Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery

13 Pages·2005·0.67 MB·English

RemoteSensingofEnvironment97(2005)39–51
www.elsevier.com/locate/rse
Classification of Amazonian primary rain forest vegetation using
Landsat ETM+ satellite imagery
Kati J. Salovaaraa,*, Sirpa Thesslerb, Riffat N. Malikc, Hanna Tuomistoa
aUniversityofTurku,DepartmentofBiology,20014Turku,Finland
bFinnishForestResearchInstitute,00170Helsinki,Finland
cEnvironmentalSciencesProgramme,FatimaJinnahWomenUniversity,TheMall,Rawalpindi46000,Pakistan
Received16September2004;receivedinrevisedform8April2005;accepted9April2005
Abstract
Alackofspatiallyandthematicallyaccuratevegetationmapscomplicatesconservationandmanagementplanning,aswellasecological
research,intropicalrainforests.Remotesensinghasconsiderablepotentialtoprovidesuchmaps,butclassificationaccuracywithinprimary
rainforestshasgenerallybeeninadequateforpracticalapplications.Herewetesthowaccuratelyfloristicallydefinedforesttypesinlowland
tropicalrainforestsinPeruvianAmazoniacanberecognizedusingremotesensingdata(LandsatETM+satelliteimageandSTRMelevation
model).Floristicdataandavegetationclassificationwithfourforestclasseswereavailableforeightlinetransects,each8kmlong,locatedin
an area of ca 800 km2. We compared two sampling unit sizes (line transect subunits of 200 and 500 m) and several image feature
combinationstoanalyzetheirsuitabilityforimageclassification.Manteltestswereusedtoquantifyhowwellthepatternsinelevationandin
thedigitalnumbersofthesatelliteimagecorrelatedwiththefloristicpatternsobservedinthefield.MostMantelcorrelationswerepositive
andhighlysignificant.Lineardiscriminantanalysiswasusedfirsttobuildafunctionthatdiscriminatesbetweenforestclassesintheeight
field-verifiedtransectsonthebasisofremotelysenseddata,andthentoclassifythosepartsofthelinetransectsandthesatelliteimagethat
hadnotbeenvisitedinthe field.Classification accuracywasquantifiedby8-fold crossvalidation.Twoofthetierrafirme(non-inundated)
foresttypeswerecombinedbecausetheyweretoooftenmisclassified.Theremainingthreeforesttypes(inundatedforest,terraceforestand
Pebasformation/intermediatetierrafirmeforest)couldbeseparatedusingthe500-msamplingunitswithanoverallclassificationaccuracyof
85%andaKappacoefficientof0.62.Forthe200-msamplingunits,theclassificationaccuracywasclearlylower(71%,Kappa0.35).The
forestclassificationwillbeusedashabitatdatatostudywildlifehabitatuseinthesamearea.Ourresultsshowthatremotelysenseddataand
relatively simple classification methods can be used to produce reasonably accurate forest type classifications, even in structurally
homogeneousprimary rain forests.
D2005Elsevier Inc.All rights reserved.
Keywords:LandsatETM+;Satelliteimaging;PeruvianAmazonia;Rainforest;Vegetationclassification;Wildlifehabitat
1. Introduction Kubitzki, 1987; Prance, 1979). The tierra firme forests,
in contrast, are structurally rather uniform. Until very
Western Amazonian rain forests have traditionally been recently, little attention was paid to the possibility that
divided into two major forest types: inundated forests and tierra firme forests might also represent ecologically
non-inundated (tierra firme) forests. Inundated forests are different forest types. However, now there is increasing
structurally very heterogeneous, which has been recog- evidence that they do consist of a heterogeneous mosaic
nized to depend on factors such as the frequency, depth of floristic communities, and that the floristic differences
and duration of inundation (Kalliola et al., 1992; between sites are related to differences in edaphic site
conditions, especially soil nutrient content (Duivenvoor-
den, 1995; Duque et al., 2002; Gentry, 1988; Phillips et
* Correspondingauthor.
E-mailaddress: [email protected](K.J.Salovaara). al., 2003; Ruokolainen et al., 1997; Ruokolainen &
0034-4257/$-seefrontmatterD2005ElsevierInc.Allrightsreserved.
doi:10.1016/j.rse.2005.04.013
40 K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51
Tuomisto, 1998; Tuomisto et al., 1995, 2003a,b,c; forest types traditionally recognized by local tribes have
Vormisto et al., 2000). been found to be distinct in satellite images (Shepard et al.,
Consequently, Amazonian tierra firme forests can be 2004).
classifiedtodifferentsitetypes,eachofwhichhasamoreor Nevertheless, the majority of Amazonia (e.g., more than
less characteristic floristic composition. In boreal and 80%ofPeruvianAmazonia;Saloetal.,1986)iscoveredby
temperateforests,suchsitetypes arerecognisedinthefield relatively uniform-looking tierra firme forests. The differ-
using indicator species (Cajander, 1926; Ellenberg, 1988) ences between tierra firme forest types that grow on
whose edaphic requirements are sufficiently well known. It different soil types have been considered to be too subtle
has recently been suggested that pteridophytes (ferns and to be discernible by remote sensing. For example, several
fern allies) could be used as forest site type indicators in studiesusingLandsatTMdatatoclassifytierrafirmeforests
Amazonian rain forests, and indeed it has been found that growing on sand and clay soils concluded that it was not
the floristic patterns of pteridophytes are highly correlated possible to separate them (Foody & Hill, 1996; Hill, 1999;
with those of other plant groups (Ruokolainen et al., 1997; Hill&Foody,1994).Inspiteofthis,ourearlierstudieshave
Ruokolainen & Tuomisto, 1998; Tuomisto et al., 1995; linkedthevariationvisibleinLandsatsatelliteimagerywith
Vormistoetal.,2000).Thislinkagebetweensoils,terrestrial floristic patterns and environmental heterogeneity in Ama-
pteridophytes and canopy plants suggests that it may be zonian tierra firme forests (Thessler et al., in press;
possible to use remotely sensed data to separate rain forest Tuomisto et al., 1995, 2003a,b), although it has also been
types that have been recognized based on their indicator suggested that the vegetation patterns visible in satellite
species composition. images may be related to forest dynamics, such as pest
Floristicandedaphicheterogeneityisimportantforland- outbreaks, disturbance or dispersal limitation,rather than to
use planning and conservation in Amazonian forests and edaphic variation (Condit, 1996).
