thisalgorithmisasimpleoverlappingspatialoperationthatfollowsaprevalenceorder(for
exemple,ifUrbanInfrastructureoverlapswithanyclass,thetheoverlappingareais
classifiedasUrbanInfrastructure).Oncetheintegratedconsolidatedannualmapsare
generated,classstatistsarecalculatedandstoredinthedatabaseasmapsandtablesfor
biomes,statesandmunicipalities.Wealsocalculateandstoremapsandstatisticsofall
possibleclasstransitionsamongpairsofconsecutiveyears.
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
4
5
5
1.2
6
1
6
1.1
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
Mangue
CorposDágua
Silvicultura
FormaçõesFlorestais
FormaçõesFlorestaisNaturais
Pastagem
12
Prevalence
LegendCode
ClassName
9
3.1.1
PastagememCamposNaturais(Combinação)
9
3.1.2
OutrasPastagens
10
2
10
3.3
10
3
UsoAgropecuário
11
4
Áreasnãovegetadas
12
4.3
13
6
FormaçõesNaturaisnãoFlorestais
AgriculturaouPastagem
Outrasáreasnãovegetadas
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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References
Almeida et al. 2016. High spatial resolution land use and land cover mapping of the Brazilian Legal
Amazon in 2008 using Landsat-5/TM and Modis data. Acta Amazônica, 46(3), 291-302.
Cardoso, M. I.. 2009. Projeto Radam: uma saga na Amazônia.
Carlotto, M. J. Reducing the effects of space-varying, wavelength dependent scattering in
multispectral imagery. International Journal of Remote Sensing, v. 20, n. 17, p. 3333-3344,
1999.
Fearnside, P.M. 2006. Desmatamento na Amazônia: dinâmica, impactos e controle. Acta
Amazônica, 36, 395-400.
IBGE. 2004. Mapa de Biomas do Brasil, primeira aproximação. Rio de Janeiro:
IBGE.
Acessível em www.ibge.gov.br.
INPE – Instituto Nacional de Pesquisas Espaciais, 2016. Projeto TerraClass – Dinâmica do uso and
cobertura da terra para o período de 10 anos nas áreas desflorestadas da Amazônia Legal
Brasileira.
(http://www.inpe.br/cra/projetos_pesquisas/dados_terraclass.php)
Acesso
in
03/13/2017.
MMA – Ministério do Meio Ambiente, 2013. Biomas. (www.mma.gov.br/biomas). Access on
03/13/2017.
MMA, 2006. Probio: dez anos de atuação = Probio: ten years of activities / Ministério do Meio
Ambiente, Secretaria de biodiversidade e Florestas. – Brasília.
Souza Jr., C., Roberts, D. A. and Cochrane, M. A. Combining spectral and spatial information to
map canopy damage from selective logging and forest fires. Remote Sensing of Environment,
v. 98, n 2-3, p. 329-343. 2005a.
Souza Jr., C. and Siqueira, J. V. ImgTools: a software for optical remotely sensed data analysis. In:
XVI Simpósio Brasileiro de Sensoriamento Remoto (SBSR). Foz do Iguaçu-PR. 8p, 2013.
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