Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold ...

15 Pages·2014·2.61 MB·English

RemoteSensingofEnvironment157(2015)170–184
ContentslistsavailableatScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Time-series analysis of Landsat-MSS/TM/OLI images over Amazonian
waters impacted by gold mining activities
FelipeL.Loboa,⁎,MayciraP.F.Costaa,EvlynM.L.M.Novob
aSpectralLab,DepartmentofGeography,UniversityofVictoria,Victoria,BC,V8W3R4,Canada
bNationalInstituteforSpaceResearch(INPE),RemoteSensingDivision,Av.dosAstronautas,1758-JardimdaGranja,SãoJosédosCampos,SP-12227-010,Brazil
a r t i c l e i n f o a b s t r a c t
Articlehistory: WatersiltationcausedbyartisanalgoldmininghasimpactedtheTapajósRiverBasininBrazilforthepast
Received30December2013 40years,howeverspatial-temporalinformationaboutchangesinwaterqualityandconsequencestotheaquatic
Receivedinrevisedform30March2014 environmentislacking.Toaddressthis,theLandsatsatellitefamilysensorswereusedtoretrievetotalsuspended
Accepted17April2014
solids(TSS)ofthewateroftheTapajósRiverfrom1973to2013.Animageprocessingapproachthatincludesat-
Availableonline9July2014
mosphericcorrection,basedonthe6Smodel,andglintremoving,basedonshortwaveinfraredcorrection,was
appliedandvalidatedwithinsituradiometricdata.Anoptimizationoftheatmosphericcorrectionhavingdark
Keywords:
denseforestspectraasreferencewasappliedandallowedarobustcorrectionofMSS,TMandOLIsignaltosurface
Landsattime-series
Atmosphericcorrection reflectancevalues.Sedimentconcentrationwasestimatedbasedonanon-linearempiricalregressionbetween
Deglinting measuredTSSandsatellitesurfacereflectanceatredband.Themulti-temporalanalysisofTSSshowedthatthe
Turbidrivers sedimentloadintheTapajósaquaticsystemisinsynchronywithminingactivities,andaconstantseasonalvar-
Suspendedsolids iationofwatersiltationisobservedthroughoutthetimeframeofthisstudy.Attheendoftherainyseason,min-
Temporalanalysis ingactivitiesintensifyand,coupledwithlowwaterflow,TSSincreases.Duringthehighwaterlevel,TSS
Goldmining
concentrationswereconsistentlylowerbecauseofhighwaterdilutionandlowminingactivity.Inadecadalanal-
TheBrazilianAmazon
ysis,apeakofsedimentconcentrationcoincideswithapeakofgoldproductioninallsitesanalyzedduringearly
1990s.Morerecently,duetothecurrentlygoldrush,anincreaseinsuspendedsolidshasbeenobservedmainlyin
theNovoandTocantinsriverswhereindustrialmininghasbeeninstalled.
©2014ElsevierInc.Allrightsreserved.
1.Introduction oftheincominglight,thusreducinglightavailabilityinthewatercol-
umn(Kirk,2011).RolandandEsteves(1998)haveshownthatanin-
TheTapajósRiverBasin,intheAmazonBasin,hasbeencontaminat- crease in suspended matter of nearly 34 mg/l in an Amazonian
edwithmercuryandimpactedwithwatersiltationduetodischargesof crystallinelake(BatataLake)raisestotallightattenuation,andconse-
artisanalgold-miningtailingsintoitstributariessince1950s(Sousa& quently reduces the phytoplankton density by approximately 50%.
Veiga,2009).Theartisanalminingactivitiesexpandedinthe1980s GuentherandBozelli(2004)suggestedthatthedecreaseinphytoplank-
whenhighgoldpricesstimulatedaround30,000workerstoextract tondensitiesrecordedinBatataLakemaynotberelatedtophytoplank-
goldinthisarea(Bezerra,Veríssimo,&Uhl,1998).Theactivityde- tonlossduetoalgal-clayaggregation,butratherisaconsequenceof
creasedinthefollowingdecades;however,duetocurrenthighgold decreasinggrowthratesbecauseoflightattenuation.Thehighlight
prices,anewgoldrushistakingplacenotonlyintheAmazon,but backscatteringinturbidwatersresultsinhighwater-leavingreflec-
also in many other countries (Schueler, Kuemmerle, & Schroeder, tance, easily detected by remote sensors as shown by Telmer and
2011;Tudesque,Grenouillet,Gevrey,Khazraie,&Brosse,2012). Stapper(2007)intheTapajósRiver.Consideringthelargescaleofthe
Previousstudiesin theTapajósBasin(Rodrigues,1994; Telmer, watersiltationimpact,TelmerandStapper(2007)haveindicatedthe
Costa,SimõesAngélica,Araujo,&Maurice,2006)reportedthatartisanal potentialofusingremotesensingdatatomonitorturbidityandtoinves-
goldminingdischargeintotheriversenormousamountsoffineinor- tigateitsconsequencestotheecosystemsoftheTapajósRiver.
ganicsedimentbyremovingtopsoillayersfromthemargins,andalso Althoughnotdesignedforwaterbodystudies,LandsatMSSandTM
byrevolvingsedimentfromthebottom.Becauseofitshighscattering havebeeneffectivelyusedtoestimatetotalsuspendedsolids(TSS)in
properties,inorganicsuspendedparticlesinthewaterbackscatterpart coastalandinlandwaters(Binding,Bowers,&Mitchelson-Jacob,2005;
Harrington,Schiebe,&Nix,1992).Detectionofwaterleavingradiance
fromturbidwaterswithhighconfidenceispossible,firstbecausethe
⁎ Correspondingauthorat:DepartmentofGeographyUniversityofVictoriaPOBox
sensor'sspatialresolution(upto80monMSS)allowsimagingrivers
3060STNCSC,Victoria,BC,CanadaV8W3R4.Tel.:[phone].
E-mailaddress:[email protected](F.L.Lobo). andestuarineareas,andsecondbecauseofthesignal-to-noiseratioof
http://dx.doi.org/10.1016/j.rse.2014.04.030
0034-4257/©2014ElsevierInc.Allrightsreserved.
F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184 171
thesesensors(250:1)(Dekker,Vos,&Peters,2002).Theuseofthese aboutatmosphericconditionstomodeltheeffectsandremovethem
sensorsforestimatingsuspendedsolidsinthewatergenerallyfollows fromthesensor'ssignal.Accordingtothe6Smodel,surfacereflectance
twoapproaches:theempiricalapproach,whichreliesondirectcorrela- (ρ )isderivedfromthefollowingequation(Vermote,Tanre,Deuze,
surf
tionbetweenmeasuredTSSandsatellitedata(Hadjimitsis&Clayton, Herman,&Morcette,1997):
2009;Mertes,Smith,&Adams,1993);oranalyticalmethods,which h i
relyonmeasuredwateropticalproperties(Albert&Mobley,2003; ρTOA¼tgasðO3; O2Þ(cid:3) ρrþaþtH2O(cid:2)trþa(cid:2)ρsurf ð4Þ
Doxaranetal.,2012).Thesestudiesshowthatgreenandredbandscor-
relatewellwithTSSuptoapproximately100mg/l.Underhighercon- whereρ istheTOAreflectance;ρ ,Rayleighandaerosolreflec-
TOA r+a
centrations,however,thesebandssaturateandNIRbandspresenta tance;t ,Rayleighandaerosoltransmittance;t (O ,O ),gases
r+a gas 3 2
betterpredictorofTSS(Wang,Lu,Liew,&Zhou,2009). transmittance;andt ,watervaportransmittance.