may also be relevant for many other organisms beside Numerous factors affect the potential success of forest
plants. For example, herbivorous animals depend on the classification using satellite images. One challenge is to
quality and quantity of plant resources available in the select the best image features to be used as the basis of the
habitat and thus site productivity and floristic composition classification. For the purposes of vegetation mapping, the
may influence their abundance and distribution. Therefore near and mid infrared bands contain more relevant
broad-scale vegetation surveys have potentially wide information than the visible bands (e.g., Singh, 1987;
applications in resource management and conservation as Thenkabail et al., 2004), but the most efficient features
well as in basic ecological research in primary rain forests. vary from one application to another. The spatial resolution
However, vegetation surveys in tropical forests are more of the study is also critical for classification success. The
demanding and time-consuming than in temperate forests sizeofthepixelwindowsusedshouldbeselectedrelativeto
because plant species diversity is extremely high, their the size of the forest patches that are to be mapped. The
taxonomyispoorlyknown,andremoteareasarelogistically spatial scales of vegetation patterns in Amazonian forests
difficulttoworkin.Asaresult,itisrarelyfeasibletocollect range from local tree fall gaps (tens of meters) through
fielddatathatcovertheareaofinterestsufficientlyandthus topographic hill–valley patterns (hundreds of meters) to
interpolation must be made between widely scattered field landscape-widepatchesofdifferentforesttypes(kilometres)
study points. Environmental reference maps or other (Tuomistoetal.,1995,2003a,b).Localheterogeneitywithin
ancillary data that could help in vegetation mapping and forest types can be considered noise in landscape-scale
spatialinterpolationareonlyrarelyavailable,especiallyata vegetation mapping and consequently more accurate clas-
relevant spatial scale. Remote sensing data is often the best sifications have been obtained when larger pixel windows
if notthe only availablesource ofsuch information(Moran (e.g., Hill&Foody,1994;Rajaniemietal.,2005;Tuomisto
& Brondizio, 1994; Tuomisto, 1998; Tuomisto et al., et al., 2003a, b) or segments (Hill, 1999; Lobo & Gullison,
2003c). For example, in our study area in Peruvian 1998) have been used. Use of pixel windows or segments
Amazonia the only available thematic map providing alsodecreasestheeffectofmisregistrationoffieldplotsand
reasonable detail is a geological map that is based on very errors in satellite image rectification, both of which can
limited field-verification (Sanchez et al., 1999). causeclassificationerror(Foody,2002;Powelletal.,2004).
Althoughperennialcloudcoveroftenposesproblemsfor However,ifthespatialresolutionofthestudyistoocoarse,
passive remote sensing in tropical areas (Asner, 2001), the classification can be blurred by high variation within
remotely sensed data have been successfully applied in pixel windows, as one window may include several
separating areas of dense forest from degraded forest and vegetation types.
non-forest land cover types (e.g., Achard et al., 2001; The number and distinctness of the forest classes to be
Laporte et al., 1995; Skole & Tucker, 1993). Seasonally mapped partly determine how accurate a classification can
inundated forests and swamps have also been successfully be achieved. In continuous tierra firme forest, easily
distinguished from non-inundated forests (de Grandi et al., recognizable limits between forest types are rare. This
2000; Kalliola et al., 1992; Lobo & Gullison, 1998; Podest makes the allocation of sites to discrete vegetation classes
& Saatchi, 2002; Tuomisto et al., 1994) and some of the difficult and often quite subjective, and therefore care
K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51 41
should be taken in defining the forest classes to be mapped forest types. Obtaining a vegetation classification for all
(Foody, 2002; Nagendra, 2001). The availability offloristic twelve transects is indispensable for the animal ecological
field data helps in this task. study.Inthepresentstudytheobjectivewastotestwhether
We have previously conducted a landscape-level vegeta- a Landsat ETM+ satellite image can be used to extend the
tionclassificationinPeruvianAmazoniaforthepurposesof vegetationclassificationtotheadditionalfourtransectsand,
wildlife habitat studies (Salovaara et al., 2004). Eight 8-km if so, to carry out the classification. Although there is an
long line transects were inventoried for their pteridophyte immediate practical need for the classification, it is also of
flora. On the basis of these floristic data, the forests were more general scientific interest to test how accurately the
divided into four main types: inundated forests, terrace forest types recognized on the basis of floristic data can be
forests,Pebasformationforestsandintermediatetierrafirme detected from remotely-sensed data.
forests. The pteridophyte species composition in the three
non-inundated forest types indicated that they differ in soil
fertility:terraceforestshaveclearlypoorersoilsthanthetwo 2. Material and methods
other forest types and Pebas formation forests have richer
soils than intermediate tierra firme forests (Salovaara et al., 2.1. Study area
2004).Sincetreespeciescompositionreactstothesekindsof
soildifferences(e.g.,Clarketal.,1998;Duqueetal.,2002; Fieldwork was conducted in an extensive (800 km2)
Phillips et al., 2003; Ruokolainen et al., 1997), the spectral uninhabited area of primary rain forest in northeastern
characteristics of the forest are expected to do so as well, Peruvian Amazonia along the Yavar´ı–Mir´ın river (Fig. 1).
