This document discusses remote sensing methods to classify terrestrial habitats in the Congo Basin Coast region of Gabon, Africa. It used Landsat 8 imagery to classify critical habitats like mangroves, water, forests, wetlands and savannah. Maximum likelihood classification of Landsat 8 bands 1-7 performed best for most habitats, while classification tree analysis of bands 4, 5, and 6 was best for mangroves. The work aims to increase understanding of land cover distributions to inform conservation efforts in the region.

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Remote&Sensing&of#Terrestrial)Habitats)!
in!the!
Congo%Basin%Coast"
"
Tim"Liponis†
,"Emily"Sturdivant°,"Ryan"Williams†
,""
"
°Graduate(School(of(Geography,(Clark(University;(†
GIS(for(Development(and(the(
Environment,(Department(of(IDCE,(Clark(University(
"
Abstract!
The(Gabonese(Coast(represents(a(diverse(ecological(and(physical(environment,(in(which(dense(
coastal(tropical(forest(intersects(with(alluvial(plains(containing(estuaries,(mudflats,(swampy(
forest(and(savanna.(While(the(885(km(coastline(is(home(to(large(concentrations(of(leatherback(
turtles,(humpback(whales,(and(elephants,(anthropogenic(influences,(including(overfishing,(
deforestation(and(offshore(resource(extraction,(threaten(the(stability(of(this(incredibly(diverse(
environment.((
Conservation(efforts(must(be(informed(by(a(comprehensive(understanding(of(ecosystem(
distributions(and(interactions.(The(study(utilized(Landsat(8(imagery(in(conjunction(with(other(
auxiliary(data(to(classify(and(map(the(coastal(terrestrial(ecosystem.(In(our(classification(efforts,(
we(evaluated(maximum(likelihood,(multiJlayer(perceptron((MLP),(and(classification(tree(analysis(
(CTA).(Ultimately(a(maximum(likelihood(classification(with(Landsat(8(bands(1J7(performed(best(
to(classify(the(landscape,(with(the(exception(of(mangroves,(which(were(best(classified(using(CTA(
on(subset(images(of(Landsat(8(bands(4,5,(and(6.(SaltJandJpepper(classification(errors(were(
mitigated(using(a(3x3(mode(filter(for(all(final(classifications.(This(work(is(intended(to(increase(
understanding(of(the(spatial(distribution(of(land(covers(in(the(Congo(Basin(Coast(and(to(inform(
conservation(efforts(using(multiJspectral(medium(resolution(imagery.((
" "

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1.!Background!
Gabon,"located"along"the"central"western"coast"of"Africa,"contains"a"mosaic"of"landscapes"
including"coastal"beaches"and"dunes,"coastal"scrub,"wetlands,"marshes,"swamps,"lakes,"lagoons,"
mangrove"forests,"littoral"forest,"secondary"forest,"and"savannahs"(Lee"et"al.,"2006)."This"habitat"
mosaic"is"home"to"around"6,000J10,000"plant"species"(Letouzey,"1968),"198"mammal"species"
(Emmons"et"al.,"1983),"680"bird"species,"98"reptile"species"(Burger"et"al.,"2006),"and"95J160"fish"
species"(Pauwels"et"al.,"2006)."Gabon"shelters"several"threatened"species,"including"the"African"
forest"elephant,"western"lowland"gorilla,"chimpanzee,"hippopotamus,"leatherback"turtle,"and"
manatee"(IUCN"2013)."The"coastal"zone,"known"as"the"Congo"Basin"Coast,"is"particularly"
important"for"these"species,"such"as"leatherback"turtles"that"nest"on"the"pristine"beaches"and"
manatees"that"shelter"in"mangroves"rimming"the"many"coastal"lagoons"and"estuaries"(Witt"et"
al.,"2009).""
Existing"biodiversity"faces"many"threats"as"a"result"of"increasing"resource"extraction."
Specifically,"major"threats"include"illegal"hunting,"illegal"inJshore"and"offJshore"fishing,"logging,"
mining,"agriculture"expansion,"and"oil"extraction"(Walsh"and"White,"1999;"WCS,"2010)."The"lack"
of"robust"land"use"planning"and"sustainable"management"strategies,"which"have"the"potential"
to"alleviate"pressures"of"resource"extraction,"indirectly"threatens"the"Congo"Basin"Coast’s"
biodiversity"(WCS,"2010)."Current"conservation"planning"objectives"at"the"Wildlife"Conservation"
Society"(WCS)"include:"expansion"of"coastal"protected"areas,"stabilize"and"increase"populations"
of"hippopotamus,"crocodiles,"Atlantic"humpback"dolphins,"and"manatees,"identify"and"protect"
leatherback"turtle"nesting"areas,"and"the"protection"of"offJshore"humpback"whale"breeding"
waters"in"accordance"with"International"Whaling"Commission"targets.""To"successfully"meet"
these"goals,"WCS"has"highlighted"the"need"to"strengthen"and"connect"protected"areas"along"the"
coast,"stop"unsustainable"resource"extraction,"and"support"local"authorities"and"private"citizens"
to"consider"conservation"in"planning"and"development"strategies"(WCS,"2010)."
Knowledge"of"the"extent"and"distribution"of"habitats"facilitates"the"majority"of"these"
conservation"objectives."Namely,"to"track"critical"habitats,"establish"conservation"priority"areas,"