H2O
GiventherecentlylaunchedOLI(OperationalLandImager)sensor The6Smodelprovidespre-definedatmosphereconditionstobe
onboardLandsat-8,withsimilarcharacteristicsofaTMsensor,theca- chosenaccordingtoscenelocationandaltitude,atmospherictype,and
pabilityofusingtimeseriesbasedonLandsatimageryforevaluation relativehumidity, to accommodate thecommon lack of inputdata
oftemporalchangesandmonitoringpurposesisextendedtothepres- aboutinsitugasesandaerosolconcentrations(Vermoteetal.,1997).
ent;atimeseriesof40years(1973–2013)ofLandsatimageryiscur- FortheBrazilianAmazon,forexample,tropicalatmosphericandconti-
rentlyavailable.However,givendifferencesinthesensor'sresolution nentalaerosolconditionsarecommonlyused(Lu,Mausel,Brondizio,&
andinatmosphericconditionsatthetimeofimageryacquisition,a Moran,2002).
propercomparisonbetweenwaterleavingsignalsrequiresthatallim- Besidesatmosphericeffects,surfacespecularreflectance,commonly
ageshavetobecorrectedforatmosphericeffectsandnormalizedtoref- calledglint,hastobeconsideredforderivingaccuratewaterreflectance
erenceimagesorreferencetargets(Hadjimitsis&Clayton,2009).Most (Hedley,Harborne,&Mumby,2005).Thedeglintingprocedureasde-
oftheimagesfromthestudyareapresentatleast40%cloudcover.At- scribedbyHochberg,Andrefouet,andTyler(2003)andHedleyetal.
mosphericcorrectionwouldbenecessarytominimizetheatmospheric (2005)reliesontwosimpleassumptions:(1)thatthebrightnessin
effectsonhistoricalLandsatimagesthatarenotvalidatedwithinsitu theinfra-rediscomposedonlyofsunglint;and(2)thattheamount
measurements. ofsunglintinthevisiblebandsislinearlyrelatedtothebrightnessin
Thispaperhasathreefoldobjective:(i)defineanimageprocessing theNIRband.TheuseofNIRfordeglintingsignalfromturbidwaters
procedurethatcorrectsLandsatdigitalnumbers(DN)tosurfacereflec- mayovercorrectthevisiblebands(Le,Li,Zha,Sun,&Yin,2009)because
tance(ρsurf(λ)),allowinginter-comparisonbetweenLandsatdatafrom itcanpresentconsiderablesignal(upto5%)whenTSSisupto25.0mg/
1973(MSS)to2013(OLI);(ii)applytheproceduretobuildareliable l,asanexamplegivenbyBale,Tocher,Weaver,Hudson,andAiken
timeseriesofwatersurfacereflectancetoretrieveTSSconcentration (1994).Forthatreason,WangandShi(2007)suggesttheSWIRband
fromhistoricalimagesintheTapajósRiverBasin;(iii)usetheretrieved (1600nm), which is less affectedor not affected by sediment-rich
suspendedsedimentconcentrationsfortemporalandspatialanalysisof waterbodies,tocorrecttheVNIRbandsfromglintingeffectbysimple
sedimentchangesandgoldminingactivityintheTapajósRiverBasin. subtraction:
2.Theoreticalbackground ρdeglintðVNIRÞ¼ ρsurfðVNIRÞ−ρsurfðSWIRÞ ð5Þ
2.1.Atmosphericeffectsandcorrectionmethods
2.2.Time-seriesforchangesdetection
Whensensingawaterbody,themeasuredradiance,L (λ),isthe
total
sumofthetargetradianceandradiancefromatmosphericattenuation: Thepotentialtousetemporalseriesofsatelliteimagestodetect
changesinsurfacewaterqualityhasbeendemonstrated,forexample,
LtotalðλÞ¼LpathðλÞþ t(cid:2)LtargetðλÞ ð1Þ by Dekker, Vos, and Peters (2001) and Wang, Xia, Fu, and Sheng
(2004).Foranabsolutemulti-temporalanalysis(i.e.,detectedchanges
whereL (λ)standsforradiancescatteredbytheatmospheregivena havetoattributesolelytovariationontarget`ssignal),onehastoper-
path
wavelength(λ),tisthediffusetransmittancefromthetargettothesen- formradiometriccorrection(includingatmosphericcorrection)follow-
sorandL (λ)istheupwellingradiancefromthewaterbody. edbynormalizationoftheimages(Hadjimitsis&Clayton,2009;Moran
target
Dependingontheatmosphericconditions,morethan80%ofthe etal.,2001).Normalizationbetweenimagesrequiresanapplicationof
total signal can be attributed to atmospheric scattering processes histogrammatchingbasedonthesignalfrompseudo-invarianttargets
(Albert & Mobley, 2003; Hu, Muller-Karger, Andrefouet, & Carder, withintheimage.Theassumptionisthatthespectraofthesetargets
2001).Notethatthetotalradiance,L (λ),canalsoincludethesurface donotchangeovertimeandarethereforeusedtoequalizehistogram
total
reflectionofthedirectsolarbeam,L (λ),and,inshallowwaters,the images(Moranetal.,2001).Thepseudo-invarianttargetscommonly
glint
effectoflightreflectedfromthebottom,L (λ). usedareconcrete,sandandbarrenlandsashighlyreflectivetargets
bottom
(Puttonen,Suomalainen,Hakala,&Peltoniemi,2009),andwaterbodies
LtotalðλÞ¼LpathðλÞþLglintðλÞþt(cid:2)LbottomðλÞþ t(cid:2)LtargetðλÞ ð2Þ asdarktargets.However,concreteandbarrenlandsareoftennotavail-
ableforthestudyarea,andusingwaterbodiesasdarkobjectscancom-
Eq.(2)isoftennormalizedtoincidentallighttoyieldadimension- promisetheabsolutesurfacereflectanceofthewaterbodiesofinterest.
lessreflectanceterm,ρ (λ)(Gordon&Wang,1994): As an alternative to histogram matching (Liew, Saengtuksin, &
TOA
Kwoh,2009),anabsoluteatmosphericcorrectionmethodisproposed
ρ ðλÞ¼π(cid:2) L ðλÞ=E (cid:2)cosθ ð3Þ basedontheassumptionthatdensedarkvegetation(DDV)isconsid-
TOA total o 0
eredaspectrallyinvarianttarget,andcanbeusedasareferencetarget
whereE isthetopofatmosphere(TOA)solarirradianceandθ isthe tooptimizeatmosphericcorrection.Thismethodwasestablishedhav-
o 0
solarzenithangle. ingAmazoniandarkdenseforestspectraasreferencetargets,including
Inordertocorrectρ (λ)toρ (λ),atmosphericcorrectionon spectrafromtheTapajósarea(Liewetal.,2009),andpostulatesthere-
TOA surf
Landsat-TMdataoverinlandwatershasbeencarriedoutusingradiative lation,ρ (blue)=0.33*ρ (SWIR),toestimatesurfacereflectance
surf surf
transfermodels(physicallybased),suchasLowtranand6S,becauseof forthevisiblebands.Comparingthecalculatedρ totheTOAreflec-
surf
accurate outputs when local atmospheric conditions are known tance,aerosolopticalthickness(AOT)canbeestimateddirectlyfrom
(Gong,Huang,Li,&Wang,2008).Thismethodrequiresinformation theimagery.Maseketal.(2006)showthatAOTderivedfromLandsat
172 F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184
TMimageryishighlycorrelatedtoinsituAOTmeasurements,andcon- propertiesinthevisiblespectrarange.Thecombinationofabsorption
firmtheapplicationofDDVasreferencetargetstooptimizeatmospher- and scattering properties of the OAC defines the ρ (Gordon &
surf
iccorrectionforamulti-temporalanalysisofρ . Brown,1975).
