www.publish.csiro.au/journals/ajb Australian Journal of Botany, 2005, 53, 337–345
Classifying Eucalyptus forests with high spatial and spectralresolution imagery: an investigation of individual species
and vegetation communities
Nicholas GoodwinA,C, Russell TurnerB and Ray MertonA
ASchool of Biological, Earth and Environmental Sciences, University of New South Wales,Sydney, NSW 2052, Australia.
BState Forests of New South Wales, Pennant Hills, NSW 2120, Australia.CCorresponding author. Email: [email protected]
Abstract. Mapping the spatial distribution of individual species is an important ecological and forestry issue thatrequires continued research to coincide with advances in remote-sensing technologies. In this study, we investigatedthe application of high spatial resolution (80 cm) Compact Airborne Spectrographic Imager 2 (CASI-2) datafor mapping both spectrally complex species and species groups (subgenus grouping) in an Australian eucalyptforest. The relationships between spectral reflectance curves of individual tree species and identified statisticaldifferences among species were analysed with ANOVA. Supervised maximum likelihood classifications were thenperformed to assess tree species separability in CASI-2 imagery. Results indicated that turpentine (Syncarpiaglomulifera Smith), mesic vegetation (primarily rainforest species), and an amalgamated group of eucalypts couldbe readily distinguished. The discrimination of S. glomulifera was particularly robust, with consistently highclassification accuracies. Eucalypt classification as a broader species group, rather than individual species, greatlyimproved classification performance. However, separating sunlit and shaded aspects of tree crowns did not increaseclassification accuracy.
IntroductionThe applications of remote sensing to forest and natural-resource management over recent years have improvedsubstantially, with advances in spatial and spectralresolutions and more advanced data-processing techniques.As a result, detailed classification maps for vegetationcommunities, individual species, species groups, as wellas forest types can be produced to provide a better sourceof information for a variety of management decisions andecological applications.
Ecological and forestry applications can benefit fromaccurate remote-sensing classifications and spatial data. Forexample, recording the spatial distribution of individualspecies and species groups can be used to identify suitableanimal habitats (Coops and Catling 1997; Scarth et al. 1999)and assess biodiversity (Nagendra 2001; Turner et al. 2003),whereas vegetation condition (Sampson et al. 2003; Coopset al. 2004; Leckie et al. 2004), fire disturbance (Henry andHope 1998; Kushla and Ripple 1998; Riano et al. 2002) andvegetation dynamics such as nutrient cycling can be usedfor a variety of environmental applications (Peterson et al.1988; Curran 1989; Wessman et al. 1989; Fourty et al.1996;
Ebbers et al. 2002). For commercial forestry purposes, forestmanagers increasingly require spatial data for activitiessuch as harvest planning, fire fighting, control of pests anddiseases, recreation management and catchment protection.For harvest planning in particular, foresters need to locatemerchantable tree species, better predict sustainable woodvolumes and ensure adequate forest regeneration.
However, in practice, several factors limit the accuracyof vegetation classifications by remotely sensed data.These include spectral and spatial resolution, leaf spectralvariability, additional reflectance surfaces to foliage (e.g.understorey), vegetation structure and crown aspect.
Spatial and spectral resolution has a strong influence onclassification accuracies of digital imagery. Spatial resolutionrefers to the pixel size, and as a rule of thumb, the pixelsize should be 2–5 times smaller than the object beingremotely assessed (O’Neil et al. 1996; Nagendra 2001). Theacquisition of higher spatial resolutions not only increases theability to detect target objects and delineate their boundaries,but also allows the analysis of spatial relationships betweenpixels within individual tree crowns (Franklin et al. 2000;Olthof and King 2000). A higher number of pixels for the
338 Australian Journal of Botany N. Goodwin et al.
same object (e.g. tree crown) will also increase the likelihoodof recording a pixel with a relatively ‘pure’ spectral signature,with limited contribution from additional reflective surfaces(e.g. understorey vegetation). Spectral resolution indicatesthe bandwidth or 50% peak response for a particular band(full-width half-maximum). Narrow spectral bands (<10 nm)can increase the number of bands recorded over the opticalregion as well as target narrow absorption and reflectivefeatures of interest (e.g. chlorophyll) that may then be usedto distinguish vegetation species.