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and"communicate"threats."An"effective"means"of"largeJscale"land"cover"monitoring"is"the"
classification"of"remote"sensed"imagery,"especially"in"partnership"with"ground"surveys."With"the"
recent"launch"of"Landsat"8"in"February"2013,"NASA"ensured"the"continuity"of"a"global"imagery"
dataset"crucial"to"the"success"of"these"monitoring"efforts"at"the"landscape."This"study"uses"
Landsat"8"imagery"to"explore"and"use"remote"sensing"methods"for"the"classification"of"critical"
habitats"in"the"region,"including"mangroves,"water,"terrestrial"forest,"wetland,"savannah,"and"
soil.""In"accordance"with"the"conservation"efforts"focused"on"mangroves,"classification"efforts"
will"focus"on"the"ability"of"Landsat"8"to"accurately"classify"mangroves"and"terrestrial"habitats"
and"document"and"document"any"challenges"and"solutions"that"are"encountered.""
1.1!Study!Area"
The"study"area"is"located"primarily"in"the"province"Nyanga,"a"narrow"alluvial"plain"in"the"
southwestern"Gabonese"coast"(Figure"1)."Covering"9093"km2
,"the"study"area"encompasses"
mangrove"forests,"dense"tropical"forests,"savannahs"and"grasslands,"wetlands"and"coastal"
beaches."During"the"wet"season,"high"temperatures"in"Nyanga"average"29.3°C,"while"lows"
during"the"dry"season"average"20.3°C."Average"annual"precipitation"in"the"region"is"175"cm,"
most"of"which"occurs"during"the"two"rainy"seasons"from"January"to"May"and"October"to"
December."Little"precipitation"occurs"during"the"cold"dry"season"from"JuneJSeptember"and"the"
little"dry"season"from"December"to"January"(FAO,"1990)."Vegetation"phenology"responds"
strongly"to"the"dual"seasonality,"particularly"in"savannah"habitats"(Mayaux"et"al."2000).""
The"study"area"represents"a"region"of"important"conservation,"as"there"are"several"protected"
areas"contained"or"in"close"proximity"to"the"study"area."Mayumba"National"Park,"located"in"the"
southeasternJmost"corner"of"the"study"area,"is"a"maritime"protected"area,"critical"for"both"
nesting"leatherback"turtles"and"migratory"humpback"whales."The"dense"tropical"forests"of"
MoukalabaJDoudou"National"Park,"in"the"center"of"the"study"region,"host"populations"of"the"
critically"endangered"western"lowland"gorilla."MoukalabaJDoudou"is"part"of"the"Gamba"
Complex"of"protected"areas,"which"includes"Loango"National"Park"and"other"sustainable"forest"

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concessions."Conservation"efforts"in"the"region"have"focused"on"the"Gamba"Complex"due"to"
habitats’"high"diversity"and"the"threat"of"oil"production,"logging,"and"illegal"bush"meat"hunting."
1.2!Literature!Review!"
Globally,"rapid"anthropogenic"ecosystem"conversion"has"led"to"increased"need"and"interest"in"
understanding"these"changes"at"broader"scales"only"measurable"from"space."(Gillespie"et"al."
2008)."This"is"also"true"for"onJ"and"offJshore"areas"near"the"fringes"of"the"planet’s"second"
largest"contiguous"rainforest"in"the"Congo"Basin"(Mayaux"et"al."2000)."While"remote"sensing"
cannot"to"replace"field"research"on"the"ground,"it"can"characterize"habitats"at"broader"scales"in"
terms"of"productivity,"disturbance,"topography,"and"land"cover"(Duro"et"al,"2007)."Remote"
sensing"data"can"become"especially"robust"when"multiple"sensors"are"combined"to"gain"a"multiJ
sensory"understanding"of"how"different"biodiversity"indicators"(NDVI,"temperature,"
geomorphology,"spectral"signature,"and"temporal"signature)"behave"in"concert"through"space"
and"time"(Gillespie"et"al."2008).""
A"variety"of"different"supervised"classification"techniques"and"datasets"have"been"used"to"
identify"tropical"terrestrial"habitats."The"normalized"difference"vegetation"index"(NDVI)"and"the"
Enhanced"Vegetation"Index"(EVI)"have"proven"especially"useful"for"connecting"spectral"
information"to"ground"measurements"of"species"richness"in"forest"ecosystems"(Duro"et"al."
2007)."MultiJdate"monitoring"of"forest"loss"is"also"possible"if"MODIS"and"Landsat"data"are"
integrated"systematically"(Hansen"et"al."2007)."Classification"tree"analysis"using"both"spectral"
and"topographical"datasets"was"useful"for"mapping"wetland"habitats"of"the"Congo"Basin"
(Bwangoy"et"al."2010)."Finally,"contextual"information,"freely"available"medium"and"high"
resolution"imagery,"and"field"knowledge"of"ground"level"ecosystem"functioning"has"proven"
effective"in"the"past"for"selecting"training"sites"and"creating"a"classification"tree"analysis"
(Helmer,"2013)."This"study"approaches"the"selection"of"training"sites"similarly"and"incorporates"
ancillary"datasets"and"contextual"knowledge"of"habitat"types."""
For"mangroves,"many"techniques"are"documented"using"medium"resolution"satellite"imagery."
Mangrove"classification"has"been"growing"dramatically"internationally"in"remote"sensing"