surf
ThehydrologicalregimeintheTapajósRiverisveryconsistentover
3.Studyarea theyears(Fig.1b),andisanimportantfactorinthebiogeochemicaldy-
namicofthewater.Thewaterreachesthehighestlevel(MarchtoMay)
TheTapajósRiverBasincoversmorethan200,000km2(Fig.1)inthe attheendoftherainyseason,andthelowestlevelduringthelowwater
centeroftheBrazilianAmazon.Itdrainslixiviatedoldrocks(N2000mil- season(fromSeptembertoNovember),withadifferenceof6.0mon
lionyears,Pre-Cambrian)whichresultsinanaturallyclearwatersys- average.Forconsistencythroughoutthedocument,thehighwater
tem with low amounts of sediments and dissolved matter (Junk, levelseasonreferstotheperiodbetweenMarchandMay,andlow
1997).Forexample,Costa,Novo,andTelmer(2013)reportedTSSup waterlevelseasonreferstotheperiodbetweenAugustandNovember
to 5.0 mg/l in the mouth of the Tapajós River as opposed to the (Fig.1b).Theonlyupstreamsite(ItaitubaCityregion,seeFig.1)that
sediment-rich (whitewater) Amazon River that shows TSS up to haslongtermsamplingforTSSestimationbytheBrazilianWaterAgen-
150mg/lduringwaterrecedingperiods.Colored-dissolvedorganic cy(ANA,2013)showsthatconcentrationreachedalmost30mg/linthe
matter(CDOMat440nm)isalsolow(upto2.1m−1)whencompared early1990sanddroppedtoarangebetween5and12mg/linthefol-
toCDOM-rich(blackwater)riverssuchastheNegro(upto10.7m−1) lowingyearsforbothseasons(Fig.1c).
(Costaetal.,2013).Therefore,becauseofitsnaturalclearwaterandbet- TheTapajósRiverBasinrepresentsoneofthelargestgoldreservesin
terlightconditions,phytoplanktonproductionintheTapajósRivercan theworld,andsincethecreationoftheGoldMiningDistrictin1983,
beconsiderablygreaterthaninwhitewaterandblackwater(Costaetal., thisareahasbeenintensivelymined.Goldproductionreachedamaxi-
2013;Junk,1997).Thelargemouth-baysoftheTapajós,forexample, mumof22.0tonsperyearin1992(12.5%ofBrazil'stotalproduction
collectnutrientsthat,alongwithrelativelygoodlightpenetration,sup- thatyear)(Sousa&Veiga,2009).Inthesameperiod,anorth–south
portseasonalphytoplanktonblooms(Novoetal.,2006).Costaetal. highway(BR-163)wasestablishedtoconnectSantarémtoCuiabáin
(2013)reportedaseasonalvariationonchlorophyll-aconcentration ordertosupportterritorycolonizationandagricultureactivities.Asare-
(chl-a)from2.1μg/lattherecedingwaterperiodto16.3μg/lduring sult,largedeforestedareasalongthehighwaycanbeobserved,mainly
theebbingseasonatSantarémarea(seeFig.1).Thevariabilityoftheop- attheJamanximsub-basin(Fig.1).Deforestationareasarealsoob-
ticallyactivecomponents(OAC),suchasTSS,chl-aandCDOM,caninflu- servedaroundSantarémandItaitubacitiesandintheGoldMiningDis-
enceρ becauseoftheirinherentlydifferentabsorptionandscattering trictasaconsequenceofminingsettlementsinthearea.
surf
properties (Mobley, 1994). TSS is a highly-scattering component, Gold-mininginthisareaistraditionallyperformedeitherbyusing
whereas chl-a and CDOM are characterized by high absorption water-jetstoremovetopsoillayers,orbyusingsmallboatscalled
Fig.1.(a)LocationoftheTapajósRiverBasinintheBrazilianAmazonwithindicationofthemainwaterbodies,samplesites(seeSection4.1),Aeronetstation(Belterra),deforestation
(INPE,2011),gold-miningdistrict,mines(CPRM,2009),andLandsatTMscenes.(b)HydrologicalandprecipitationregimeoftheTapajósRiveratItaitubagaugestation(centerof228/
63scene)forthepast10years,periodsoffieldcampaignsarealsoshown.(c)Historical(1992–2011)TSSconcentrationintheTapajósRiver-ItaitubaCityarea.(Sourcefor(b)and(c):
ANA-BrazilianNationalAgencyforWaterResources2013.)
F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184 173
‘balsas’thattakethesedimentfromthebottomoftheriversusingsuc- 2011, during high water level (23 sample points); and September
tionandseparatethegoldbygravity(AraújoNeto,2009;Telmeretal., 2012,duringlowwaterlevel(16samplepoints)(seeFig.1forsample
2006).Bothtechniquesdischargelargeconcentrationsoffinesediment pointlocations).Thefieldcampaignsweredefinedbasedonperiods
intothemaintributaries(Crepori,Jamanxim,andTocantins)thatcanbe whenthewatersystemislessdynamicandchangesinwaterquality
measuredkilometresdownstream(Telmeretal.,2006).Accordingto are slower compared to receding or flooding periods. This choice
Bezerraetal.(1998),atotalof67.0millionm3/yearofsedimenthave wouldcontributetomatchinginsitudatawithconcurrentsatelliteim-
beenremovedfromthemarginsofmanytributariesoftheTapajós ages.Itwouldalsohelpwiththeinter-annualcomparisonbetweenim-
Riverinthe1980sduetogoldminingactivities.Furthermore,Telmer ages.Thesamplepointlocationsweredefinedinordertocoverthe
etal.(2006)showedthatthesedimentplumegeneratedduringmining spatialdistributiononthemainTapajósRivertributariesbeforeand
operationsiscomposedmostlyoffineinorganicparticlesthatcancarry aftertheirdischarge,andalongtheTapajósRivertocoveritslengthwise
significantamountsofmercury,whichisusedintheamalgamationpro- variation.
cess.Telmeretal.(2006)alsodemonstratetheapplicabilityofmonitor- Foreachsamplepoint,twowatersamplesweretakenatadepthof
ing the sediment plume using Landsat TM data, and indicate that 0.3mtodetermineTSSconcentrationsaccordingtothegravimetric
satelliteimageryshowsgreatpromisetobeusedasamodernmonitor- method(APHA,2005).Foreachwatersampletaken,triplicatesofpre-
ingsystemgiventhelargeandremotearea. weighted(0.7μm)filterswereusedtodetermineTSSaverageandstan-
darddeviationsinthelaboratory.Bydoingso,atotalfullprecisionofTSS
estimationisachievedbyaccountingforvariabilityoffilteringmethods,
4.Methods
andforheterogeneityofwatersamples.
Three main steps were conducted in order to define an image Todeterminetheinsitusurfacereflectance,above-waterdownward
irradiance(E),andacontinuousdepthprofileofin-waterupwellingra-
processingmethodforLandsatdatathatallowsmulti-temporalanalysis s
ofwatersurfacereflectanceandsuspendedsolidsinTapajósRiverBasin diance,Lu,weremeasuredusingSatlanticHyperPro(SatlanticInc.).