becausetheyaremostlydeterminedbytheforestcanopy. The Yavar´ı–Mir´ın is a tributary of the Yavar´ı river, which
A mammal census was conducted simultaneously with formstheborderbetweenPeruandBrazil.Theareaiswithin
thevegetationsurveyintheeightfloristictransectsandinan the proposed Yavar´ı conservation unit and has extremely
additionalfourtransectsthatlackfloristicfielddatainorder high species diversity (Pitman et al., 2003 and references
to compare the structure of mammal communities between therein). Although restricted selective logging and other
Fig.1.Colorcomposite(bands4,5and7allocatedtored,greenandblue,respectively)oftheLandsatETM+satelliteimagecoveringthestudyareainNE
PeruvianAmazonia.Eachsquareinthelinetransectsrepresentsone500(cid:1)400msamplingunit.Thethreeforestclassesareshownasfollows:inundated
forestsinblack,terraceforestsindarkgrayandPebasformation/intermediatetierrafirmeforestsinlightgray.Classificationofthe500-msamplingunitsin
transects4–8and10–12wasbasedonfloristicdata(Salovaaraetal.,2004),whereastransects1,2,3and9wereclassifiedinthepresentstudywiththehelpof
remotelysenseddata.Theredsquareinthereferencemapindicatesthelocationofthestudyarea.
42 K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51
small-scale extractive activities take place in the area, they All transects were georeferenced in the field by taking
have not caused visiblechanges inthe forest structure. GPS readings at 500-m intervals. To improve the accuracy
Elevation in the area ranges from 100 m to ca 180 m oftheGPSdata,aminimumoftworepeatedmeasurements
above sea level and terrain varies from practically flat to were taken on different dates at each point and averaged.
hilly. Most of the river basin is covered by non-inundated When the transects were overlaid on the Landsat ETM+
tierra firme forest. Areas close to the Yavar´ı–Mir´ın river their locations were deemed sufficiently accurate (<90 m
channel are inundated seasonally and the floodplains of the error) by visual estimation.
major creeks sporadically after heavy rains. Palm swamps Notopographicalmapscoveringthestudyareaexist,but
dominated by Mauritia flexuosa L. f. are patchily distrib- a digital elevation model of the Shuttle Radar Topography
utedthroughoutthelandscape,mainlyclosetotheriverand Mission (SRTM DEM) is freely available (http://srtm.usgs.
major creeks. The vegetation and geomorphology of the gov/) and was acquired as unfinished data in 2003. The
area are described in more detail in Salovaara et al. (2004). SRTM DEM is based on the C-band radar data and has a
The nearest weather station is in the city of Iquitos, ca 150 horizontal resolution of 90 m. However, the study area is
km northwest of the study area. There the mean monthly relativelyflatandhasonlyminortopographicalvariation,so
temperature varies between 25 and 27 -C and mean annual topographical correction at this spatial resolution cannot be
precipitation is about 3100 mm. The highest long-term accomplishedusingSRTMdata.Wealsotestedwhetherthe
mean monthly precipitation is in March (350 mm) and the radiance values of the satellite image were correlated with
lowest in June (180 mm), but years when the driest month incidence angle calculated using the DEM and known sun
receives less than 100 mm of rain are not uncommon elevation, but no statistically significant correlation existed,
(Marengo, 1998). and consequently topographical correction was not deemed
necessary. No atmospheric correction was applied to the
2.2. Data LandsatETM+image,becauseonlyonescenewasusedand
the lack of atmospheric data for the time of image
Twelve 8-km long transects (Fig. 1) were cleared in the acquisition would have prevented reliable within-scene
study area to allow a floristic survey and census of large- correction (Song et al., 2001).
bodiedherbivorousmammals(ungulates,primates,rodents) Two different window sizes were used to extract digital
to be conducted. Avegetation classification for eight of the number (DN) values from the satellite image. A balance
transects was produced with the help of floristic data needed to be found between solving two problems: larger
(pteridophyte species abundances) collected in contiguous pixel windows are more often mixtures of more than one
2-mwideand100-mlongsamplingunitsalongthetransects foresttype,butsmallerwindowsaremoreseverelyaffected
in March–April 2002 (Salovaara et al., 2004). The field by local noise. The smaller windows used here were 5(cid:1)5
work had to be ended prematurely because of logistical pixels (150(cid:1)150 m) in size, and the larger windows ca
difficulties,leavingfourtransects(numbered1,2,3and9in 12(cid:1)15 pixels (360(cid:1)450 m). A window size 5(cid:1)5 was
Fig. 1) without floristic data. The present paper reports the selected because smaller pixel windows (3(cid:1)3 and single
use of satellite-derived data to assign the vegetation along pixels) were found to perform poorly by Hill & Foody
these four transects to the forest classes recognized by (1994),andthelargerwindowsizeiscomparabletothatused
Salovaara et al. (2004). byTuomistoetal.(2003b).Thesmallerpixelwindowswere
A Landsat Enhanced Thematic Mapper (ETM+) image usedinanalysesincludingthe200-mlongfloristicsampling
covering the study area (path 005, row 63, acquisition date units from the transects and the larger windows in analyses
25 June 2001) was used in the analysis. The image was ofthe500-mfieldsamplingunits.Theimagesamplingunits
obtained as geometrically and radiometrically corrected were made slightly shorter than the corresponding field
(level 1G) data in digital number (DN) values. The image sampling units in order to avoid including the same image
was rectified using 1st order polynomial rectification. pixels intwo adjoining pixel windows.