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research"of"the"last"decade"due"to"a"lack"of"field"data"in"inaccessible"locations,"the"increased"
awareness"of"the"economic"and"ecological"important"of"their"function,"their"rapid"destruction"
globally,"and"their"sensitivity"to"climate"change"(Kuenzer"et"al.,"2011)."In"terms"of"methods,"the"
maximum"likelihood"classifier"is"described"as"the"“most"effective"and"robust”"especially"when"
multiple"methodologies"and"datasets"are"incorporated"(Kuenzer"et"al.,"2011)."For"instance,"
band"principal"components"(Green"et."al;"Binh"et"al."Kovacs"et"al.;"Béland"et"al),"tasseled"cap"
transformations,"and"objectJbased"classifications"(Myint"et"al."2008)"have"all"proven"effective"
tools"in"classifications."Additionally,"a"mangrove"recognition"index"incorporating"the"tasseled"
cap"transformation"bands"of"greenness"and"wetness"was"developed"to"take"advantage"of"
mangrove"sensitivity"to"water"content"changes"at"differing"levels"of"tide."However,"it"was"
recognized"that"there"is"great"difficulty"in"finding"multiJdate"cloud"free"imagery"in"tropical"areas"
where"mangroves"are"typically"found"(Zhang"and"Tian,"2012).""
The"USGS"conducted"a"global"mangrove"classification"using"1000"Landsat"images"from"1997"to"
2000,"converted"to"topJofJatmosphere"reflectance."The"images,"along"with"ground"truth"data,"
existing"maps,"and"databases"were"used"in"hybrid"supervised"and"unsupervised"classification"
techniques."They"validated"the"results"with"existing"GIS"data"and"published"literature"(Giri"et"al."
2011)."
Jones"et"al."(2012)"used"both"unsupervised"and"supervised"classification"on"Landsat"ETM+"
bands"1J5"and"7"to"identify"mangroves"and"other"vegetation"classes"in"Madagascar."They"
isolated"the"analysis"to"within"7"km"of"the"coast"and"30"m"of"sea"level."Observations"of"the"
spectra"revealed"that"NIR"&"SWIR"are"especially"useful"for"mangrove"classes."SWIR"
differentiated"mangroves"from"terrestrial"vegetation."Unsupervised"classification"grouped"
pixels"into"similar"spectral"classes,"which"were"used"to"remove"water"and"shadow"pixels."
Green"et"al."(1998)"in"their"efforts"to"explore"methods"for"mangrove"mapping"in"Turks"and"
Caicos"in"the"Caribbean,"utilized"band"ratios"as"part"of"a"principal"components"analysis"(PCA)."
The"spectral"distinction"of"mangroves"in"Landsat"TM"bands"3"(Red),"4"(NIR)"and"5"(SWIR)"were"
exploited"with"two"band"ratios"of"3/5"and"5/4."The"same"bands"were"used"for"visual"

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interpretation"and"discrimination"of"mangroves"and"nonJmangroves."Consistent"with"other"
literature"(Gray"et"al."1990),"the"band"ratios"of"3/5"and"5/4"were"used"with"Red,"NIR"and"SWIR"
bands"as"inputs"to"the"PCA."The"resulting"first,"second,"and"fourth"principal"components"
summarized"more"than"95%"of"the"data"variance"and"were"used"in"a"maximum"likelihood"
classification"to"determine"extent"of"mangroves"in"the"Caribbean."Using"the"PCA"and"band"
ratios,"Green"et"al."(1998)"achieved"an"accuracy"of"92%."Similarly,"our"study"will"focus"on"
exploring"Red,"NIR,"and"SWIR,"and"PCA"band"ratios"and"combinations"to"tease"out"the"
mangrove’s"unique"spectral"properties.""
!
2.!Methods!!
2.1!Data!
One"Landsat"8"image"was"selected"from"the"two"dozen"available"for"the"coastal"region"based"on"
the"possibility"of"extracting"data"despite"the"presence"of"haze"and"clouds"that"are"prevalent"in"
all"images"of"the"region."The"selected"image"was"acquired"on"July"17th,"2013"at"Landsat"tile"
(185,"62)."The"names"and"characteristics"of"all"ancillary"data"used"to"assist"classification"efforts"
are"presented"in"Table"1."They"include"ASTER"GDEM"for"elevation,"UNEP"polygons"of"mangrove"
and"wetland"distributions,"Madagascar"land"cover"spectra"provided"by"Dr."Trevor"Jones,"and"
publically"available"highJresolution"imager"and"user"photos."Flash"Earth"imagery,"available"at"
flashearth.com,"uses"high"spatial"resolution"orthographic"photos"from"a"collection"of"sources."
GeoJEye"imagery"is"available"from"DigitalGlobe."UserJsubmitted"geoJlocated"photography"via"
Panaramio"(www.panoramio.com).""
2.2!Cloud!Masking!
We"used"an"ISO"Cluster"unsupervised"classification"(ISOCLUST"in"Idrisi)"with"3"iterations"on"
bands"1J7"to"derive"20"classes."Operators"visually"examined"the"resulting"clusters"in"conjunction"
with"the"original"imagery"and"ancillary"data"to"determine"which"clusters"represented"clouds"in"