(Fig.2):1)fieldcampaignsforinsituradiometricandTSSdatacollection Theseopticalsensorsmeasurehyperspectralquantitiesintheinterval
from396to800nmwith10nmresolution.Therawdatawerecalibrat-
(box(a)inFig.2);2)atmosphericcorrectionofhistoricalLandsatimag-
edtosensorspecification,correctedfortareconditionsandbinnedto
erybasedonreferenceimagesthatwerevalidatedwithinsituradio-
depthintervals.Inordertominimizethewavefocusing/defocusingef-
metricdata(boxes(b),(c),and(d)inFig.2);and3)seasonaland
decadalanalysisofρ (λ)andsuspendedsolids(TSS)intheTapajós fect(Hedleyetal.,2005),anduncertaintiesattributedtotheLusensor
surf
tilt,triplicatemeasurementsweretakenateachsamplepoint,andsig-
RiverBasin(box(e)inFig.2).
nals measured with a tilt higher than 15° were removed from the
dataset.Afterbeingcorrectedandbinnedtodepthintervals,Luvalues
4.1.RadiometricandTSSdata
werethenusedtocalculateupwardirradiance,E ,asfollows:
u
(cid:2) (cid:3) (cid:2) (cid:3)
TwofieldcampaignswereconductedintheTapajósRiverBasinto E 0þ; λ ¼4:5 (cid:2) L ð0−; λÞ(cid:2) 1−ρðλ;θÞ=n2ðλÞ ð6Þ
measureradiometricquantitiesandTSSconcentrations:March/April u u w
Fig.2.Flow-chartofthemethodologyappliedinthisstudy.Fielddataoftwocampaigns(a)wasusedtocalibratetheatmosphericcorrection(b)thatwasappliedtoLandsat-5TMdatabase
basedonforestreferencespectra(c).AfterincorporatingcorrectedMSSandOLIsurfacereflectanceintothedatabase(d),amulti-temporalanalysisofρsurf(λ)andofTSSinfoursub-basins
andalongtheTapajósRiverwasperformed(e).
174 F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184
whereρ(λ,θ)isFresnelreflectanceindexofthewater(0.021)and for evaluation were the determination coefficient (R2), regression
n2(λ)isFresnelrefractiveindex(1.34)whichrelatestothefractionof slopes,andtheRMSE(rootmeansquareerror).Afteratmosphericcor-
w
theincidentirradianceofacollimatedbeamthatisreflectedbyalevel rection,glinteffectwasremovedaccordingtoEq.(6)andtheirstatisti-
surface(Mobley,1994).Next,surfacereflectance,ρ (λ),valueswere calparameterswerealsocompared.
surf
calculatedforeachprofileaccordingto: ForthefieldworkperformedinMarch/April2011duringthehigh
waterlevelseason,threeLandsat-5TMimages(row/orbit:227/63;
(cid:2) (cid:3) (cid:2) (cid:3)
ρ ðλÞ¼E 0þ; λ =E 0þ; λ : ð7Þ 228/63;and228/64)acquiredonMarch19,2011wereusedintheval-
surf u s idationprocess.However,forthecampaignperformedinSeptember
2012,Landsat-5wasnolongeractive,whichpreventedfullseasonal
analysisofLandsatdata.Alternatively,cloud-freeimagesacquiredby
4.2.Imageprocessing IRSLISS-IIIinthesameperiodofthecampaignwereusedasreference
imagesforthelowwaterlevelseason.
Theimageprocessingforatmosphericcorrectionandnormalization Theinputdataforthe6Smodelwereprovidedbyiterativelytesting
ofallselectedLandsatdata(1973–2013)fromDNtoρ wasper- measured ranges of water vapor values and AOT from the closest
surf
formedinfoursteps:i)imageryselectionandcompilationintoadata- AERONET (AErosol RObotic NETwork) station (NASA/GSFC, 2013),
base;ii)atmosphericcorrectionoftwoimagesets(fromhighwater nearBelterra(S3°06′,W55°03′,seeFig.1).Dataavailablefrom1999
levelandlowwaterlevelseasons—calledreferenceimages)coincident to 2005 shows water vapor values of 4.2 ± 0.6 cm, and AOT of
withinsituradiometricmeasurements;iii)correctionofatmospheric 550nmvaryingfrom0.1upto0.5(dimensionless).ForLandsat-5TM
effectsfromallLandsat-5TMimages(1984–2011)usingthereferences (March19,2011),awatervaporvalueof3.8cmandAOTequalto
imagesandAERONETdatatooptimizethe6Sinputatmosphericparam- 0.22werechosen,minimizingtheaveragedifferencebetweeninsitu
etersforeachimage;iv)correctionofMSSandOLIdatafromDNtoρ and image reflectance output. Using the same criteria,LISSimages
TOA
followedbynormalizationtoρ basedonforestspectraderivedfrom (September16,2012)wereatmosphericallycorrectedbyaphysical-
surf
referenceimages. basedmethodhavingwatervaporvalueandAOTequalto3.7cmand
0.19,respectively.
4.2.1.Imagerydatabase
ThedetectablewaterbodiesintheTapajósRiverBasinextendover 4.2.3.AtmosphericcorrectionofhistoricalLandsat-5TMdata(1984–2011)
sixLandsatTMscenes(Fig.1).LandsatMSSandTMimagesacquired
Oncetheatmosphericcorrectionsofthereferenceimageswereval-
from1973to2011weredownloadedfromDGI/INPE(2013)foranalysis idated,theywereusedtoiterativelydefinethesetofinputvaluesfor
oftwospecificseasons:highwaterlevelseason(MarchtoMay)andlow
runningtheatmosphericcorrectiononthe6Smodel.Thesetofwater
waterlevelseason(AugusttoNovember).Recentcloudfreeimages vaporvaluesandAOTforeachTMimageweredefinedbyminimizing
fromtheOLIsensor,onboardLandsat-8,acquiredinAprilandSeptem-
thedifferencesbetweenforestspectrafromthereferenceimagesand
ber2013,weredownloadedfromEarthExplorerwebsite(USGS,2013a).
theimagesubjectedtoatmosphericcorrection.
Atotalof77images(31fromhighseasonand46fromlowseason)were
Theassumptionis that densely forested areaspresentinvariant
incorporatedintothedatabase(Table1).
spectraoverdecadesthatcanbeusedasreferencespectratooptimize
atmospheric correction in individual scenes (Holben et al., 1998;
4.2.2.Atmosphericcorrectionvalidation
Kaufmanetal.,1997;Liewetal.,2009).AlthoughAmazonianforest
Inordertodefineimagesthatcanbeusedasreferencestocorrect
spectraisverysteadyoverdecades,thereareslightdifferencesbetween
historicalLandsatdata,twogroupsofsatelliteimagescorresponding
spectra taken in rainy and dry seasons (Asner, 1998; Lu, Mausel,
tothefieldworkperiodswerecorrectedforatmosphericeffects,and
Brondízio,&Moran,2004;Luetal.,2002)thatmustbetakenintoac-
validatedwithinsituradiometricdata.Thestatisticalparametersused
countforproperimagerycorrection.Assuch,theimagesacquireddur-
ingthehighwaterlevelseason(orrainyseason)wereoptimizedto
Table1
forestspectrafromLandsat5-TMacquiredinMarch19,2011,whileim-
Numberofsatelliteimagesofsixorbit/rowsacquiredinhighandlowwaterseasons
agesacquiredduringthelowwaterlevelseason(ordryseason)were
between1973and2013usedintheimageprocessing.Notethatonlymonthsthat
representatleastoneimageareshown. optimized using LISS-III acquired in September 2012 as reference.
Afteroptimizingtheatmosphericparameters(watervaporandvisibili-
Highwaterlevel Lowwaterlevel
ty)foreachTMimage,vegetationspectrawereextractedandaveraged
Mar Apr May Aug Sep Oct Nov differencescomparedwiththereferenceimages.Imagesthatdidnotfall
MSS 1973 4 withinanacceptablerange(upto50%differenceatredband,whichcor-
1975 2 respondstoadifferenceofupto1.5%inρ andhaveminimumimpact
surf
1979 2 1
on TSS estimation) were re-corrected with new atmospheric
1980 1 1
parameters.