Ground control points (GCPs) were obtained in the field ThemeanandstandarddeviationoftheDNvalueswithin
with a hand-held Global Positioning System (Garmin GPS the pixel windows were extracted from the Landsat ETM+
12) and from reference points read from a map that was imageforbands2,4,5and7.Theratioofbands4and5was
based on Landsat MSS (Multispectral Scanner) images also extracted. Bands 1 and 3 were not used, because they
(IFG, 1984). Although the map manufacturer reports a had either pronounced striping (band 1) or coherent noise
relativelylowspatialaccuracy(125m)forthesemaps,they (band 3), which originate from the Landsat detector. It was
have been useful in our earlier applications. A total of 12 not possible to replace the faulty image, because no other
GCPs were used as control points and additional four as cloud-free Landsat ETM+ scenes were available from the
checkpoints,whoserootmeansquareerrorwas<1pixel,or time period when the field study was conducted. Thermal
lessthan30m.Riverchannelsandoxbowlakesaretheonly band6wasalsoexcludedandtheriverchannelwasmasked
usablelandmarksintheregionandasomewhatlargerscene outtoexclude pixels representing open water.
than the one shown in Fig. 1 was used for rectification to The DN values inside the 5(cid:1)5 pixel windows were
allow more even dispersion of GCPs. obtained by filtering. A moving window kernel of 5(cid:1)5
K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51 43
pixels was applied over the image and the DN value of the and standard deviations of DN values, ratio of bands 4 and
pixelatthemid-pointofeach200-mlongtransectsampling 5, and elevation in the pixel windows. Each variable was
unit was used as the image data corresponding to that usedonitsowntoconstructaEuclideandistancematrixand
sampling unit. The 12(cid:1)15 pixel windows were delineated additional Euclidean distance matrices were constructed
manually on the image so that the long axis of each pixel using a combination of several variables. In the latter case,
window was aligned with and centered on the correspond- the combination of variables to be included was selected
ing 500-m transect sampling unit (Fig. 1). Elevation data using multiple regression on distance matrices with back-
wereextractedfromtheSRTMDEM.Thevalueofthepixel ward elimination and the individual variables were stand-
containing the mid-point of each 200-m sampling unit was ardized to give them equal weight before computing the
used as the elevation data corresponding to that sampling distances.
unit. For the 500-m sampling units, the mean and standard In the multiple regression on distance matrices, the
deviation of elevation were extracted using the same pixel dependent matrix was the floristic distance matrix, and the
window size as in the case of the DN values (360(cid:1)450 m, independent matrices were the Euclidean distance matrices
corresponding to 4(cid:1)5 pixels in the DEM). based on each spectral and elevation feature separately.
Initially,allindependentdistancematriceswereincluded.At
each iteration of the backward elimination, the independent
3. Data analyses matrix with the least significant coefficient of partial
determination was excluded, until the coefficients of all
3.1. Mantel tests matrices remaining in the model were statistically signifi-
cant at the p<0.05 level after Bonferroni correction. The
Alltheanalyseswererunseparatelyusingthe200-mand statistical significances were estimated by Monte Carlo
500-m sampling units (N=317 for the 200-m sampling permutation, using either 999 permutations (in the case of
units, N=127 for the 500-m sampling units). Mantel tests 500-m sampling units) or 99 permutations (in the case of
were run to analyze how well the patterns in the digital 200-m sampling units).
numbers of the satellite image and in elevation correlated
with the floristic patterns observed in the field. The Mantel
testcomputesacorrelationcoefficient(Mantel’sr)between 4. Discriminant analysis
two resemblance matrices, in the present case a floristic
resemblance matrix and a satellite-derived resemblance Discriminant analysis was used to build a discriminant
matrix. Mantel’s r is similar to Pearson’s product–moment function, which consists of a linear combination of
correlation, except for the important difference that corre- explanatory variables (in this case, spectral features and
lationsarenotcalculatedbetweentheoriginalvariables,but elevation) that best discriminates among a number of
between resemblance matrices based on these variables. predefinedclasses(inthiscase,theforesttypesofSalovaara
Resemblance matrices consist of pair-wise comparisons of et al., 2004). The parameters of the model are estimated on
all sampling units using a resemblance measure that is the basis of a training data set consisting of field-verified
appropriate for the data at hand (Legendre & Legendre, sampling units with known class membership (the eight
1998). The statistical significance of the Mantel correlation classified floristic transects). The resultant model can then
coefficientisestimatedwithaMonteCarlopermutationtest; be used to predict class membership for unvisited sampling
in the present case 999 permutations were used to test for units(thefourtransectslackingfloristicdata)andtheentire
significance at the p<0.001 level. study area (Legendre & Legendre, 1998).
The floristic resemblance matrices were calculated using The training data set consisted of the existing floristic
theBray–Curtisindex, whichisameasureofthedegree of classification of the 100-m long vegetation sampling units
differenceinspeciesabundancesbetween twosites(Legen- from the eight transects that were inventoried for pterido-
dre & Legendre, 1998): phytes (Salovaara et al., 2004). Because the size of the
p sampling units was different between the vegetation and
X
jy1j(cid:6)y2jj imagedata,someadjustmentswerenecessary.Whena500-
j¼1 mimagesamplingunitcontainedmorethanoneforesttype,
Dðx ;x Þ¼ ; where ð1Þ
1 2 p
X it was allocated to the forest type to which most of the
jy þy j
1j 2j corresponding 100-m vegetation sampling units belonged.