Variability in the spectral signature of an individualspecies (i.e. the characteristic spectral-reflectance curve) willaffect the ability of remote-sensing to classify species. Ideally,the spectral variability within a species for at least onewavelength should be less than between species to enablediscrimination of species. However, foliage reflectance canvary within a single tree (Cochrane 2000), making speciesmapping a difficult or near impossible task for somespecies. In addition, the potential to discriminate individualspecies in digital imagery will strongly depend on thewavelengths contained in the dataset. Mixed results havebeen achieved for discriminating a number of differentvegetation species (O’Neil et al. 1990; Gong et al. 1997;Martin et al. 1998; Datt 1999a; Scarth et al. 1999; Gong et al.2001; Dennison and Roberts 2003; Underwood et al. 2003;Coops et al. 2004).
The signal recorded by a remote-sensing sensor is affectedby the composition and relative proportion of surfaces withinthe instrument’s field-of-view. For example, soil is frequentlypresent in remotely sensed images and has a spectral curvethat differs substantially from vegetation spectra. Pixelscontaining both vegetation and soil will therefore record amixed spectra, which increases the difficulties in matchingknown pixel spectra to these pixels.
Forest structure influences the composition of forestsurfaces viewed by a sensor and this may provide advantagesand disadvantages for classifying vegetation. Differentstructural characteristics (e.g. crown size, crown shape, leafarea index (LAI)) may be used to distinguish tree crowns thathave similar spectra. Classification of species in forests withan open canopy structure may be more difficult than in a forestwith a closed canopy, since open canopies allow more light topenetrate the forest canopy and result in the return of a mixedsignal to the sensor. The importance of forest structure forclassifying vegetation is dependent on the species and studyarea examined.
Li and Strahler (1992) investigated the effects on imageryof shadowing within tree crowns at the tree scale. Theirwork indicated that, in addition to illumination and viewingpositions, the shape and density of tree crowns and thecontrast between crown brightness and background alterthe spectral response received by a sensor. As a consequence,the spectral signatures of sunlit and shaded tree crownsmay not be uniform, and separating these two components
may increase the ability to discriminate individualspecies in high spatial resolution data. Numerousstudies have examined the optimal approach forextracting crown spectra, using of the whole crown,maximum pixel brightness within each crown, meansunlit spectra and mean sunlit and shaded spectra forclassification purposes (Li and Strahler 1992; Gougeon1995; Gong et al. 1997; Leckie et al. 2004). For example,research has shown stronger classification accuracies withsunlit-crown than with the whole-crown spectra to classifyconiferous tree species (Gougeon 1995; Gong et al. 1997;Leckie et al. 2004).
Current knowledge of the optical properties of Australianvegetation is limited, although considerable advances arebeing made. Eucalypt leaves generally hang vertically(termed pendulous), making their canopies semi-transparentwhen viewed from above (Greaves and Spencer 1993;Kumar 1998). The spectra of mature eucalypt leaves arealso frequently influenced by a range of damage-inducedsymptoms from leaf pathogens and herbivorous insects (Datt1999b; Stone et al. 2001), although insect damage is notrestricted to eucalypts and will be a recurring issue for themajority of tree species. Physiological and morphologicalchanges associated with aging of the eucalypt leaf have beenshown to affect spectral variability (Datt 1999a).
Kumar (1998) and Datt (1999a) analysed high spectralresolution reflectance measurements from several Eucalyptusspecies. A common finding was that vegetation indices (e.g.NDVI: a difference index between reflectance in a visible redand near-infrared wavelength) produced poor estimations ofchlorophyll. The work of Datt (1999a), however, identifiedthe maximum sensitivity of pigments contained in eucalyptsto be ∼550 and 708 nm. New chlorophyll indices weresubsequently developed and a leaf maturity index (LMI)was produced to account for red-edge shifts to shorterwavelengths in response to new foliage growth (Datt 1999a,1999b, 1999c).