1981 3
TM 1984 1 1 1 2 1
1985 2 1
1986 1 4.2.4.CorrectionofMSSandOLIdata
1987 1 1 1 1 The6Sphysical-basedmodeldoesnothavethefunctionalitytocor-
1989 2 2 rectMSSandOLIimagery;assuch,analternativeprocedurehadtobe
1990 1 1 adoptedforatmosphericcorrectionoftheseimages.MSSimageswere
1993 5 1 4
firstconvertedfromDNtoL(λ)(Markham&Barker,1986),andsubse-
1995 1
1996 1 quentlycorrectedtoρTOA(λ)(seeEq.(3))accordingtotheradiometric
1997 3 2 1 specificationsandthedateofimageacquisition.OLIDNdatawerecon-
1998 1 vertedtoρ (λ)accordingtoUSGS'swebsite(USGS,2013b).Forboth
TOA
1999 1 MSSandOLIρ (λ),theatmosphericeffectswereremovedfromVIS
2000 3 TOA
2001 1 1 bandsbyapplyinganoffsettomatchdenseforestspectrawiththose
2005 1 1 2 2 fromoptimizedsurfacereflectance(seeFig.4a)whichresultedinρ-
2011 3 1 1 (λ).Inthiscase,weassumedthatthedifferencesbetweenthesurface
surf
OLI 2013 2 5 reflectance forest spectra extracted from reference images and the
F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184 175
forestρ (λ)fromMSSandOLIdatacanbeattributedtoatmospheric 5.Results
TOA
effects.
5.1.Validationofatmosphericandglintcorrection
4.3.Multi-temporalanalysisofsurfacereflectanceandTSSconcentration Theevaluationoftheatmosphericandglintcorrectionrevealedthat
thesatelliteρ withoutglintcorrectionwasincloseagreementwithin
surf
OncesurfacereflectanceatvisibleandNIRwerederivedfromthe situρ values,especiallyontheredandgreenwavebands(Fig.3).
surf
imagery database (Landsat-MSS, Landsat-TM, and OLI), a temporal HighR2(N0.78)andlowRMSE(b1.80inρ unit)wereobservedfor
surf
analysis was performed in four different sub-basins and along the bothseasonswhereregressioncoefficient(slope)valueswerearound
TapajósRiver(seeFig.1),foratotalofeightdifferentsites.Thesites 0.85atthesebands(Table2showsthestatisticalresultsforthelinear
werechoseninordertocoverthefourmaintributariesintermsof regressionsshowninFig.3).Attheblue(LandsatTMonly)andNIR
waterdischargeandminingactivities(Crepori,Jamanxim,Tocantins, bands,thestatisticsshowrelativelypoorresults,withgenerallyhigher
andNovorivers)andalsofourothersitesalongtheTapajósRiverthat ρ forsatellitecomparedwithinsitumeasurements(Fig.3aandb).
surf
represent the river's longitudinal variation from upstream to the Fig.3canddshowexamplesofinsituρ spectraunderdifferent
surf
mouth(Jacareacanga,Itaituba,Aveiro,andSantarém).Foreachsite,sev- TSSconcentrationsplottedwithglintcorrectedsatelliteρ (λ)forthe
surf
eralpixelsdistributedinthesampleareawereselectedandaveragedto twofieldcampaigns.Highaccordancebetweensatelliteandinsitu
reducethevariabilitycausedbyadjacencyorbottomeffects,andalsoto ρ (red)isobserved.AccordingtoTable2,thecorrectionforglinteffect
surf
diminishthenaturalvariabilityofthewaterbody.Thisprocedurecan significantlyimprovedthestatisticalresults.Attheblueband,R2values
alsominimizethedifferentspatialresolutionofMSS(80m)andTM increasedfrom0.44(noglintcorrection)to0.80(withglintcorrection),
(30m)data.Furthermore,inordertoavoidadjacencyeffectonthese- andRMSEdecreasedfrom1.34to0.75(inρ unit).Improvements
surf
lectedpixels,theanalyseswererestrictedtoriverswithatleast3pixels werealsoobservedforthegreenandredbands,whereR2werehigher
width.Forthatreason,MSSdatacouldnotbeextractedatriverswith than0.88andRMSEwerelowerthan1.0(inρ unit)forbothseasons.
surf
widthsupto200m(e.g.,Jamanxim,Novo,andTocantinsrivers),for BecausetheimagesacquiredinMarch2011(Landsat-5TM)present
whichanalyseswererestrictedtoTMandOLIdata(1984–2013). highcloudcoveragedistributedunevenlyoverthescene,theSWIRsig-
InordertoretrieveTSSconcentrationfromsurfacereflectanceinthe nalcanbeattributednotonlytoglinteffect,butalsotoatmosphericef-
eightsamplesites,anon-linearregressionwasestablishedbetweenTSS fects that have not been removed by the 6S model, and therefore
andρ (red)derivedfromreferenceimages(seeFig.6).Theapplica- minimizestheheterogeneitydistributionofatmosphericconditions
surf
tion of ρ (red)to estimate TSSin coastal and inlandwater using (Ruddick,Ovidio,&Rijkeboer,2000).ForLISS-IIIimages,theρ (SWIR)
surf surf
empirical regressions has been extensively reported for MSS valueswerealwayslowerthan1.0(inρ unit),indicatingthatglint
surf
(Harrington etal.,1992;Mertes etal.,1993)andTMdata (Dekker wasnotaffectingtheimagequality.Thestatisticalresultscorroborated
etal.,2002;Maseketal.,2006).AveragedTSScollectedduringthe this,howeverwefollowedtheimageryanalysiswiththeglintcorrected
twofieldworks(n=39)wereusedtoestablishtheempiricalregres- LISS-IIIimagegiventhattheobservedinterceptvaluesdecreasedwhen
sioncurvethatallowsretrievingTSSwithassociatederrorestimation compared with no glint correction. All the linear regressions of
(within95%levelofconfidence). deglintingdata(showninFig.3)weretestedfornormaldistribution
Fig.3.ScatterplotsbetweenmeasuredρsurfandcorrectedsatelliteρsurfatVNIRchannelsfor(a)Landsat-5TM,(b)IRS-LISSIII.ρsurfbefore(emptycircles)andafter(filledcircles)glint
removingisalsoshown.Linearregressionandstandarddeviationareplottedonlyfordeglinteddata(bestresults).SeeTable2forstatisticalparametersoflinearregressions.Notethe
differentρsurfrangebetween(a)and(b).Examplesofmeasuredsurfacereflectancespectraplottedwithcorrespondentglintcorrectedsatellitedataforhighwaterlevel(c)andlow
waterlevel(d)seasons.
176 F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184
Table2
Statisticalparameters(intercept,slope,R2,RMSE)forlinearregressionsbeforeandafter
deglintingbetweenmeasuredρsurf(λ)andρsurf(λ)derivedfromtwoimagerysets:
Landsat-5(highwaterlevel)andLISS(lowwaterlevel).
Landsat-5TM(Mar.19,2011)– IRSLISS-III(Sep.16,2012)–
Highwaterlevel(n=23) Lowwaterlevel(n=16)
Channel Intercept Slope R2 RMSE Intercept Slo R2 RMSE
pe
Blue 1.26 0.76 0.44⁎ 1.34 – – – –
deglint −0.51 1.16 0.80 0.75
Green 2.1 0.82 0.78 1.56 1.57 0.87 0.88 1.77
deglint 0.5 0.96 0.93 0.85 1.31 0.87 0.88 1.79
Red 1.7 0.88 0.88 1.36 1.35 0.82 0.97 0.89
deglint 0.8 0.99 0.93 0.87 1.06 0.82 0.97 0.89
⁎
NIR 2.8 1.4 0.27 1.36 1.77 1.41 0.75 1.46
deglint 1.38 1.84 0.82 0.79 1.47 1.41 0.76 1.42
⁎Determinationcoefficientsnotsignificantat0.05level.
ofresidualsata0.05levelofsignificance.Theresultsconfirmthathigh
andlowwaterleveldataweredrawnfromanormallydistributedpop-
ulation(testsnotshown),thusindicatingnooverorunderestimationof
reflectancevalues.