j¼1
In the case of ties in the 200-m image sampling units,
x =sampling unit i preference wasgiven totheforesttype that was rarer inthe
i
y =abundance of species j in sampling unit i entire data set. Consequently, 2% of the 200-m sampling
ij
p=total number of species. unitsand9%ofthe500-msamplingunitsweremixedpixel
windowsthatcontainedmorethanoneforesttype.Thiscan
Several Euclidean distance matrices were constructed increase misclassification (Smith et al., 2003), so the
usingthespectralfeaturesandelevationdata,i.e.,themeans percentage of misclassified sampling units was calculated
44 K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51
separatelyforthemixedandthenon-mixedimagesampling selected.Oncethebestfeaturecombinationhadbeenfound,
units. the accuracy of the corresponding discriminant model was
The 500-m section closest to the river on transect 4 had assessed by 8-fold cross-validation. Seven of the field-
been classified as intermediate tierra firme forest on the verifiedtransectswereusedtobuildthediscriminantmodel
basis of floristic data (Salovaara et al., 2004), but this was and the remaining transect was used as the test data set.
clearlyamisclassification,becausetheareawasobservedto Eachofthefield-verifiedtransectswasusedasthetestdata
beinundatedintherainyseason.Thiserrorcoulddistortthe set in turn and classification accuracy was derived as the
discriminant model and cause false misclassifications, percentage of correctly classified sampling units across all
especially because the number of sampling units belonging the eight runs. This method of cross validation was used
totheinundatedforestclasswaslow.Therefore,thetransect because the ultimate purpose of the discriminant analysis
section in question was transferred to the inundated forest was to classify the sampling units of the uninventoried
class prior to the following analyses. The main results transects and a realistic estimate of classification accuracy
obtained with the uncorrected data are also reported for the was needed for a situation where the nearest field-verified
sake of comparison. sampling units are several kilometres away.
A total of eleven spectral and elevation features were Classificationresultsandaccuraciesweresummarizedin
availableformodelbuilding(Table1),sothefirststepinthe error matrices and by Kappa values. An error matrix shows
analysiswastoselectthecombinationoffeaturesthatwould the number of correctly classified image sampling units in
give the best classification accuracy with the fewest the diagonal and the number of image sampling units that
features. Thiswas done bya stepwise discriminant analysis were erroneously either included in (lower triangle) or
using backward elimination. At each step, the feature that excluded from (upper triangle) each class. Producer’s
contributed least to the discriminatory power of the model accuracy indicates the percentage of image sampling units
was removed, until only one feature remained. Discrim- thatwereassignedtothecorrectclass.User’saccuracygives
inatorypowerwasmeasuredwithWilks’ Lambda,whichis thepercentageofimagesamplingunitsassignedtoacertain
the spectral (and elevation) variance among the sampling class that actually belong to that class according to the
units that is not explained by the model (Legendre & floristic classification.
Legendre, 1998). Every step produced one feature combi- The kappa coefficient (j) was calculated from the error
nation that was validated by testing how well it classified matrixEq.(2).Kappaindicatestowhatextentclassification
the sampling units whose class memberships were already accuracy is due to true agreement of the field data and the
known. The classification accuracies obtained in this classified data, and to what extent it could have been
manner are unrealistically high because the same sampling achieved by chance (Lillesand et al., 2004). Its value varies
units were used for both model building and testing, but between0and1andavalueof0.5meansthatthereis50%
their purpose was only to help in selecting the optimal better agreement than expected by chance alone.
model for each sampling unit size.
r r
X X
The feature combination with the highest classification N X (cid:6) ðx 4x Þ
ii iþ þi
accuracy was then selected; when two or more models
j¼ i¼1 i¼1 ; where ð2Þ
performedequallywell,theonewiththefewestfeatureswas r
X
N2(cid:6) ðx 4x Þ
iþ þi
i¼1
Table1
Selectionoffeaturecombinationsandcorrespondingclassificationaccuracy
using the entire data set for building and validating the discriminant
function r=number of the rows/columns in the error matrix
200-msamplingunits 500-msamplingunits xii=number of observations in the cell ii (row i and
column i)
Numberof 317 127
samplingunits xi+=marginal totals of row i
Includedfeatures m2,m4,m5,m7,sd2, m2,m4,m5,m7,sd2, x =marginal totals of column i
+i
sd4,sd5,sd7,m4/m5, sd4,sd5,sd7,m4/m5, N=total number of observations.
elevation elevation,sdofelevation
Selectedfeature Elevation,m7,m4/m5, m7,m4,m5,elevation,
Once the model building, cross validation and accuracy
combination m5 sd4,sd5,sd2,m2
Classification 73.8% 92.1% evaluation of the discriminant models were completed for
accuracy the 200-m and 500-m image sampling units, the models
Elevation was obtained from the SRTM DEM and DN values from a wereappliedtoobtainclassificationsoftransects1–3and9.
LandsatETM+satelliteimage.Thenumbersshowwhichwavelengthbands Classification of the entire study area was only produced
wereused:mstandsformeanandsdforthestandarddeviationoftheDN using the larger sampling unit size.
valuesofthebandinquestion.Valueswereextractedfrompixelwindows
For the classification of the entire study area the band
of150(cid:1)150mforthe200-msamplingunitsand360(cid:1)450mforthe500-
andelevationlayersthat produced thehighestclassification
msamplingunits.Totaldegreesoffreedomweren(cid:6)1,withinclassesn(cid:6)3
andbetweenclasses2. accuracy were filtered by a mean and/or standard deviation
K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51 45
kernel of 12(cid:1)15 pixels and the pixel data were imported 3) Pebas formation/intermediate tierra firme forests; non-
to the discriminant model. The data from the eight inundated forests growing on relatively nutrient-rich
classified transects was then used as a training data set clayey to loamy soils.