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the"image."The"resulting"12"cloud"classes"were"removed"from"the"imagery"by"creating"a"
Boolean"cloud"mask."See"Figure"2"for"before"and"after"imagery"of"cloud"masking"technique.""
2.3!Classification!!
Two"different"classification"methods"were"developed"specifically"for"mangrove"and"nonJ
mangrove"vegetation."The"final"land"cover"classification"for"the"entire"Landsat"scene"was"
produced"by"combining"the"final"mangrove"classification"with"the"final"nonJmangrove"
classification."See"Figure"3"for"full"methodology"flowchart"for"classification"methods.""
NonJmangrove(classification:(
We"evaluated"the"spectral"separability"of"the"landscape"with"an"ISO"Cluster"unsupervised"
classification"to"10"classes"from"the"cloudJmasked"imagery."We"manually"digitized"training"sites"
for"8"classes"(water,"forest,"forest"shadow,"wetland,"wetland"shadow,"secondary"agricultural"
complex,"beach/bright"soil,"medium"soil,"dark"soil,"savannah,"and"savannah"shadow)"based"on"
exploration"of"the"imagery"and"comparison"with"ancillary"data."Forest"and"wetland"classes"were"
oversampled"in"order"to"capture"the"spectral"heterogeneity"of"these"spectrally"similar"classes."
We"extracted"the"spectral"signatures"of"the"training"sites"for"a"maximum"likelihood"
classification."See"Table"2"for"each"nonJmangrove"class"and"the"number"of"pixels"collected"for"
training"the"classifier.""
Mangrove(classification:(
The"Landsat"scene"was"subset"to"focus"classification"efforts"on"mangroves."Image"subset"
boundaries"were"determined"based"on"spatial,"spectral,"and"environmental"gradients."This"
resulted"in"northern"and"southern"image"subsets"focused"on"two"coastal"waterbodies"where"
USGS"and"World"Atlas"indicated"mangrove"presence."See"Figure(4"for"methods"flowchart"for"
Mangrove"classification."The"following"method"was"applied"for"each"of"the"two"image"subsets:"
Similar"to"the"initial"unsupervised"classification,"an"ISO"cluster"classification"of"10"classes"
automatically"delineated"spectrally"similar"land"covers."The"resulting"image"classification"

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included"mangrove"and"nonJmangrove"vegetation"classes."We"selected"only"those"classes"with"
a"broadly"defined"possibility"for"mangroves"and"masked"the"subset"to"these"pixels."We"were"
careful"not"to"mask"out"any"vegetation"unless"the"class"had"no"proximity"to"water,"to"reduce"
the"chance"of"commission"errors.""
We"distributed"manual"training"sites"geographically"and"spectrally"across"historical"mangrove"
areas"(USGS,"World"Atlas)"to"ensure"a"comprehensive"representation"of"the"mangrove"
signature."We"created"training"sites"for"three"classes:"mangroves,"nonJmangrove"vegetation,"
and"nonJvegetation."The"combination"of"these"UNEP"2000"mangrove"classifications"and"visual"
inspection"of"a"Red,"NIR"and"SWIR"(Landsat"8"bands"4J6)"composite"provided"confirmation"for"
training"site"development."After"conducting"classification"analyses"with"these"training"sites,"
polygons"were"revised"to"address"omission"and"commission"errors.""
The"classification"employed"a"classification"tree"analysis"(CTA)"with"a"Gini"split"rule"on"bands"4J
6."The"output"of"the"initial"CTA"on"bands"4J6"was"used"to"refine"the"training"sites."After"final"
classifications"were"established,"the"resulting"output"of"both"mangrove"and"nonJmangrove"
classifications"were"smoothed"with"a"3x3"modal"filter"to"reduce"noise"in"the"classification.""
!
3.!Results!and!Discussion"
3.1!Cloud!Masking!!
Traditionally"atmospheric"correction"is"performed"to"convert"raw"satellite"DN"to"atJsurface"
reflectance,"thereby"reducing"the"influence"of"atmospheric"scattering"on"radiance"values."
However,"due"to"the"extreme"heterogeneity"of"cloud"cover,"atmospheric"correction"was"
ineffective"in"removing"haze"within"the"scene."Therefore,"no"atmospheric"correction"was"
performed"in"order"to"maximize"the"available"data"for"analysis.""
Cloud"must"be"eliminated"from"the"imagery"to"improve"the"variation"of"land"cover"data."We"
attempted"three"unsuccessful"methods"to"identify"clouds."The"QA"band"is"a"categorical"image"
coded"based"on"the"likelihood"of"clouds,"cirrus,"snow/ice,"vegetation"or"water"presence"in"the"