In a study by Coops et al. (2004), spectral differentiationat the canopy scale has been examined for a eucalyptforest that contains rainforest elements. This tested thestatistical differences between 10 narrow spectral bandsfor three rainforest species, a rainforest class (crownsindistinguishable), Syncarpia glomulifera, Angophorafloribunda Smith and three eucalypt species (Eucalyptussaligna Smith, E. pilularis Smith and E. paniculataSmith) by using whole tree crown mean reflectancevalues. The results indicated that eucalypts were noteasily distinguished from one another, although in at leastone band each eucalypt species could be discriminated;720 and 740 nm provided the highest discrimination.No statistically different bands were identified betweenSyncarpia glomulifera and either E. saligna or E. paniculate,and the spectral band 550 nm (the green peak) provided nodiscriminatory power.
Classifying Eucalyptus forests with high-resolution imagery Australian Journal of Botany 339
In this paper we investigated the potential of highspatial resolution Compact Airborne Spectrographic Imager2 (CASI-2) to classify species within a native eucalypt foreston the New South Wales central coast. The objective wasto evaluate CASI-2 data (consisting of 10 narrow bandsranging from 450 to 850 nm) to see whether individual treesor species groups could be consistently discriminated. Byusing a field-located training sample (including sunlit andshaded crown components), we investigated the relationshipsamong spectral reflectance curves of individual tree species.Spectral information for individual species was analysed withANOVA to identify bands that were significantly differentamong species. Finally, a series of supervised maximumlikelihood classifications were performed to assess treespecies separability.
Materials and methods
Study site
Olney State Forest is on the central coast of New South Wales, Australia,(33◦9′00′′S, 151◦20′30′′E), approximately 12 km east of Morisset, NewSouth Wales. The overstorey is dominated by Eucalyptus acmenoides(Schauer), E. pilularis (Smith), E. saligna (Smith), E. paniculata(Smith), E. sideroxylon (Woolls), E. deanei (Maiden) and Syncarpiaglomulifera (Smith). At the time of image capture, spring leaf flush hadnot initiated and the tree crowns generally consisted of mature leaves.The study area is a regrowth forest (last harvested in the 1970s) andhas been the focus of previous research (Stone 1999; Stone et al. 2000,2001; Coops et al. 2003, 2004).
The prevailing climate of the region is maritime temperate, withhigh summer and low winter rainfall (average annual rainfall of1200 mm) and mean temperatures of 27 and 15◦C in summer andwinter, respectively (Coops et al. 2004). The study area is characterisedby undulating topography, an elevation range between 80–280 and600–700 m above sea level, with soils derived from HawkesburySandstone (State Forest of New South Wales 1995).
Sensor characteristics
CASI-2 is a push-broom imaging spectrograph, operating between413 and 958 nm and with a 37.8◦ field-of-view across track. TheCASI-2 data was captured in October 1999, during which sunlightsolar zenith angles ranged between 40.3 and 40.6◦. Although CASI-2is capable of recording 288 narrow bands (about 10-nm intervals), only10 bands covering the visible to near-infrared portion of the spectrumfrom 449 to 850 nm were captured at a spatial resolution of 80 cm.The imagery, which contained some cloud effects, was subsequentlyprocessed to correct for geometric and atmospheric distortions, by
Table 1. Field data collected for CASI classifications
Tree species No. of tree crowns No. of training pixels
White mahogany (Eucalyptus acmenoides) 20 2976Blackbutt (E. pilularis) 11 2276Sydney blue gum (E. saligna) 3 369Mugga ironbark (E. sideroxylon) 3 312Grey ironbark (E. paniculata) 1 265Round leaved gum (E. deanei) 1 133Turpentine (Syncarpia glomulifera) 11 1158
Total 50 7489
using an empirical line calibration by Ball Advanced Imaging andManagement Solutions.