5.2.AtmosphericcorrectionofhistoricalLandsat-5TMdata(1984–2011)
Oncetheatmosphericcorrectionforthereferenceimageswasvali-
datedwithinsitudata,theforestspectraextractedfromtheseimages
(Fig.4a)wereusedasareferencetooptimizethe6Satmosphericinputs
(AOT,watervapor)forcorrectionoftheother76historicalLandsat-5
TMimages(Table1).Forthisstep,theimagerydatabasewasseparated
intotwogroupsaccordingtothetimeofimageryacquisition:highand
lowwaterlevelseason.Therationalforthisdivisionwastheobserved
slightlyhigherforestspectravaluesatlowwaterlevel(LISS-III)com-
paredtoforestspectraathighwaterlevelseason(Landsat-5TM).For
example,ρ (green)reachedvaluesashighas5.2%inthelowwater
surf
level,whereasfortheimagesacquiredduringhighwaterlevelseason,
valuesof3.1%wereobserved(Fig.4a).
Theforestρ extractedfromthehistoricalimages(Fig.4b)was
surf
comparedwiththeforestspectrafromthereferenceimages,again
dividedintolowandhighwaterlevelseasons(Tables3and4).High
accordancebetweenforestspectraextractedfromthecorrectedim-
agesandthereferenceimagesforbothlow(n=30)andhighwater
Fig.4.(a)Darkdenseforestspectraextractedfromhighwaterlevelseason(Landsat)and
levels=(n=28)periodswasachieved.Thehighestdifferencesbe-
lowwaterseason(LISS-III)usedasreferenceforoptimizingatmosphericparameterson
tween reference and individual images were observed at the blue Landsathistoricaldata.ExamplesofuncorrectedMSSandOLITOAreflectance,and
band,wheretheaveragedifferencewasabout33%.Fortheremaining denseforestfromtheBrazilianAmazonfoundinliterature(Luetal.,2002,2004)are
bands, the differences were on average 9% for both low and high alsoshown.(b)Denseforestspectraforhighandlowwaterseasonsextractedfromhistor-
icalLandsat-5TMimageshavingspectrashownin(a)asreference.SeeTables3and4for
waterlevelseasons.
differencesbetweenreferenceimagesandhistoricalTMdata.
5.3.MSSandOLIimagerycorrection
fromsurroundingvegetationonthewaterreflectancefromthenarrow
Intotal,14MSSand7OLIρ (λ)imageswereincorporatedinto rivers.Therefore,fortheserivers,timeseriesanalysisofρ (λ)startsin
surf surf
thedatabasebyapplyingoffsetsonuncorrectedρ (λ)basedonfor- 1984withTMdata.
TOA
estspectra.Becausetheatmosphericscatteringeffectsaremoreprom- Ingeneral,besidesthesignaldifferencesamongrivers,twomajor
inentinthebluewavebands,thecorrectionoffsetwereconsistently temporalvariationsofwaterρ (λ)wereobserved:seasonal,between
surf
higherforbluebandwhencomparedtogreenandredbands.Fig.4a lowandhighwaterlevelseason,andannual(Fig.5).Althoughdiffer-
depictsMSSandOLIρ (λ)correctedtoρ (λ).Forexample,MSS encesinmagnitudeamongtheriverswereobserved,ρ (VNIR)values
TOA surf surf
andOLIρ (green)werecorrectedfrom8.1and6.2%,respectively, fromthelowwaterseasonwereconsistentlyhigherthanthosefromthe
TOA
toρ (green)of4.6%. highwaterseason,especiallyatredandgreenbands.Intermsofinter-
surf
annualanalysis,aclearpeakofρ inallbandswasobservedbetween
surf
5.4.Spatialandtemporalanalysisofsurfacereflectance 1985and1995fortheeightsitesanalyzed,withtheleasteffecton
Jacareacanga,locatedupstreamofTapajósRiverbeforetheconfluence
Oncethecorrectionforimagerydatabase(LandsatMSS,TM,and withtheheavilyminedCreporiRiver.
OLI)wasvalidated,amulti-temporalanalysisofρ (λ),from1973to FortheJamanximRiver,locatedinasub-basinwithlowminingac-
surf
2013,wasperformedforfourtributaries(Jamanxim,Tocantins,Crepori, tivity,theρ (red)valueswerelowerthan4.0%inbothseasons,buta
surf
andNovorivers)andalongtheTapajósRiver(seeFig.1).Giventhe significantdifference(pb0.05)wasobservedbetweenhigh(1.7%)
coarse spatial resolution of the MSS data (80 m), the Jamanxim, andlow (3.0%) water level seasonsfrom 1984to 2013(Fig. 8).At
Tocantins,andNovoriverswerenotsampledduetosignalinterferences theotherextreme,intheCreporisub-basin,characterizedbyintense
F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184 177
Table3
Differencesbetweendenseforestspectra(ρsurf(λ)),extractedfromhighwaterlevelseasonreferenceimages(L5TMMarch2011)andhistoricalimagesacquiredatthesameseason.SD–
standarddeviation.
Blue Green Red NIR
Referenceimage(Landsat5Mar2011) 1.2±0.2 3.1±0.3 2.0±0.3 27.3±2.3
ρsurf±SD
Orbit Row Date Season Percentagedifferencefromreferenceimage
(xm−xi/xm).100
L5-TM 229 64 04/06/1984 High 59% 9% 0% −8%
L5-TM 228 64 30/05/1985 High 27% 28% 27% 0%
L5-TM 227 64 10/05/1986 High 79% 31% 31% −4%
L5-TM 229 64 09/04/1987 High 63% 25% 28% −2%
L5-TM 228 64 09/03/1990 High −24% 12% −14% 6%
L5-TM 229 64 11/05/1993 High 77% 30% 17% 6%
L5-TM 229 64 19/05/1996 High −6% −1% −25% 4%
L5-TM 228 64 31/05/1997 High −4% −2% 3% −13%
L5-TM 227 64 30/05/1999 High 44% 9% −2% 2%
L5-TM 228 64 10/05/2001 High −27% 18% 28% 2%
L5-TM 228 64 03/04/2005 High 68% 27% 29% −6%
Historicalaverageρsurf±SD 1.0±0.4 2.8±0.5 1.9±0.4 27.2±1.8
miningactivity,theρ (red)valuesincreasedfrom4.0%in1973to 5.5.SpatialandtemporalanalysisofTSS
surf
27.0%in1984,reachingitsmaximumvaluein1993(36.0%),during
lowwaterlevelseason.Althoughvaluesdecreasedinthefollowing TSSconcentrationsweremeasuredat39samplepointsandcorre-
decades, their magnitudes were still high (up to 20%) until 2013. latedwithreflectancederivedfromsatellitesensors(Landsat-5TM
TheTocantinsandNovoriverspresentedsimilarresults,thehighest dataforhighwaterlevelandLISS-IIIdataforlowwaterlevelseason).
of ρ (green) and ρ (red) in the early 1990s. However, for the MeasuredTSSconcentrationswerehigherinthoseriverswithintense
surf surf
TocantinsandNovorivers,arecent(2005–2013)increaseofreflec- gold mining activity, such as the Crepori River. During high water
tancewasobservedforthesamebands,indicatingthatconsiderable level,TSS values of 35.3 mg/l wereobservedin this river,whereas
miningactivityiscurrentlytakingplaceinthesesub-basins. inthelowwaterperiod,concentrationsupto115.2mg/lweremea-
Theanalysisofρ (λ)alongtheTapajósRiver(Jacareacanga,Itaituba, sured.Minimumvaluesaresimilarforbothperiods(Table5).