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grid"cell"so"clouds"are"selected"using"the"code"for"all"types"of"clouds"in"the"imagery""
(landsat.usgs.gov/L8QualityAssessmentBand.php)."The"Mahalanobis"typicality"technique"
determines"the"probability"of"cloud"for"every"pixel"using"manual"digitization"of"training"sites"
from"a"visual"assessment."Level"slicing"condenses"data"values"to"a"userJspecified"amount"of"
discrete"bins,"to"which"clouds"can"be"estimated"using"a"threshold."
The"Quality"Assessment"(QA)"band,"Mahalanobis"typicalities,"and"level"slicing"all"performed"
poorly,"due"to"the"variety"of"cloud"types"in"the"scene."Ultimately,"the"ISO"Cluster"technique"best(
accounted"for"cloud"heterogeneity."Unsupervised"classification"uses"an"iterative"selfJorganizing"(ISO)"
approach"to"develop"automatically"identify"classes"of"spectrally"similar"pixels."The"algorithm"finds"
classes"by"maximizing"the"spectral"Euclidean"distance"between"to"create"better"spectral"separability."Of"
the"band"combinations"evaluated,"Landsat"8"bands"1J7"proved"most"effective."The"coastal"blue"
band"(Band"1)"was"especially"helpful"in"classifying"clouds"over"water."See"Figure"5"for"full"
methodologies"tested"for"cloud"masking"and"image"preprocessing.""
3.2!Classification!
Mangrove(vs.(NonJmangrove(classification(
In"the"image,"there"is"little"spectral"distinction"between"mangrove"and"terrestrial"forest"classes"
most"likely"due"to"the"timing"of"image"acquisition"during"the"dry"season."Since"no"field"data"was"
available,"classification"methods"were"dependent"solely"upon"the"userJinterpretation"of"
imagery"and"spectral"characteristics"of"the"land"covers."In"an"effort"to"better"distinguish"
mangrove"and"nonJmangrove"vegetation,"the"Landsat"scene"was"subset"to"focus"classification"
efforts"on"mangroves."Image"subset"boundaries"were"determined"based"upon"historical"
mangrove"classifications"indicated"by"UNEP"polygons."It"is"hypothesized"that"the"separation"of"
nonJmangrove"and"mangrove"vegetation"into"different"image"subsets"allowed"for"two"distinct"
classification"methodologies"unique"to"classes"of"interest"(see"Figures"3"and"4)."Therefore"two"
different"classification"methods"were"developed"specifically"for"mangrove"and"nonJmangrove"
vegetation."The"final"land"cover"classification"for"the"entire"Landsat"scene"was"determined"by"

10. 10"
"
combining"the"final"mangrove"classification"with"the"final"nonJmangrove"classification."See"
Figure"3"for"full"methodology"flowchart"for"classification.""
NonJmangrove(classification(and(validation(
Unsupervised"classification"is"appropriate"for"general"exploratory"purposes,"but"does"not"
accurately"categorize"land"covers"that"are"spectrally"similar."Understanding"Gabon’s"prominent"
habitats"types"and"manually"digitizing"suspected"locations"facilitated"scene"characterization"and"
successful"classification."To"manually"delineate"these"training"sites"and"to"assess"feasibility"of"
training"site"locations,"we"compared"highJresolution"publically"available"imagery"from"Google"
Earth"and"Flash"Earth,"geoJlocated"userJsubmitted"photography"from"Panaramio,"Landsat"8"
imagery,"Landsat"8"derived"vegetation"indices,"and"ASTER"DEM"imagery."The"spectral"signatures"
for"these"training"sites"were"assessed"by"comparing"the"spectral"data"collected"in"the"field"for"
similar"classes"in"Madagascar"(Jones"et"al.,"2014),"as"seen"in"Figures"6"and"7.""
Maximum"likelihood"was"most"effective"of"the"three"tested"classifiers."As"a"result"of"the"
spectral"similarity"of"training"sites,"multiJlayered"perceptron"(MLP)"both"overJclassified"
wetlands"and"soils"and"underJclassified"forests."Classification"Tree"Analysis"(CTA)"was"also"
unable"to"split"the"training"sites"spectral"data."Maximum"likelihood"supervised"classification"was"
most"effective"at"discriminating"between"spectrally"similar"classes"and"producing"classification"
imagery"consistent"with"highJresolution"imagery"and"existing"USGS"and"World"Atlas"datasets"
focused"on"wetlands"and"mangroves."
Mangrove(classification(
Preliminary"attempts"to"classify"mangroves"from"the"entire"scene"were"unsuccessful,"in"part"
because"mangroves"have"small"spatial"extent"in"the"scene"and"because"they"can"be"spectrally"
similar"to"upland"vegetation"or"shadowed"forest,"depending"on"environmental"conditions."
These"characteristics"prevent"a"classification"of"mangroves"for"the"entire"image."As"described"
previously,"the"image"subset"technique"successfully"classified"mangroves"over"a"smaller"extent"
with"a"higher"expected"proportion"of"mangroves"than"in"the"entire"extent.""