Field survey
To validate the supervised classifications, a series of sample crowns weresubjectively selected for each of the six tree species. Initially, the CASI-2imagery was examined by ENVI 4.0 (The Environment for VisualisingImages, Research Systems Inc., Colorado) to identify potential visiblydiscrete crowns. Hardcopy prints of the CASI-2 imagery were takeninto the field and used in conjunction with aerial photography(1 : 5000 colour and 1 : 20 000 near-infrared) and a handheld global-positioning system (GPS), to field-locate sample trees. Once located andspecies type confirmed by State Forest of New South Wales staff, thecrown boundaries were drawn onto the CASI-2 prints. Crown outlineswere later digitised into a geographic information system (GIS). Thecrown vectors were also divided into sunlit and shaded components.Since each crown had to be large enough to be identifiable in the CASI-2imagery, the samples were characteristically large mature trees. Table 1shows the number of sample crowns per tree species, as well as thenumber of pixels utilised in the training process.
Data analysis
Initially, the crown-boundary vectors were used as a sampling tool toextract pixel values (sunlit and shaded) for each identified tree species.This (1) provided data to investigate the spectral properties of thetarget species and (2) any statistical differences among the spectralproperties, and (3) provided a source of training data for the supervisedclassification.
To observe the spectral properties of the tree species, meanreflectance values in each of the 10 wavelengths were extracted from thecrown pixel samples, including sunlit and shaded crown portions. Thespectral curves of the sample trees were plotted to assess tree speciesseparability. Additional classes representing mesic vegetation, soil andshadow, and a merged eucalypt class, were included for comparativepurposes.
The pixel data were also transferred to a statistical package Statistica(StatSoft 2002) and an ANOVA was completed. Statistical analysis of thespectral properties of vegetation classes was used to identify statisticallydifferent spectral bands for species separation.
Finally, a series of supervised maximum likelihood classificationswere applied to the CASI-2 dataset by using the 10 spectralbands and the training dataset. This classification approachcalculates the statistical probability of each pixel belonging to aparticular class (ENVI 4.0, Research Systems Inc.). All pixelswere subsequently assigned to the species class, with the highestprobability subject to a probability threshold. A confusion orerror matrix was also produced for all supervised classifications,indicating the overall accuracy (producer’s and user’s) and the Kappacoefficient, in addition to the errors of omission and commission(Foody 2002; ENVI 4.0, Research Systems Inc.). Supervised
340 Australian Journal of Botany N. Goodwin et al.
classifications were also performed on whole and sunlit/shaded aspectsof tree crowns.
Results
Vegetation spectral curves
A visual comparison of the spectral curves for each speciesclass revealed strong similarities among all eucalypt samples,but a distinct separation for S. glomulifera and mesicvegetation (Fig. 1). This demonstrates that the eucalyptspectra exhibit minimal variability in terms of magnitude inthe visible wavelengths and a large rise in variability as thewavelengths increase towards the near-infrared wavelengths(especially 759–850 nm). A distinct separation between theeucalypts and both S. glomulifera and mesic vegetation ispresent at 550 nm. Both S. glomulifera and mesic vegetationshow similar reflective peaks at 550 nm and absorptiontroughs at 449 and 679 nm. However, a separation is evidentin the near-infrared wavelengths between 759 and 850 nm(near-infrared plateau), which may indicate differentiationof internal structure (Stone et al. 2001). The spectralvariability relative to the mean is close to constant for mesicvegetation, whereas there is a slight increase for eucalyptsand S. glomulifera with increasing wavelengths.