for predicting the forest classes for the filtered image
pixels. The produced forest classification was filtered with 5.2. Mantel tests
a majority kernel of 5(cid:1)5 pixels. Since the field sampling
may not have covered all of the variation present in the The results of the Mantel tests between the floristic
study area, the pixels with values outside the sampled distance matrices and the corresponding image feature
ranges of spectral and elevational features were removed distance matrices are shown in Table 2. Mantel correlations
from the classification. This was done by plotting the DN were calculated both using each image feature individually
values of a pair of spectral features for all the pixel and using those feature combinations that were retained in
windows. A convex object (i.e., a polygon formed as if a the multiple regression on distance matrices after backward
string isstretched around the field-verified pixels) was then elimination. Of the individual spectral features, the mean
drawn around the field-verified pixels and all pixel DNvaluesofband7showedthehighestMantelcorrelation
windows that were outside the convex object were masked with both sampling unit sizes. The mean DN values of the
out from the image (see Thessler et al., in press). This was other bands also showed relatively high correlation coef-
repeated for all pairs of spectral and elevational features. ficients,asdidelevationandtheratioofbands4and5.The
The excluded areas were presented as unclassified pixels in correlation coefficients obtained with the 200-m sampling
the produced map. Preprocessing of the Landsat image and units were invariably, and usually considerably, lower than
GPS data was done using the Erdas Imagine 8.6 and those obtained with the 500-m sampling units. Those
ArcGIS programs. Resemblance matrices and Mantel tests distance matrices that combined several features resulted
were computed with Le Progiciel R (Casgrain & Legendre, in the highest correlation coefficients: 0.47 and 0.30 with
2001), and multiple regressions on distance matrices were the 500 and 200-m sampling units, respectively.
run with Permute!. Both programs are available through
the www site <http://www.bio.umontreal.ca/legendre/ 5.3. Building of discriminant models
indexEnglish.html>. The discriminant analyses were run
in the SAS 8.2 (SAS Institute Inc., 1999). Table 1 presents the best feature combinations selected
by stepwise discriminant analysis using backward elimi-
5. Results
Table2
MatrixcorrelationsasmeasuredbyaManteltestbetweenfloristicdistances
5.1. Simplifying the vegetation classification
anddistancesbasedonremotesensingdata(theDNvaluesofaLandsat
ETM+ satellite image and a digital elevation model) in NE Peruvian
The original classification of Salovaara et al. (2004) Amazonia
produced four main forest classes separated by floristic Spectralorelevationfeatures Mantel’sr
characteristics: inundated forests, terrace forests, Pebas
500-msampling 200-msampling
formation forests, and intermediate tierra firme forests. All units units
the current analyses were first run using these four classes,
m2 0.28*** 0.11***
but it turned out that Pebas formation forests and inter- m4 0.25*** 0.14***
mediate tierra firme forests were difficult to separate from m5 0.24*** 0.14***
eachotheronthebasisofremotelysenseddata.Theoverall m7 0.33*** 0.18***
sd2 0.12** 0.00
classification accuracy for the four-class classification was
sd4 0.21*** 0.05*
66% with the 500-m sampling units and even lower (49%)
sd5 0.15** 0.11**
with the 200-m sampling units. sd7 0.05 0.02
The intermediate forests covered 16% of the area Ratioofbands4and5 0.23*** 0.17***
sampledinthefieldandweremainlyfoundinsmallpatches Elevation 0.26*** 0.24***
sdofelevation 0.22*** –
surrounded by Pebas formation forest. As these two forest
m2,m4,m5,m7,elevation, 0.47*** –
types were also floristically more similar to each other than
sdofelevation
to the other forest types, it seemed appropriate to combine m4,m7,sd5,elevation – 0.30***
them. All the analyses reported below are, therefore, based
Floristic distances were based on the Bray–Curtis index. Spectral and
on a three-class classification: elevationaldistanceswerebasedontheEuclideandistancecomputedfrom
themean(m)orstandarddeviation(sd)oftheDNvaluesofoneormore
1) inundated forests; including both river floodplain and ETM+bandsand/orelevationwithineachpixelwindow(150(cid:1)150mfor
the200-msamplingunits,360(cid:1)450mforthe500-msamplingunits).
swamp forests
*P<0.05.
2) terrace forests of tierra firme; non-inundated forests
**P<0.01.
growing on relatively nutrient-poor loamy soils ***P<0.001.
46 K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51
nation and the overall classification accuracy when these Table4
features were used to classify sampling units into floristi- Error matrix of the classification using spectral and elevation features to
assign the 200-m sampling units (N=317) to floristically defined forest
cally defined forest types. The same features were used in
typesinNEPeruvianAmazonia
the 8-fold cross-validation. In the cross validation, classi-
Actualclass Predictedclass Producer’s
ficationaccuracyvariedconsiderablybetweensamplingunit
accuracy(%)
In Te P/I Rowtotal
sizes and depending on which transect was used as the test
data set (Table 3). For the 200-m sampling units, accuracy Inundatedforest 23 3 7 33 69.7
Terraceforest 2 18 18 38 47.4
ranged between 53% and 95% (overall accuracy 71%) and
Pebasformation/ 13 50 183 246 74.4
for the 500-m sampling units between 63% and 100%
intermediatetierra
(overall accuracy 85%). The Kappa coefficient was 0.35 in firmeforest
the classification of the 200-m sampling units and reached Columntotal 38 71 208 317
0.62 in the classification of the 500-m sampling units. User’saccuracy(%) 60.5 25.4 88.0
The error matrix of the 200-m sampling units shows a Thediagonalshowsthenumberofcorrectlyclassifiedsampleunitsforeach
low classification accuracy for the terrace forest class: class.Producer’saccuracyisthepercentageofthesamplingunitspredicted
tobelongtothecorrectclass,anduser’saccuracyisthepercentageofthe
producer’s accuracy was 47% and user’s accuracy 25%
samplingunitspredictedtobelongtoaparticularclassthatactuallybelong
(Table 4). Terrace forest sampling units were often
to that class. The best linear discriminant function based on the feature
erroneously classified to the Pebas formation/intermediate combinationindicatedinTable1wasused.Theerrormatrixwascalculated
tierra firme class. Pebas formation/intermediate tierra firme bysummingtheclassificationresultsofeightseparaterunswhereeachof
forest showed the highest producer’s and user’s accuracy thetransectswasusedasatestdatasetinturn.