11. 11"
"
The"classification"process"explored"both"the"maximum"likelihood"classifier"and"classification"
tree"analysis"(CTA)."For"maximum"likelihood,"the"following"band"combinations"were"assessed:"
1J7,"4J7,"and"4J6."CTA"incorporates"a"broader"range"of"data"to"inform"its"nonJparametric"
classification."CTA"with"a"Gini"split"rule"was"used"with"the"following"input"combinations:"bands"
1J7,"4J7,"4J6,"4J6,"the"wetness"index"(NDWI),"and"a"combination"of"first"three"principal"
components"of"a"principle"components"analysis"(PCA)."The"PCA"inputs"included"band"ratio"of"
Red/SWIR"(4:6)"and"SWIR/NIR"(6:5)"with"bands"4J6,"as"had"been"previously"used"by"Green"et"al."
(1998)"to"classify"mangroves."For"both"maximum"likelihood"and"CTA"classification"methods,"
Landsat"8"bands"4J6"produced"the"best"mangrove"classification"results."When"maximum"
likelihood"was"compared"with"CTA,"CTA"appeared"more"biophysically"reasonable"and"better"
aligned"with"the"USGS"mangrove"polygons"than"the"maximum"likelihood"with"the"same"bands.""
"
In"regard"to"postJclassification"efforts"to"smooth"the"image,"two"modal"filter"sizes,"3x3"and"5x5,"
were"evaluated"in"their"ability"to"effectively"clean"'salt"and"pepper'"noise,"which"are"sparsely"
occurring"pixels"within"the"image."The"3"x"3"modal"filter"best"eliminated"false"mangrove"
classification"without"overJsimplifying"the"output"(Figure"8).""
"
Final(classification(
The"resulting"land"cover"classifications"include"inland"lagoons"and"estuaries"(303"km2
),"
terrestrial"forest"(4088"km2
),"wetlands"(1111"km2
),"Secondary"agricultural"complex(200"km2),"
beach/bright"soil"(597"km2
),"medium"soil"(13"km2
),"dark"soil"(78"km2
),"grassland"savannah"(256"
km2
),"and"mangroves"(10"km2
)."The"spatial"distribution,"quantity,"and"arrangement"were"
plausible"given"our"understanding"of"the"ecosystem.""This"is"understood"by"the"complex"
heterogeneity"of"the"landscape"(Figure"11)."This"was"especially"true"of"the"secondary"
agricultural"complex"class,"which"the"field"literature"indicated"clustered"around"road"networks"
and"surround"savannah"and"wetland"patches"(Figure"9)."Further,"Grassland/savannah"classes"
were"interspersed"with"soil"classes"and"followed"a"pattern"of"typical"of"the"dry"season"field"
descriptions"of"the"class"(Mayaux"et"al."2000)"(Figure"10)."(

12. 12"
"
4.!Conclusions!
The"following"methods"were"found"to"be"most"effective"for"masking"heterogeneous"clouds:"
1. ISO"Cluster"unsupervised"classification"with"about"20"classes."
2. Reclassification"of"the"cloud"clusters"to"a"Boolean"mask,"which"is"applied"to"the"imagery."
The"following"methods"were"found"to"be"most"effective"for"classifying"nonJmangrove"terrestrial"
central"African"tropical"forests:"
1. Create"training"sites"based"on"operator’s"thorough"inspections"of"key"band"composites"
and"indices"(RJGJB,"RJNIRJSWIR,"NDVI,"EVI),"highJresolution"imagery"(if"the"area"has"not"
experienced"rapid"land"cover"change),"and"geoJlocated"photographs."Compare"the"
resulting"spectral"signatures"of"training"sites"to"signatures"measured"in"the"field"or"in"
similar"investigations"from"other"locations."Oversampling"for"spectrally"similar"classes"
helps"the"classifier"to"discriminate.""
2. Run"a"maximum"likelihood"classification"using"the"first"7"bands"of"Landsat"8"(Coastal"
blueJ"SWIR2).""Iteration"of"the"maximum"likelihood"classification"is"critical"to"improving"
training"sites"and"therefore"classification"results.""
3. When"the"classification"produces"a"satisfactory"output"based"on"a"visual"assessment"of"
agreement"between"the"classification"and"ancillary"data,"use"a"3"x"3"mode"filter"to"
remove"saltJandJpepper"in"the"classification.""
"
If"mangroves"exist"in"small"patches"or"are"sparsely"distributed"in"the"scene,"subset"the"scene"
based"on"best"knowledge"of"mangrove"presence"and"spatial"and"biophysical"differences."Follow"
the"next"steps"for"developing"a"mangrove"classification"for"each"mangrove"subset.""The"
following"methods"were"found"to"be"most"effective"for"classifying"mangroves"without"verified"
ground"data:""
"