surf
Aveiro,andSantarém)showedvaluesnothigherthan5%fromtheup- ThebestempiricalcorrelationbetweenTSSandρ (λ)wasgiven
surf
stream(Jacareacanga)tothemouth(Santarém)atthehighwaterperiod byapowerfunction(R2=0.94,RMSE=1.33%,seeFig.6)usingthe
fortheentiretimeseries(1973–2013).Duringthelowwaterperiod, redband.Althoughthecurveisbasedonsatelliteρ (red)upto22%,
surf
however,significantly(pb0.05)highervalues(ρ (green)=5%onav- weassumedthatthisfunctioncanbeextendedtovaluesupto35%,
surf
erage)wereobservedcomparedtothehighwaterlevelseason(3%onav- whichcorrespondstoapproximately300mg/lofTSS.Itisworthnoting
erage).Althoughlowρ (λ)wereobservedupstream(Jacareacanga)at theverysimilarcorrelationofTSSwithinsituρ (red)depictedbythe
surf surf
greenandredbands,aconsiderableincreaseofreflectancewasobserved dashedcurveinFig.6.TheTSSrangeavailableduringfieldworkwas
inItaitubaand,lesspronounced,inAveiro(whichis200kmdownstream onlyupto110mg/l,whichcorrespondstoapproximately24%ofsatel-
fromItaituba),mainlyintheperiodbetween1985and1995.Takinginto litesurfacereflectancedata.Above35%,theempiricalregressioncurve
accounttheriver'snetworkanddischarge,theρ (λ)variationfromup- doesnotprovidereliableTSSestimationbecausetheregression'sconfi-
surf
streamTapajós(Jacareacanga)observedforbothseasonsisinaccordance dencerange(confidencelevel=95%)yieldserrorsupto50%(seecon-
withthespatialvariationofthetributariesshowninFig.5a. fidenceintervalinFig.6).
Table4
Differencesbetweendenseforestspectra(ρsurf(λ)),extractedfromlowwaterlevelseasonmasterimages(LISS-IIISeptember2012)andhistoricalimagesacquiredatthesameseason.
LISS-IIIsensordoesnothaveablueband.SD,standarddeviation.
Blue Green Red NIR
Referenceimage(LISS-IIISept.2012) – 4.6±0.3 3.1±0.2 30.6±1.8
ρsurf±SD
Orbit Row Date Season Percentagedifferencefromreferenceimage
(xm−xi/xm)∙100
L5-TM 228 64 16/09/1984 Low – −8% −9% −11%
L5-TM 227 64 14/10/1985 Low – −2% 8% −20%
L5-TM 228 62 11/10/1987 Low – 1% −1% −8%
L5-TM 229 64 21/09/1989 Low – 37% 37% 9%
L5-TM 229 64 16/09/1993 Low – 17% 24% −4%
L5-TM 229 64 05/08/1995 Low – −8% −18% −5%
L5-TM 228 64 03/08/1997 Low – 12% 18% −4%
L5-TM 227 64 31/08/1998 Low – 11% 7% −2%
L5-TM 229 64 18/08/2000 Low – 26% 25% 3%
L5-TM 229 64 19/10/2005 Low – −11% −28% 0%
L5-TM 229 64 01/08/2011 Low – 35% 11% −6%
Historicalaverageρsurf±SD 2.6±0.9 4.3±0.7 3.0±0.6 31.9±2.7
178 F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184
Basedonthedefinedpower-lawfunction(equationinFig.6),TSS totheinabilityofthephysical-basedapproachtomodelaerosoland
valuesfromthehistoricalLandsatimageryattheeightsitesanalyzed gasesconcentration(Bailey&Werdell,2006;Ruddicketal.,2000),or
wereretrieved(Fig.7).Followingsimilarobservedρ (red)dynamic tounavailabilityofasuitableaerosolmodel(Guanteretal.,2010).The
surf
(Fig.4),TSSexhibitedhigherconcentrationsatlowwaterlevelthanat effectswillbemoreaccentuatedinmoderateconcentrationsofabsorb-
high water level periods (Fig. 7). In the low water level season of ingaerosolsashigherconcentrationsareoftenidentifiedasclouds.
1989,forexample,TSSvaluesofabout301.0mg/lwereestimatedfor AttheNIRregionofthespectrum,thepoorcorrelationbetween
theCreporiRiver.AftertheCreporidischargeintotheTapajósRiver, measuredandcorrectedsatelliteρ (NIR)islikelyduetothelowmag-
surf
thehighTSSismixedwiththerelativelylowTSSwatersoftheTapajós, nitude(upto5%)andtotheadjacencyeffectonthereflectancesignal.
andatapproximately260kmdownstream,theTSSconcentrationde- Thelowmagnitudeandlowvariationofρ (NIR)ismostlydueto
surf
creasedtoabout33.5mg/latItaitubaCityanddownto6.6mg/latthe strong absorption of NIR radiation by water molecules (Mobley,
Santarémarea.Similarvalueswereobservedforthehighwaterseason, 1994).Althoughconsiderableρ (NIR)hasbeenobservedinturbid
surf
butatlowerTSSconcentrations(Fig.7);theCreporiRiverexhibitedTSS waters,asreportedinliterature(Dekkeretal.,2001;Liewetal.,2009;
valuesupto100.0mg/l,andaftertheconfluencewiththeTapajósRiver, Wang & Shi, 2007), the NIR spectrum from clear waters (TSS
TSSvaluesdecreasedto4.4mg/linItaitubaandto2.5mg/linSantarém. b7.0mg/l)shouldbeclosetozero.Giventhesmallwidthofthetribu-
ThespatialandseasonaldynamicisillustratedinFig.8,whichcorre- tariesinthisstudy,theoverestimatedsatelliteρ (NIR)intheclearwa-
surf
spondstotheseasonalρ (red)-basedTSSvariation(equationinFig.6) terscanthusbeattributedmostlytotheadjacencyeffect.Santerand
surf
intheCreporiRiverandTapajósRiver,fromLandsat-8imagesacquired Schmechtig(2000)investigatedthecontributionoftheadjacencyeffect
onJune12th(highwater)andSeptember16th(lowwater)2013.An totheNIRwaterreflectanceinthemiddleofacircularlake.Foralake
evidentincreaseofTSSconcentrationswasobservedfromJune2013 with5kmradius,thesupplementarycontributionofthesurrounding
(TSS=38.0mg/l)toSeptember2013(96.0mg/l).Itisworthnoting reflectancetotheNIRwatersignalwasroughlyhalfofthewatersignal.
thatthesedimentplumeinSeptemberreaches100kmdownstream The adjacency contribution can be even higher at lower distances,
intheTapajósRiveraftertheconfluencewiththeCreporiRiver,atacon- whichisthecaseoftheTapajósRiverBasin,wheresometributaries
centrationofapproximately45.0mg/l,decreasingtoabout15.0mg/lat arenolargerthan110minwidth(e.g.,TocantinsandNovorivers).
theriver'sconfluence(indicatedintheFig.8). TherepresentationoftheadjacencyeffectisminimalonVISbands,be-
causeforestspectrapresentlowρ (upto5%)asopposedtoNIR,
surf
6.Discussion where values are as high as 40%.As such, caution should betaken
whenusingNIRbandstoderiveturbidityfromnarrowrivers,given
6.1.Atmosphericissues thedifficultyindefiningthespatialcontributionoftheadjacencysignal
totheρ (NIR)fromwaterbodies.