The strong similarity in spectral curves for all eucalyptspecies suggested it would be difficult to achieve individualspecies classification. Hence, all species were combined into
E. pilularisE. sideroxylonE. salignaE. acmenoidesS. glomuliferaMesic vegetation
449 550 635 679 700 721 740 759 780 850
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Ref
lect
ance
Fig. 1. Spectral curves of four eucalypt species, Syncarpia glomuliferaand mesic vegetation. Eucalyptus deanei and E. paniculata werenot included because only one tree crown of each of thesespecies was identified at the study site, whereas mesic vegetationrepresents a spectral mixture of mesic vegetation species. Vertical barsindicate ± one standard deviation.
a single eucalypt group. Figure 2 compares the mean spectrafor the merged eucalypt group with that of S. glomuliferaand mesic vegetation. These results show separabilityat 550 nm, and at 759, 780, and 850 nm (the near-infrared shoulder), which could be used to distinguish thesevegetation classes.
An examination of Fig. 3 shows differences among themean spectral curves of sunlit and shaded componentsfor eucalypts, mesic vegetation and S. glomulifera, and,for comparative purposes, shadow and soil. Although ofa slightly lower magnitude, the spectral curves for sunlitand shaded vegetation have similar absorption troughs andreflective peaks at the same wavelength. In contrast, thespectral curves of shadow and soil are most notably differentacross most of the 10 spectral bands.
ANOVA results
The ANOVA results shown in Table 2 demonstrate thatS. glomulifera has multiple spectral bands that differsignificantly both from those of the eucalypt speciesand mesic vegetation. Bands at 550 and 700 nm differedsignificantly between the eucalypts and S. glomulifera,whereas mesic vegetation could be separated from eucalyptsat the green peak 550-nm wavelength (also evident in Fig. 2).The relative separation between S. glomulifera and mesicvegetation is minimal in the visible wavelengths; however,there is increasing separability in the three longer near-infrared wavelengths (759, 780 and 850 nm).
EucalyptsS. glomuliferaMesic vegetation
449 550 635 679 700 721 740 759 780 850
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Wavelength (nm)
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lect
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Fig. 2. Mean spectra for a merged eucalypt class, Syncarpiaglomulifera and mesic vegetation. Vertical bars indicate ± one standarddeviation.
Classifying Eucalyptus forests with high-resolution imagery Australian Journal of Botany 341
0
0.1
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Eucalypts Sunlit
Eucalypts Shaded
S. glomulifera Sunlit
S. glomulifera ShadedMesic vegetationSunlitMesic vegetaionShadedSoil
Shadow
449 550 635 679 700 721 740 759 780 850
Wavelength (nm)
Ref
lect
ance
Fig. 3. Mean sunlit spectral characteristics for regions of interest.
An examination of the results for eucalypt species(Table 2) shows that E. sideroxylon has a number of bandssignificantly different from those of the other three eucalyptspecies. In comparison, E. acmenoides, E. saligna andE. pilularis have relatively few statistically different bandsand E. saligna is only separable with one other eucalypt
Table 2. Spectral wavelength ANOVA results among four eucalypt species, Syncarpia glomulifera and mesic vegetationWavelengths (nm) included indicate α < 0.05; *α < 0.001
Species/species group E. pilularis E. acmenoides E. saligna E. sideroxylon S. glomulifera
species in two bands (supporting earlier observations on thesimilarity of spectral cures; refer to Fig. 1). Overall, the bandsat 700 and 721 nm are most frequently separable among thefour eucalypt species. The inclusion of 700 nm as a separablewavelength may infer a relationship with chlorophyll and,therefore, leaf age composition.
Table 3 shows the comparison of the merged eucalyptgroup with S. glomulifera and mesic vegetation, indicatinga larger number of statistically different spectral-bandcombinations. This includes seven bands (449–740 nm),with six being highly significant for S. glomulifera and all10 bands identified as highly significant for mesic vegetation.This supports previous observations regarding the strongseparability in the spectral curves (Fig. 2).