13. 13"
"
1. Run"an"unsupervised"classification"with"10"classes."Ten"classes"provide"enough"spectral"
distinction"to"accurately"determine"mangrove"clusters"and"nonJmangrove"clusters."Mask"
the"subset"to"all"classes"with"even"a"slight"chance"of"including"mangroves."The"classes"to"
eliminate"will"tend"to"include"soils,"cloud"edges,"and"water.""
2. Create"training"sites"for"mangroves"and"nonJmangrove"classes."Use"the"operator’s"best"
judgment,"integrating"historic"mangrove"classifications"(USGS,"World"Atlas),"available"
ground"photos,"any"highJresolution"imagery,"and"a"RedJNIRJSWIR"composite"to"
manually"digitize"training"site"polygons."When"selecting"mangrove"areas,"only"select"
areas"that"are"definitively"mangroves"and"attempt"to"geographically"disperse"the"
selection"among"all"areas"where"mangrove"locations"are"near"proven.""
3. Generate"a"classification"tree"from"the"training"sites"for"Landsat"8"bands"4"(Red),"5"(NIR),"
and"6"(SWIR)"and"run"the"classification."The"resulting"classification"is"prone"to"confusion"
between"mangroves"and"terrestrial"forest,"or"wetland"forests"that"spectrally"resemble"
mangroves."Therefore"it"is"important"to"consider"mangrove"classification"an"iterative"
process"and"to"use"the"information"from"the"classification"tree"analysis"to"inform"the"
next"iteration"of"training"sites."Digitize"additional"training"sites"to"reduce"the"ambiguity"
of"areas"with"clear"classification"errors.""
4. Rerun"the"classification"tree"analysis"with"the"new"training"sites.""
5. When"the"classification"produces"a"satisfactory"output,"based"on"a"visual"assessment"of"
agreement"between"the"classification"and"ancillary"data,"use"a"3"x"3"mode"filter"to"
remove"saltJandJpeppering"in"the"classification.""
"
We"intend"that"the"classification"image"created"by"this"study"may"be"validated"with"field"data"
and"inform"classifications"of"the"Congo"Basin"Coast"in"the"future."It"is"with"these"and"future"
classifications"that"the"WCS"Congo"program"may"augment"and"strengthen"their"conservation"
efforts"to"monitor"ecosystems"in"the"vast"and"often"inaccessible"region"at"broader"scales"and"
meet"their"long"term"goals"of"stabilizing"vulnerable"species"populations"and"connecting"
protected"areas."
!

14. 14"
"
5.!References!
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Binh,"T.;"N."Vromant,"N.T."Hung,"L."Hens,"E.K."Boon."2005."Land"cover"changes"between"1968"and"2003"in"
Cai"Nuoc,"Ca"Mau"Peninsula,"Vietnam."Environ.(Develop.(Sustain."7,"519J536."
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amphibian"fauna"of"the"Gamba"Complex"of"Protected"Areas,"Gabon."In:"Alonso,"A.,"M.E."Lee,"P."
Campbell,"O.S.G."Pauwels"and"F."Dallmeier,"eds.,"Gamba,(Gabon:(Biodiversity(of(an(Equatorial(
African(Rainforest."Bulletin"of"the"Biological"Society"of"Washington,"No."12."
Bwangoy,"J.R.B.,"M.C."Hansen,"D.P."Roy,"G."De"Grandi,"C.O."Justice."2010."Wetland"mapping"in"the"Congo"
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Sensing(of(Environment"114(1):"73J86."
Duro,"D.C.,"N.C."Coops,""M.A."Wulder,"T."Han."2007."Development"of"a"Large"Area"Biodiversity"
Monitoring"System"Driven"by"Remote"Sensing."Progress(in(Physical(Geography"31.3:"235–260."
Gillespie,"T.W."G.M."Foody,"D."Rocchini,"A.P."Giorgi,"S."Saatchi."2008."Measuring"and"Modelling"
Biodiversity"from"Space."Progress(in(Physical(Geography"32(2):"203–221.""
Giri,"C.,"E."Ochieng,"L."L."Tieszen,"Z."Zhu,"A."Singh,"T."Loveland,"J."Masek"and"N."Duke."2011."“Global"
Mangrove"Distribution"(USGS).”"US"Geological"Survey"(USGS)"Earth"Resources"Observation"and"
Science"Center"(EROS),"United"Nations"Environment"Programme."Global(Ecology(and(
Biogeography"20(1)("
"Green,"E.P.;"P.J."Mumby,"A.J."Edwards,"C.D."Clark,"A.C."Ellis."1997."Estimating"leaf"area"index"of"
mangroves"from"satellite"data."Aquat."Bot."58,"11J19.""
Green,"E.P.,"C.D."Clark,"P.J."Mumby,"A.J.""Edwards,"A.C."Ellis."1998."Remote"sensing"techniques"for"
mangrove"mapping."Int.(J.(Remote(Sens.(,"19,"935J956."
Hansen,"M.C.,"D.P."Roy,"E."Lindquist,"B."Adusei,"C.O."Justice,"A."Altstatt.""2008."A"Method"for"Integrating"
MODIS"and"Landsat"Data"for"Systematic"Monitoring"of"Forest"Cover"and"Change"in"the"Congo"
Basin."Remote(Sensing(of(Environment"112(5):"2495–2513."""
Hazevoet,"C.J.,"B."Granavita,"P.L."Suarez,"F.W."Wenzel."2011."Seasonality"of"humpback"whale"Megaptera"
novaeangliae"(Borowski,"1781)"records"in"Cape"Verde"seas:"evidence"for"the"occurrence"of"
stocks"from"both"hemispheres?."Zoologia(Caboverdiana(2(1)"(2011):"25J29.""
Helmer,"E."2013."Detailed"Maps"of"Tropical"Forests"Are"within"Reach:"Forest"Tree"Communities"for"
Trinidad"and"Tobago"Mapped"with"Multiseason"Landsat"and"Google"Earth."Atbc,"2013.""