surf
Atmosphericcorrectionisakeycomponentpriortotheanalysisof Theglinteffectcorrectionapproachwassuccessful,asshownbythe
satelliteimagerytime-series.However,difficultyrisesduetothelack improvedcorrelationsbetweenmeasuredandsatellitereflectancefor
ofinformationaboutpastatmosphericconditionsatthetimeofimagery the whole spectral range. The improvement was more evident on
acquisition,especiallyforremoteareassuchastheAmazon.Inorderto Landsat-5TM(March2011),whenconsiderablecloudcoverwasob-
overcometheabsenceofinformationaboutatmosphericconditionsre- servedintheseimages.Wesuggestthat,besidestheinfluenceofapos-
quiredasinputtophysical-basedatmosphericmodels,weappliedan sibleglintsignal,theSWIRband(usedinthedeglintingprocess)might
approachthatreliesoninitialvalidationofasetof6Satmospheric- alsocontainatmosphericsignalthatwasnottotallyremovedintheat-
correctedsatelliteimagesacquiredconcurrentlytoinsitureflectance mosphericcorrectionapproach.Becausethe6Scorrectionmethodcon-
data. sidersAOTandwatervaportobehomogenousintheentirescene,this
Theinsitureflectancepresentedcomparablevaluestothosereport- deglintingprocedurecancorrectnon-homogenousatmosphericeffects
ed in literature for Amazonian waters (Barbosa, Novo, Melack, withinasatelliteimage,andassuchprovidesamoreaccurateestimate
Gastil-Buhl,&Filho,2010;Lobo,Novo,Barbosa,&Galvão,2012;Rudorff, ofwaterρ (Kutser,Vahtmäe,Paavel,&Kauer,2013;Wang&Shi,
surf
Galvão,&Novo,2009)whereinsituspectradatausedforvalidationof 2007).Thisisparticularlyimportanttoremotesensingstudiesinthe
thesatelliteimagespresentedhighvariabilityathighmagnitudevalues Amazon,wherehighhumidityandhighcloudcoverintroducesatmo-
(seeFig.3).Thisislikelyduetosignal-to-noiseratioreductionrelatedto sphericvariabilitywithinthesamescene(Luetal.,2002).
measuring under-water radiance in high TSS conditions (Reinart,
Paavel,Pierson,&Strombeck,2004;Sunetal.,2009).Inhighturbidity 6.2.Multi-temporalanalysisofρ (λ)andTSSintheTapajósRiverBasin
surf
Amazonianwaterconditions,theunder-waterlightisintensivelyatten-
uatedinthevisiblespectrum,reducingsignal-to-noiseratioandin- TheresultsofthecorrectionsperformedinhistoricalLandsatimag-
creasingdatavariability.Costaetal.(2013),forinstance,haveshown ery (1973–2013) demonstrated that forest spectra from historical
thatsediment-richwaters(TSS=138.8mg/l)presentadiffuseattenu- Landsat-5TM(1984–2011)behavedasaninvariantspectralfeature
ationcoefficient(K )fivetimeshigher(15.3m−1)thaninaclear overdecades,consideringtheseasonality.Therefore,anyρ (λ)varia-
d(blue) surf
waterriver(TSS=4.1mg/l;K =3.0m−1). tioninspectraextractedfromwaterbodiescanbeattributedmostlyto
d(blue)
Despitethevariabilityofinsitureflectance,highcorrelationsbe- variationinopticalconstituentsinthewaterandnottoenvironmental
tweeninsituandsatelliteρ atthegreenandredbandsindicate conditions,suchasatmosphericandglinteffects.Accordingtothevali-
surf
thattheatmosphericcorrectionapproachwassuccessful.Greenand dationprocedure(Section5.1),thecertaintyofcorrectedρ ishigher
surf
redbandsalsocorrespondtothespectrumwheresuspendedsolids fortheredandgreenbandscomparedtotheblueandNIR,wheresatel-
presentahighscatteringproperty,whichincreasesthesignal-to-noise liteρ areovervalued.
surf
ratioofthesignaldetectedbyremotesensors.Ontheotherhand,for Themagnitudeandspectralshapeofρ (λ)derivedinthisstudyis
surf
blueandNIRbandstheatmosphericcorrectionresultedinageneral similartootherstudiesintheAmazonianwaters(Loboetal.,2012;
overestimationofρ whencomparedtoinsituρ .Theweakcorre- Rudorffetal.,2009).Ingeneral,increasingTSSconcentrationupto
surf surf
lationbetweenmeasuredandsatelliteρ (blue)suggeststhattheat- 50.0mg/laffectsprimarilythegreenandredwavebands.Undervery
surf
mosphericmethoddidnotproperlymodeltheeffectsofaerosoland high turbidity conditions (TSS N100.0mg/l), the light scattered by
gasesscattering(Guanteretal.,2010)andabsorptioneffectsbynitro- suspendedparticlesalsoconsiderablyaffectstheNIRspectrumregion.
gen dioxide (Gao, Montes, Davis, & Goetz, 2009; O'Neill & Costa, Forinstance,spectraderivedfromsediment-richwatersinthisstudy
2013).Studiesthatvalidatedatmosphericcorrectionwithinsituradio- (TSSupto110.0mg/l)presentvalues(upto23.1%atredband)inac-
metricmeasurementsattributedtheerrorsatbluewavelengthseither cordancewithotherstudiesintheAmazonRiverfloodplain(Barbosa
F.L.Loboetal./RemoteSensingofEnvironment157(2015)170–184 179
etal.,2010;Mertesetal.,1993;Rudorffetal.,2009).Theobservedre- mattertothewaterisdeforestation(Neilletal.,2011).However,webe-
flectancevaluesarelikelyassociatedwiththeoriginoftheinorganic lievethatthepossibleincreaseinTSScausedbydeforestationisnotat
particles.TSSderivedfromminingactivitiesintheTapajósiscomposed thesamemagnitudeastheincreaseinTSScausedbyminingactivity.
mostlyofsilt/clayparticles(Telmer&Stapper,2007),whichinturnare Forexample,theJamanximRiver,whichischaracterizedbyhighly
more efficient at scattering light than organic and sand particles deforestedareasbutwithlowminingactivityinthesub-basinarea
(Gordonetal.,2009).Arecentlaboratoryinvestigationonthescattering (seeFig.1),presentslowρ (λ)thataresimilartothosefromTapajós
surf
propertiesoftwosuspendedparticlesizeshasshownthatclay/siltpar- upstream(Jacareacanga),eveninthelowwaterlevelseasonwhenmin-
ticlesare40%moreefficientinscatteringlightthanmedium-sizedsand ingactivitycanbemoreintense.Wewouldliketohighlightthatthenat-
particlesat660nm(Bowers&Binding,2006;Loboetal.,2014).Asare- uralclearwaterconditionfacilitatesthedetectionofsedimentplume
sult,ahighρ (red)isexpectedfromclay-richwaterthanfromsand- causedbygoldmining.Thesamerationaleislikelynotvalidforwhite
surf
richwaters,forexample. water(muddywater)rivers,suchastheMadeiraRiverintheAmazon.
Theimagery-derived surface reflectancefrom theTapajós River BecausetheMadeiraRiverisnaturallyveryturbidasaconsequenceof
Basinsuggeststhatitsvariabilityismainlyaffectedbythescattering itsdrainageintheAndes,detectionofgoldminingtailingsintothe
propertiesofsuspendedsolidsthatvaryseasonallyandarealsoin- riveris likely amoredifficult task when comparing to theTapajós
creasedduetominingactivities.Anotherpossiblesourceofinorganic clearwatersystem.
Fig.5.Seasonalandinter-annualvariationofρsurf(VNIR)bandsinfoursub-basins:Jamanxim,Novo,Tocantins,andCreporirivers;andalongtheTapajósRiver:Jacareacanga,Itaituba,
Aveiro,andSantarémcitiesusedforlocationreference(seeFig.1).Theρsurfvariesinthecolorpalettefrom0(blue)upto40%.Theverticaldashedlinesrepresenttheavailabledata
usedforplotting.Yearswithnodataavailablearerepresentedbythedashedbar.