values (74% and 88%, respectively) and inundated forests
showed intermediate values (70% and 61%, respectively).
selectedfeatureswerethesameasintheanalysesdescribed
A higher classification accuracy was obtained when
above, but classification accuracy was slightly lower.
using the 500-m sampling units (Table 5). For the terrace
Overall accuracy decreased from 85% to 84%. Producer’s
forest class, producer’s accuracy was 75% and user’s
accuracy of inundated forest decreased from 73% to 60%,
accuracy 48%. Pebas formation/intermediate tierra firme
whereas in terrace forest it was 81% and in intermediate/
forest again showed the highest producer’s and user’s
tierra firme forests 86%. For these same forest types the
accuracy values (88% and 95%, respectively) and inun-
user’s accuracies were 67%, 50% and 95%, respectively.
dated forests showed intermediate values (73% and 89%,
With the uncorrected 200-m data the feature m4/m5 was
respectively).
replaced by m4 in the model and the overall accuracy was
The total number of 500-m sampling units was 127 and
68%,ascomparedto71%withthecorrecteddata.Thus,the
11oftheseweremixedpixelwindowscontainingtwoforest
corrected data were used also in the subsequent classifica-
types. Of the 11 mixed sampling units, four (36%) were
tions of the entire study area and the four transects that
misclassified, whereas 15% of the 116 sampling units
lacked floristic data.
located in the forest class interior were misclassified. There
were seven mixed plots among the 317 200-m sampling
5.4. Classification of four transects and the study area
units,and57%oftheseweremisclassified,whereas29%of
the sampling units that included one forest type were
Overall, in the classification of transects 1–3 and 9,
misclassified.
which lacked floristic field data, the results for both
Whenthe500-msection closesttotheriverintransect4
sampling unit sizes agreed relatively well. Only 20% of
was not transferred to the inundated forest class but was
the200-msamplingunitswereassignedtoadifferentforest
retained as Pebas formation/intermediate forest, the classi-
fication accuracies were consistently lower than with the
corrected data set. In case of the 500-m sampling units, the
Table5
Error matrix of the classification using spectral and elevation features to
Table3
assign500-msamplingunits(N=127)tofloristicallydefinedforesttypesin
Eight-foldcrossvalidationofthediscriminantmodelswhenusingLandsat
NEPeruvianAmazonia
ETM+DNvaluesandSRTMDEMelevationfeaturestoclassifysampling
unitstofloristicallydefinedforesttypesinNEPeruvianAmazonia Actualclass Predictedclass Producer’s
accuracy(%)
200-msamplingunits 500-msamplingunits In Te P/I Rowtotal
Overallaccuracy 70.7% 85.0% Inundatedforest 8 2 1 11 72.7
Range 52.5–95.0% 62.5–100.0% Terraceforest 0 12 4 16 75.0
Standarddeviation 16.4 14.5 Pebasformation/ 1 11 88 100 88.0
Kappacoefficient 0.35 0.62 intermediatetierra
firmeforest
The selected feature combinations in Table 1 were used in the analyses.
Columntotal 9 25 93 127
Eachoneoftheeightfloristictransectswasusedasthetestdatasetinturn
User’saccuracy(%) 88.9 48.0 94.6
andthetablesummarizestheresultsfortheeightruns.Correspondingerror
matricesareshowninTables4and5. SeeTable4formoredetailedexplanation.
K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51 47
Fig.2.ClassificationofthefourtransectslackingfloristicfielddataintothreevegetationtypesinNEPeruvianAmazonia.Theresultsareshownseparatelyfor
the200-and500-msamplingunits.Theclassificationisbasedonalineardiscriminantmodelusingthebestcombinationofspectralandelevationfeaturesfor
eachsamplingunitsize,asindicatedinTable1.Theresultsforthe200-msamplingunitsareshownabovethoseofthe500-msamplingunits.Thesampling
unitsassignedtothethreeforesttypesareshownasfollows:inundatedforestsinblack,terraceforestsindarkgrayandPebasformation/intermediatetierra
firmeforestsinlightgray.Thefirst200-munitoftransect1wascoveredbytherivermaskandwasexcludedfromtheanalyses.
class than that of the corresponding 500-m unit (Fig. 2). In Landsat ETM+ color composite. It is apparent that the
the case of these four transects, classification accuracy obtained forest classification corresponds well to the
cannot be formally estimated because neither field data nor visually observable patterns in the satellite image. Large
existing vegetation maps are available. However, the swamp forests (in a reddish tone) were all correctly
accuracy is probably close to that obtained in the model classified to inundated forest, as were river floodplain
validation for the eight field-inventoried transects when forests, even though the latter are less clearly visible in the
each transect was excluded in turn. This yielded an overall image.Anabrupttransitionfromdarkergreenterraceforest
classification accuracy of 85% and Kappa value of 0.62 for to lighter green Pebas formation/intermediate tierra firme
the 500-m sampling units. forest is visible in transects 3 and 11 and somewhat less
Because less accurate results were obtained in all clear in transects 1 and 7.
analyses for the 200-m sampling units than for the 500-m The forest classification map for the entire area is
units, the latter sampling unit size can be considered presented in Fig. 3. Generally, the classification results
preferable for use in future wildlife studies. In Fig. 1 the agreeratherwellwiththepatternsseenintheLandsatcolor
classified 500-m sampling units are placed on top of the composite in Fig. 1. The inundated forests are concentrated
Fig.3.ForestclassificationoftheLandsatETM+satelliteimageshowninFig.1.Thelinetransectswhoseforestclassesweredefinedbyfloristicfielddata
(transects4–8and10–12)orbyremotely-senseddata(transects1–3and9)areoverlaid.Thethreeforestclassesareshownasfollows:inundatedforestin
black,terraceforestindarkgrayandPebasformation/intermediateforestinlightgray.Whiteareasareeitheropenwaterorareasthatwereunclassifiedbecause
theirspectralandelevationalfeatureswereoutsidetherangescoveredbyfieldsampling.