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Jones,"T.G,"H.R."Ratisimba,"L."Ravaoarinorotsihoarana,"G.""Cripps,"A."Bey."2014."Ecological"Variability"and"
Carbon"Stock"Estimates"of"Mangrove"Ecosystems"in"Northwestern"Madagascar."Forests"5(1):"
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us/~/media/Files/prospectuses/CongoBasinCoast_Prospectus2010_101810.pdf""
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Aerial"surveying"of"the"world’s"largest"leatherback"rookery:"A"more"effective"methodology"for"
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space."Current(Science("105(8)"(2013):"1149J1155"

16. 16"
"
6.!Tables!
Table!1.!Data!sources!used!in!the!analysis.!
Data! Source!
Spatial!
Resolution!
Temporal!
Relevance!
Landsat"8"imagery" NASA" 30"m" July"17,"2013"
ASTER"Digital"Elevation"Model"(DEM)" NASA" 30"m" 2011"
Global"Mangroves"
USGS,"World"
Atlas"
Vector" 2010"
Global"Wetlands" UNEP" Vector" 2005"
Gabon"protected"areas" OFAC" Vector" 2009"
Congo"Basin"Boundary" WCS" Vector" 2013"
Madagascar"Land"cover"Spectra" Trevor"Jones" N/A" 2013"
Google"Earth"" Google" 2.5"J"15"m" N/A"
Flash"Earth" Microsoft" 2.5J30"m"" N/A"
Panaramio"Imagery" Panaramio" Digital"photos" N/A"
!
Table!2.!Each!nonImangrove!training!class!and!number!of!pixels!selected!
Class" No.$of$Pixels"
Water" 76728"
Forest" 82244"
Forest"Shadow" 1343"
Wetland" 44135"
Wetland"Shadow" 223"
Secondary"Complex" 2689"
Bright"Soils" 2582"
Soils"Medium" 578"
Soils"Dark" 1684"
Savannah" 5551"
Savannah""
shadow"
102"
!
!
Landsat 8 Image

17. 17"
"
7.!Figures!
!
Figure!1.!Map!of!the!study!area!in!Gabon,!as!bounded!by!the!red!outline!of!the!landsat!8!image!and!
the!green!coastal!zone!of!the!Congo!Basin.!Dark!green!and!beigeIgreen!represent!the!USGS/World!
Atlas!mangroves!and!UNEP!wetlands!respectively.!Lighter!pink!polygons!indicate!protected!areas,!
which!were!created!in!2002!in!an!effort!to!improve!conservation!within!Gabon.!!
""

18. 18"
"
!
Figure!2.!Before!(left)!and!after!(right)!of!isoclust!cloudmasking!
"
"
!
!
!
Figure!3.!Methods!flow!chart!for!both!mangrove!and!nonImangrove!classifications.!Details!for!
mangrove!classification!displayed!in!Figure!4.!
!

19. 19"
"
!
!
!
!
!
Figure!4.!Methods!flowchart!of!mangrove!image!subsets.!!!
"
" !!
Figure!5:!Methods!flowchart!for!cloudImasking!techniques!tested!
!
!

20. 20"
"
"
Figure!6:!Spectral!signatures!collected!in!the!field!by!Jones!et!al!in!Madagascar.!!
"
Figure!7:!Spectral!signatures!for!each!class!of!interest!across!the!first!7!bands!of!the!Landsat!8!image!
(path=185,!row=62).!!
0"
0.05"
0.1"
0.15"
0.2"
0.25"
0.3"
0.35"
0.4"
0.45"
Band"1"
(Blue)"
Band"2"
(Green)"
Band"3"""""
(Red)"
Band"4"""
(NIR)"
Band"5"
(SWIR)"
Band"7"
(SWIR)"
AtIsurface!spectral!reflectance!
Landsat!7!ETM+!bands!
savannah"
acyve"crops"
terrestrial"
forest"
exposed"soil"
exposed"mud"
4000"
6000"
8000"
10000"
12000"
14000"
16000"
18000"
1" 2" 3" 4" 5" 6" 7"
DN!
Landsat!8!Band!
Water"
Forest"
Wetland"
Secondary"Complex"
Soils"Bright"
Soils"Medium"
Soils"Dark"
Savannah"

21. 21"
"
!
(
Figure!8:!Final!classification!of!mangroves!in!expected!spatial!distribution!near!leeward!sides!of!
estuaries!and!within!tidal!lagoons.!!

22. 22"
"
!
Figure!9.!Final!classification!zoom!shows!expected!clustering!of!secondary!complex!around!dirt!roads!
through!forest.!!

23. 23"
"
!
Figure!10:!Final!classification!zoom!shows!expected!interspersed!pattern!of!grassland!and!savannah!
during!long!dry!season.!!

24. 24"
"
!
Figure!11:!Final!Classification!showing!distribution!of!classes!across!the!entire!image.!Figure!8,!9,!and!
10!are!framed!in!black.!!
!!