Classification accuracies
The final comparison involved the classification of individualspecies in CASI-2 imagery by using the maximumlikelihood algorithm (Table 4). With all 10 spectral bandsand individual eucalypt species as input, supervisedclassifications successfully discriminated S. glomulifera,mesic vegetation, roads and shadow. Moreover, the wholecrown and sunlit/shaded classification results for theseclasses were comparable with overall accuracies of 80.0and 81.7%, respectively. However, the maximum likelihoodclassifier failed to distinguish the six eucalypt species.E. acmenoides was the only exception, with several correctlyclassified clusters of pixels representing coherent treecrowns. By merging all eucalypts into one group, theclassification accuracy and Kappa coefficient increased(see Table 4).
Table 4 shows that slightly higher overall accuracies andKappa coefficient scores where achieved by using wholecrowns rather than distinguishing sunlit and shaded aspects
342 Australian Journal of Botany N. Goodwin et al.
Table 3. Spectral wavelength ANOVA results among eucalypts,Syncarpia glomulifera and mesic vegetation
Wavelengths (nm) included indicate α < 0.05; *α < 0.001
Species/species group Eucalypts S. glomulifera
S. glomulifera 449*, 550*, 635*, n.a.679*, 700*, 721*,
(Table 4). The errors of commission and omission weremore related to classes misclassifying their own sunlit orshaded cover types, rather than to confusion among differentvegetation classes. This suggests that there is some degree ofrobustness between the training spectra and the intensity ofreflectance for the pixel to be classified.
Figure 4 shows a subset of three classification imagesfor the study area: whole crowns with 10 spectralbands, sunlit/shaded crowns with 10 spectral bands andsunlit/shaded crowns with only the highly separable spectralband 550 nm and a single, near-infrared wavelength on thenear infrared plateau (in this example 850 nm). An overallclassification accuracy of 78.9% has been recorded forthis image (Kappa coefficient = 0.74). The comparison alsoreveals that the sunlit/shaded classification (Fig. 4c) containsgreater textural information which aids visual interpretation.Finally, an examination of Fig. 4a, c demonstrates thatby using only two spectral bands as an input to themaximum likelihood classifier a similar level of detail canbe produced.
Discussion
This research has demonstrated CASI-2 can effectivelydiscriminate S. glomulifera in a floristically and spectrallymixed two-tier eucalypt forest. The user’s and producer’saccuracies for S. glomulifera were demonstrated to be
Table 4. Maximum likelihood classification results
Input class Classifier Overall accuracy (Kappa coefficient)Whole crown Sunlit/shaded
All 10 bands: eucalypt species Maximum likelihood 80.0 (0.77) 81.7 (0.80)(individually), S. glomulifera, mesicvegetation, soil and shadow
Eucalypts species (one class): Maximum likelihood 95.4 (0.94) 88.8 (0.86)S. glomulifera, mesic vegetation,soil and shadow
550 and 850 nm: eucalypt species Maximum likelihood 90.1 (0.87) 78.9 (0.74)(one class), S. glomulifera, mesicvegetation, soil and shadow
consistently higher than the other species examined whenusing the maximum likelihood classifier (>89%). MappingS. glomulifera achieved a high level of accuracy as a resultof the strong spectral differences from the other vegetationclasses. In visible wavelengths, S. glomulifera has similarreflectance to mesic vegetation but higher reflectance thaneucalypts. However, in the near infrared S. glomuliferahas a lower reflectance than mesic vegetation. The closedcanopy and horizontally oriented leaves of S. glomuliferaare attributes likely to assist in the separability fromeucalypt species.
Research by O’Neil et al. (1990) has also shown thatthe green peak (550 nm) is an effective wavelength fordiscriminating S. glomulifera. This study undertaken at theleaf scale indicated structural differences between leaf cross-sections of S. glomulifera and several native Australianeucalypt and rainforest species. Most notably, S. glomuliferacontained few intercellular spaces and displayed a dark anddull green upper leaf surface.