48 K.J.Salovaaraetal./RemoteSensingofEnvironment97(2005)39–51
along the river and major creeks. The terrace forests are transects conformed well both to the general vegetation
concentrated on the southern side of the river, whereas patterns observed in the field and to the patterns visible in
Pebas forests/intermediate tierra firme forests are more the Landsat ETM+ image.
common on the opposite side of the river. Only 4% of the Itisnotstraightforwardtocomparethepresentresultsto
study area remained unclassified, because the values of the those ofearlier studies because thenumbersanddefinitions
spectral and elevational features were outside the ranges of the recognized vegetation classes are unique in each
covered by field sampling. These areas seem to correspond study. When more vegetation classes are recognized, the
to the light-colored areas in the Landsat image (Fig. 1) and probability of erroneous class assignments increases. The
probablyrepresentareasofopenvegetation,e.g.,largegaps presentstudyonlyincludedthreeforestclasses.Ontheother
produced by strong storms. hand,vegetationclassesthatarestructurallyclearlydifferent
are intrinsically easier to separate than structurally similar
ones. All of the forest types in this study were structurally
6. Discussion quite homogeneous, closed-canopy rain forest,even though
there is some structural heterogeneity within the inundated
BoththeManteltestsandthediscriminantanalysesgave forests. The spatial scale and sampling design also vary
clearly better results with the 500-m sampling units than among studies, but unfortunately information on sampling
with the 200-m units. Increasing the size of the sampling design or measures of accuracy are not always provided,
unit increases the risk that more than one vegetation types makingcomparisonswithotherstudiesdifficult.Withthese
exist within it. It was indeed confirmed that the sampling reservations in mind, some comparisons can be attempted.
units located at transition between two forest types were Few other studies have tried to separate floristically
more often misclassified than those in the forest class defined forest types within Amazonian primary tierra firme
interior. However, the classification error caused by this forests. The only other studies that employed forest classes
tendency was more than compensated for by the ability of comparable to ours (tierra firme on nutrient-poor clay soils
thelargersamplingunitstodampentheeffectoflocalnoise vs. moist sand or sandy clay soils) concluded that these
(e.g., tree fall gaps). These results are well in line with the foresttypescouldnotbeseparatedfromLandsatTMimages
studyofHill&Foody(1994),whoconcludedthatlow-pass (Foody & Hill, 1996; Hill, 1999; Hill & Foody, 1994).
filtering increased the separability of Amazonian forest However, tierra firme forests on sand or sandy clay could
types. oftenbeidentifiedvisually(Foody&Hill,1996),whichwas
Theclassificationaccuracyachievedinthepresentstudy also the case for terrace forests and Pebas formation/
was higher than we had expected, considering the fact that intermediate tierra firme forests in our study. Foody &
the forests were structurally rather similar, and that many Cutler(2003)achievedahighclassificationaccuracy(96%)
previousstudieshaveobtainedlowaccuracies.Forthe500- for nine floristically defined forest classes in lowland
m sampling units the overall classification accuracy of the dipterocarpforestsinBorneowhenusingLandsatTMdata.
finaldiscriminantmodelwas85%andthelowestproducer’s However, parts of the study area were heavily impacted by
accuracy for an individual forest class was 73%. There are logging,whichcouldcausetheclassestobestructurallyand
nogenerallyacceptedlimitsonhowaccurateaclassification notjustfloristicallydissimilar.Furthermore,noindependent
should be in order to qualify as reliable, but usually an test data was used in accuracy assessment.
overall accuracy exceeding 85% is considered reasonable, Separation of Amazonian inundated and tierra firme
often with the additional criterion that the accuracy should forests has been very successful. Lobo & Gullison (1998)
not be lower than 70% for any class (Foody, 2002; wereabletoclassifyseasonallyfloodedlowlandforestsand
Thomlinson et al., 1999; Smits et al., 1999). These seasonal evergreen dense forests 100% correctly using a
recommendations are meant for general use in remote- Landsat TM satellite image in Bolivia and reached an
sensingapplications,whichinmostcasesintendtoseparate overall classification accuracy of 94–98% with eight land
structurallydistinctclasses,e.g.,forestandopenfield,from cover types (water, swamps and several savanna and
each other. tropicalforestclasses).Hessetal.(2003)mappedinundated
The user’s accuracy for the terrace forest class was only and non-inundated forests with 63–90% producer’s and
48%, indicating that half of the sampling units assigned to user’s accuracies in the central Amazon basin from dual-
that class from transects 1, 2, 3 and 9 (Fig. 2) could in fact season radar imagery.
belong to another forest type, most probably to the Pebas Successional stages of moist tropical forests have also
formation/intermediateclass.Therewere,indeed,three500- beenclassifiedquiteaccurately.Luetal.(2003)achievedan
m sampling units in the middle part of transect 1 and two overallaccuracyof78%(rangeofproducer’saccuracy58–
units in the latter part of transect 3 that based on field 99%) when classifying three successional and one mature
observations most closely resembled the Pebas formation/ forestclassinBrazilianAmazonia.Thenkabailetal.(2004)
intermediateforestclass.Nonetheless,theywereassignedto discriminated fallows and successional stages of tall forest
the terrace forest class in the discriminant analysis. Except (eight classes in total) in Cameroon with an accuracy of
for these five sampling units, the classification of the four 96% using hyperspectral Hyperion images, but classifica-