The eucalypt species proved more difficult to discriminatefrom one another. From the six eucalypt species investigatedwith CASI-2 data, only E. acmenoides was readilydistinguishable. One reason for this could be the highernumber of crowns used in the training process (Table 1). Thisis only a minor departure from previous research findingswhere many eucalypt species have been found to have similarspectral signatures. For example, Datt (1999a) concludedthat of the 21 eucalypt species studied, no individualspecies displayed a unique spectral reflectance. The best-case scenario showed the discrimination of two or moreeucalypt species. Kumar and Skidmore (1998) also indicatedthat some combinations of eucalypt species do not exhibitsignificant differences across wavelengths between 400 and2500 nm. However, both of these studies were conducted onleaf samples in a laboratory environment.
The results of this study also differ from those by Coopset al. (2004), particularly for distinguishing E. saligna fromE. pilularis, S. glomulifera from the three eucalypt species,and for the discrimination ability of the 550-nm spectral
Classifying Eucalyptus forests with high-resolution imagery Australian Journal of Botany 343
Fig. 4. CASI-2 maximum likelihood classifications: (a) 10 spectral bands and sunlit and shaded crowns,(b) 10 spectral bands and whole crowns and (c) two spectral bands (550 and 850 nm) and sunlitand shaded crowns.
band. This highlights the difficulty of separating individualeucalypt species, as the reflective characteristics appear tobe variable. One factor contributing to these differencescould be the inclusion of a number of crowns with reducedcondition (increased crown transparency, leaf necrosis, foliaranthocyanin content and insect herbivory) in the trainingprocess by Coops et al. (2004).
The ability to discriminate eucalypts as a group frommesic vegetation and S. glomulifera was high. This strongdifferentiation is due to the lack of spectral differences amongeucalypt species and the large spectral differences among theother vegetation classes. For example, at 550 nm, eucalyptreflectance is clearly lower than for mesic vegetation andS. glomulifera (Figs 1, 2). Another possible factor improvingthe accuracy in discriminating a single eucalypt class, incontrast with individual species, could be related to the
overlap of neighbouring eucalypt species in the canopy. Alarge proportion of the tree crowns appeared to be singlein image printouts were in fact mixed species in the field.Intuitively, this could explain some of the difficulties indistinguishing eucalypt species and the improved resultswhen combining the eucalypts into a single group.
One of the more interesting results arising from this studyhas been the effectiveness of species discrimination amongeucalypt, S. glomulifera and mesic crowns by using only afew key spectral bands (550, 759, 780 and 850 nm). Thissuggests it is feasible for a limited number of bands toprovide a cost-effective classification tool based on cheaper4-band multi-spectral sensors mounted with spectral filters.By lowering costs and increasing availability, such remote-sensing technology could become operational for large-scaleforest-classification projects.
344 Australian Journal of Botany N. Goodwin et al.
Conclusion
This study has demonstrated that CASI-2 can be used toclassify an individual species (S. glomulifera) as well asto distinguish eucalypt tree crowns from mesic vegetation.The highest spectral separability among these vegetationclasses occurred around the green wavelength (550 nm)and the near-infrared shoulder (759–850 nm). Although thediscrimination of S. glomulifera was robust, with consistentlyhigh classification accuracies, mapping eucalypt speciesindividually was largely unsuccessful.
An important finding from this study is that twowavelengths, 550 and 850 nm, are capable of discriminatingeucalypts, S. glomulifera and mesic vegetation at thecanopy scale in high spatial and spectral resolution imagery.Furthermore, separating sunlit and shaded aspects of treecrowns has not increased the overall classification accuracyor the Kappa coefficient score.
Acknowledgments
We thank Murray Webster (State Forest of New South Wales)for assistance in tree identifications as well as Will Cuttyand Tom Bourne for field assistance. The critical review byNicholas Coops (CSIRO) and Christine Stone (State Forestof New South Wales) is most appreciated. The imageryused in this study was supplied by State Forest of NewSouth Wales and we thank Christine Stone for approving thedata request.
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Manuscript received 17 June 2004, accepted 4 April 2005
